welcome back guys to comic n TV's pop reviews I'm your host Shannon and today we're gonna be taking a look at the pop animation the power puff girls number 126 bubbles vinyl figure stay tuned all right guys welcome back and as I said we are taking a look at the pop animation the power puff girls number 126 bubbles vinyl figure as you can see you got bubbles here on the front you got bubbles on the side 126 pop animation on the back you've got the power puff girls blossom is number 125 bubbles is 126 and Buttercup is 127 Mojo Jojo is 201 and him is 202 collect the entire line of Funko pop power puff girls super stylized vinyl figures from Funko and Cartoon Network then on the side here you've got bubbles you got the Cartoon Network logo you got pop animation you got 126 let's open this girl up and take a look inside all right guys so I'm not a big power puff girls fan but my kids love them to death so my daughter Ali picked this one out for her we were at Walmart the other day and GameStop is right next door I wanted to go through by their browse around take a look inside see what kind of Funko pops they had and as soon as I stepped through the door I saw one that's been on my list of my wish list forever and that's the Space Ghost Funko pop and with Easter being just a couple days away I decided to let my kids pick out their Easter presents and so my son picked out the Blue Ranger morphing exclusive Ali picked out the bubbles power puff girl and Marissa picked out a metals die cast spider-man so let's take a look at bubbles here she does come on display case or display base because she does have the round feet I tried standing her up without the base and she toppled right over and see her blonde hair and pigtails here got the blue twisty ties black eyes around got the mouth which is unusual for Funko but since power puff girls are animated without noses she needed a mouth got the blue dress with the black sash white leggings black shoes she's in that little cutesy pose there take a look around back you got the X at the top of the head to mark to show kind of the splitting her hair I guess very nice her head can turn all the way around no neck as with most Funko pops very nice figure like I said I don't really know much about power puff girls I'm not a big fan but my kids love them so this was the pop animation power puff girls number one 26 bubbles vinyl figure like I said we did get her at GameStop just the other day so I hope you enjoyed this review guys not a bad figure we'll see you again next Tuesday take care guys


Novak Djokovic will spend Saturday that January 15th night in immigration detention before he seeks a court ruling to stop his deportation. Then he can keep alive his bid for a record 21st major title at the Australian Open. Immigration Minister Alex Hawke decided to cancel his visa because his present could foster opposition to COVID-19 vaccination in Australia. This is according to court documents released after an initial hearing the federal court on Saturday. It will be the second stint in detention for Djokovic.


THE SUNCAN GARDEN AND OTHER POMES by Walter de la Mer Read for LibriVox.org by Bruce Gachuck THE LITTLE SALAMANDER TO MARGO When I go free I think it will be a night of stars and snow and the wildfires of frost shall light my footsteps as I go. Nobody, nobody will be there with groping touch or sight to see me in my bush of hair dance burning through the night. End of poem. This recording is in the public domain. THE SUNCAN GARDEN by Walter de la Mer Read for LibriVox.org Speak not, whisper not. Here bloweth time and bergamot Softly, on the evening hour, secret herbs their spices shower dark-spiked rosemary and myrrh, lean-stalked purple lavender hides within her bosom too all her sorrows bitter rue. Breathe not, trespass not, of this green and darkling spot latticed from the moon's beams perchance a distant dreamer dreams perchance upon its darkening air the unseen ghosts of children fair faintly swinging sway and sweep like lovely sea flowers in its deep while unmoved to watch and ward. Minutes gloomed and daisied sward stands with bowed and dewy head that one little leaden lad. End of poem. This recording is in the public domain. THE REDLERS by Walter de la Mer Read for LibriVox.org Thou solitary the blackbird cried from the happy rain, linnet and black cap would lark thrush purged all upon a sweet briar bush have come at cold of midnight tide to ask thee why and when griefs moat thy heart so thou dost sing in solemn hush of evening so sorrowfully lovelorn thing, nay, nay, not sing but rave, but wail most melancholic nightingale do not the do's of darkness steep all pinings of the day in sleep why then when rocked in starry nest we mutely couch secure at rest doth thy lone heart delight to make music for sorrow's sake. A moon was there so still her beam it seemed the whole world lay a dream laud by the watery sea and from her leafy night-hung nook upon this stranger soft did look the nightingale side he to strange my friend the kingfisher but yestermoor and conjured me here out of his green and gold to say why thou in splendor of the noon wearest of color but golden shone and else dost the array in a most somber suit of black surely he sighed some load of grief past all our thinking and belief must weigh upon his back. Do then in turn tell me if joy thy heart as well as voice employ why dost thou now most sable shine in plumage woefuler far than mine thy silence is a sadder thing than any dirge I sing thus then these two small birds perched there breathed a strange riddle both did share yet neither could expound and we who sing but as we can in the small knowledge of a man have we an answer found nay some are happy whose delight is hid even from themselves from sight and some win peace who spend the skill of words to sweeten despair of finding consolation where life has but one dark end who in rapt solitude tell or a tale as lovely as for lore into the midnight air and a poem this recording is in the public domain. Mrs. Grundy by Walter de la Mare read for LibriVox.org step very softly sweet quiet foot stumble not whisper not smile not by this dark ivy stoop cheek and brow still even thy heart what seaest thou high coft broad browed aged suave yet grim a large flat face eyes keenly dim staring at nothing that's me and yet with a hate one could never know never forget this is my world my garden my home hither my father bad mother to come and bear me out of the dark into light and happy I was in her tender sight and then thou frail flower she died and went forgetting my pitiless banishment and that old woman and aunt she said came hither lodged fattened and made her bed oh yes thou most blessed from Monday to Sunday has lived on me prayed on me Mrs. Grundy called me dear nephew on each of those chairs has gloated in righteousness heard my prayers why ditched thou dare the thorns of the grove timidest trespasser huntress of love now thou has peeped and now dust know what kind of creature is thine for foe not that she'll tear out thy innocent eyes poison thy mouth with devil trees watch thou wait though soon will begin the guile of a voice hark come in come in and a poem this recording is in the public domain The Dark House by Walter de la Mer read for LibriVox.org see this house how dark it is beneath its vast bowed trees not one trembling leaflet cries to that watcher in the skies remove remove thy searching gaze innocent of heaven's ways brood not moon so wildly bright on secrets hidden from sight secrets sighs the night wind vacancy is all I find every keyhole I have made way lasubons faint and sad no voice ever answers me only vacancy once once the cricket shrills and far and near the quiet fills with its tiny voice and then hush falls again mute shadows creeping slow mark how the hours go every stone is mouldering slow and the least winds that blow some minutest atom shake some fretting ruin make in roof and walls how black it is beneath these thick bowed trees and a poem this recording is in the public domain mistress fell by Walter de la Mer read for LibriVox.org whom seek you here sweet mistress fell one who loved me passing well dark as I wild his face stranger if in this lonely place by such and one then pretty say I am come here today many his like mistress fell I did not look so cannot tell only this I surely know when his voice called me I must go touched me his fingers and my heart lept at the sweet pain smart why did he leave you mistress fell magic laid its dreary spell stranger he was fast asleep into his dream I tried to creep called his name soft was my cry he answered not one sigh the flower and the thorn are here fall of the night dew cold and clear out of her bower the bird replies mocking the dark with ecstasy's see how the earth's green grass doth grow praising what sleeps below thus have they told me and I come as flies the wounded wild bird home not tears I give but all that he clasped in his arms sweet charity all that he loved to him I bring for a close whispering and a poem this recording is in the public domain the stranger by Walter Dottemaire read for LibriVox.org in the woods as I did walk dappled with the moon's beam I did with a stranger talk and his name was dream spirit his heel dark his cloak shady wide his bonnets brim his horse beneath a silvery oak grazed as I talked with him softly his breast brooch burned and Sean hill and deep were in his eyes one of his hands held mine and one the fruit that makes men wise wonderfully strange was earth to see flowers white as milk did gleam spread to heaven the Assyrian tree over my head with dream dews were still betwixt us twain stars a trembling beauty shed yet not a whisper comes again of the words he said and a poem this recording is in the public domain the flight by Walter Dottemaire read for LibriVox.org how do the days press on and lay their fallen locks at evening down while as the stars in darkness play and moonbeams weave a crown a crown of flower like light in heaven where in the hollow arch of space mourns mistress dreams and the pleads seven stand watch about her place stand watch old days no number keep of ours when this dark clay is blind when the world's clocks are dumb in sleep tis then I seek my kind and a poem this recording is in the public domain the remonstrance by Walter Dottemaire read for LibriVox.org I was at peace until you came and set a careless mind aflame I lived in quiet cold content all longing in safe banishment until your ghostly lips and eyes made wisdom unwise not was in me to tempt your feet to seek a lodging quite forgot lay the sweet solitude we too in childhood used to wonder through times cold had closed my heart about and shot you out well and what then oh vision grave take all the little all I have strip me of what in voiceless thought life's kept of life unhoped unsought reverie and dream that memory must hide deep in dust this only I say though called and bear the haunted house you have chosen to share still neath its walls the moonbeam goes and trembles on the untended rose still or its broken roof tree rise the starry arches of the skies and neath your lightest word shall be the thunder of an ebbing sea and of poem this recording is in the public domain the exile by Walter Dottemaire read for LibriVox.org I am that Adam who with snake for guest hid anguished eyes upon Eve's piteous breast I am that Adam who with broken wings fled from the seraph's brazen trumpetings betrayed and fugitive I still must roam a world where sin and beauty whisper of home all from wide circuit shall at length I see pure daybreak lighten again on Eden's tree loosed from remorse and hope and love's distress enrobe me again in my lost nakedness no more with wordless grief a loved one grieve but to Heaven's nothingness re-welcome Eve and of poem this recording is in the public domain eyes by Walter Dottemaire read for LibriVox.org all strange devices that alone divide the seer from the scene the very highway of earth's pomp and pride that lies between the traveller and the cheating sweet delight of where he longs to be but which bound hand and foot he close on night can only see and of poem this recording is in the public domain the trist by Walter Dottemaire read for LibriVox.org why in my heart oh grief dust thou in beauty by dead is my well content and buried deep my pride cold are their stones beloved to hand and side the shadows of even are gone shot are the day's clear flowers now have her birds left mute their singing bowers lone shall we be we twain in the night hours thou with thy cheek on mine and dark hair loosed shall see take the far stars for fruit the Cyprus tree and in the use black shall the moon be we will tell no old tales nor heed if in wandering air die a lost song of love or the ones fair still as well water be the thoughts we share and while the ghosts keep trist from chill sepulchres dreamless our gaze shall sleep and sealed our ears heart unto heart will speak without tears oh thy veiled lovely face joys strange disguise shall be the last to fade from these wrapped eyes air the first dart of daybreak pierce the skies and of poem this recording is in the public domain the old man by Walter Dottemaire read for LibriVox.org hold and alone sit we caged little red men lost to earth's listen and see thoughts where for and when only far memories stray of a past once lovely but now wasted and faded away like green leaves from the bow vast brews the silence of night the ruinous moon lifts on our faces her light when saw dreaming is gone we speak not trembles each head in their sockets our eyes are still desire as cold as the dead without wonder or will and one with a land horn draws near at clash with the moon in our eyes where art thou he asks I am here one by one we arise and none lifts a hand to withhold a friend from the touch of that foe heart cries unto heart thou art old yet reluctant we go and a poem this recording is in the public domain the fools song by Walter Dottemaire read for LibriVox.org never know never listen too long to the chattering wind in the shadows the night bird song to sad in sooth to lie under the grass but none too glad some to wake and grow cold where life shadows pass dumb the old tall woman squats and for every green copper battered and worn dolls out never's and knots I know a blind man too who with a sharp ear listens and listens the whole world through oh sit we snug to our feast with flatter and finger and spoon and good vettles at least and a poem this recording is in the public domain the dreamer by Walter Dottemaire read for LibriVox.org Oh thou who giving helm and sword gaves to the rusting rain and starry dark salt tender dues to blunt and stain out of the battle I am sped unharmed yet stricken sore a living shape mid whispering shades on Lethe's shore no trophy in my hangs I bring to this sad sighing stream the names and the trumps and cries where but a dream a dream traitor to life of life betrayed oh of thy mercy deep a dream my all the all I ask is sleep and a poem this recording is in the public domain motley by Walter Dottemaire read for LibriVox.org come death I'd have a word with thee and thou for innocence see and love a lad with broken wing and pity to the fool shall sing to you as fools will sing I music has small sense and a tunes soon told and earth is old and my poor wits are dims yet have I secrets dark my dear to breathe you all come near and lest some hideous listener tells I'll ring my bells they're all at war yes yes their bodies go neath burning sun and I see star to chanted songs of woe dragging cold cannon through a mire of rain and blood and spouting fire the new moon glinting hard on eyes wide with insanities hush I use words I hardly know the meaning of and the mute birds are glancing at love from out their shade of leaf and flower trembling at treacheries which even in noon day coward heed not what I said of frenzied hosts of men more fools than I on envy hatred fed who kill and die spake I not plainly then yet pity whispered why thou silly thing off to the daisies go mine was not news for child to know and death no ears hath he had sucked where creep eyeless worms in hush of sleep yet when he smiles the hand he draws a thwart his grinning jaws faintly the thin bones rattle and there there park and how my bells in the air drive away care nay but a dream I had of a world all mad not simple happy mad like me who am mad like an empty scene of water and willow tree where the wind hath been but that foul Satan mad who wroughts in his own head and counts the dead not honest one and two but for the ghosts they were brave faithful true when head in air in earth's clear green and blue heaven they did share with beauty who bad them there there now death goes may have I've wearied him I and the light doth dim and asleep's the rose and tired innocence in dreams is hence come love my lad nodding that drowsy head his time thy prayers were said and home this recording is in the public domain to E. T. nineteen seventeen by water dot a mare read for LibriVox dot org you sleep too well too far away for sorrowing word to soothe or wound your very quiet seems to say how longed for a peace you have found else had not death so lured you on you would have grieved twist joy and fear to know how my small loving son had wept for you my dear and a poem this recording is in the public domain Alexander by water dot a mare read for LibriVox dot org it was the great Alexander capped with a golden helm sat in the ages in his floating ship in a dead calm voices of seamades singing wondered across the deep the sailors laboring on their oars rode as in sleep all the high pump of Asia charmed by that siren lay out of their weary and dreaming minds faded away like a bold boy say their captain his glamour withered and gone in the souls of his brooding mariners while the song pined on time like a falling dew life like the scene of a dream laid between slumber and slumber only did seem oh Alexander then in all us mortals to wax thou not bold too bold on the wave dark blue come the calm infinite night who then will hear ought save the singing of the seamades clear end of poem this recording is in the public domain for all the grief by water dot a mare read for LibriVox dot org for all the grief I have given with words may now a few clear flowers blow in the dust and the heat and the silence of birds where the lonely go for the thing said that heart asked of me be a dark cool water calling calling to the foot sore benighted solitary when the shadows are falling oh be beauty for all my blindness a moon in the air where the weary wind and dues burdened with loving kindness in the dark of the end and a poem this recording is in the public domain farewell by Walter de la mare read for LibriVox dot org when I lie where shades of darkness shall no more assail mine eyes nor the rain make lamentation when the wind sighs how will fare the world whose wonder was the very proof of me memory fades must the remembered perishing be oh when this my dust surrenders hand foot lip to dust again may these loved and loving faces please other men may the rusting harvest hedgerow still the travelers joy entwine and as happy children gather posies once mine look thy last on all things lovely every hour let no night seal thy sense in deathly slumber till to delight thou have paid thy utmost blessing since that all things thou would praise beauty took from those who loved them in other days and a poem this recording is in the public domain clear eyes by Walter de la mare read for LibriVox dot org clear eyes do dim at last and cheeks outlive their rows time heedless of the past no loving kindness knows chill unto mortal lip still see flows griefs to but brief while stay and sorrow being or its salt tears shed away wounded the heart no more stealthily leave those waters that solemn sure ah then sweet face burn on while yet quick memory lives and sorrow air thou art gone know that my heart forgives air yet grown cold in peace it loves not nor grieves and a poem this recording is in the public domain music by Walter de la mare read for LibriVox dot org when music sounds gone is the earth I know and all her lovely things even lovelier grow her flowers in vision flame her forest trees lift burdened branches still with ecstasy when music sounds out of her rise niads whose beauty dims my waking eyes wrapped in strange dream burns each enchanted face with solemn echoing stirs their dwelling place when music sounds all that I was I am air to this haunt of brooding dust I came while from time's woods break into distance song the swift winged hours as I hasten along and a poem this recording is in the public domain in a churchyard by Walter de la mare read for LibriVox dot org as children bidden to go to bed puff out their candles light since that the natural dark is best for them to take their flight into the realm of sleep so we God's bidding did obey not without fear our tired eyes shot and wait and wait the day and a poem this recording is in the public domain two houses by Walter de la mare read for LibriVox dot org in the strange city of life two houses I know well one where in silence a garden hath and one where dark doth dwell roof unto roof they stand shadowing the where vanity flaunts her gilded boots in the noontide glare and heat green graped upon their walls the ancient hoary vine hath clustered their carbon likeness stones with tendril serpentine and ever and anon dazed in that clamorous throng I thirst for the soundless fount that stills those orchards mute of song knock knock nor knock in vain heart all thy secrets tell where silence a fast sealed garden hath where dark doth dwell and a poem this recording is in the public domain and of the sunken garden and other poems by Walter de la mare


Thank you everyone for joining. My name is Alexandra Gogonia, Product Marketing Engineer responsible for sensors, and I'm based in ST Bay Area office. Today, we will talk about the sensor tile.box and the function packs available, which allow to use this device in what we call professional mode. I will start with a quick introduction on Mems and Sensors Landscape, and how ST is addressing key market verticals, as well as providing some high level details on sensor tile.box. Then, I will focus on sensor tile.box and professional mode of usage, referencing two software function packs that are available for this hardware platform. We will also show you a quick demo on how to use it as an asset tracking device based on the tools available. I will conclude with few final remarks. Let's start by talking about the IoT trend, where familiar devices are evolving to become connected and smarter by leveraging the massive computing capability of the cloud. This is impacting every industry and even creating new ones. One of the key elements of this market trend is the need to acquire and understand data, and therefore, sensors play a key role within IoT. With the expansion of IoT, the variety of sensors deployed in the field is facing significant growth, and ST is one of the key players. From a market segment perspective, we cover consumer, industrial, and automotive industries. And the goal is to offer devices where key parameters such as accuracy, noise density, size, and power consumption are the best in class. Here are some of the key sensor categories that ST can support. Motion sensors, like accelerometers, gyroscope, six-axis inertial measurement units, magnetometers, and e-compasses. Environmental sensors, like pressure, humidity, and temperature sensors. Acoustic sensors with our MEMS microphones, and proximity sensors through our Time of Flight product portfolio. So if you're looking to develop an application using sensors, we have the right device for you. Our sensors address all three key market segments. Automotive, with products qualified according to AEC Q100 standard. Industrial, where we offer devices with extended temperature range and 10 years longevity commitment. And consumer, where technology with advanced embedded features offer devices with best in class accuracy and lower power consumption in the industry. This allows us to enable a wide range of companies to explore sensors across several different applications, each with specific requirements. Consider all these sensors. How are you going to evaluate them and create a rapid prototype? It would be great if we could just be able to collect data from sensors without writing any line of code. And maybe have some flexibility to create a proof of concept through minimal configuration settings. And then explore rapid prototyping and have access to full customization capabilities as well as being able to rely on STM32Q and tools such as AlgoBuilder and the Unicleo graphical user interface. Well, we got you covered. As this is exactly what Sensortile.box can do. So, Sensortile.box is one of the main hardware platforms that can enable this design flow. A flexible plug-and-play IoT node that allows users to jumpstart applications by relying on a user-friendly mobile app interface called STBLE Sensor App, which is available on both Google Play and App Store. Now, let's take a quick look inside the Sensortile.box. Here is the board. On one side, some of the latest motion sensors from ST. Environmental sensors, analog wideband MEMS microphone, an STM32L4-plus microcontroller, a STBLE energy-based BLE module, and a microUSB connector. On the other side, a microSD card, 500mAh battery, and the ST-Link V3 connector. Now, let's take a deeper look at the main components on the Sensortile.box, featuring the latest motion and environmental sensors from ST. Starting with the motion sensors. LSM60SOX, six-axis inertial measurement unit, best in class in terms of power consumption and featuring a high-performance accelerometer and gyroscope combo. It also enables a new concept in smart features by embedding a programmable finite state machine and decision trees. The board also features two standalone accelerometers, the LIS3DHH, a high-performance inclinometer based on a ceramic package, ideal for precise inclination measurement over temperature and time. And the LIS2DW12, an ultra-low-power accelerometer featuring less than 500 microamps in active mode. The platform also benefits from our standalone magnetometer, LIS2MDL, the perfect companion for the LSM60SOX, moving towards a full nine-axis implementation. LIS2MDL offers a high dynamic range, increasing the robustness of the part. From the environmental sensor perspective, the Sensortile.box includes our latest high-performance pressure sensor, LPS-22HH, offering high accuracy, low noise and low power consumption. In addition to the pressure, measuring temperature is one of the most common sensor implementation in the industry. The STTS751 can bring high accuracy and low power consumption to IoT devices. We also included our small-front factor and low-power humidity and temperature sensor combo, the HTS221, featuring 3.5% relative humidity accuracy and 0.5 Celsius degree accuracy, and finally, but not the least, the MP23ABS1, analog wine-band MEMS microphone, capable of capturing voice and noise generated in the environment. From a processing perspective, all the sensors are managed by an ultra-low-power STM32L4-plus microcontroller in conjunction with the Bluetooth Low Energy module based on Blue Energy system on chip. Additionally, as mentioned earlier, the Sensortile.box is battery-powered and it features our STBC02 battery charger technology. I will now provide a quick overview on Sensortile.box operating modes. The entry mode is the out-of-the-box experience of the Sensortile.box. There is no need for programming. You will just need to connect the board to the smartphone mobile app and select one of the pre-programmed functions available and are also listed here. Pedometer, human activity recognition, sensor fusion, compass and level, data recorder, vibration monitor, barometer, and two AI-based implementations, in-vehicle baby alarm and the baby crime detector. The next mode is the expert working mode that increases complexity and flexibility of the mobile app interface, still without writing a single line of code. In this case, the user benefits from the STBLE sensor app graphical user interface to create and customize new applications. It's possible to define additional applications by selecting the necessary building blocks and allocating the function that does make sense for each specific use case. Some examples are power optimization by selecting which sensors are activated or shut down. Vibration analysis through fast Fourier transform and even pattern recognition by benefiting from algorithms such as ST sensor fusion that can be enabled as needed. The last working mode of the Sensortile.box is the professional or pro mode. It's fully compatible with STM32 cube and you can find Sensortile.box among the starter kits in the configurator, CubeMX, allowing the user to properly set up the hardware and the microcontroller peripherals as needed. The platform supports a dedicated function pack featuring a wide range of examples that benefit from the Sensortile.box hardware. In addition to these features, the pro mode now also supports the algo builder and uniquely graphical user interface. Additionally, this is the mode where the user can benefit from the embedded ST-Link V3 connector for programming and debugging. If you'd like to explore more about the Sensortile.box and the three operating modes, go ahead and register for this on-demand webinar. Just follow the link on the bottom of this slide. During this presentation, we will be focusing on the pro mode. Here are the main advantages of the professional working mode of the Sensortile.box. Compatibility with the STM32 cube, so you can program the STM32 microcontroller and benefit from a dedicated STM32 cube function packs. There are three function packs currently available. We'll cover in detail the FPSSNS STBOX1 and the FPATR BLE1. But let me provide you first a summary on the function pack FPAI Sensing 1. This function pack is dedicated to artificial intelligence. The package enables advanced application, such as human activity recognition or audio sync classification, based on output generated by neural networks. The neural networks are implemented by a multi-network library generated by the XQBI extension of the STM32 cube MX tool. Pro mode also offers compatibility with PC-based tools, such as the AlgoBuilder and Uniclaw graphical user interface. The function pack SNS STBOX1 is available online and I have captured here the snapshot of the webpage. On the bottom of the slide, you have the full link for download. The function pack SNS STBOX1 is an STM32 cube function pack for the pro mode of the sensor tile.box, which helps you build custom applications. The package benefits from the all key components on the sensor tile.box. Software examples are running on the STM32 microcontroller and it also includes all the drivers needed for the sensor tile.box evaluation kit. The function pack features a complete set of sample applications, like the one listed here. Easily send data via Bluetooth Low Energy. Visualize the sensor data with the Uniclaw graphical user interface via PC Serial Terminal or use Rural Power Implementation based on RTOS for transmitting the data via Bluetooth Low Energy connectivity. Create a bootloader and an application for firmware over the error updates. Use the dual bank flash feature for rollback after a firmware over the error update or program the LSM60SOX machine learning core or its finite state machine and control the output of its register and transmit the results via Bluetooth Low Energy and also save the sensor data to the SD card. I will just present here the first two and let you discover the rest. As I mentioned, one example is to send data from the board over Bluetooth Low Energy. I have used here the binary file available in the function pack and programmed it into the STM32 on the sensor tile.box. Here are the snapshots from the phone app showing environmental data, motion data and the RSSI. The battery was disconnected as the device was powered over USB. Datalog extended example, part of the function pack can be used to stream data via USB or BLE to the Uniclaw graphical user interface. A custom algorithm can be built using AlgoBuilder graphical user interface. I will provide here a brief overview on these tools that I have just referenced. The AlgoBuilder is an application for the graphical design and testing of algorithms. It relies on existing algorithms and libraries provided by ST and user-defined data processing blocks. Unicleo GUI is a graphical user interface that allows the user to configure sensors, display sensors data like the plot over time, interrupt generation through a logic analyzer and more and also display of output data from algorithms created using AlgoBuilder. Another function pack available for sensor tile.box in Pro mode is the function pack ATR BLE1. Before we get into details, I want to frame better the use of such software example. The main goal is to address asset tracking applications. Asset tracking is referred to a very broad range of use cases from monitoring shipping containers, parcels, luggage, food, medicine, tools and utensils, to livestock and pets. Basically, we refer to any method of tracking physical assets from most simple scanning barcode labels attached to the asset at various inspection point to using tags with technologies like GPS, BLE, RFID along with cellular and ultra wide area network connectivity like Lora and SiegFox. Outdoor real-time monitoring applications like containers, e-bikes or scooters or fleet management require the most investment in technology with real-time positioning and wide area band connectivity. Industrial logistics like pallets, luggage, smart parcels and goods guarantee like the cold chain and medical shipment might require a lower cost solution and still a fairly smart sensor. In more case reusable, while some application like personal letters, packages or parcels, hardly can just justify a substantial cost for the service and would require a one-time disposable solution. Let's look here in more detail from the technologies point which are the requirements for each type of asset tracking application. Our goal is to make all these applications smarter and to choose the optimal set of processing, sensors, connectivity and power management for the task. Let's focus on the sensing technology. If a container was to carry perishable food items, then temperature and humidity monitoring are relevant. Motion sensors also can play a big role. Imagine a container with fragile objects. The smart tracker would need to sense if the container is not handled correctly or even drop down and be able to record the event. To do so, an approach is used to the embedded functionality of our accelerometers to automatically detect a free fall or a shock and alert the main processing unit, a microcontroller. And to record the event. Auto real-time monitoring would also require a position information with GNSS to complete what, when, where of any event. Another interesting sensing technique applicable to industrial logistics is the use of a barometer in conjunction with an accelerometer and dedicated algorithms. They can be used to identify taking off or landing if a cargo is on a plane. The functional blocks for an asset tracker are listed here on top. ST use an equally positioned to offer for some solution of the components along with turn key evaluation platforms. Using the ST evaluation board, it is an effective way to jump start your design for an asset tracker application. NFC, BLE and Subligar solutions are available and today we'll be focusing on BLE sensor non-tracking device with multiple sensors implemented with the sensor tile.box that we discussed previously. The function pack developed with asset tracking application in mind is the FP ATR BLE one. The software package can be downloaded from ST website and I have referenced the link on the bottom of this slide. The following folders are included in the software package. Documentation contains a compiled HTML file generated from the source code which details the software components and the APIs. Drivers contain the HAL drivers and the board specific drivers for each supported board or hardware platform. Middleware contains libraries and protocols for do energy dash to do energy, the real time operating system and the file system. Projects contains one example application for creating a customized low power asset tracking application controlled by the tool low energy for the sensor tile.box development kit. The application is available for IR embedded workbench and STM32Q by DE multi-OS development tool. This software package offers a complete application example on how to create an asset tracking application and record events like tilt and orientation change or record excursion of temperature and humidity outside the certain range. All of this is configurable from the ST asset tracking application available for Android and for iOS is coming soon. This app supports the configuration of a cloud dashboard they will discuss in the next slide and also supports other connectivity options like SIGFOX and NFC with the NFC sensor node. Having a sensor node connected to a smartphone app is a good demo but we can really jumpstart a proof of concept of an asset tracking end to end application is the availability of a cloud dashboard component. ST has built and published DSH asset tracking a sandbox application powered by AWS and free for our customer to use and experiment with connecting their sensor nodes. It's compatible with the sensor tile.box via the ST asset tracking app. Say for example, you bought some very nice bottle of wine from the Columbia Valley and the shipper equips them with a sensor tile.box. I have now a way to know what happened to the box at any given time. Was it left in the sun, kept upside down, exposed to excess humidity, all information that allows you the receiver to know if there is a possibility that the wine was spoiled in transit. And you have access to this information just looking at the dashboard data when you receive the package. But what's better than seeing the real demo? Now I will hand it over to Manuel to show you the real stuff. Thanks, Alexandra. My name is Ernesto Manuel Cantone, Product Marketing Manager for Mems and Sensors for GeoAccounts out of Portland, Oregon. In this demo, we'll show you how to use a sensor tile.box in professional mode as an asset tracker in an end-to-end example of a sensor-to-cloud application. For this demo, we'll need a sensor tile.box out of its usual enclosure this time as we're going to use the user button, an Android phone, I'm using a Pixel phone with Android 9, and a laptop with a browser. I'm going to use Chrome. The sensor tile.box is running the FunctionPack FP-ATR-BLE1 and is communicating via Bluetooth low energy to the ST asset tracking app on your Android device. The app will then funnel the sensor data to a cloud application powered by AWS and the very same data is accessible via the dashboard asset tracking that you can find on www.ST.com. In today's demo, we'll use Google login to the dashboard while in its final four, a MyST.com account will be required. On the dashboard, let's navigate on the top left menu and login with your Google account. As you see, no devices are present. On the smartphone, here mirrored on the screen, let's open the ST asset tracking app and tap on the cloud icon to also login. Nothing to see here also. Let's go back to the menu menu and tap on the Bluetooth icon. Allow Bluetooth access to the app and let's press the user button on the sensor tile box to start advertising. You see now ATB underscore 100, that is the default name for the sensor tile running this FunctionPack. We can now configure our tracker based on sensor tile box. Let's set the sensor reading to one minute. We can now add the features and triggers to the tracker. For example, temperature greater than 30 degrees Celsius. Humidity greater than 50%. Adding a wake up interrupt based on shock greater than 2G. And finally, a detection of a tilt event. Next, let's hit the save button and the sensor tile box will disconnect and start logging. Let's focus now on the sensor tile feed on the bottom right of the screen. Every time that an event is triggered, a blue light is blinking. For a sake of time, let's speed up now and fast forward a couple of minutes in the future. Back on the application, let's tap on the Bluetooth icon and press the user button on the sensor tile box to connect it to the app. I will now move to the data tab. And as you see, the collected data are now being transmitted to the app. The app will ask if we want to sync cloud data. Let's tap on OK. Next, I need to give a unique name to my tracker. In this case, ST underscore devcon. The app is now communicating with the AWS cloud and creating a thing associated to the sensor tile box in my account and exchanging the security certificates to guarantee the uniqueness of my tracker. Once connected, the upload phase starts and the data now residing on the phone are being transferred to the cloud. There you go, uploaded, completed. And as you can see, we can visualize directly on the app both the sensor data and the alerts triggered by events. Let's go back now on the browser. I click on Devices and refresh the browser status. As you can see now, the thing STDevcon is available and the same data we've seen on the smartphone now augmented with the location data from the phone can be accessed from anywhere. Now, for the last part of our demo, I left the sensor tile box collecting data for a couple of hours. And I'm following the same procedure I liked it before, tapping on the Bluetooth button on the app and pressing the user button on the sensor tile box and then navigating to the data tab once connected. Now, unnoticeable amount of data is being transferred from the sensor tile box to the app via Bluetooth low energy. And this can take some time due to bandwidth limitation as we see. Again, sync completed and then notice all the historic data collected what can be a snapshot of the status of your package during shipment. Let's upload to the cloud app for further analysis since we have already created the AWS thing this time the app won't ask for the name as the sensor tile box is recognized by its MAC address. Here's the data synchronized to the cloud now. Let's go back on the dashboard and this time let's click on telemetry. We can select the STDevcon sensor tile box and the feature of interested with this timeframe in this case temperature for the last three hours. Let's click on the play button to fetch this data. Next, let's select humidity again for the same three hours. Let's click then on the events and again select the STDevcon for the last three hours. We can observe here all the events triggered wake up and tilt. In today's demo we showed how to build an end-to-end asset tracking solution with the dashboard asset tracking and sensor tile box. The dashboard supports other RF connectivity options on top of BLE, SIGFOX, LORA, LTE and NFC. We'd like here to allow it that this dashboard is compatible with the function pack sensors smart tag one running on the nuclear hardware or on the form factor ST NFC sensor. Thanks for your time today and back to you, Alexandra. I hope you enjoyed the demo. You are now ready to order a sensor tile box and start playing. Before we end this presentation, I want to quickly summarize few key points. Today you have been introduced to the sensor tile box and the professional mode using softer function packs along with the smartphone apps available and the asset tracking dashboard. For reference, all the main components inside the sensor tile box are listed here. Sensor tile box is an open source platform where all the relevant hardware documentation is available on ST.com. Visit the sensor tile box webpage to download the schematic, Gerbers and Bill of Materials along with all the user manual and related software. Follow the link on the bottom of this slide for more details. For more information, you can also access the dedicated YouTube videos on the sensor tile box. These videos walk you through entry, expert and professional modes in detail. On top of this contact for support, please refer to our online support center. Visit our longevity program webpage highlighting the industrial grade sensor product portfolio. We also have a dedicated repository for Android, Linux and platform-independency drivers available for our MEM sensors. Don't forget to visit ST.com slash sensors to get the latest updates. I will end this presentation with a couple of takeaways. The internet of things brings many opportunities to enable sensor to cloud connectivity. ST plays a key role in this marketplace by bringing the necessary building blocks in terms of products ranging from consumer, industrial and automotive, and by providing development tools that jumpstart your understanding, evaluation and development when targeting a new application. We are always focus focused on MEMS innovation and we are constantly expanding our development ecosystem. Thank you for joining us today. We will now open the Q&A session.


I'll say it's your turn, you'll pull this out, claymore, claymore, claymore, put your finger in here as you're saying it, push, twist, pull.


In this lecture we are going to provide a broad overview of a class of techniques that has come to be called 3D war. War for variational 3D essentially tells you we are concerned with problems in the 3 dimensional spatial variable at a given time. So, time is not a factor here. So, the 3D war and 4D war, war always represents variational method is a acronym for variational method. 3D essentially no time only space 4D 3 plus 1, 3 for space 1 plus 1 for time. So, you can think of 3D war to be a data simulation scheme done over the spatial domain at a given time. 4D war is a data simulation scheme that is done as a system evolves in time in the 3 dimensional space. Again, why are we talking about 3, 4 and so on? Most of the physical why most all the problems of interest in geophysical sciences happens on and around the earth. So, the space of interest is fundamentally a 3 dimensional space XYZ and many of the physical processes evolve in time. So, meteorologists in the early days when data simulation schemes were developed in order to be able to bring out the distinction between when time is a factor, when time is not a factor they concocted this notion of 3 and 4. The way VAR essentially stems from the fact some of the earliest thought processes that led to the data simulation scheme arose from the variational approach which we have indicated when we did forward sensitivity method 4D war method or adjoin method and that is the door by which these acronyms are kept into the vocabulary. So, you understand my thought is the row smells the same by whatever name you call. So you can if they want to call 3D war survey what is the idea? The idea is the underlying idea in 3D data simulation is always I would like to be able to estimate the unknown true state of a field variable. So, I have a spatial domain I have a grid embedded in the spatial domain I have numbered the grid 1 through n at each of the grid points I have a pressure value or temperature value at each grid value I am interested in a scalar variable I am going to collate all the values and create a vector action. In the case of 1D the grids are along the 1 dimension I would like to talk about for a minute. So, in here the grid is like this in case of 2D over the grid. So, in here we have n points along one line in the case of 2D I have the x dimension nx points along the x ny points along y. So, n is equal to nx times ny in this example 1 2 3 4 nx is equal to 4 ny is also equal to 4. So, n is equal to 16 in the case of a 3D I have a grid I have a grid I have a grid. So, this is nx this is ny this is nz therefore, n is equal to nx times ny times nz. So, you can see as you go from 1 dimension to 2 dimension to 3 dimension the size explodes let me give you a quick example let us assume nx is equal to 100 ny is also equal to 100 and nz is equal to 50. So, n is equal to 1 2 3 4 5 5 times 10 to the power of 5 that is half a million. So, the whole question is this. So, what is the nx the number of points along the x direction. So, let us now consider a spherical coordinate system. So, I would like to be able to embed a spherical coordinate system built on the earth. The earth diameter is 8 roughly 8000 miles. So, the circumference of the earth at the equator is roughly 24000 miles. If you consider a grid of 100 points covering 24000 miles the distance between 2 grid points is 24000 divided by 100 that is a large distance 24000 miles divided by 100. So, that is 240 miles. So, the distance between 2 grid points is 240 miles. Now, let us take the distance from Bangalore to Madras for example it is roughly 200 miles. So, there will be 1 grid point in Madras 1 grid point in Bangalore 1 grid point in probably Bangalore can that accurately capture the variations no. So, if you now change the number. So, if you want better accuracy you have to increase nx you have to increase ny you have to increase the nz. What is nz? nz is the number of vertical levels generally in meteorology the vertical levels is limited to 50 or 100 that is all. But what is the general problem for global models nx ny the product of nx ny with the nz the number of points. So, that is why in atmospheric sciences in oceanographic sciences the value of n quickly in a hurry becomes a million become tens of millions. So, there are 2 things 1 is how to solve conceptually a given problem. Secondly knowing conceptually how to solve a given problem how do we solve the problem for which we know that there is an algorithm but a problem of huge size. Not every conceptual algorithm will be applicable directly to problems of large size. So, the course of dimensionality often times limits what one can do we have already alluded to these things. So, how do we decide what is the value of n I want to do many things whatever I want to do I must be able to do in my lifetime earlier we saw to multiply 2 matrices of million by million on a machine with a teraflop power it will take about 12 days to multiply 1 matrix to multiply 1 pair of matrices. Can we afford to solve a problem for 12 days can we afford to solve a problem for one day can we afford to solve a problem in 6 hours no we want to be able to make forecast every 6 hours every 12 hours every 24 hours. So, what is the time period within which you have to generate forecast that provides the time horizon the computer provides you the computing power. So, the time horizon and the computing power together limits the value of n that you can consider in solving problems. So, whenever we talk about problems of size n I want you to keep track of this n I mathematically I can make this n as large as possible but physically to be able to put it through the computer there are other limitations. So, let us pretend I have a global domain I have a field variable of interest I have correlated all the field variables of interest in a vector vectors of size n n if it is a one dimensional problem two dimensional problem three dimensional problem how relates to 1d 2d 3d that is what we have talked about so far. So, Rn is called the model space as we have been talking about all through there are our notation is pretty common through the whole thing there are two pieces of information about the unknown one is the background or a prior and see the new observation again this is nothing new we have talked about it within the Bayesian context at great length. Let me give an example suppose you are planning a trip to Paris in the middle of January you want to be able to pack the right kind of dress. So, what did you do you would like to look at the Almanacs that talks about the average low temperature average high temperature in the middle of January in downtown Paris and that information is available this day and age the Almanac is essentially Google Wikipedia they all provide you lots of information how are these information collected these are summaries over a long time and that is the information that is called background the background essentially comes from a summary of the prior information it is also called climatology. So, the background can come from several different directions from previous knowledge climatology previous forecast and so on and so forth. So, all the gobbledygomix of everything I know up until this time if I can spread it over the grid and embed this as a vector that I call as a background. Z is the new information RM is called the observation space again we have talked about it at great length in several occasions the prior info so I am going to call XB as a prior. So, you can see the Bayesian framework comes into play sneaks in the XB has been derived from the previous forecast on the climatology X is the unknown X is the unknown I want to be able to estimate sorry X I am interested in trying to estimate X or find X. XB is the prior knowledge about X so X minus XB is X tilde B X tilde B is called the difference between the unknown and the background. The expected value of XB is 0 the covariance of X tilde B is B B is a matrix B is a n by n matrix. So, B is a n by n matrix that encapsulates the spatial covariance that underlie the current knowledge of XB and that should be very clear to us from the previous slide in optimal interpolation as well what was C there C was a covariance matrix that is derived from the knowledge of the long time series. So, XB so if you look into Google you can talk about what is the maximum temperature in downtown Paris noon every day that is a prediction. So, you can from this data you can compute B by doing simple statistical analysis. So, I am assuming the I know XB I know B what is B B is represent the spatial variability of XB but then you and I look at Google to go to Paris in the middle of January we do not we do not worry about the variance B simple as a the maximum temperature what did you say the maximum temperature in downtown Paris in middle of January could be 5 degree Celsius plus or minus 1 the plus or minus 1 is indication of XB 5 degrees is XB. So, we have plus or minus gives you the variation at a given location I also know the mean value. So, I am going to now assume PX is the prior prior has the mean XB the covariance is B I am going to pretend it is normally distributed let us consider the let us discuss that for a moment normal distribution what is the beauty of normal distribution. Normal distribution is probably the only distribution I think it is the only distribution that is uniquely determined by the mean and the variance. So, there is a general result in probability theory given a density function PFX sorry given a density function PFX given a density function PFX I can compute all the moments what are the moments first moment second moment third moment central moment and non-central moments. So, what is the expression for the kth moment the expression for the kth moment is expected value of X to the power k which is equal to integral let us assume scalar variable that is equal to X to the power of k PX DX. I can also consider central moments or centred moment X minus EFX to the power k PFX DX I can compute this for every k. So, given P I can compute all moments now let us consider the converse how many moments should I know in order that I can build the probability density function. So, given the probability density function I can compute all moments simply evaluation of the integral some integrals can be evaluated explicitly some integral almost all the integrals can be evaluated to a very high degree of approximation numerically. So, the passage from distribution to moment I can do anything that the converse question is a fundamental question that question has come to be known as moment problem moment problem. What is the statement of the moment problem the moment problem tell as the following question how many moments should I know in order to be able to build the underlying probability density function that is a very good proof you know in the first volume of Feller's book that essentially tells you one needs to know infinitely many moments to be able to reconstruct PFX. So, the converse problem of going from moments to distribution is a much tougher problem. So, within the context of this moment problem now I want to mention that normal distribution Gaussian distribution is the only unique distribution that is determined by the mean in the variance why if I say x is a Gaussian distribution x is uniquely decided by the mean and the covariance or other variance. Therefore in many meteorological application a very statistical application if I am given a bunch of data from the data I can grind the mean from the data I can grind the sample mean and the sample variance sample covariance if I have a sample mean and sample covariance I am going to pretend that underlying random variable is distributed with normally with a sample mean as the mean and sample covariance as a covariance. So, that is the reason why even though we only know the first moment and second moment we take liberty we take a big liberty in assuming that the background is the background is normally distributed with the mean x b and the covariance b it is a bit of a stretch all we know is only x b and b to go from x b and b to the normality with x b as the mean and the b and the is the leap in faith but we oftentimes do that. So, for those of us who would like to look at the specificity of the distribution function to say p of x is normal is to say p of x has this functional form 1 over 2 pi to the power n by 2 determinant. So, this is the determinant of the matrix b determinant of the matrix b exponential of minus j b of x where j b is given by this quadratic functions. Now please understand this kind of quadratic functions we have seen several times over and over and over and over and again this the so j b looks like the least square criterion this j b which is the which looks like the least square criterion comes as an exponent to an exponential function. Look at this now least square where invented by gas gas in distribution is normal. So, the least square functional form trying to describe the functional form of the normal distribution it is not an accident it is all it is all invented by the same person. So, it is this j b the mean square type objective function gives rest of the bell shaped curve. So, this is the functional form it is very important to understand this functional form. So, this is the information about the background let us go to the observation z is the observation z is equal to h of x plus v z contains information about the state. So, is the indirect measurements of the state I am going to assume v is normally distributed. So, z because of the additivity because of the uncorrelated nature of x and v the conditional distribution p x given p of z given x is given by this distribution where j o is given by this you can see the relation between 5 and 6 and the relation between 8 and 9. So, 5 and 6 relates to prior the background is taken as the prior. So, what is the assumption here the prior information is the normal is normally distributed with the known mean and the known covariance the conditional distribution given x that means given x the conditional distribution is given by 8 and 9. So, I want you to take a good look at j b in 6 and j o in 5 in particular I would like to draw attention to the j o in 9 j b b refers to background j o o refers to observation. So, z minus h of x I am sure you will recall this z minus h of x is the residual this is the residual. So, this is essentially least square criterion we have already used but we did not say anything about least squares we simply talked about exponent of a underlying normal distribution. So, I would like you to develop an appreciation least squares normality they are all intimately associated with each other we are also. So, what an assumption x b and v are uncorrelated x b what is x b x b is the background the background and the observation noise uncorrelated these are standard assumptions why are these standard assumption they are very meaningful x x b and v and 0 v and x b that means there is no correlation between v and x b. So, x b b refers to the prior z r refers to the new information these are two pieces of information about the unknown true state please understand when we did not have prior we only had observation we know how to do data simulations for static problem we usually squares now we are going to invoke to the Bayesian type of analysis I have prior I have new information we are going to use pfx the prior conditional distribution in a Bayes rule to get the posterior distribution and that is the underlying philosophy of 3D war. So, recall the Bayes rule the posterior this is the posterior that is the prior that is the conditional distribution pfz is a normalizing constant I can now substitute look at this now I have expression for pfx I have expression for conditional distribution both are normal I can substitute everything if I did that I get this multiplying constant times exponential exponential minus gfx minus gfx is simply j0 of x plus jb of x where c is some normalizing constant we have looked at this kind of ratio and under normal distribution we have done several exercises in in the chapter on Bayes least squares estimation so I am trying to do very thing very much similar to what I did in the past now what is that I would like to do I would like to be able to so now look at this now before I go further what is this that is a posterior you can think of as a likelihood a like Fischer are with me place so Fischer did not have background so he only had the conditional distribution so he would he considered the notion of likelihood and maximized the likelihood here we have posterior within the Bayesian context we have already seen the posterior mean is the best estimate so I would like to be able to I would like to be able to maximize minimize appropriate quantities given the setup that is the optimization problems of interest so I hope the expression 11 and 12 are very clear now 12 what is 12 I want to draw your attention to 12 12 is simply sum of two quality forms j0 arising from observation jb arising out of background so I would like to be able to maximize px of z with respect to x we can see px of z that means I want to find the location where the px of z is maximum when px of z a pre-remember px of z is proportional to barring a constant exponential minus j of x and j of x is equal to j0 of x plus jb of x so maximizing exponential of minus j of x is equal to minimize in j of x therefore therefore the algorithm reduces to minimizing this term this is the background term this is the observation term when hfx is equal to linear hfx both the terms are quadratic even hfx is not equal to linear the first term could be non-linear but second term is still quadratic I am interested in minimizing jfx which is given in 13 this minimization problem has come to be called the 3D war problem the 3 dimensional variation of simulation problem is essentially minimizing the two quadratic forms one coming from background another coming from observation and so minimize jfx with respect to x is an unconstrained minimization problem a lot easier there is no constraint we can compute the gradient gradient of the sum is some of the gradients Hessian of the sum is some of the Hessians so gradient of j0 gradient of jb substituting summing and equating that to 0 I get the general equation now look at this now the right hand side involves xb zb Jacobian of h and r everybody is known and the left hand side b is known r inverse is known Jacobian of h is known x I do not know x I do not know when hfx when hfx is equal to hfx this reduces to a linear relation which can be solved by any one of the methods in principle 17 is a non-linear system is a non-linear algebraic system whose solution gives you the optimal analysis you with the non-linear algebraic system the only way to solve a non-linear algebraic system is by numerical methods so there are tons of numerical methods one knows to solve 17 in the special case I would like to when I reemphasize that in the special case when hfx is equal to hfx in this in so when when hfx is equal to hfx the Jacobian of h is equal to h if everything simplifies therefore the matrix like this times x times this matrix and that is the optimal solution is given by this this system I have to solve this system is like ax is equal to b again ab is spd we already know how to solve spd systems and it is no accident that this is exactly the same relation that we derived in the model space formulation in the Bayesian scheme so this see the model space formulation so this is equivalent to Kalman filter equation so the model space formulation so what does this tell you as we will see when we come to Kalman filter equation Kalman filter equation Kalman filtering essentially consists of the following step namely the background is replaced by a forecast a new observation comes in how do you combine forecast and and the observation that is the Kalman filter equations so you can readily see what happens at the filtering stage of the Kalman filter Kalman filter has multiple stages one of the stages in Kalman filter is called the filtering phase or a filtering stage the filtering stage of the Kalman filter is equivalent to 3D war that is the story that is the story and 3D war is very much related to the Bayesian framework and it is no surprise here we are coming back to Kalman because we have already established the relation between Bayesian least square and minimum linear minimum variance estimate one did it in the model space another did in the observational space we built the bridge between the two using the matrix identity called Sherman-Maurice-Rodbury formula so now I believe the whole thing is falling in place Bayesian linear minimum variance 3D war Kalman filter all these things are all close cousins of each other and the even though the ideas come in different with different labels and different names and the underlying mathematics are very nearly similar or same the so the equation to this gives you an so the equation to 19 gives you I am sorry the equation to 19 gives you the analysis the optimal solution for this is called the analysis so that is called XA analysis XA is the analysis please realize analysis is the fancy term that geophysicists use for the posterior estimate so what statisticians calls as posterior estimate is essentially analysis in geophysical sciences so we have already seen this the the Hessian of the J function again I would like you to verify we have already seen when we did statistical least squares the inverse of the Hessian is indeed the analysis covariance of the analysis so I have XA I have PA PA is essentially X inverse that is the Hessian so this is the analysis covariance so this is PA analysis covariance so I have analysis XA from the previous slide given by equation 19 the analysis covariance is given by 12 why am I interested in analysis covariance because we are interested we have been talking about statistical methods in a statistical methods mean alone will not cut I need to be able to provide information about the underlying variability as well so in most of the stochastic predictions we are not only interested in the level given by the mean but also we are interested in getting some feel for the underlying covariance so I have both the mean and the covariance so this is the best we could do you gave me the first two moments of background and up the first two moments of both the background as well as observation now I have combined the first two moments of the background of the observation to create the first moment and the second moment of the analysis the mean of the analysis the covariance of the analysis how do I use the covariance analysis analysis covariance matrix is a matrix the diagonal elements are all the individual variance the sum the trace the sum of the diagonal elements which is called a trace the total amount of variance variance across all the components that provides you a good measure of the quality of the of the analysis if that variance is large you cannot put too much faith on your analysis if that variance is smaller analysis has lot more credibility lot lesser variation so that is how we would like to be able to we would like to be able to whenever we generate an analysis what is an analysis analysis are kind of a prediction what good is a prediction if I do not know good what is the degree of variability in my prediction with respect to lunar eclipse lunar eclipse lunar eclipse and solar eclipse variability is 0 we know precisely can we predict the temperature on noon on March 15th at at at at at downtown London we cannot we we can I can of course I can generate a number but what good is that number unless I know what is going to be underlying variance associated with it so prediction deterministic prediction stochastic prediction deterministic prediction we are content with one number in stochastic prediction we also need to have a number that gives you the level also we need to give the variability so if you look at current predictions of local weather they will say tomorrow there is a chance of 1 inch rain 80% probability so 1 inch rain is the measure of the mean level of the rain 80% what that is a very very high percentage very high percentage so we need to be able to not only give the level but also an associated variance which is the measure of the variability in my our confidence in my prediction so in stochastic predictions I need both the mean as well as the variance or the covariance once we get this there are is a quick exercise I can talk about the incremental form of 3D war in the model space so let me run through this very quickly this is a largely related to computational advantages and disadvantages of whichever formulation we want to follow let this be the increment this is called incremental formulation substituting this 23 in 20 my J becomes this that you can readily see where D is equal to H Z-H of XB that is what is called the observation increment or the innovation so we can work with the incremental variable it can be verified that the optimal increment delta X given by the solution of this in other words if I minimize this the minimizer is given by the solution of this what is the idea now the analysis is given by background plus the increment the increment comes from this analysis that is that is another way of looking at it one of the difficulty in here in solving this is this system as well as the previous system let me go back we need to know B inverse we need to know R inverse so that is the question one can ask myself so that from a computational point of view do I know XB do I know XB and B do I know XB and RB inverse where B inverse comes in B inverse comes in as the weight in my objective function in the least squares so my while I know B my data simulation scheme uses B inverse B is a very large matrix n by n n is a million the inverse of a million by million matrix so computationally now one has to ask oneself the question hey are you going to really invert this matrices is there a way to reformulate the problem without worrying about B inverse but using B only I hope you appreciate this problem now we know XB so what is that we have been given I know the background information XB and B I know the observation Z and R but my J0 uses R inverse my JB uses B inverse my JB uses B inverse B inverse R inverse who is going to give you the theory is beautiful but you need to be able to compute B inverse R inverse look at this now the left hand side matrix in here involves these inverses so where in the world are you going to get these so the question here is that is there a way to reformulate I like the formulation but I do not like the computational demands of it is there a way to reformulate it to an equivalent formulation that does not depend on some of the inverses so that is a question that drives some of these so look at this now AM so I am going to now call B inverse plus H transpose R inverse H as AM sorry as AM B and R are a speedy I am assuming H is a full rank therefore AM is a speedy if AM is a speedy this combined matrix AM has n eigenvalues the least of the eigenvalues is positive the condition number for this matrix is given by lambda 1 by lambda n please understand this is the matrix with which I have to solve AM X is equal to the right hand side right hand is the vector so the quality of the solution depends on the condition number of this matrix AM the condition number please recall lambda 1 by lambda n while AM could be symmetric and positive definite since lambda n positive but lambda n can be very small so the condition number could be very large since lambda n is positive but small so lambda n can be small but still positive so let us assume lambda n as an example is 10 to the power of minus 6 is still positive but it comes in the denominator so condition number will be very large so condition number for SPD matrix is becomes very large because the smallest eigenvalue while remaining positive may become very small in those cases I will have headache computationally so large condition number leads to instabilities in computation to tame the conditioning we use a suitable transformation called preconditioning so preconditioning is the methodology by which I am going to get around the challenges posed by an ill conditioned matrix or a matrix with large condition number what is the matrix of interest here AM what is the AM AM is the sum of B inverse plus H transpose R inverse H these are all mathematical considerations so how am I going to do develop a preconditioning to avoid the challenges of ill conditioned matrices or nearly ill conditioned matrices that is what we are going to discuss now so we have assumed B is known we are assumed R is known if B is known let us pretend I know the Choloski factors G G transpose every SPD as a Choloski decomposition so G is also called Choloski factor or a square root of B we have already seen that when we did that so I am now trying to define a new variable U in terms of delta X you can think of this as a linear transformation or you can think of this as a preconditioning transformation whichever way you want to call it so with this delta X is equal to GU look at this now if B is equal to G G transpose in 28 B inverse is going to be G to the G minus T G inverse minus T means transpose of the inverse so if I substitute this linear transformation my GU becomes this look at this now in the first term there is no B B is gone in the new variable and what is the minimizer the minimizer of this is given by this so I am not trying to show you all the steps this is the quadric 30 is a quadric function can compute the gradient equate the gradient to 0 equated to 0 you get the optimal solution in the new variable U to be given by 31 I hope you keep track of everything now what is the matrix AM AM is given by G transpose H transpose R inverse H G now let us look at this matrix I AM so I have the matrix I so let us instead of AM I will consider the matrix A what is the general resultant matrix theory if lambda is an eigenvalue of A implies 1 plus lambda is an eigenvalue of I plus A that is a fact so if let us assume now if lambda bar 1 is greater than equal to lambda bar 2 greater than equal to lambda bar n greater than equal to 0 so if AM is a symmetric positive definite it is eigenvalue the least eigenvalue is lambda bar n please understand I use lambda 1 lambda 2 lambda 3 for A I am using lambda bar 1 bar 2 bar n for A bar m so the eigenvalues let this be the eigenvalues of this the eigenvalues of I plus AM are 1 plus lambda 1 bar greater than equal to 1 plus lambda 2 bar greater than equal to 1 plus lambda n bar this is greater than 0 now look at this now the least eigenvalue is 1 plus lambda n bar 1 plus lambda n bar can never be close to 0 lambda n bar is positive but 1 plus that means I am bounding the value of the least eigenvalue that means my condition number is not going to explore this new space therefore I have tamed the condition number by a useful transformation so such transformations are called preconditioning methods preconditioning methods have been known for a long time that is essentially motivated by to tame the instabilities that may arise because of large or a large condition cases so I have already talked about all this so the condition number for the condition number for the new matrix is this and this is always the least eigenvalue is always bounded away from 0 instead of being bounded away from 0 so this matrix is well tamed if this matrix is well tamed I should not expect any difficulty in numerical analysis in numerically trying to compute the solution when I use any method any meaningful method to solve. So with that we come to the end of the discussion of the so called 3D war so in summary 3D war is another way of looking at the Bayesian formulation which we have already talked about within the context of statistical estimation theory especially development of Bayes least squares theory we are trying to reinforce it by using a new language that is prevalent in atmospheric sciences in data simulation called 3D war and we talked about the nature of the solution we talked about the potential for ill conditioning we also talked about how do we tame using a preconditioning transformation to be able to induce better conditioning for matrices so that I can have reasonably good stable numerical results I would like to quickly bring to your attention to the Sherman-Marrison-Nutbury formula you can see the importance of this formula again I want to tell you something Sherman-Marrison-Nutbury formula was developed in mathematics for the sake of interest in matrix identity what is that they were interested in they were interested in the following if A is a matrix if I know A inverse if I perturb that matrix by adding another matrix if I am interested in A inverse can I find the inverse of A plus B which is the perturbed matrix simply as a function of A inverse and B that is the question they were interested in in other words if I know if I have a routine to compute A inverse of a matrix I can plow the entire new matrix A plus B through the algorithm and can compute it but that is not that what they were interested in I know the inverse of A A has been updated by B how do I update A inverse to get the inverse of A plus B that is the idea the idea is very similar to what we do in Taylor series what is the example of in Taylor series I know the value of the function at FFX I would like to be able to know the value the function at FFX plus H and what we do we simply say this is FFX plus H times F prime of X plus H square divided by 2 times F double prime of X so what is the idea in general both here as well as in here knowing what I know how can I extrapolate my knowledge in a neighbourhood of around what I know so I know A inverse from A A plus B is a matrix which is close to A so if I know somebody close to somebody who is invertible can I express my inverse based on the inverse of a fellow whom I know is invertible and I know his inverse that is the idea same thing in here I know the value of function at the point X I am considering neighbourhood of a point F of X plus H how do I extrapolate the value of FFX to FFX plus H I can do it by knowing the value of the function I am just derivative here I simply want to be able to use A inverse and B and that is the beauty of Sherman-Marrison's beauty formula when they developed it they had no idea whether it will be used at all they did it for the sake of mathematics for the sake of beauty in fact Sherman-Marrison formula plays such a crucial role in data simulation especially in trying to relate the data simulation formulation from model space to observation space why is this relation needed the model space is n dimensional observation space is m dimensional which is seldom m is equal to equal to n so either m is greater than n or m is less than n so what is that we would like to take advantage of it is always cheaper to perform computation in a smaller dimensional space so n is smaller you do the operations in model space if m is smaller do the operations in the observation space who provides this freedom to go between these two world Sherman-Marrison's very formula that is beauty it has no physics it has no meteorology it has nothing it is yes a beautiful mathematical enterprise that helps you to build the bridge between these two world and that relieves this of the pain of having to do all the computation only in one world irrespective whether m is greater m is less it is this power of the Sherman-Marrison's very formula I would like you to understand appreciate and see how repeatedly we have used in our discussion of in our discussion of data simulation I would like to make one or two comments ends up in the United States which is called the National Center for Environmental Protection they use the preconditioned incremental model space for the spectral model so it is mouthful let me tell you this now what is the model they use they use spectral model for the phenomenon what is the spectral model so consider the primitive equation in a spherical domain you express the solution in the spherical harmonics as a Fourier series you substitute the expansion in the spherical domain in the spherical harmonics into the differential equation you reduce the system of partial differential equation to a system of ordinary differential equation on the amplitudes of the spherical harmonics so what is the expansion of the field variable such as velocity such as pressure such as temperature so what is that I am assuming I can encapsulate the spatial variation by involving spherical harmonics what do I did not what I what I do not know it is amplitude so amplitude we assume the spherical the spatial variation can be captured by a clever combination of spherical harmonic functions amplitude is something I do not know so by substituting this Fourier series expansion in spherical domain into the primitive equation model I can reduce the infinite dimensional partial differential equation to a finite dimensional ODE the resulting finite dimensional ODE's have come to be called either low order models or spectral models so spectral models are essentially resulting from the application of the Fourier analysis to the continuous domain so they consider the spectral model they do a model space formulation they use an incremental version what is the incremental version delta x they use the preconditioned incremental model space formulation of the spectral model I hope you are now appreciate that that means there is a background I want to have the analysis I want to go from prior to posterior background is prior posterior is analysis I would like to be able to express the analysis the posterior as background plus an increment it is that increment in this incremental formulation that is being computed that is what we have expressed that because the computational problem they use the preconditioned version and they also use model space formulation so model space formulation of the spectral model precondition increment as opposed to NSF in NASA they use the observation space formulation so different places develop programs systems in different formulation because of that belief for the targeted applications in mind so I would like to leave you with a very interesting computational project it could be a very nice master's thesis in fact compare so what is that you have a 3D var problem I can do an iterative method customer like iterative method I can do a 3D var like method so it behooves us to ask a question to compare the results of 3D var with iterative methods and also I not only would like you to compare the quality of the result but also the computational requirements I think these kinds of projects at the master's level would be very educated very inspiring people to be able to look at different kinds of methodologies that could be brought to bear to solve different types of data simulation problems of interest in daily life thank you


Hello Isabelle speaking I find your number dog walker. Yes your walker dog. Okay.   He need exercise and yard is small and I need him walks by say tomorrow. You free No, it's a little townhouse with the dog and I walk need walking or it go crazy. It's a fact No, no, no you come back to me darling. You do not leave Arnold fine on the phone. My pig need to be walked. Where do you live? You


hat Commonwealth Institute is now the design museum, not actually sure if the gallery is still there or if it's been completely stripped as well. But I think that it that you know that it was important I think to signify. You know, again, you have these really old fashioned dioramas, you know these like you go into like the, you know, the Natural History Museum and Kensington Road or whatever and you've got these the same sort of dioramas that you have at the Commonwealth Institute these old funny duddy dioramas and then you walk into this modernist space, and you see these abstract paintings. I think there is definitely something he said there for that architecture. And as for new vision itself. I have no idea what the place looked like I know that it was in a basement and or that part of it was in a basement and not until Kenneth Smith came along. Did they start showing sculpture regularly as well. So, yeah, I haven't seen really very many images of the inside of that particular space. But that's something to think about as well. I hope you heard me all. Thank you I did that was loud and clear and David thank you so much for typing your answer at speed I know that was quite difficult but I think I just really hear you about your your point about spaces together as well which seem rather distant in in these times and as we're finding out obviously zoom gives us this incredible way of connecting internationally but they can be frustrations and barriers and challenges with it anyway so. I don't know whether we can hear you still. Can you hear me. Yes. I think, I mean, this was a very, very rich panel, starting with, you know, the two presentations the keynotes. And I just wanted to, I mean, all the sessions were too rich to be summed up, you know, in, in a paragraph or so, but I'm going to just give it a try. I just wanted to raise this question how to construct specific concept of solidarity which involves a new methodology of seeing the world. And what is crucial to note is how the scene happens to the lens of decoloniality. And this is the question which our, you know, speakers had wonderfully addressed. The scene involves history memory that recognizes art and aesthetics as double edged. So if they can be used as tools vampire and enslavement. They also work as powerful tools for resistance and emancipation, and it is this ambivalence which I thought was most important to be present in Michael Dracobitz's work, which came out in the discussion. You know, on his works. But going by the presentations today by David and Mariam based on new archival research and oral histories, which were collected off, you know, from his organizations like artists for democracy and the young contemporary Commonwealth artists. They actually give us a kind of a somber and a realistic picture in spite of all the informal community and sociability around food for political solidarity. There were so many roadblocks, which, you know, one has to also take into account the discontent between the cultural workers who had little knowledge of politics, but they saw it as an opportunity to take a bit. On one hand, on the other hand, your political radicals were not really interested in art and poetry. And I think this gaps made it very, very, you know, challenging for, for the actual kind of organic political art to kind of really emerge. However, I would really like to conclude that despite all these odds which were faced by the artists in the public, they were in our ground for intersectionality across anti imperialist feminist and radical artistic concerns. And I think we can see some rays of hope, even within the claims of failures made by the artist and the critics of the time. And I think to conclude on a positive note is actually good politics. Thank you. Thank you very much, Carol, for some incredible chairing as well and weaving these threads of connection across the papers and also into the future. I think that was, yeah, a kind of resounding note to end on and echoed actually some of the things that Michael was saying as well about an Omar about what it means to do this kind of work and looking into the past, digging into archives to think about what is ahead, and what kinds of futures we want to make through our work and our research so thank you for that prompts and, and to all our speakers today for such rich papers. And to you the audience for joining us and staying with us we had a little bit of a technical hitch at the start and then a few things throughout but it's brilliant that so many people stayed on the on the line, and were part of the conversations. And, and hopefully you will join us as well tomorrow, and throughout the next few weeks for future London Asia art world panels. We've also got an informal zoom meeting after this webinar if you want to join us just to say hello we realize that webinars are quite formal, you know we can't have that conviviality that we would if we were meeting together in another place but the zoom meeting allows us to do have a bit more informal chat so you're very welcome. Shauna and Danny have put the link up in the chat so please do join us even if it's just for two seconds to say hello to to us and to some of our panelists. That would be lovely to see you there but yes thank you all. Thanks to Hamad and to Ming for all their core organization and of course to Shauna and Danny for all this amazing work that goes on behind the scenes to make a gathering such as this happens so thanks everyone for joining us today.


There are three main points. The first one being that we want to address blockchain fragmentation. Blockchains were designed with the idea of destroying silos and now we just have tons of different networks that are siloed from each other. Cactus can help those different networks talk to each other. It also saves the application developers from having to reinvent the wheel every time they want to do something new. Every time they want to do something, every time they want to iterate over what they have and finally, they heavily lower the risk of adopting specific DLT technologies by businesses. This means that if today you choose to build your application on top of a specific blockchain protocol, let's say that you build your solution on top of fabric and you have a client that just has a BESU network and they want you to move to their hyper-layer BESU network, you can do so with close to zero effort. There are three different entities that are interacting with each other through a supply chain that each of them are connected exclusively to their own blockchain network. So right now on my machine, I'm running three blockchain networks for hyper-layer BESU, one for GoQuorum and another one for hyper-layer fabric. Each of them has a separate role and we're going to be seeing how we can create an asset on BESU. We can turn that asset into a different asset into Quorum and how we can ship that using fabric. Right now I have the role of the bamboo harvester and I can submit a new bamboo harvest details. When I create this, it appears here and now this is only in the scope of my hyper-layer BESU network. Now if I go to the, if I want to create a bookshelf, here I can select which bamboo harvest I want to use for that hyper-layer from the hyper-layer BESU network and here I have created my bookshelf and I want to ship it using fabric because the shipment companies, they use fabric. So here I just defined my bookshelf that I just created and I click on submit and there it appears. So here what we've shown is how we can use three different, is how we can have the same application with three different entities. The easiest way to run this and to test this is to run this command because as we've done for absolutely everything, everything in cactus is localized. So from our unique tests to our more complex demo applications to the individual plugins, you can instantiate and use and run everything as a microservice if you want as well because everything can live within a container. You can visit that link and every day we spend some time with the community doing pet programming in case they have a specific problem. So we encourage everyone to talk to us through a rocket chat obviously, to join on the main list obviously as everyone else.