A warm welcome to this session continuing the theme of wavelets and multi resolution signal processing and multirate filter banks. It is my proud privilege today to introduce a guest speaker that we have in this series and I would like first to mention some of his biographical details before bringing him before you and also to stress and emphasize the role, the very important role that he has played in the construction of this course, in the construction of this series of sessions and lectures. Professor Aditya Abhyankar is going to come before you today and the most important qualification that he has is that he is very well known in the area of wavelets and filter banks in the country. It has been my proud privilege to have him as a reviewer for this series and he has carefully and meticulously given suggestions on the content and the manner in which the lectures have been constructed and therefore, it is both out of gratitude and pride that I have invited him today to speak on the theme and to present some of his own experience and some of his own thoughts in this broad field of wavelets, multi resolution signal processing, image processing, filter banks and so on. Professor Aditya Abhyankar obtained his degree of Doctor of Philosophy from Clarkson University, New York, USA in 2005. He has for quite some time been on the faculty of the University of Pune. In fact, currently he is a professor and head of department in the University of Pune in the department of technology. In particular as I said he has worked to a great extent on the themes of signal processing pattern recognition and I know for sure that he has made some very important contributions to the area of biometrics specifically fingerprints. You will recall that at some stage we had said little bit about the importance that wavelets have had in fingerprint analysis, fingerprint pattern recognition and so on. Professor Aditya Abhyankar has important contributions in this field. In addition he has been leading several initiatives in the University of Pune and as I said earlier on a very important role that he has played in this series is to review and give us very valuable inputs on these lectures. And therefore without taking too much time in this session I would like straight away to put before you Professor Aditya Abhyankar who will now talk to you on some themes related to wavelets. Thank you. Hello and welcome to this session on the topic of wavelets and in the broader perspective a topic that deals with joint time frequency analysis. It's my proud privilege that I was called upon to review the beautiful video lectures given by Dr. Gadre who is a professor at doubly department IIT Bombay. And there is a reason as to why I am saying this. We have quite a quite a few number of books written on wavelets. In fact today wavelets has become a buzzword. However, almost all the material that has been written on wavelets it is written by mathematicians and predominantly it is written for mathematicians. For engineers to understand the concept behind these mathematical formulas it was required by someone to simplify those mathematical formulas and bring out the physical significance associated with these mathematical formulas. It was pleasant experience going through the beautiful video lectures recorded by Dr. Gadre and that's because he had simplified so many beautiful concepts associated with wavelet transform. It's also my privilege that he has given me an opportunity to record few of my thoughts on this beautiful subject of wavelets and this very first lecture. We have titled this lecture as zoom in and zoom out using wavelet transform. Let's begin. Nowadays wavelet transform has become a buzzword. I honestly believe that if last 100 years were the 100 years of Fourier transform the coming 100 years are going to be 100 years of wavelet transform. And that's because wavelet transform has so many beautiful facets so many beautiful characteristics associated with it. And in this session we try to bring out one such beautiful facet which is the zoom in and zoom out feature of wavelet transform. If we compare wavelet transform with the conventional methods of representing the signals or functions then relatively it's a new field. And then we might pose few questions and those questions will be answered by a beautiful property associated with wavelet transform which is known to us as multi resolution analysis or very popularly it is known as MRA. Let's pose more fundamental question first. Why transform? Not all the operations are called as transforms. For example, when it comes to image processing there are certain operations like histogram equalization. We don't name that operation as histogram equalization transform. There are only very few certain class of operations which are termed as transforms. And why at all take this pain of transforming information from one domain to another domain? There are multiple reasons. However, the strongest reason is pure convenience. For the analyzer it becomes extremely convenient to understand the representation of the information in one domain rather than another domain. And we have already seen one example, the example of music. We don't understand music as just few time domain signals with varying voltage. Not really. We understand music as a sequence of frequencies. And so it makes sense to actually transform the signal into frequency domain and then the analyzer will be more comfortable dealing with those frequencies. Ultimately, we want to design those beautiful filters and it's more convenient to design filters in frequency domain compared to time domain or spatial domain. So, the main theme behind doing most of these transformations is purely the convenience of the analyzer. Wavelet transform in a way is strikingly different than most of the conventional transforms. If we go through most of the conventional transforms that we have studied with the likes of Fourier transform, Laplace transform, Z transform and probably the first transform to which we get formally introduced to is the logarithmic transform. For all these transforms, the basis function comes out of a beautiful constant, a constant E. And in a way, all these transforms, they have some common thread in all of them. All the basis functions, the kernel functions, they are of the nature E to the power some variable. It could be frequency or it could be any variable. However, Wavelet transform differs from all these transforms and we have to go a little beyond the purview of all these transforms to really make sense of the Wavelet framework. So, why at all do this? We already know how to analyze linear time invariant systems. And if we see this particular slide, traditionally we have two methods based on convolution or based on difference equations. And typically, convolution comes out of the fact that given any signal x of n, I can decompose that signal into sequence of shifted impulses. And once I do that, I can very well write x of n as the scale summation of shifted version of impulses. Remember, we can do this because we are talking about linear and shift invariant systems. Because my system is linear, I can go on accumulating the information, I can add up everything because the system would follow superposition theorem, the additive and homogeneity properties. And because my system is time invariant or shift invariant, I can go on shifting my impulses. And once I do this, what we say is if I understand how my system reacts to these shifted versions of impulses, I can characterize my system completely. This is what is known to us as an impulse response. And if I know the impulse response of my system and let us say, we call this impulse response as h of n. And if I excite my linear shift invariant system with an exponential, then what kind of output the system would produce? That was the question that was posed by Dr. Fourier. And if we call this output as y of n, then we realize that this is a very interesting situation that we are in. We have linear shift invariant system. We have characterized that system by virtue of impulse response. And we are stimulating that system with an exponential, with a known frequency e to the power j omega 0 n and omega 0 is the known frequency. This formulates one very interesting pair of Eigen value and Eigen system for linear and shift invariant systems. And that is purely because if I split the term e to the power j omega 0 n minus k, then definitely the summation happens for variable k. And I can take out e to the power j omega 0 n outside the summation and this is very interesting. The system was excited with e to the power j omega 0 n and what comes out of the system is again the same exponential with the same frequency plus something. This exponential e to the power j omega 0 n is known to us as an Eigen function and what it produces along with it is known to us as an Eigen value. And this Eigen value in broader sense is known to us as Fourier transform. What is really interesting is to observe the common thread in all these transforms. And like we said before for all the different transforms like Fourier transform, Laplace transform and Z transform, the kernel function remains almost same. In case of Z transform, the basis function is z to the power minus n, where z is again equal to e to the power j omega. So, we are talking about essentially similar looking kernel function or basis function. Same holds true for Laplace transform and where exactly this constant e comes from that is an interesting thing to notice. It is a combination of the efforts put in by three great genius scientists. That is a vague story about how Dr. Bernoulli was able to invent this constant e. For that matter most of the inventions and discoveries are pure accidents or we can say they are apparent accidents, they appear as accidents. However, there are tremendous dedicated efforts by the respective scientists or researchers that actually goes in and only then the elevated minds will be able to grasp and capture that particular idea. Something similar happened in this case also. This is a vague story and there is no authentic source to this story. However, it is very interesting. Dr. Bernoulli was trying to help out his banker friend and his business was not actually picking up and he gave him a beautiful solution which was based on the formula of compound interest and typically we know how the formula actually looks. The formula goes like this. If you are investing 1 rupee as the principal amount then the formula for compound interest is 1 plus 1 upon n bracket rest to n and Dr. Bernoulli simply brought in the series expansion by putting limit as to when n tends to infinity. We can very well solve this limit. Let us very quickly do a small exercise in MATLAB. As defined for n is equal to 1 to some large number, we cannot go all the way till infinity and then let us implement this formula 1 plus 1 upon n bracket rest to n and let us end this for loop and then we will see that after few iterations it will saturate to value of 2.7183. This is the value which is known to us as the constant E. It is a simple compound interest formula and Dr. Bernoulli was able to help out his banker friend but in the meanwhile he was also able to discover this beautiful constant E. That was not enough and then comes the second genius in this story. As can be seen from the slides, his name is Dr. Euler and Dr. Euler he gave a different meaning altogether to this constant E and he gave us this beautiful identity which is known to us as Euler's identity and the identity goes like this. He told us E to the power i pi is equal to minus 1. This is what is known to us as Euler's identity. For almost last 300 odd years or so mathematicians they have been trying their level best to come up with a formal mathematical proof of this identity but they are not yet successful. So, once again an elevated mind and he was able to capture the essence of that particular idea. What is so special about this identity is an extended version of Euler's identity. He told us that E to the power i theta can be disintegrated into cos theta plus i sin theta and we know cos theta and sin theta together they form an orthogonal system. So, the beauty associated with this exponential curve is you can take any point on this curve and draw a tangent. The y intercept and the slope of this tangent will match and we know an equation of a straight line is y is equal to m x plus c that essentially indicates that on this curve at any point on this curve I can resolve that entity along two axis which are orthogonal in their nature cos and sin and it is this beautiful Euler's identity that made all the transforms orthogonal in their nature. That was also not enough and then came the third genius scientist in the story his name is of course, Dr. Fourier as can be seen from the slide Dr. Fourier told us how to analyze periodic or aperiodic functions or signals. The legacy of transforms that we have with us is a contribution of these three great genius scientists. We can summarize and that can be seen from the slide that we have this constant E which creates an Eigen value Eigen function system and the Eigen value is nothing else, but the Fourier transform which can be implemented using convolution which is at the heart of any DSP processor. This convolution is possible only if we have linear time invariant system and then we are talking about band limited aperiodic signals a specific class of signals. They should obey sampling theorem and then there will be no aliasing in reconstruction which guarantees sparse representation which in a way would guarantee inverse Fourier transform which can then once again be implemented using convolution. The phase changes are marked as the directional changes which is once again a property of being an Eigen function of the system that produces an Eigen value that takes us back to this constant E. So, at the heart of all these transforms we have this beautiful constant E and we have this beautiful legacy of transforms. For periodic signals we have series representation for aperiodic signals or functions we have transforms. So, if we already have series representations and transforms then comes one important question and that important question is why at all wavelet transform. Let us go back to the slides and we will realize that one beautiful feature associated with wavelet transforms is it decomposes signal into two separate series. A single series to represent most course version which leads us to scaling function or which is also very popularly called as the father function and the double series to represent the details or the refined version that leads us to the wavelet function or wavelet function is also popularly known as the mother wavelet function and the father and the mother would then in a way give the whole wavelet family. However, there are two fundamental questions which are still required to be answered. The first is are in the conventional methods to represent signals or functions good enough and what is strikingly special about wavelet representation. Let us take these questions one at a time and let us very quickly revisit the conventional methods that we have with us. Probably the most basic way of representing the signals or the functions is by virtue of using the Taylor series expansion and we probably learn this at the beginning of our calculus course. We know that signal representation is known for a long time and one particular example of Taylor series expansion at x of 0 is equal to 0 is shown for a function e to the power x and that gives us infinite coefficients for this particular series 1 plus x plus x square by 2 and it goes on. Every single coefficient can be looked upon as decomposed piece and we can make use of these decomposed pieces for doing the reconstruction back of the original signal or function. If we make use of only some finite number of coefficients let us say first three coefficients in the series 1 x and x square by 2 and using only these three coefficients if we try and reconstruct the original signal then this is how it is going to look and it is shown in the diagram on the left hand side. This dotted curve essentially represents the reconstruction using only first three coefficients and this line in blue essentially is the original function which is e to the power x. Please ignore these discontinuities over here because the resolution used is lesser but we can always use a higher resolution and we can take care of this part. Now, as against this instead of using just first three coefficients if I use first 12 coefficients then you can clearly see the representation goes very close to the actual representation and there comes the question of the cooperation of the series expansion and what we say is in Taylor series this cooperation to build better representation is rigid. Why this is rigid? Number one that is because we do not have freedom but to add large number of terms. I cannot play around with the individual term I am restricted with the scale and the translation parameters of every single term and I do not have freedom to change this. In contrast to this in wavelet analysis a beautiful combination of the scaling function and its associated wavelet function makes the entire representation very very flexible. I have flexibility in terms of selecting the scale selecting the translation parameter and then I can also bring in the dilation by virtue of which I can then create the nested subsets and this is going to lead us to MRA multi resolution analysis. In wavelet analysis the scale 1 upon 2 to the power j is dependent on the analyzer to what degree we require the refinement to be added to the actual representation and one example could be if we want to determine the spike in the signal we can think of using a very high value of j and then we can bring in the translation parameter tau j comma k which is equal to k upon 2 to the power j and this can be used to focus on that specific part in the signal. This combination together scale and translation parameter is so beautiful that I can go and look at any particular part of the signal I can change and alter the scale and I can zoom in or zoom out and by virtue of making use of a beautiful combination of scaling and translation parameter what we get is the zoom in and the zoom out facility of wavelet transform. As can be seen from the slides someone might argue that Fourier series has a noteworthy advancement over Taylor series and that is all the elements of Taylor series they do not necessarily and always form an orthogonal set. However, when it comes to Fourier series the set 1 cos of n x and sin of n x n ranging from 1 to infinity is always orthogonal on range minus pi to plus pi. This is absolutely true however the fundamental query remains the same I cannot change the scale and the translation parameters associated with the basis function or the kernel function when we are trying to represent the information. However, we can derive some useful information from the Fourier series and that is why they always say wavelet transform in a way stands on the shoulders of Fourier series and Fourier transform. From the slides we can clearly see that a very special relationship actually exists between the sin and cosine parts of Fourier series. A similar special relationship also exists between the scaling function and the wavelet function when it comes to wavelet series. This relationship is quite trivial but very very interesting and the interested viewers can dig further into this. So, in a way we have answered the first question in a way we have also answered the second question, but we will take the second question further in order to really bring out what is strikingly special about wavelet representation. The scaling and the translation parameters are indeed the hallmarks of wavelet transform and when we add up the dilation parameter in totality they would lead us to the multi resolution analysis framework which is popularly also known as MRA. The central theme of MRA is as shown in the slide. We are talking about the piece wise constant approximations on unit intervals. However, wavelet transform is not just about finding out the piece wise constant approximations otherwise there would not have been any difference in wavelet transform and a simple process of quantization. Once we carry out the successive approximation then comes the very interesting part and that is filling in the details. So, the piece wise constant approximation will be given by the scaling equation phi of t and then the details will be added with the help coming from the wavelet equation which is psi of t. The filling in of the details can be then called as the zoom in feature. Losing out on details can be called as the zoom out feature. Increasing the resolution would lead us to zoom in and decreasing the resolution would lead us to zoom out. The very concept of zooming in and zooming out has become a well known phenomenon these days and that is because we live an era where we make use of digital cameras. In fact, most of the scanners and most of the sensors they have gone digital and by virtue of using digital camera we often capture digital images and then zoom on to a particular portion in that image to really understand what kind of activity is going on there. Correspondingly we can also zoom out by losing out on details if we want to get a generic feel about that particular picture. So, we understand what is zoom in and zoom out. However, when it comes to signal analysis many of the times it is a requirement that we should have a framework by virtue of which we should be able to zoom on to a specific part in the signal. So, as to understand what is really going on there consider a case of ECG signal may be we have a large recording of one hour and in that large recording of one hour if there are only few samples which shows some abnormality then as a signal analyzer we should be able to focus only on that part from the large recording that we have with us. So, this beautiful property of zooming in and zooming out is of great importance, great significance. From the slide we will understand that the whole point in doing this exercise is an ability of the analyzer of going arbitrarily close to the original signal. And in fact in a book written by Stephen Malat on this beautiful subject of wavelets he has used one beautiful word he says one should be able to go tantalizingly close to the original signal, beautiful. How to achieve this that is what we are going to uncover in the reminder of this particular lecture. So, in a way the introductory part is over and now we will begin our journey towards understanding how we can achieve this zoom in and zoom out features. We have understood the requirement we have also understood why at all it is essential and now it is actually time to dirty our hands to actually see the framework and then to be able to understand how we can carry out this task how we can actually do this zoom in zoom out using the framework that we are going to lay down. From the slide let us define linear space and let us say it is a linear space that we have named as v of 0 and then this would contain the functions which are square integrable that is the functions which in a way obey the L 2 R norm. So, to say and the piecewise constant approximation will be done on an open interval from n to n plus 1 where n is an integer what is really interesting is the size of this interval. If we are in a linear space v of 0 the size of this interval and we can call this as the analysis window this analysis window will be 2 to the power 0 from v of 0 we can move on to v of 1 and if we are in linear subspace v of 1 then correspondingly the size of the interval will be 2 to the power minus 1 that is half and we can continue doing this activity and we can generalize this notion and we can say we are talking about a linear space v of m where the size of the interval is 2 to the power minus m and that leads us to very spatial relationship and this relationship in general is called as the nested subsets and we have already seen in the lectures by Dr. Gadre that if we are looking at this ladder of subspaces which are the nested subspaces then we can think of either moving up the ladder or moving down the ladder. If we move up the ladder the analysis window will become smaller and smaller and smaller and will go on adding up the details and eventually we should be able to achieve the L to R norm as against that if we move down the ladder then obviously we are talking about the resolution getting coarser and coarser and eventually that is going to lead us to a trivial subspace and we will probably lose out all the details. We can convey this mathematically using this formula and the phenomenon of moving up the ladder with closure as was given by Dr. Ingrid Dobasz is captured in this mathematical formula and what is really striking about the wavelets is you could be talking about a particular function and its corresponding projection in any space any subspace that can be very neatly and nicely constructed using just one single function and that is psi of t and how to do that we have already seen this. We can do this using hallmarks of wavelet transform that is scaling, translation and also dilation. We will talk about it more once we start putting down the framework. Well, this is all true when it comes to psi of t that is the wavelet functions and we can very well span the w subspaces but who will span v subspaces. There has to be a function who would do that task for us and that function is phi of t which is also known to us as the scaling function. Now who gives this scaling function? How we can go about finding out the coefficients of the scaling function that is one interesting question and probably down the line in this series we are going to also try and uncover this part. The whole point of having the scaling function as can be seen from the slide is to be able to span that particular subspace v of m and this in a way guarantees the generation of ladder of subspaces. And we are saying this term again and again because this ladder of subspaces is eventually going to lead us to MRA that is multi resolution analysis. Let us quickly run through the axioms of MRA because the framework that we are going to see in this particular lecture in a way depends on these axioms. So, once we understand how the ladder of subspaces are formed then the first axiom is obviously moving up the ladder, the second axiom is obviously moving down the ladder. The third axiom guarantees the existence of a scaling function that will help us span all the v subspaces. Correspondingly we have to ensure that phi of t in a way generates the orthogonal set of series and then that will lead us to axiom number 5 and 6. The axiom number 5 and 6 we are going to see the direct implication of these two axioms in the framework that we are going to go through. And based on these axioms we have the MRA theorem that tells us given these axioms there exists a wavelet function psi which is once again a square integrable function and using this function I can span those W subspaces and bring out the details in the underlying signal or function. One typical way of implementing this MRA philosophy is using a two band filter bank structure. And if we have some input let us say p of n then I can very well go about doing the analysis by first of all passing this through the analysis low pass filter followed by a down sampler, analysis high pass filter which is shown by g 0 of z followed by a down sampler. And if I want to reconstruct back the original signal then I can run the synthesis phase where I can have an up sampler followed by the synthesis low pass filter which is shown as h 1 of z and the synthesis high pass filter which is g 1 of z and by virtue of doing the orthogonal summation we can very well reconstruct back the original signal as the two filters are complements or duels of each other. With this introduction now let us try and understand how we can go about building the framework. One serious drawback of this two band filter bank is as follows. As can be seen from the slide if we focus only on the analysis part and let us say input to our system is some signal p of n it is a digital signal of finite duration and let us say this signal belongs to some subspace v 1. The moment you run this signal through analysis low pass filter followed by a down sampler by a factor of 2 and analysis high pass filter and a down sampler by a factor of 2 you in a way end up with signals p 0 of n that would belong to v 0 and q 0 of n that would belong to a subspace w 0. So, in this process let us really try and understand what exactly is happening. Let us say you have some signal x of n and you are passing this signal through the two band filter structure and this will be followed up with a down sampler by a factor of 2. What we are saying is if this signal x of n belongs to some subspace say v of j then by virtue of doing this kind of an arrangement we end up with x j minus 1 n which would obviously belong to v of j minus 1 and let us say y of j minus 1 n which belongs to w of j minus 1. And if we try and understand what is happening over here and if we see the nested subsets that we have already seen and so on. And let us say this v of j is v of 0 that means we are starting over here then by virtue of doing these operations we start moving in the leftward direction and that is because if I am starting in v of 0 I will generate projections in v of minus 1 and w of minus 1. So, in a way we start moving towards the left direction we start moving down the ladder not always it is desirable to move in the downward direction. In fact, for many of the applications it is required to actually move up the ladder and only by virtue of moving up the ladder we can go on adding up the details from v of 0 we can move on to v of 1 from v of 1 we can think of moving to v of 2. And we can go on adding up the details and then we can think of going tantalizingly close to the actual signal and its corresponding representation. This is one interesting journey and we will have to build the whole framework in order to be able to achieve this. What we have seen in this particular lecture is how we can interpret the well known two band filter bank structure and understand it from the point of view of nested subsets. And how typically we end up only moving down the ladder and for many of the applications it is required it is desired to move actually up the ladder. And if we want to move up the ladder we will have to design the framework and that we are going to cover in the next class. However, we will write down one important mathematical formula which is going to be at the heart of what the kind of framework that we are going to build in the next class. And that formula is by looking at the two band hard structure we can very well write down that v of j is equal to v of j minus 1 orthogonally added with w of j minus 1. This is the formula which is of great importance, which is of great significance. And the whole multi resolution analysis structure is in a way based on this formula. We will stop here and we will continue building the framework by virtue of which we can possibly think of moving up the ladder, add the details and really think of going tantalizingly close to the signal or function under analysis. Thank you.


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So, I love DMA because it comes right out of the community. It comes from people who care about media, who care about information, who care about making sure that stories are told that aren't usually told, whether it's music we don't normally hear on the radio or it's news stories we don't normally hear or it's people we don't normally see on a television screen. I care about DMA because these stories and these people get heard and seen and appreciated. I think it's an important piece of our democracy and as a nation of people who are thinking and curious and creative to build our connections with each other and to strengthen our communities on a very local level. This year started with bad news on the national media scene, woeful tales about net neutrality and mega mergers between telecom giants. Earlier this year I wrote, what role does one, rather small, community media center play in the larger media landscape? Of what import are things such as an archive of 10,000 locally produced programs, a quarter century of local election programming and a non-commercial space on the radio dial and why does any of this matter? The answer was local content matters. Commercial media is often costly, doesn't invite participation and represents a narrow spectrum of voices owned by a very few companies. Non-commercial community media feels a critical need, teaching both digital literacy and media production all while highlighting the voices of non-profits, community activists, marginalized voices and perspectives outside the mainstream. Davis Media Access, or DMA, fills this need in Yolo County partnering with schools, local governments, community groups and people like you. As DMA celebrated its 25th anniversary this year and in September kicked off Kader's 10th anniversary year we worked hard to demonstrate our value in this community. Throughout the year DMA's staff and large volunteer base produced two cycles of local election coverage in June and November. We trained kids and youth in video production, stop motion animation and location based recording. We trained community members in video production, post-production, social media and radio broadcast. We helped highlight musicians from near and far providing an audience for them and music for all of us. DMA was on the job to cover congressional town halls, local forums and TEDx events, school sports and performing arts, civic gatherings and so much more. I offer my heartfelt thanks to DMA's board, its staff and volunteers, its underwriters and supporters and to the city of Davis, the Davis Joint Unified School District and our many community partners. Here's to the year that was and all those yet to come, please enjoy this look back at 2014. Davis Media Access is a tremendous community resource. I've been fortunate to work with them on recording things like the annual Martin Luther King community celebration, the Davis High School annual commencement and even the US Bicycling Hall of Fame induction ceremonies. The folks there, the volunteers are passionate and knowledgeable and it's a great resource for our community and I can't conceive of Davis without it so let's hope for many more years of success and community support. Davis Media Access is a community media center serving Davis, California and surrounding areas. DMA's main projects are DCTV channel 15 on the Comcast cable system, public access television, educational access channel 17, both channels also air on AT&T U-verse at menu 99. DMA operates KDRT LP 95.7 FM low power community radio for Davis and beyond. Through partnerships with the City of Davis and Davis Joint Unified School District, DMA provides the public with access to video and television equipment, training and unique programming. We round out our offerings with digital equipment and social media, topical workshops, several local programming archives, participation in open source projects, youth media projects, local studio productions, event coverage and advocacy and information for broader media issues. Davis Media Access gave me an outlet to show my skills and creativity and I was able to do things with the youth, providing their services and I'm very grateful for the opportunity of being able to produce television programs on this station. Thank you. My name is Jesse Drew and I'm a professor here at UC Davis. I teach a radio production class and we produce a program here with students called TechnoCult Radio. Davis Media Access has been a fantastic resource to get students off campus and into the community. One of the requirements of the class is they produce segments of interest to the greater community of Davis as well as other segments on campus life, developments in science and technology, sports, music, culture, etc. I have used Davis Media Access for years and I hope it continues for many years. Thanks. Hi, my name is Peter Pasteur. I'm a volunteer on the KDRT side of Davis Media. Davis Media has given me a tremendous opportunity to become part of a family that contributes to the community here of Davis. As a matter of fact, the larger community around Davis. I interview musicians on a weekly basis and it's interesting to see how these musicians at many times, it's their first time on radio and they just come alive. You can see it in their eyes, you can hear it in their music. The other show is Live in the Lone where Doug Kelly and I interview musicians that are traveling through the area and that's why it's called a pop-up show. We are creating the show time to accommodate their time frame and not ours. So thank you Davis Media for a wonderful opportunity, not just for me but for the artistic community in Davis. Davis Community Television, DCTV, is the heart of DMA and where it all started 25 years ago. As a public access channel, DCTV welcomes all members of the community to learn all aspects of hands-on video production from planning to studio production to editing and distribution. Founded in 1988, DCTV has grown from a single public access channel to the media center it is today. DCTV airs non-commercial programming produced by local volunteers and sponsored programming produced elsewhere but sponsored by a community member. To provide regular training opportunities and a platform for exploring issues of local interest, DMA produces four episodes of In the Studio each month. These shows are managed by DMA staff but crude and sometimes produced by volunteers who contributed over 450 hours of time to this effort in 2014. DMA also produces local election programming and events such as PSA Day which highlights local nonprofits. As resources allow, DMA covers local events of note from public meetings to lectures, forums and celebrations. In 2014, we launched From the Vault to highlight some of the more than 10,000 locally produced programs in the DCTV archive. Hello, I'm Ruth Chambers and I'm one of the many volunteer producers here at Davis Media Access. I started probably about 10 years ago. It was something to do, something I was interested in. And I started with Granny Muffin Reads. I was trying to deal with what can I be now that I'm an older person? Well, I could be a granny. So I was granny of Granny Muffin Reads. And then after that I did Granny Muffin's traveling puppet show involving puppets of course. And of course it was a means of self-expression, a chance to share. I like sharing and I like reading stories. And so we proceeded on to a show that I produced with Alex Silva-Satter. And it was a seasonal literature show like all the holidays, the different holidays of the year. And we did that. And then I started my radio show, The Chambers Street Theater. And that's on right now so you can tune in. Then there's, well, I was very fortunate. I was invited to participate with Bryce Parker. So not only was I doing my things, but I got to be a participant in someone else's show. And his show is called The Rectangular Age. And I'm sure you can find it online with Davis Media Access. You can be a doer. You can be a viewer. We need viewers. And of course you can always be a doer and come on down. It's Davis Media Access. We'll see you. DMA is responsible for the day-to-day management of Educational Access Channel 17. In partnership with school district staff, DMA staff manages the production and promotion of Channel 17's schedule, its high school internship program, its website at djusd.davismedia.org, and related initiatives. In addition, we work with many DJUSD teachers and parent liaisons. Many recordings are made under DVDs, which are then sold to benefit parent booster groups, such as Davis Schools' Orchestral Music Association. DMA's internship is competitive and well utilized. Interns from Davis High, DaVinci, and King High schools are trained in production, post-production, and digital file management. And with their help last year, we recorded programs such as volleyball and football games. Davis Community Idol, Everybody Dance Now, DHS Jazz Extranfaganza, Attribute to Tony Fields, All High School Graduations, and Secondary Orchestra and Band Performances. So when I graduated from college, I didn't really know what I was going to do. And I majored in communications. And I just kind of felt a little bit lost. And I think that's the case for a lot of college students. But I came back home to Davis. And suddenly I heard about Davis Media Access. And I actually hadn't heard of it before, even though I grew up in Davis. And I decided to check it out. So I contacted the station and they just said, come on in and you'll learn the ropes and just see if you like being here. So I was like, okay, sure. So I just came in and everyone was extremely warm, extremely knowledgeable.  Like I never did anything related to media specifically. But when I came in, I just kind of felt like this is the right place. From a scrappy station that struggled to get on the air, 10 years on KDRT has found an audience and, thanks to social media and online archives and ever expanding listener base. KDRT is proud of its role in facilitating critical audience development for independent musicians. In 2000, what was then the DCTV Board of Directors authorized our application for a low power FM frequency. We launched the station on September 24th, 2004. And KDRT, where the grassroots grow, took root in our community. Originally located at 101.5 FM, the station is found today at 95.7 FM. DMA holds the license and provides organizational support for KDRT, though KDRT's day to day operations are the purview of the all volunteer KDRT management team. Since launching, the station has diversified the community's media options. Davisites have a choice of media, varied training programs for different ages and abilities and an opportunity to broadcast and receive local content by a free radio. KDRT is nationally known for its leadership on low power FM radio and locally has become a bright light and center of alternative arts and culture in Davis. I'm Rocket, I'm 14 years old and I'm the host of Rocket Radio at KDRT. I'm Penelope Drew and I've been helping Rocket, my brother, do his radio show. I started this when I was 10 and one of my first shows was doing the interviews at the Science Fair in elementary school and I did that a couple years afterwards and it's been a great thing to do and I've loved doing it always. And Penelope, anything? I started helping Rocket when I was eight. And Penelope's been on the show frequently since and it's great to hear from her because she has a very keen eye from the elementary school perspective and I'm in junior high, I go to Holmes Junior High School and I really love doing the show. I think it's a great contribution to the community and I think it's an excellent thing to do. You really don't see a lot of kids on the radio or hear a lot of kids on the radio and I really wish you did and so I'm really glad that I get to do this and I think it's changed me. I think it's made me a very open person and I really love doing it. I think it makes me like talking and like sharing and like being around people. I think it's definitely motivated me to be involved with some theater things. I was in school production in seventh grade and I'm going to be in production in ninth grade and I've been involved with Acme, the local theater company and I think that this has definitely helped with that too. I also think this has been a great experience. So we love doing it and we hope to continue doing it for a lot longer.


Good morning I'm today talking about stack spatial temple asset catalog Has anybody of you heard about stack already or? Well, yeah, that's great. Well, do you have heard about it? Where have you heard about it? Anyway So that's an introductory talk. I'm just giving the basics today and The basic for this was what is annoying about metadata, right? so if you have data and want to expose that to search engine so that users can use it so you need to basically now a metadata standard and expose it or write XML and stuff like that and Also if you use it you need to understand the metadata format and We're trying to tackle that with focus on search and discovery of metadata We may get trapped with this of course So if there is like 40 metadata standards, then we do a new one and then there's 15 competing metadata standards. So that might happen, but we tried To avoid this and of course give good reasons for why we're doing this So first of all like at the moment when you're trying to search for geospatial data, then you're probably getting to view any of these portals that are out there like Dozens of portals where you can download your data like it Copernicus open access hub for Sentinel data or the NASA CMR and so on and so on but of course to find your data, you need to know all these portals, right? so otherwise you won't be able to find the data anyway and We could also like go and see whether there is something like an Dictionary where everybody puts in his data, but or like in like a person that like Views all over the data and puts together a dictionary about them But then we already realized that there was too much data out there like yeah Who is not a thing anymore where people? Looked at the data and put into everything into a dictionary. So We now have crawlers like for Google for example There everything is in one place and you can just visit Google and find everything So we think finding things via Google or any other search engine is better than going through all of these portals to find data And of course like there is going on so much Satellites at the moment that there are petabytes of data and you just need good tools to find all the data So for example, if you're going through looking into a Sentinel data, then you get This from ESA where it says if you just want a single granule a single tile when you get all this data and What you really want in the end is just maybe the metadata and the actual data file, right? So that's just these two things and this whole bunch of information that you get and Then you have to look through all these fires like if you have central to metadata XML files It's 20 megabytes of XML that you need to go through First step that's only 22 kilobytes that you can as a normal user can really understand Like for example for comparison the plaintext Bible is just 4,000 kilobytes. So That's quite a lot to read if you want to understand the data And even I need to find some kind of like documentation how this all works And what it actually is the data that you're finding the metadata so Why we're doing this now there are so many standards and proprietary solutions also for API is that you can like the portals There's very similar scopes and capabilities But it would be a good idea to basically unify them and make them interoperable so that a client can access all these API's and all these data That's a barrier for adoption. And so we thought about stack could be a good idea to evolve So what is stack actually it's basically defining a metadata standard for Specification it's not a standard because we're not like working for standardization of a company But it's for just a specification of what we think is useful for due special catalogs and assets With the focus on search and discovery so in most case you won't find any information how to process the data There you can still link from stack to the original metadata for processing, but to actually first find and Discover the data you can use stack. So It's very simple. It's Jason based so most people can really read Jason as it's just a very thin layer on top of the metadata and It's extensible So you don't need to write any like for example for previous things when when you had XML You needed to write an XSD schema and adopt it so you can add things But now it's just Jason where you can put your own things into that in addition to what we have standardized already Also a different thing from other previous standards is that you have also a static catalog that you control so You can basically put your metadata files together with your data or put it on top of that like if you have exposed central data for example in an S3 storage bucket you can open another S3 stored storage bucket with your static files That are conformant to stack and then you can crawl through all these metadata files. They are linked together with links and It's such that you don't need a server to run it You just can put it on your like file storage and then it's there and Google can make use of it You don't need to write any software for that or something like that That's the static catalogs and then there is dynamic APIs of course as well because if there is like Thousands and thousands of files you probably need to put them into the database and index them so that you can Better search for them. So you also expose an API which is based on the recent version of OGC API features the former WFS web feature service from the OGC And we just put a thin layer on top of this standard to make it searchable and It's an open specification. Of course. That's why we're here open source everybody can contribute and So what is it not? It's not a full-fledged meta step. Not as I said, it's not for processing stuff or so It's basically really focusing on search and discovery although you can if you want put your processing information into a bit It's extensible So also, it's not a replacement for the data providers internal metadata So you can basically from your Item file you can link to your X other metadata or other files that you have previous and stuff like that it's not the single source of truth in this case and It's also not for all kinds of data sets. So it's just for spatial temporal and you don't Really can expose things like additional document like you can link to additional documents, but it's not meant for like Putting other things in spatial temporal data into your stack catalog So and as such it's also not a replacement for ISO standards for example and stuff like that or CCSW There is the recent innovation plan for OTC catalogs or records Which is also a new API and we try to align also with this effort So what the state of stack at the moment the moment we're at version 0.9 just released some days ago and We're heading towards releasing the first stable version 1.0 in mid like third or fourth quarter of the this year And there is also plans to separate the actual Specification work for the metadata and the API in the next weeks so that they're more Streamlined towards their use cases So what is this specification actually about what do we expose there? So there's first there are catalogs Collections items the API and extensions and best practices. So what is all this about? A catalog is basically a very rough Or very small thing for cataloging you can group your Collections and items with it As very simple. It's like basically just an idea description and additional links to whatever you want to group and Then a collection is basically an addition of a catalog It extends it and adds collection level metadata to it. For example the extent spatial and temporal License provider And all these things that you have like for for example if you want to expose central data you want to talk that about what the central data actually is like that which platform it is using which Temporal coverage it has it's spatial coverage it has which license where you can find licensing information who's a provider and so on and so on Then This can be used standalone. So if you don't expose any assets granules Whatever you can only use it to expose your collections as well like for example Google Earth engine if you know that Just exposes their collections and then you need to use their tools to actually use this data So you can find this data as collection then but then it tells you that you need to use your tool so if there is data or so which you can download in the Traditional sense then you can at least use it in any cloud provider that is out there and exposes that it is stack collection and Collections are also useful for summarizing the actual item data that is Exposed and items itself are the actual granules. So the individual tiles and Items are basically Geo JSON features. So the feature is basically then the geometry of the asset that is exposed and an asset for example in an item could be the file for band 1 and then another asset is band 2 and so on and so on and All these assets can then be downloaded Provided with additional links like for example the the provider specific metadata in iso format or whatever and This actually is very nice if you combine it with cloud optimized geotifs So if you can see here, I'm not sure whether yeah, that It's basically just a browser that is working on a geotif Yeah, and the cloud optimized geotif and the cloud optimized geotif is basically a Geotif that is a bit different Structures and with HTTP get requests range requests. You can basically Without any like server software you can browse it on a map. So for example, this is I think leaflet and If you can see it if it zooms out then it basically if you zoom in now It loads the data just the data it needs so that there could be a 500 megabyte file Behind that and it just downloads the things you see here So that's of course pretty nice if you don't want to expose the WMS Especially for that and you can just download the data that you need and View it while discovering it whether it contains the data you need or not The IP API itself is as I said aligned with all you see API features It's pretty simple. I think there is a landing page where with capabilities There is collections that you can actually expose for example, that would be second and central to and then the items would be each granular yet you can Download here as data and then there is the stack specific search endpoints where you can basically search for whatever is in the files like whether the cloud cover or The extend or the provider or the license and so on That's defined as open API documents are pretty easy to use with the open API ecosystem as well to implement And then we have basically for items the metadata fields are very slim like there is title and Extends and There you can specify some things like when the metadata has been created or updated But then basically this thing is in the core is very slim and then you extend it with extensions so for example for content we have extensions for Describing data cubes for EO data, which is in this case electro optical Then for machine learning to specify the labels Point cloud data for a SAR data Then we have a specific one for satellite data, which is basically Inherited from EO and SAR and for scientific data like exposing doys and stuff like that And then for the API, of course This is also in the core very slim and then you can extend it via fields for example that you can say I don't only want to a certain set of fields in my response so that it gets smaller You can query it via some specifics You can sort it And there is a transaction extension to basically add and remove fields and update fields items and stuff like that and Of course also for versioning if there is like different versions of assets and you can version that There is a growing ecosystem behind stack You have a for example already a validator where you can just put your catalog into it and it validates it whether it's Okay, and according to the stack and there is an extension for intake. I don't know intake, but It was said it's a big thing In the Python world Then there is pystack for catalog creation and all work with stack catalogs And similarly works set stack Then there is a number of clients for example stack browser, which you already saw when we had this cloud-optimized duty preview there this is basically human readable version for the Catalog for the JSON files which also expose for example schema.org Translation so that they can be crawled by Google and their new Google data set search There is a QGIS plug-in the set search for searching data They're set fetch for fetching the data or downloading the data and then there are set API browser, which is basically so stack browser was more for the for the static catalogs and set API browser is more for the API part because there you can also search and stack this browser is just for like Going through the links that are in the data Then there is a couple of server implementations which you can use to expose your data for example staccato in Java Stacc API, which is I think a Node.js application and set API postgres scale which is basically I think Python with a postgres database behind it and This for example is a QGIS plug-in where you can basically just specify your acquired parameters and then It searches for data and basically loads the data directly as cloud-optimized duty into your QGIS Instance to work with Then you can as it's a cloud-optimized duty also directly as zoom into that and it loads the proper data and so on It we're working on basically making use several catalogs available openly available at the moment there are central one and two Lens at eight and use GS lens that collection two is directly Offered a stack and cock catalogs from usgs the seabirds 4 which is a Chinese Brazil satellite for observation NAAP NASA CMR is also Translated into stack and there are a couple of more things that are coming and are in preparation And maybe in the future also your data. It's pretty simple to expose such things So if you have data that one you want to be found then it's a good idea I think to expose it as stack catalog Here's an overview who is already exposing their data as stack and working with stack quite a number of entities And now I maybe just Show you a single example here Like for example, this one is the is a catalog. So It basically just is Jason you can I guess most of you can read what is in there and it's basically Gives you an idea. It's Copernicus as to here and then the title a description What is it is about a license keywords provider information the extent information temple and All the other summary data that is like for example the how to site what the ground sampling distance for the individual things is the constellation platform names Projection information The bands for example, which bands are in the actual assets that you can download then one then two and so on common names wave lengths and the links that you can basically Visit to get more information and that's a collection and now you can basically also look at the items Which is then an individual granular? It says For example, which idea it has which collection it belongs to and there is again links to get additional information the bounding box of the granular geometry Assets that you can download basically now which is Jason files or the JPEG 2000 either some nail or the actual data and Is there anything else in here? Yeah, some additional properties like cloud cover values and so on so that's how it's working and And Yeah, I'm happy to take a question if there are any Thank you very much for listening to this talk


This is the VOA Special English Economics Report. On November 30th, the European Central Bank took action to support the worldwide financial system. Central banks in Britain, Canada, Japan, Switzerland, and the United States also acted. The banks made it less costly for other countries to borrow American dollars. The United States Federal Reserve said the joint effort was meant to help central banks in Europe provide financial support to nations that need it. The action sent American stock prices up more than 3%. On December 1st, European Central Bank President Mario Draghi suggested that the ECB may provide new financial support for European countries struggling with debt. But first, he said, Euro area countries must establish trust by enacting big financial reforms. He said, a credible signal is needed to give ultimate insurance over the short term. What I believe are economic and monetary union needs is a new fiscal compact, a fundamental restatement of the fiscal rules together with the mutual fiscal commitments that Euro area governments have made. 17 members of the European Union share the Euro currency. Concern over its future has been building in recent years the Greek debt crisis, rescue loans for Ireland and Portugal, and worries over Italy's debt have all increased fears that Europe may have to abandon the Euro. The Euro is the world's second most traded and second most used reserve currency after the dollar. European finance ministers failed to agree in November on a plan to provide economic support for debt-troubled countries. Financial officials say a lack of economic and financial cooperation is at the heart of the two-year-old debt crisis. Dutch finance minister Jan Kiesdiager said there is a strong need for tougher rules. Lack of economic reforms and lack of fiscal discipline has brought us into this crisis. So you also have to address these fundamental issues. That means Euro nations may have to surrender some power over their budgets to the European Union. For VOA Special English, I'm Carolyn Presuti. We have more programs at voaspecialenglish.com where people can read, listen, and learn English with texts, MP3s, and activities. We also offer English lessons at the VOA Learning English page on Facebook.


The case study examined how an integrated health and social services centre in Quebec responded to the Covid-19 pandemic. The centre faced high mortality rates during both the first and second wave of the pandemic, leading them to develop resiliency strategies. These strategies included reorganising their services and spaces to accommodate more Covid-19 patients, managing contagion risks for patients and staff, and managing PPE, supplies and medications. The centre's response was heavily influenced by the 2015 healthcare reform in Quebec, which had resulted in a centralised model of governance, budget cuts to long-term care facilities, and a shortage of human resources.