Distributed coding is a new paradigm for video compression, based on Slepian and Wolf's and Wyner and Ziv's information-theoretic results from the 1970s. This paper reviews the recent development of practical distributed video coding schemes. Wyner-Ziv coding, i.e., lossy compression with receiver side information, enables low-complexity video encoding where the bulk of the computation is shifted to the decoder. Since the interframe dependence of the video sequence is exploited only at the decoder, an intraframe encoder can be combined with an interframe decoder. The rate-distortion performance is superior to conventional intraframe coding, but there is still a gap relative to conventional motion-compensated interframe coding. Wyner-Ziv coding is naturally robust against transmission errors and can be used for joint source-channel coding. A Wyner-Ziv MPEG encoder that protects the video waveform rather than the compressed bit stream achieves graceful degradation under deteriorating channel conditions without a layered signal representation.

Distributed Video Coding

The authors consider classes of signals that have a finite number of degrees of freedom per unit of time and call this number the rate of innovation. Examples of signals with a finite rate of innovation include streams of Diracs (e.g., the Poisson process), nonuniform splines, and piecewise polynomials. Even though these signals are not bandlimited, we show that they can be sampled uniformly at (or above) the rate of innovation using an appropriate kernel and then be perfectly reconstructed. Thus, we prove sampling theorems for classes of signals and kernels that generalize the classic "bandlimited and sinc kernel" case. In particular, we show how to sample and reconstruct periodic and finite-length streams of Diracs, nonuniform splines, and piecewise polynomials using sinc and Gaussian kernels. For infinite-length signals with finite local rate of innovation, we show local sampling and reconstruction based on spline kernels. The key in all constructions is to identify the innovative part of a signal (e.g., time instants and weights of Diracs) using an annihilating or locator filter: a device well known in spectral analysis and error-correction coding. This leads to standard computational procedures for solving the sampling problem, which we show through experimental results. Applications of these new sampling results can be found in signal processing, communications systems, and biological systems.

/pdf/sampling-signals-with-finite-rate-of-innovation-1e8p47iotp.pdf

Sampling signals with finite rate of innovation

Ultra-wideband (UWB) radio is a fast emerging technology with many unique attractive features that promotes major advances in wireless communications, networking, radar, imaging, and positioning systems. Research in UWB is still in its infancy stages, offering limited resources in handling the challenges facing the UWB communications. Understanding the unique properties and challenges of UWB communications as well as its application in competent signal processing techniques are vital in conquering the obstacles towards developing exciting UWB applications. UWB research and development has to cope with the challenges that limit their performance, capacity, throughput, network flexibility, implementation complexity, and cost. This tutorial focuses on UWB wireless communications at the physical layer. It overviews the state-of-the-art UWB in channel modeling, transmitters, and receivers of UWB radios, and outlines the research directions and challenges that needs to be overcome. Since a signal processing expertise is expected to have major impact in research and development of UWB systems, emphasis is placed on the DSP aspects.

Ultra-wideband communications: an idea whose time has come

Compressed sensing (CS) is an emerging field that has attracted considerable research interest over the past few years. Previous review articles in CS limit their scope to standard discrete-to-discrete measurement architectures using matrices of randomized nature and signal models based on standard sparsity. In recent years, CS has worked its way into several new application areas. This, in turn, necessitates a fresh look on many of the basics of CS. The random matrix measurement operator must be replaced by more structured sensing architectures that correspond to the characteristics of feasible acquisition hardware. The standard sparsity prior has to be extended to include a much richer class of signals and to encode broader data models, including continuous-time signals. In our overview, the theme is exploiting signal and measurement structure in compressive sensing. The prime focus is bridging theory and practice; that is, to pinpoint the potential of structured CS strategies to emerge from the math to the hardware. Our summary highlights new directions as well as relations to more traditional CS, with the hope of serving both as a review to practitioners wanting to join this emerging field, and as a reference for researchers that attempts to put some of the existing ideas in perspective of practical applications.

Structured Compressed Sensing: From Theory to Applications

Chapter 1. Introduction and Overview of Wireless Sensor Networks. Chapter 2. Commercial and Scientific Applications of Wireless Sensor Networks. Chapter 3. Basic Wireless Sensor Technology. Chapter 4. Wireless Sensors Networks Protocols: Physical Layer. Chapter 5. Medium Access Control Protocols for Wireless Sensor Networks. Chapter 6. Sensors Network Protocols: Routing Protocols. Chapter 7. Transport Control Protocols for Wireless Sensors Networks. Chapter 8. Middleware for Sensor Networks. Chapter 9. Network Management for Wireless Sensor Networks. Chapter 10. Operating Systems for Sensor Networks. Chapter 11. Performance and Traffic Management Issues. Appendix A: Analysis. Appendix B: Discussions. Index.

/pdf/wireless-sensor-networks-technology-protocols-and-dh5el2590j.pdf

Wireless Sensor Networks: Technology, Protocols, and Applications

Distributed nature of the sensor network architecture introduces unique challenges and opportunities for collaborative networked signal processing techniques that can potentially lead to significant performance gains. Many evolving low-power sensor network scenarios need to have high spatial density to enable reliable operation in the face of component node failures as well as to facilitate high spatial localization of events of interest. This induces a high level of network data redundancy, where spatially proximal sensor readings are highly correlated. We propose a new way of removing this redundancy in a completely distributed manner, i.e., without the sensors needing to talk, to one another. Our constructive framework for this problem is dubbed DISCUS (distributed source coding using syndromes) and is inspired by fundamental concepts from information theory. We review the main ideas, provide illustrations, and give the intuition behind the theory that enables this framework.We present a new domain of collaborative information communication and processing through the framework on distributed source coding. This framework enables highly effective and efficient compression across a sensor network without the need to establish inter-node communication, using well-studied and fast error-correcting coding algorithms.

Distributed compression in a dense microsensor network

We consider the problem of efficiently transmitting sets of spatially correlated observations in a distributed sensor network without requiring inter-node communication to exploit the correlation. Specifically, we provide a construction for quantizer design given a training set, and a distributed compression scheme to efficiently relay the quantized observations to a central decoder.

Distributed compression for sensor networks

Revisiting Wireless Internet Connectivity: 5G vs Wi-Fi 6

In this work, we consider the problem of channel estimation by using the recently developed theory for sampling of signals with a finite rate of innovation. We show a framework which allows for lower than Nyquist rate sampling applicable for timing and channel estimation of both narrowband and wideband channels. In certain cases we demonstrate performance exceeding that of algorithms using Nyquist rate sampling while working at lower sampling rates, thus saving power and computational complexity.

/pdf/sampling-with-finite-rate-of-innovation-channel-and-timing-2wtu95ja54.pdf

Sampling with finite rate of innovation: channel and timing estimation for UWB and GPS

We consider the problem of low-sampling rate high-resolution channel estimation and timing for digital ultra-wideband (UWB) receivers. We extend some of our recent results in sampling of certain classes of parametric non-bandlimited signals and develop a frequency domain method for channel estimation and synchronization in ultra-wideband systems, which uses sub-Nyquist uniform sampling and well-studied computational procedures. In particular, the proposed method can be used for identification of more realistic channel models, where different propagation paths undergo different frequency-selective fading. Moreover, we show that it is possible to obtain high-resolution estimates of all relevant channel parameters by sampling a received signal below the traditional Nyquist rate. Our approach leads to faster acquisition compared to current digital solutions, allows for slower A/D converters, and potentially reduces power consumption of digital UWB receivers significantly.

Julius Kusuma

Papers

Distributed compression in a dense microsensor network

Distributed compression for sensor networks

Revisiting Wireless Internet Connectivity: 5G vs Wi-Fi 6

Sampling with finite rate of innovation: channel and timing estimation for UWB and GPS

Low-sampling rate UWB channel characterization and synchronization