Energy-Efficient Computing for Mobile Signal Processing

Mobile devices have rapidly proliferated, and deployment of handheld devices continues to increase at a spectacular rate. As today's devices not only support advanced signal processing of wireless communication data but also provide rich sets of applications, contemporary mobile computing requires both demanding computation and efficiency. Most mobile processors combine general-purpose processors, digital signal processors, and hardwired application-specific integrated circuits to satisfy their high-performance and low-power requirements. However, such a heterogeneous platform is inefficient in area, power and programmability. Improving the efficiency of programmable mobile systems is a critical challenge and an active area of computer systems research. SIMD (single instruction multiple data) architectures are very effective for data-level-parallelism intense algorithms in mobile signal processing. However, new characteristics of advanced wireless/multimedia algorithms require architectural re-evaluation to achieve better energy efficiency. Therefore, fourth generation wireless protocol and high definition mobile video algorithms are analyzed to enhance a wide-SIMD architecture. The key enhancements include 1) programmable crossbar to support complex data alignment, 2) SIMD partitioning to support fine-grain SIMD computation, and 3) fused operation to support accelerating frequently used instruction pairs. Near-threshold computation has been attractive in low-power architecture research because it balances performance and power. To further improve energy efficiency in mobile computing, near-threshold computation is applied to a wide SIMD architecture. This proposed near-threshold wide SIMD architecture-Diet SODA-presents interesting architectural design decisions such as 1) very wide SIMD datapath to compensate for degraded performance induced by near-threshold computation and 2) scatter-gather data prefetcher to exploit large latency gap between memory and the SIMD datapath. Although near-threshold computation provides excellent energy efficiency, it suffers from increased delay variations. A systematic study of delay variations in near-threshold computing is performed and simple techniques-structural duplication and voltage/frequency margining-are explored to tolerate and mitigate the delay variations in near-threshold wide SIMD architectures. This dissertation analyzes representative wireless/multimedia mobile signal processing algorithms, proposes an energy-efficient programmable platform, and evaluates performance and power. A main theme of this dissertation is that the performance and efficiency of programmable embedded systems can be significantly improved with a combination of parallel SIMD and near-threshold computations.Ph.D.Electrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/86356/1/swseo_1.pd

TechNews digests: Jan - Mar 2010

TechNews is a technology, news and analysis service aimed at anyone in the education sector keen to stay informed about technology developments, trends and issues. TechNews focuses on emerging technologies and other technology news. TechNews service : digests september 2004 till May 2010 Analysis pieces and News combined publish every 2 to 3 month

Cross-layer Optimized Wireless Video Surveillance

A wireless video surveillance system contains three major components, the video capture and preprocessing, the video compression and transmission over wireless sensor networks (WSNs), and the video analysis at the receiving end. The coordination of different components is important for improving the end-to-end video quality, especially under the communication resource constraint. Cross-layer control proves to be an efficient measure for optimal system configuration. In this dissertation, we address the problem of implementing cross-layer optimization in the wireless video surveillance system. The thesis work is based on three research projects. In the first project, a single PTU (pan-tilt-unit) camera is used for video object tracking. The problem studied is how to improve the quality of the received video by jointly considering the coding and transmission process. The cross-layer controller determines the optimal coding and transmission parameters, according to the dynamic channel condition and the transmission delay. Multiple error concealment strategies are developed utilizing the special property of the PTU camera motion. In the second project, the binocular PTU camera is adopted for video object tracking. The presented work studied the fast disparity estimation algorithm and the 3D video transcoding over the WSN for real-time applications. The disparity/depth information is estimated in a coarse-to-fine manner using both local and global methods. The transcoding is coordinated by the cross-layer controller based on the channel condition and the data rate constraint, in order to achieve the best view synthesis quality. The third project is applied for multi-camera motion capture in remote healthcare monitoring. The challenge is the resource allocation for multiple video sequences. The presented cross-layer design incorporates the delay sensitive, content-aware video coding and transmission, and the adaptive video coding and transmission to ensure the optimal and balanced quality for the multi-view videos. In these projects, interdisciplinary study is conducted to synergize the surveillance system under the cross-layer optimization framework. Experimental results demonstrate the efficiency of the proposed schemes. The challenges of cross-layer design in existing wireless video surveillance systems are also analyzed to enlighten the future work. Adviser: Song C

Cross-layer Optimized Wireless Video Surveillance

A wireless video surveillance system contains three major components, the video capture and preprocessing, the video compression and transmission over wireless sensor networks (WSNs), and the video analysis at the receiving end. The coordination of different components is important for improving the end-to-end video quality, especially under the communication resource constraint. Cross-layer control proves to be an efficient measure for optimal system configuration. In this dissertation, we address the problem of implementing cross-layer optimization in the wireless video surveillance system. The thesis work is based on three research projects. In the first project, a single PTU (pan-tilt-unit) camera is used for video object tracking. The problem studied is how to improve the quality of the received video by jointly considering the coding and transmission process. The cross-layer controller determines the optimal coding and transmission parameters, according to the dynamic channel condition and the transmission delay. Multiple error concealment strategies are developed utilizing the special property of the PTU camera motion. In the second project, the binocular PTU camera is adopted for video object tracking. The presented work studied the fast disparity estimation algorithm and the 3D video transcoding over the WSN for real-time applications. The disparity/depth information is estimated in a coarse-to-fine manner using both local and global methods. The transcoding is coordinated by the cross-layer controller based on the channel condition and the data rate constraint, in order to achieve the best view synthesis quality. The third project is applied for multi-camera motion capture in remote healthcare monitoring. The challenge is the resource allocation for multiple video sequences. The presented cross-layer design incorporates the delay sensitive, content-aware video coding and transmission, and the adaptive video coding and transmission to ensure the optimal and balanced quality for the multi-view videos. In these projects, interdisciplinary study is conducted to synergize the surveillance system under the cross-layer optimization framework. Experimental results demonstrate the efficiency of the proposed schemes. The challenges of cross-layer design in existing wireless video surveillance systems are also analyzed to enlighten the future work. Adviser: Song C

Efficient Algorithms for Large-Scale Image Analysis

This work develops highly efficient algorithms for analyzing large images. Applications include object-based change detection and screening. The algorithms are 10-100 times as fast as existing software, sometimes even outperforming FGPA/GPU hardware, because they are designed to suit the computer architecture. This thesis describes the implementation details and the underlying algorithm engineering methodology, so that both may also be applied to other applications

QR code detection under ROS implemented on the GPU

Tato diplomová práce se zabývá vývojem a implementací algoritmu pro detekci QR kódů s integrací do platformy ROS a výpočty běžícími na grafické kartě. Z rešerše současně dostupných nástrojů a technik je vybrán vhodný postup a algoritmus je napsán jako modul v programovacím jazyce Python, který je snadno integrovatelný do ROS. Ke zprostředkování výpočtů na vícejádrovém hardware, jako jsou grafické karty či vícejádrové procesory, je využita knihovna OpenCL.This master's thesis deals with the design and implementation of a QR code detection algorithm under the ROS platform with computations running on a graphical processing unit. Through a comparative survey of available tools and techniques, a suitable approach is chosen and the algorithm is written as a module in the Python programming language, ready to be implemented under the ROS platform. The OpenCL parallel computing platform is used to facilitate parallel computation on multi-core hardware, such as graphical processing units or multi-core CPUs.

Topics in Adaptive Optics

Advances in adaptive optics technology and applications move forward at a rapid pace. The basic idea of wavefront compensation in real-time has been around since the mid 1970s. The first widely used application of adaptive optics was for compensating atmospheric turbulence effects in astronomical imaging and laser beam propagation. While some topics have been researched and reported for years, even decades, new applications and advances in the supporting technologies occur almost daily. This book brings together 11 original chapters related to adaptive optics, written by an international group of invited authors. Topics include atmospheric turbulence characterization, astronomy with large telescopes, image post-processing, high power laser distortion compensation, adaptive optics and the human eye, wavefront sensors, and deformable mirrors