Digital compression algorithms for HDTV transmission

Digital compression of video images is a possible avenue for high definition television (HDTV) transmission. Compression needs to be optimized while picture quality remains high. Two techniques for compression the digital images are explained and comparisons are drawn between the human vision system and artificial compression techniques. Suggestions for improving compression algorithms through the use of neural and analog circuitry are given

Video data compression using artificial neural network differential vector quantization

An artificial neural network vector quantizer is developed for use in data compression applications such as Digital Video. Differential Vector Quantization is used to preserve edge features, and a new adaptive algorithm, known as Frequency-Sensitive Competitive Learning, is used to develop the vector quantizer codebook. To develop real time performance, a custom Very Large Scale Integration Application Specific Integrated Circuit (VLSI ASIC) is being developed to realize the associative memory functions needed in the vector quantization algorithm. By using vector quantization, the need for Huffman coding can be eliminated, resulting in superior performance against channel bit errors than methods that use variable length codes

Combinatorial pulse position modulation for power-efficient free-space laser communications

A new modulation technique called combinatorial pulse position modulation (CPPM) is presented as a power-efficient alternative to quaternary pulse position modulation (QPPM) for direct-detection, free-space laser communications. The special case of 16C4PPM is compared to QPPM in terms of data throughput and bit error rate (BER) performance for similar laser power and pulse duty cycle requirements. The increased throughput from CPPM enables the use of forward error corrective (FEC) encoding for a net decrease in the amount of laser power required for a given data throughput compared to uncoded QPPM. A specific, practical case of coded CPPM is shown to reduce the amount of power required to transmit and receive a given data sequence by at least 4.7 dB. Hardware techniques for maximum likelihood detection and symbol timing recovery are presented

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The exact analysis of second order bandpass modulator with sinusoidal in-puts is performed and the results show the quantization error is neither uniformly distributed nor white and its spectrum is purely discrete and symmetric with w = 2. The location of each spectrum frequency component or idle tone depends strongly on the input amplitude but the spectrum magnitude is not input dependent. As the amplitude of input signal increased, the largest secondary tones will approach tothe center frequency and may corrupt desired signal. The crosscorrelation between quan-tization error and input do exist in our study of bandpass system with sinusoidal inputs. However, it can be cancelled out by the noise shaping function, so the mod-ulator output will not be a ected by this crosscorrelation. However, for input signal with certain bandwidth, the crosscorrelation will degrade the system performance. For higher order systems or noisy passband communication signals, the Gaussian assumption for the input to the quantizer is reasonable even for bandpass modu-lators. The analysis of quantization error power under this assumption shows it is not only the function of output step size but also the input level of quantizer. The mini-mum power of quantization error can be achieved when a speci c value of quantizer input power is applied. Two applications, direct decoding technique and improved Hartley image-reject receiver, for bandpass modulators are also introduced and analyzed. i

Towards Radar-Enabled Sensor Networks

Ultrawideband radar-enabled wireless sensor networks have the potential to address key detection and classification requirements common to many surveillance and tracking applications. However, traditional radar signal processing techniques are mismatched with the limited computational and storage resources available on typical sensor nodes. The mismatch is exacerbated in noisy, cluttered environments or when the signals have corrupted spectra. To explore the compatibility of ultrawideband radar and mote-class sensor nodes, we designed and built a new platform called the Radar Mote. An early prototype of this platform was used to detect, classify, and track people and vehicles moving through an outdoor sensor network deployment. This paper describes the sensor’s theory of operation, discusses the design and implementation of the Radar Mote, and presents sample signal waveforms of people, vehicles, noise, and clutter. We demonstrate that radar sensors can be successfully integrated with mote-class devices and imbue them with an extraordinarily useful sensing modality

Design of a Wireless Sensor Network Platform for Detecting Rare, Random, and Ephemeral Events

We present the design of the eXtreme Scale Mote, a new sensor network platform for reliably detecting and classifying, and quickly reporting, rare, random, and ephemeral events in a largescale, long-lived, and retaskable manner. This new mote was designed for the ExScal project which seeks to demonstrate a 10,000 node network capable of discriminating civilians, soldiers and vehicles, spread out over a 10km 2 area, with node lifetimes approaching 1,000 hours of continuous operation on two AA alkaline batteries. This application posed unique functional, usability, scalability, and robustness requirements which could not be met with existing hardware, and therefore motivated the design of a new platform. The detection and classification requirements are met using infrared, magnetic, and acoustic sensors. The infrared and acoustic sensors are designed for low-power continuous operation and include asynchronous processor wakeup circuitry. The usability and scalability requirements are met by minimizing the frequency and cost of human-in-the-loop operations during node deployment, activation, and verification through improvements in the user interface, packaging, and configurability of the platform. Recoverable retasking is addressed by using a grenade timer that periodically forces a system reset. The key contributions of this work are a specific design point and general design methods for building sensor network platforms to detect exceptional events. 1

Differential Vector Quantization of Real-Time Video Using Entropy-biased ANN Codebooks

We describe hardware that has been built to compress video in real time using full-search vector quantization (VQ). This architecture implements a differential-vector-quantization (DVQ) algorithm which features entropy-biased codebooks designed using an artificial neural network (ANN). A special-purpose digital associative memory, the VAMPIRE chip, performs the VQ processing. We describe the DVQ algorithm, its adaptations for sampled NTSC composite-color video, and details of its hardware implementation. We conclude by presenting results drawn from real-time operation of the DVQ hardware