Systolic VLSI compiler (SVC) for high performance vector quantisation chips

An overview is given of a systolic VLSI compiler (SVC) tool currently under development for the automated design of high performance digital signal processing (DSP) chips. Attention is focused on the design of systolic vector quantization chips for use in both speech and image coding systems. The software in question consists of a cell library, silicon assemblers, simulators, test pattern generators, and a specially designed graphics shell interface which makes it expandable and user friendly. It allows very high performance digital coding systems to be rapidly designed in VLSI.

A 75-81 GHz, 14.3 dBm Peak Output Power, 21 dB Peak Gain, SiGe Power-Combining Amplifier Operating from a Single 3.3 V Supply

A compact differential 4-way power combiner with 2.3 dB loss and high common-mode rejection characteristic for use in mm-wave PAs is presented. A complete circuit comprised of a power splitter, two-stage cascode PA array, and a power combiner was implemented in SiGe technology. Measured small-signal gain of at least 17 dB was obtained from 74.5 GHz to 80.5 GHz with a peak 21 dB at 79 GHz. The prototype delivered 13.2 dBm P1dB and 14.3 dBm Psat when operated from a single 3.3 V supply at 75 GHz.

Mean shift based gradient vector flow for image segmentation

In recent years, gradient vector flow (GVF) based algorithms have been successfully used to segment a variety of 2-D and 3-D imagery. However, due to the compromise of internal and external energy forces within the resulting partial differential equations, these methods may lead to biased segmentation results. In this paper, we propose MSGVF, a mean shift based GVF segmentation algorithm that can successfully locate the correct borders. MSGVF is developed so that when the contour reaches equilibrium, the various forces resulting from the different energy terms are balanced. In addition, the smoothness constraint of image pixels is kept so that over- or under-segmentation can be reduced. Experimental results on publicly accessible datasets of dermoscopic and optic disc images demonstrate that the proposed method effectively detects the borders of the objects of interest.

Generic Ergodic Capacity Bounds for Fixed-Gain AF Dual-Hop Relaying Systems

This paper elaborates on the ergodic capacity of fixed-gain amplify-and-forward (AF) dual-hop systems, which have recently attracted considerable research and industry interest. In particular, two novel capacity bounds that allow for fast and efficient computation and apply for nonidentically distributed hops are derived. More importantly, they are generic since they apply to a wide range of popular fading channel models. Specifically, the proposed upper bound applies to Nakagami-m, Weibull, and generalized-K fading channels, whereas the proposed lower bound is more general and applies to Rician fading channels. Moreover, it is explicitly demonstrated that the proposed lower and upper bounds become asymptotically exact in the high signal-to-noise ratio (SNR) regime. Based on our analytical expressions and numerical results, we gain valuable insights into the impact of model parameters on the capacity of fixed-gain AF dual-hop relaying systems. © 2011 IEEE.

Hardware Acceleration of Background Modeling in the Compressed Domain

In intelligent video surveillance systems, scalability (of the number of simultaneous video streams) is important. Two key factors which hinder scalability are the time spent in decompressing the input video streams, and the limited computational power of the processor. This paper demonstrates how a combination of algorithmic and hardware techniques can overcome these limitations, and significantly increase the number of simultaneous streams. The techniques used are processing in the compressed domain, and exploitation of the multicore and vector processing capability of modern processors. The paper presents a system which performs background modeling, using a Mixture of Gaussians approach. This is an important first step in the segmentation of moving targets. The paper explores the effects of reducing the number of coefficients in the compressed domain, in terms of throughput speed and quality of the background modeling. The speedups achieved by exploiting compressed domain processing, multicore and vector processing are explored individually. Experiments show that a combination of all these techniques can give a speedup of 170 times on a single CPU compared to a purely serial, spatial domain implementation, with a slight gain in quality.

Documents and Dependencies: an Exploration of Vector Space Models for Semantic Composition

In most previous research on distributional semantics, Vector Space Models (VSMs) of words are built either from topical information (e.g., documents in which a word is present), or from syntactic/semantic types of words (e.g., dependency parse links of a word in sentences), but not both. In this paper, we explore the utility of combining these two representations to build VSM for the task of semantic composition of adjective-noun phrases. Through extensive experiments on benchmark datasets, we find that even though a type-based VSM is effective for semantic composition, it is often outperformed by a VSM built using a combination of topic- and type-based statistics. We also introduce a new evaluation task wherein we predict the composed vector representation of a phrase from the brain activity of a human subject reading that phrase. We exploit a large syntactically parsed corpus of 16 billion tokens to build our VSMs, with vectors for both phrases and words, and make them publicly available.