VLSI architectures for digital image coding

A number of high-performance VLSI architectures for real-time image coding applications are described. In particular, attention is focused on circuits for computing the 2-D DCT (discrete cosine transform) and for 2-D vector quantization. The former circuits are based on Winograd algorithms and comprise a number of bit-level systolic arrays with a bit-serial, word-parallel input. The latter circuits exhibit a similar data organization and consist of a number of inner product array circuits. Both circuits are highly regular and allow extremely high data rates to be achieved through extensive use of parallelism.

A bit-level systolic architecture for implementing a VQ tree search

A bit-level systolic array system for performing a binary tree Vector Quantization codebook search is described. This consists of a linear chain of regular VLSI building blocks and exhibits data rates suitable for a wide range of real-time applications. A technique is described which reduces the computation required at each node in the binary tree to that of a single inner product operation. This method applies to all the common distortion measures (including the Euclidean distance, the Weighted Euclidean distance and the Itakura-Saito distortion measure) and significantly reduces the hardware required to implement the tree search system. © 1990 Kluwer Academic Publishers.

JPEG encoder system-on-a-chip demonstrator

The design of a System-on-a-Chip (SoC) demonstrator for a baseline JPEG encoder core is presented. This combines a highly optimized Discrete Cosine Transform (DCT) and quantization unit with an entropy coder which has been realized using off-the-shelf synthesizable IP cores (Run-length coder, Huffman coder and data packer). When synthesized in a 0.35 µm CMOS process, the core can operate at speeds up to 100 MHz and contains 50 k gates plus 11.5 kbits of RAM. This is approximately 20% less than similar JPEG encoder designs reported in literature. When targeted at FPGA the core can operate up to 30 MHz and is capable of compressing 9-bit full-frame color input data at NTSC or PAL rates.

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.

Transmit and receive filter design for OFDM based WLAN systems

We analyze the effect of different pulse shaping filters on the orthogonal frequency division multiplexing (OFDM) based wireless local area network (LAN) systems in this paper. In particular, the performances of the square root raised cosine (RRC) pulses with different rolloff factors are evaluated and compared. This work provides some guidances on how to choose RRC pulses in practical WLAN systems, e.g., the selection of rolloff factor, truncation length, oversampling rate, quantization levels, etc.

Freehand core biopsy in breast cancer: an accurate predictor of tumour grade following neoadjuvant chemotherapy?

Core biopsy is an increasingly used technique in the pre-operative diagnosis of breast carcinoma, as it provides useful prognostic information with respect to tumour type and grade. Neoadjuvant chemotherapy is being used in the treatment of large and locally advanced breast cancers but little is known regarding the correlation between tumour histology on pre-treatment core biopsy and that in residual tumour following primary chemotherapy and surgery. This study aimed to evaluate the accuracy of core biopsy in predicting these features in patients treated with primary chemotherapy. One hundred and thirty-three patients with carcinoma of the breast diagnosed on clinical, radiological and cytological examination underwent core biopsy, followed by primary chemotherapy (with cyclophosphamide, vincristine, doxorubicin and prednisolone) and surgery. The false-negative rate for pre-treatment core biopsy was 14%, with 91% agreement between the grade demonstrated on core biopsy and that in the residual tumour following completion of chemotherapy. Tumour type in the residual post-chemotherapy tumour was predicted by core biopsy in 84%. This study suggests that pre-treatment core biopsy histology accurately predicts residual tumour histology following primary chemotherapy and surgery in patients with breast cancer. (C) 2002 Elsevier Science Ltd. All rights reserved.

Predicting forest attributes In southeast Alaska using artificial neural networks

Artificial neural network (ANN) methods are used to predict forest characteristics. The data source is the Southeast Alaska (SEAK) Grid Inventory, a ground survey compiled by the USDA Forest Service at several thousand sites. The main objective of this article is to predict characteristics at unsurveyed locations between grid sites. A secondary objective is to evaluate the relative performance of different ANNs. Data from the grid sites are used to train six ANNs: multilayer perceptron, fuzzy ARTMAP, probabilistic, generalized regression, radial basis function, and learning vector quantization. A classification and regression tree method is used for comparison. Topographic variables are used to construct models: latitude and longitude coordinates, elevation, slope, and aspect. The models classify three forest characteristics: crown closure, species land cover, and tree size/structure. Models are constructed using n-fold cross-validation. Predictive accuracy is calculated using a method that accounts for the influence of misclassification as well as measuring correct classifications. The probabilistic and generalized regression networks are found to be the most accurate. The predictions of the ANN models are compared with a classification of the Tongass national forest in southeast Alaska based on the interpretation of satellite imagery and are found to be of similar accuracy.