Packed hyper-ellipsoid classifiers

Pattern classification is frequently performed using the k-nn algorithm or a neural network. The choice of parameters for the former is often difficult and the amount of data which has to be stored in the classifier can be high. Neural network classifiers can overcome some of these problems but learning is often unreliable and slow. An alternative which combines some of the best features of the k-nn and neural network classifier is described by the authors. The classifier is called a packed hyper-ellipsoid classifier     

Vector algorithm for Reed-Muller expansions

A novel vector algorithm for Reed-Muller (RM) expansions is proposed which can save as many as a factor of 2^n-1 memory elements compared with previous matrix algorithms     

An OFDM scheme with a half complexity

The paper deals with an OFDM (orthogonal frequency division multiplexing) system based on filter-bank architecture. The known implementation uses a DFT (discrete Fourier transform) processor and a polyphase network (PPN). Even if it is based on complex components, in the final step it operates the real part extraction of the incoming signal. This leads to redundant operations in the DFT processor and in the PPN. Specifically, for the transmission of N complex symbol sequences at a given rate ½F, an N-point DFT processor and an N-branch PPN, both working at the rate F, are required. This implementation can be improved with a complexity reduction by a factor of two. In fact, in the paper an architecture is presented based on an N-point DFT processor and N-branch PPN at the rate F, for the transmission of 2N (in place of N) complex symbol sequences at the rate ½F     

A stochastic deconvolution method to reconstruct insulin secretionrate after a glucose stimulus

Insulin secretion rate (ISR) is not directly measurable in man but it can be reconstructed from C-peptide (CP) concentration measurements by solving an input estimation problem by deconvolution. The major difficulties posed by the estimation of ISR after a glucose stimulus, e.g., during an intravenous glucose tolerance test (IVGTT), are the ill-conditioning of the problem, the nonstationary pattern of the secretion rate, and the nonuniform/infrequent sampling schedule. In this work, a nonparametric method based on the classic Phillips-Tikhonov regularization approach is presented. The problem of nonuniform/infrequent sampling is addressed by a novel formulation of the regularization method which allows the estimation of quasi time-continuous input profiles. The input estimation problem is stated into a Bayesian context, where the a priori known nonstationary characteristics of ISR after the glucose stimulus are described by a stochastic model. Deconvolution is tackled by linear minimum variance estimation, thus allowing the derivation of new statistically based regularization criteria. Finally, a Monte-Carlo strategy is implemented to assess the uncertainty of the estimated ISR arising from CP measurement error and impulse response parameters uncertainty     

Arithmetic coding for data compression

Arithmetic coding provides an effective mechanism for removing redundancy in the encoding of data. We show how arithmetic coding works and describe an efficient implementation that uses table lookup as a first alternative to arithmetic operations. The reduced-precision arithmetic has a provably negligible effect on the amount of compression achieved. We can speed up the implementation further by use of parallel processing. We discuss the role of probability models and how they provide probability information to the arithmetic coder. We conclude with perspectives on the comparative advantages and disadvantages of arithmetic coding     

Silver nitrate-acetonitrile and iodine-benzene complexes as studied by NMR in thermotropic liquid crystals

Silver nitrate-acetonitrile and π iodine-benzene complexes in thermotropic liquid crystals have been studied by ¹H, ²H, and ¹³C NMR spectroscopy and by optical microscopy. Evidence for at least two silver complexes in each liquid crystal is presented.     

A single-chip 12.7 Mchips/s digital IF BPSK direct sequencespread-spectrum transceiver in 1.2 μm CMOS

This paper describes a fully integrated digital-spread spectrum transceiver chip fabricated through MOSIS in 1.2 μm CMOS. It includes a baseband spread spectrum transmitter and a coherent intermediate frequency (IF) receiver consisting of a Costas loop, an acquisition loop for the pseudo-noise (PN) sequence, and a clock recovery loop with a 406.4 MHz onchip numerically controlled oscillator (NCO). The transceiver is capable of operating at a maximum IF sampling rate of 50.8 MS/s and a maximum chip rate of 12.7 R Mchips/s (Mcps) with selectable data rates of 100, 200, 400, and 800 kbps. At the maximum operating speed of 50.8 R MS/s, it dissipates 1.1 W. In an additive white Gaussian noise channel the IF receiver achieves a receiver output SNR within 1 dB of theory and can acquire code with a wide range of input SNR from -17 dB to over 30 dB. The transceiver chip has been interfaced to an RF up/down converter to demonstrate a wireless voice/data/video link operating in the 902-928 MHz band