Bearing and Range Estimation Algorithm for Buried Object in Underwater Acoustics

A new algorithm which associates (Multiple Signal Classification) MUSIC with acoustic scattering model for bearing and range estimation is proposed. This algorithm takes into account the reflection and the refraction of wave in the interface of water-sediment in underwater acoustics. A new directional vector, which contains the Direction-Of-Arrival (DOA) of objects and objects-sensors distances, is used in MUSIC algorithm instead of classical model. The influence of the depth of buried objects is discussed. Finally, the numerical results are given in the case of buried cylindrical shells.

Improved Parameter Estimation for First-Order Markov Process

This correspondence presents a linear transformation, which is used to estimate correlation coefficient of first-order Markov process. It outperforms zero-forcing (ZF), minimum mean-squared error (MMSE), and whitened least-squares (WTLSs) estimators by controlling output noise variance at the cost of increased computational complexity.

Design of an Adaptive Predistorter for Solid State Power Amplifier in Wireless OFDM Systems

Orthogonal frequency division multiplexing (OFDM) is a powerful modulation choice for wideband wireless communication systems. However, its high peak-to-average power ratio greatly limits the high power amplifier (HPA) power efficiency. Here, we present the design of an adaptive predistorter to compensate the distortion caused by the HPA. Specifically, we deal with the implementation issue of the proposed predistorter in Lee and de Figueiredo's work (2006). The performance improvement by predistorter is verified by both floating-point simulation and fixed-point simulation, where the latter includes the distortion effects from the hardware. The bit widths for OFDM signals, ADC, and DAC are evaluated, and the bit width of 10 is shown to be sufficient for the hardware design.

Using Multiscale Product for ECG Characterization

This paper introduces a new method for R wave's locations using the multiscale wavelet analysis, that is based on Mallat's and Hwang's approach for singularity detection via local maxima of the wavelet coefficients signals. Using a first derivative Gaussian function as prototype wavelet, we apply the pointwise product of the wavelet coefficients (PWCs) over some successive scales, in order to enhance the peak amplitude of the modulus maxima line and to reduce noise. The R wave corresponds to two modulus maximum lines with opposite signs (min-max) of multi-scale product. The proposed algorithm does not include regularity analysis but only amplitude-based criteria. We evaluated the algorithm on two manually annotated databases, such as MIT-BIH Arrhythmia and QT.

Selective Decoding Scheme-MAC Protocol in Ad Hoc Networks with MIMO Links

A problem encountered in the IEEE 802.11 MAC is the collision of simultaneous transmissions from either neighboring nodes or hidden nodes within the same contention floor. This paper presents the selective decoding schemes in MAC (SDS-MAC) protocol for MIMO ad hoc networks. It is able to mitigate interferences by means of a minimum mean-squared error (MMSE) technique. As a result, it allows a pair of simultaneous transmissions to the same or different nodes to yield the network utilization increase. The decoding schemes and time line operations are properly selected corresponding to the transmission demand of neighboring nodes to avoid collision. Transmission demand can be determined by the number of RTS packets and type of CTS packets. Simulation results are given to illustrate the SDS-MAC performance.

Singular Spectrum Analysis of Gene Expression Profiles of EarlyDrosophila embryo: Exponential-in-Distance Patterns

We present investigation of gene expression profiles by means of singular spectrum analysis (SSA). The biological problem under investigation is the decomposition ofbicoidprotein profiles ofDrosophila melanogasterinto the sum of a signal and noise, where the former consists of an exponential-in-distance pattern and is close to constant nonspecific component, or “background.” The signal processing problems addressed are (i) trend extraction from a noisy signal, (ii) batch processing of similar data, and (iii) analytical approximation of the signal components by the sum of exponential and constant-like functions. The proposed methods are evaluated on the given 17 series.

NLOS Mitigation for TOA Location Based on a Modified Deterministic Model

Wireless location becomes difficult due to contamination of measured time-of-arrival (TOA) caused by non-line-of-sight. In this letter, TOA measurements seen at base stations are adjusted by scale factors, and a modified deterministic model is built. An effective numerical solution is proposed to resolve the scale factors and mobile position. A simulation comparison of four algorithms indicates that the proposed algorithm outperforms the other three algorithms.

An Adaptive Resolution Computationally Efficient Short-Time Fourier Transform

The short-time Fourier transform (STFT) is a classical tool, used for characterizing the time varying signals. The limitation of the STFT is its fixed time-frequency resolution. Thus, an enhanced version of the STFT, which is based on the cross-level sampling, is devised. It can adapt the sampling frequency and the window function length by following the input signal local characteristics. Therefore, it provides an adaptive resolution time-frequency representation of the input signal. The computational complexity of the proposed STFT is deduced and compared to the classical one. The results show a significant gain of the computational efficiency and hence of the processing power.

Efficiently Synchronized Spread-Spectrum Audio Watermarking with Improved Psychoacoustic Model

This paper presents an audio watermarking scheme which is based on an efficiently synchronized spread-spectrum technique and a new psychoacoustic model computed using the discrete wavelet packet transform. The psychoacoustic model takes advantage of the multiresolution analysis of a wavelet transform, which closely approximates the standard critical band partition. The goal of this model is to include an accurate time-frequency analysis and to calculate both the frequency and temporal masking thresholds directly in the wavelet domain. Experimental results show that this watermarking scheme can successfully embed watermarks into digital audio without introducing audible distortion. Several common watermark attacks were applied and the results indicate that the method is very robust to those attacks.

On Optimal Bit Allocation for Classification-Based Source-Dependent Transform Coding

An optimal bit allocation is presented for classification-based source-dependent transform coding. A vector of transform coefficients is considered to have been produced by a mixture of processes. The available bit resource is distributed optimally in two stages: (1) bit allocation is performed for each class of coefficient vectors, and (2) bit allocation is performed for each vector coefficient. The solution for low bit rates imposing nonnegative bit resource is also presented. The rate-distortion bound of the classification-based source coding is derived.