Articles in This Issue
Abstract
In this paper new semi-empirical formulas are developed to evaluate the variation of both real and imaginary parts of soil complex permittivity with depth inside the earth's surface. Computed values using these models show good agreement with published measured values for soils of the same textures and same frequency band. Use of these models may serve to handle more accurate results especially in the ground probing radar (GPR) applications and other applications relating the detection of buried objects inside the earth's surface, where the use of a single average value of the soil complex permittivity had not necessarily led, for most of the times, to accurate results for the electromagnetic fields propagated inside the earth's surface.
Abstract
In order to reduce the impact of watermark embedding on the perceptual fidelity of the marked signal, watermarking systems process the generated watermark to match it to the local properties of the underlying host signal prior to embedding. However, this adaptation process could distort the watermark, affecting its robustness and information content. In this paper, a new watermark coding technique is proposed, that enables the application of some mark- nondistorting host-adaptation processing, where the intensity of the watermark could be redistributed according to the local properties of the underlying host without changing the way of interpreting the watermark to be embedded. This completely eliminates the need to equalize adaptation distortions prior to decoding, and hence, to pass any side information about the adaptation processing to the decoder, too.
Abstract
This paper presents a comprehensive analysis for the performance of heterojunction bipolar phototransistor (HPT) as an essential element for optoelectronic switch configurations. The theory of operation of the (HPT) is reviewed. Analytical expressions are drived for transistor current components, optical gain $G_{opt}$ and DC current gain $h_{FE}$ in common emitter configuration. The analysis enables one to study the influence of various structure parameters and incident optical power on the optical gain characteristics of the (HPT). Simulation results are presented for a $1.3~\mu m$ $\text{In}_{0.53}\text{Ga}_{0.47}\text{As}/\text{InP}$ structure.
Abstract
The aim of this paper is to investigate the switching characteristics of hetrojunction phototransistor (HPT). First, the static characteristics of the HPT are given under ideal conditions to get a physical insight on the main parameters affecting it's response. Then the speed of response of HPT is addressed and supported by simulation results reported for $1.3~\mu m$ InGaAs/InP transistor.
Abstract
The dynamic performance of vertical-cavity surface emitting lasers (VCSEL) diodes can be enhanced by incorporating multiquantum-well (MQW) structure in the active region. This paper addresses the transient response of MQW-VCSEL by solving the laser rate equation in the large-signal regime. The analysis makes use of the energy band structure and optical gain spectrum obtained by applying Schrödinger equation to both conduction and valance bands. Simulation results are presented for $1.3~\mu m$ InGaAs/InP VCSEL and indicate clearly that a MQW laser has higher switching speed compared with bulk laser and this finding is more pronounced with small number of wells.
Abstract
Brain machine interface provides a communication channel between the human brain and an external device. Brain interfaces are studied to provide rehabilitation to patients with neurodegenerative diseases; such patients loose all communication pathways except for their sensory and cognitive functions. One of the possible rehabilitation methods for these patients is to provide a brain machine interface (BMI) for communication; the BMI uses the electrical activity of the brain detected by scalp EEG electrodes. Classification of EEG signals extracted during mental tasks is a technique for designing a BMI. In this paper a BMI design using five mental tasks from two subjects were studied, a combination of two tasks is studied per subject. An Elman recurrent neural network is proposed for classification of EEG signals. Two feature extraction algorithms using overlapped and non overlapped signal segments are analyzed. Principal component analysis is used for extracting features from the EEG signal segments. Classification performance of overlapping EEG signal segments is observed to be better in terms of average classification with a range of 78.5% to 100%, while the non overlapping EEG signal segments show better classification in terms of maximum classifications.
Abstract
This work presents aneural and fuzzy based ECG signal recognition system based on wavelet transform. The suitable coefficients that can be used as a feature for each fuzzy network or neural network is found using a proposed best basis technique. Using the proposed best bases reduces the dimension of the input vector and hence reduces the complexity of the classifier. The fuzzy network and the neural network parameters are learned using back propagation algorithm.