On the Development of a New Adaptive Channel Equalizer using Bacterial Foraging Optimization Technique

The present paper introduces the Bacterial Foraging Optimization (BFO) technique to develop an efficient forecasting model for prediction of various stock indices. The connecting weights of the adaptive linear combiner based model are optimized by the BFO so that its mean square error(MSE) is minimized. The short and long term prediction performance of the model is evaluated with test data and the results obtained are compared with those obtained from the multilayer perceptron (MLP) based model. It is in general observed that the proposed model is computationally more efficient, prediction wise more accurate and takes less training time compared to the standard AMP based model.

This paper introduces a new variable step-size LMS (NVSS LMS) algorithm in an adaptive channel equalizer. This variant of VSS LMS algorithm is based on the weighting coefficients bias/variance trade-off. We will show here that the adaptive equalizer with the NVSS LMS algorithm has favorable performance. Simulation results are provided to support the proposed implementation of the NVSS LMS.

A new modified particle swarm optimization algorithm for adaptive equalization

This paper applies neural networks to the adaptive channel equalization of a bipolar signal passed through a dispersive channel in the presence of additive noise. The paper describes two neural networks which might be considered as adaptive equalizers. The simulation results confirm that the neural network equalizers offer a performance which exceeds that of linear structures. More specifically, this paper highlights the effects of delay order on the BER performance of nonlinear structures.

Current studies on modulation and detection schemes in molecular communication mainly focus on the scenarios with static transmitters and receivers. However, mobile molecular communication is needed in many envisioned applications, such as target tracking and drug delivery. Until now, investigations about mobile molecular communication have been limited. In this paper, a static transmitter and a mobile bacterium-based receiver performing random walk are considered. In this mobile scenario, the channel impulse response changes due to the dynamic change of the distance between the transmitter and the receiver. Detection schemes based on fixed distance fail in signal detection in such a scenario. Furthermore, the intersymbol interference (ISI) effect becomes more complex due to the dynamic character of the signal which makes the estimation and mitigation of the ISI even more difficult. In this paper, an adaptive ISI mitigation method and two adaptive detection schemes are proposed for this mobile scenario. In the proposed scheme, adaptive ISI mitigation, estimation of dynamic distance, and the corresponding impulse response reconstruction are performed in each symbol interval. Based on the dynamic channel impulse response in each interval, two adaptive detection schemes, concentration-based adaptive threshold detection and peak-time-based adaptive detection, are proposed for signal detection. Simulations demonstrate that the ISI effect is significantly reduced and the adaptive detection schemes are reliable and robust for mobile molecular communication.

In this paper, we proposed a home energy management (HEM) scheme for minimization in electricity bills and reduction in peak load. This can be achieved by scheduling the usage timings of appliances (APP) for shifting load from peak hours (PHs) to OFF-peak hours (OPHs). In this study we proposed a technique which is hybrid of two bio-inspired optimization techniques chicken swarm optimization (CSO) and bacterial foraging optimization (BFA). Simulation results shows that proposed hybrid technique reduces the cost and load peaks by shifting load from PHs to OPHs and reduction in load peaks.

Discrete multitone modulation allows for a simple receiver structure if the impulse response of the communication channel is shorter than the cyclic prefix. In ADSL, a time-domain equalizer (TEQ) is used to fulfil this requirement. Without a TEQ, unwanted intercarrier interference (ICI) and intersymbol interference (ISI) occur. In this paper, quadratic forms are developed to calculate the contributions of ICI and ISI to the desired signal. Based on these expressions, a new method for calculating the TEQ coefficients, which minimizes the effects of ICI, ISI, and additive noise, is derived. Simulations in the context of ADSL compare the performance of this new algorithm with one that is state of the art.

In this paper, we present a new evolutionary instance-based learning method by integrating the bacterial foraging optimization (BFO) technique with fuzzy k-nearest neighbor classifier (FKNN), termed as BFO-FKNN. In the proposed approach, the issue of parameter tuning problem in FKNN is tackled using the BFO technique. The effectiveness of the proposed method has been rigorously evaluated against the Parkinson’s disease (PD) diagnosis problem. The simulation results have shown that the proposed approach outperforms the other two counterparts via 10-fold cross validation analysis. In addition, compared to the existing methods in previous studies, the proposed method can also be regarded as a promising success with the excellent classification accuracy of 96.39%.

Nonlinear distortions must be compensated in many real-life systems, such as in digital satellite and microwave channels and others, so adaptive equalization is of considerable practical interest. Radial basis function neural networks have the ability to equalize nonlinear channels, and a simplified version is proposed for online implementation. We focus on the stochastic gradient technique, and get a good performance by adjusting only one coefficient and one center closest to the input vector. Simulations are included to verify the algorithm.

The space-time adaptive processing (STAP) can improve the performance of airborne early warning (AEW) radar. But, the receiver channel mismatch will give rise to much degradation of the performance for STAP. This paper introduces an efficient adaptive channel equalization method which can compensate the channel mismatch automatically by least squares theory. The effect of equalizer parameters on equalization performance is analysed in detail, and a sample of adaptive channel equalizer is given.

This paper proposes a new approach to enhance peak signal to noise ratio of highly corrupted image affected by impulse noise. The proposed technique is implemented using an adaptive median Alter and the bacterial foraging optimization (BFO) technique. The adaptive median Alter is used to identify pixels affected by noise and replace them with median value to keep the information uncorrupted. The BFO technique minimizes errors between adaptive median filter output image and noisy image to maintain an error percentage of 0.0001. It has been observed that results of the proposed method are superior to conventional methods in terms of perceptual image quality as well as clarity and smoothness in edge regions of the resultant image. This technique can remove the salt-and-pepper noise of highly corrupted images with a noise as high as 90%.

Assuming the intersymbol interference (ISI) has been removed by an adaptive equalizer which employs a suitable blind equalization algorithm, the output constellation may still be rotated by an arbitrary phase angle. These angles are the result of the existence of undesirable local minima in the phase parameter space. A simple technique to identify all such angles is presented. In addition, the stability of the rotated solutions and the range of phase angles that leads to such solutions can be determined too. Three blind algorithms, namely the multi-modulus algorithm (MMA), the reduced constellation algorithm (RCA), and the constant modulus algorithm (CMA) are analyzed using this approach. The immunity of the MMA towards such solutions is verified analytically while the susceptibility of the RCA towards them, especially the 45deg-solution, is verified through simulations. Our analysis also agrees with the well known fact that the CMA is indeed phase-blind.

This paper presents a new design technique of high-speed interconnects with controlled intersymbol interference (ISI) to create efficient signaling over a band-limited channel. Performance of high-speed electrical links is limited by conductor loss, dielectric dispersion, and reflections in the board, package, and connector. These nonidealities result in significant ISI. In current systems, the effect of ISI is either mitigated through complex equalization, signal conditioning, and coding techniques, or through costly impedance control and manufacturing processes. In the proposed approach, instead of eliminating ISI, we shape the response of the channel into a set of channel characteristics with controlled ISI using simple passive structures in the board and the package. The resulting controlled ISI is exploited at the transmitter and receiver to simplify the architecture of the system and to achieve high data rates. The techniques to design interconnects with controlled ISI are reasonably simple to implement in conventional interconnect technologies. Simulation examples are given to demonstrate the validity and advantages of the design technique using duobinary and analog multitone (AMT) signaling methods.

Free Space Optical (FSO) communication is an emerging technology to provide high bandwidth, secure and high quality communication. In this work we have determined the capacity of the Free Space Optical communication system with Multiple Input Multiple Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) technique under weak atmospheric turbulence conditions. To model the weak turbulence condition of the atmosphere lognormal channel model is used throughout the work. The capacity of MIMO-OFDM FSO is evaluated in the presence of Inter Symbol Interference (ISI) and is compared with MIMO FSO with ISI. The capacity of MIMO-OFDM FSO system increases in comparison to MIMO in the presence of ISI. It is found that the effect of ISI is mitigated by using OFDM and the capacity is enhanced. Since MIMO uses spatial divervity to mitigate the fading caused by turbulence hence the capacity of both MIMO-OFDM FSO and MIMO FSO also increases as the number of receiving antennas is increased.

This paper presents analytical results of the effects of jitter and inter symbol interference (ISI) on a millimeter-wave impulse radio (IR) transceiver, compared with performance of the developed 10-Gb/s W-band IR transmitter prototype. The compact and cost-effective IR transmitter consists of a pulse generator (PG) that creates a short pulse with a duration of <10 ps, a band-pass filter (BPF) that shapes the short pulse to the desired millimeter-wave pulse (wavelet), and a power amplifier (PA) which is deployed as an option. Jitters of the PG and ISI from the BPF are a great issue to make the IR transceiver robust and to obtain excellent performance. One analysis verified that, thanks to a novel retiming architecture, the random jitter and the data dependent jitter from the PG give only a small penalty of <0.5-dB increase in signal to noise ratio (SNR) for achieving a bit error rate (BER) of <10−12. The alternative analysis on the effect of ISI from the BPF indicated that using a Gaussian BPF enables transmission with a BER of <10−12 up to a data rate of 1.4 times as large as a bandwidth of the BPF, whereas the ISI analysis suggested that mismatching of the skirt characteristics of the developed BPF to those of a Gaussian BPF causes tail lobes following the wavelet, resulting in an on/off ratio of <15 dB and hence an SNR penalty of <6 dB.