Adaptive hierarchical artificial immune system and its application in RFID reader collision avoidance

This paper proposes an efficient adaptive hierarchical artificial immune system (AHAIS) for complex global optimization problems. In the proposed AHAIS optimization, a hierarchy with top-bottom levels is used to construct the antibody population, where some antibodies with higher affinity become the top-level elitist antibodies and the other antibodies with lower affinity become the bottom-level common antibodies. The elitist antibodies experience different evolutionary operators from those common antibodies, and a well-designed dynamic updating strategy is used to guide the evolution and retrogradation of antibodies between two levels. In detail, the elitist antibodies focus on self-learning and local searching while the common antibodies emphasize elitist-learning and global searching. In addition, an adaptive searching step length adjustment mechanism is proposed to capture more accurate solutions. The suppression operator introduces an upper limit of the similarity-based threshold by considering the concentration of the candidate antibodies. To evaluate the effectiveness and the efficiency of algorithms, a series of comparative numerical simulations are arranged among the proposed AHAIS, DE, PSO, opt-aiNet and IA-AIS, where eight benchmark functions are selected as testbeds. The simulation results prove that the proposed AHAIS is an efficient method and outperforms DE, PSO, opt-aiNet and IA-AIS in convergence speed and solution accuracy. Moreover, an industrial application in RFID reader collision avoidance also demonstrates the searching capability and practical value of the proposed AHAIS optimization.     

Color differences based fuzzy filter for extremely corrupted color images

In this paper, a color difference based fuzzy filter is presented for fix and random-valued impulse noise. Noise detection scheme of two stages was applied to detect noise efficiently whereas for noise removal an improved Histogram based Fuzzy Color Filter (HFC) is presented. Pixels detected as noisy by the noise detection scheme are deliberated as candidate for the removal of noise. Candidate noisy pixels are then processed using a modified Histogram based Fuzzy Color Filter to estimate their non-noisy values. The idea of using multiple fuzzy membership functions is presented, so that best suitable membership function for local image statistics can be used automatically. In the proposed technique we have used three different types of fuzzy membership functions (bell-shaped, trapezoidal-shaped, and triangular-shaped) and their fuzzy number construction algorithms are proposed. Experimentation is also performed with three, five, and seven membership functions. Type and number of suitable fuzzy membership functions are then identified to remove noise. Comparison with the existing filtering techniques is established on the basis of objective quantitative measures including structural similarity index measure (SSIM) and peak-signal-to-noise-ratio (PSNR). Simulations show that this filter is superior to that of the existing state-of-the-art filtering techniques in removing fix and random-valued impulse noise whereas retaining the details of the image contents.     

Swarm intelligence based approach for sinkhole attack detection in wireless sensor networks

Swarm intelligence, a nature inspired computing applies an algorithm situated within the context of agent based models that mimics the behavior of ants to detect sinkhole attacks in wireless sensor networks. An Ant Colony Optimization Attack Detection (ACO-AD) algorithm is proposed to identify the sinkhole attacks based on the nodeids defined in the ruleset. The nodes generating an alert on identifying a sinkhole attack are grouped together. A voting method is proposed to identify the intruder. An Ant Colony Optimization Boolean Expression Evolver Sign Generation (ABXES) algorithm is proposed to distribute the keys to the alerted nodes in the group for signing the suspect list to agree on the intruder. It is shown that the proposed method identifies the anomalous connections without generating false positives and minimizes the storage in the sensor nodes in comparison to LIDeA architecture for sinkhole attack detection. Experimental results demonstrating the Ant Colony Optimization approach of detecting a sinkhole attack are presented.     

Source cell-phone recognition from recorded speech using non-speech segments

In a recent study, we have introduced the problem of identifying cell-phones using recorded speech and shown that speech signals convey information about the source device, making it possible to identify the source with some accuracy. In this paper, we consider recognizing source cell-phone microphones using non-speech segments of recorded speech. Taking an information-theoretic approach, we use Gaussian Mixture Model (GMM) trained with maximum mutual information (MMI) to represent device-specific features. Experimental results using Mel-frequency and linear frequency cepstral coefficients (MFCC and LFCC) show that features extracted from the non-speech segments of speech contain higher mutual information and yield higher recognition rates than those from speech portions or the whole utterance. Identification rate improves from 96.42% to 98.39% and equal error rate (EER) reduces from 1.20% to 0.47% when non-speech parts are used to extract features. Recognition results are provided with classical GMM trained both with maximum likelihood (ML) and maximum mutual information (MMI) criteria, as well as support vector machines (SVMs). Identification under additive noise case is also considered and it is shown that identification rates reduces dramatically in case of additive noise.     

Fusing navigation and vision information with the Transferable Belief Model: Application to an intelligent speed limit assistant

The present paper focuses on the fusion, based on imprecise and uncertain information, between a Geographic Information System (GIS) and a Speed Limit Sign Recognition System (SLSRS), performed on camera images. This study is dedicated to the development of a Speed Limit Assistant (SLA) in the context of vehicle driving aid. The proposed SLA is developed within the Evidence Theory framework. The information from the sources is interpreted as belief functions using a non-antagonistic basic belief assignment (bba) in the Transferable Belief Model (TBM) semantics. This bba ensures that the conflict which could appear after the global fusion is exclusively due to source discordances. The present paper proposes a way to manage these discordances by formalizing a conflict-related constraint decision rule. As far as the application is concerned, a two-level (decentralized) fusion architecture is developed. The sensor relevancy is estimated in a first step, followed by the GIS intra-sensor fusion with a maximum of Credibility decision which determines the context-compliant speed candidate considering the road information given by the digital map. This allows the detection of possible errors of the GIS. The multi-sensor fusion then combines the GIS and SLSRS information considering that the sensors are independent and specialized in one speed, each. For the decision, two strategies are adopted. The first one considers the conflict as a threshold for the final speed selection, and so allows the SLA to stay undecided in case of highly conflicting situations. The second strategy employs the 5th version of the Proportional Conflict Redistribution operator. The SLA has been tested in simulation and in real-time experiments by qualitative and quantitative performance evaluations.     

EvoSeedRNSAII: An improved evolutionary algorithm for generating detectors in the real-valued Negative Selection Algorithms

In the previous work, a detector generation algorithm, named as the EvoSeedRNSA, is proposed. A genetic algorithm is adopted in the EvoSeedRNSA to evolve the random seeds to generate an approximately optimal detector set. This paper proposes an improved EvoSeedRNSA, named as the EvoSeedRNSAII, to generate a more efficient detector set. A multi-group random seed encoding scheme is designed to represent the individuals and different detector generation sequences are discussed. The experiments demonstrate that the EvoSeedRNSAII has a better performance than the EvoSeedRNSA.     

Automatic abstraction controller in reinforcement learning agent via automata

Reinforcement learning (RL) for solving large and complex problems faces the curse of dimensions problem. To overcome this problem, frameworks based on the temporal abstraction have been presented; each having their advantages and disadvantages. This paper proposes a new method like the strategies introduced in the hierarchical abstract machines (HAMs) to create a high-level controller layer of reinforcement learning which uses options. The proposed framework considers a non-deterministic automata as a controller to make a more effective use of temporally extended actions and state space clustering. This method can be viewed as a bridge between option and HAM frameworks, which tries to suggest a new framework to decrease the disadvantage of both by creating connection structures between them and at the same time takes advantages of them. Experimental results on different test environments show significant efficiency of the proposed method.     

Stochastic feature compensation methods for speaker verification in noisy environments

This paper explores the significance of stereo-based stochastic feature compensation (SFC) methods for robust speaker verification (SV) in mismatched training and test environments. Gaussian Mixture Model (GMM)-based SFC methods developed in past has been solely restricted for speech recognition tasks. Application of these algorithms in a SV framework for background noise compensation is proposed in this paper. A priori knowledge about the test environment and availability of stereo training data is assumed. During the training phase, Mel frequency cepstral coefficient (MFCC) features extracted from a speaker's noisy and clean speech utterance (stereo data) are used to build front end GMMs. During the evaluation phase, noisy test utterances are transformed on the basis of a minimum mean squared error (MMSE) or maximum likelihood (MLE) estimate, using the target speaker GMMs. Experiments conducted on the NIST-2003-SRE database with clean speech utterances artificially degraded with different types of additive noises reveal that the proposed SV systems strictly outperform baseline SV systems in mismatched conditions across all noisy background environments.     

Received signal strength based positioning for multiple nodes in wireless sensor networks

We address the problem of locating multiple nodes in a wireless sensor network with the use of received signal strength (RSS) measurements. In RSS based positioning, transmit power and path-loss factor are two environment dependent parameters which may be uncertain or unknown. For unknown transmit powers, we devise two-step weighted least squares (WLS) and maximum likelihood (ML) algorithms for node localization. The mean square error of the former is analyzed in the presence of zero-mean white Gaussian disturbances. When both transmit powers and path-loss factors are unavailable, two nonlinear least squares estimators, namely, the direct ML approach and combination of linear least squares and ML algorithm, are developed. Numerical examples are also included to evaluate the localization accuracy of the proposed estimators by comparing with two existing node positioning methods and the Cramér–Rao lower bound.     

The Podhale geothermal reservoir simulation for long-term sustainable production

The Podhale geothermal system, located in the southern, mountainous part of Poland, is the most valuable reservoir of geothermal waters discovered in the country to date and the one with the highest capacities in Central and Eastern Europe. Over 20 years of continuous operation has proved its stable operating parameters – a small drop in pressure and an unnoticeable temperature change. Production of over 500 m³/h of geothermal water with an 86 °C wellhead temperature is current practise, while drilling a new production well and reconstruction of an injection well allows for production that may significantly exceed 600 m³/h. To utilize these vast resources, a binary power cycle for electricity and heat production is considered by group of researchers. The results of numerical modelling of heat extraction from the Podhale reservoir are presented in the article as a preliminary step to the detailed analysis of combined heat and power production through a binary power cycle.