ORCID Profile
0000-0002-1707-5485
Current Organisations
Wageningen University & Research
,
Leibniz Institute of Freshwater Ecology and Inland Fisheries
,
University of Kufa
,
Edith Cowan University
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Publisher: Science Publications
Date: 08-2019
Publisher: IEEE
Date: 12-2007
Publisher: Scientific Research Publishing, Inc.
Date: 2009
Publisher: Institution of Engineering and Technology (IET)
Date: 2004
DOI: 10.1049/EL:20045803
Publisher: IEEE
Date: 12-2007
Publisher: MDPI AG
Date: 09-08-2022
DOI: 10.3390/S22165935
Abstract: An adaptive deep neural network is used in an inverse system identification setting to approximate the inverse of a nonlinear plant with the aim of constituting the plant controller by copying to the latter the weights and architecture of the converging deep neural network. This deep learning (DL) approach to the adaptive inverse control (AIC) problem is shown to outperform the adaptive filtering techniques and algorithms normally used in adaptive control, especially when in nonlinear plants. The deeper the controller, the better the inverse function approximation, provided that the nonlinear plant has an inverse and that this inverse can be approximated. Simulation results prove the feasibility of this DL-based adaptive inverse control scheme. The DL-based AIC system is robust to nonlinear plant parameter changes in that the plant output reassumes the value of the reference signal considerably faster than with the adaptive filter counterpart of the deep neural network. The settling and rise times of the step response are shown to improve in the DL-based AIC system.
Publisher: Elsevier BV
Date: 11-2014
Publisher: IEEE
Date: 10-2010
Publisher: Springer US
Date: 2006
Publisher: IEEE
Date: 2006
Publisher: MDPI AG
Date: 17-03-2021
DOI: 10.3390/COMPUTATION9030035
Abstract: Accurate, fast, and automatic detection and classification of animal images is challenging, but it is much needed for many real-life applications. This paper presents a hybrid model of Mamdani Type-2 fuzzy rules and convolutional neural networks (CNNs) applied to identify and distinguish various animals using different datasets consisting of about 27,307 images. The proposed system utilizes fuzzy rules to detect the image and then apply the CNN model for the object’s predicate category. The CNN model was trained and tested based on more than 21,846 pictures of animals. The experiments’ results of the proposed method offered high speed and efficiency, which could be a prominent aspect in designing image-processing systems based on Type 2 fuzzy rules characterization for identifying fixed and moving images. The proposed fuzzy method obtained an accuracy rate for identifying and recognizing moving objects of 98% and a mean square error of 0.1183464 less than other studies. It also achieved a very high rate of correctly predicting malicious objects equal to recall = 0.98121 and a precision rate of 1. The test’s accuracy was evaluated using the F1 Score, which obtained a high percentage of 0.99052.
Publisher: Elsevier BV
Date: 11-2007
Publisher: Elsevier BV
Date: 03-2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2002
Publisher: Informa UK Limited
Date: 2015
Publisher: Springer US
Date: 2006
Publisher: Springer US
Date: 2006
Publisher: IEEE
Date: 2004
Publisher: IEEE
Date: 11-2009
Publisher: IEEE
Date: 11-2005
Publisher: MDPI AG
Date: 11-10-2021
DOI: 10.3390/INFO12100415
Abstract: This work combines compressive sensing and short word-length techniques to achieve localization and target tracking in wireless sensor networks with energy-efficient communication between the network anchors and the fusion center. Gradient descent localization is performed using time-of-arrival (TOA) data which are indicative of the distance between anchors and the target thereby achieving range-based localization. The short word-length techniques considered are delta modulation and sigma-delta modulation. The energy efficiency is due to the reduction of the data volume transmitted from anchors to the fusion center by employing any of the two delta modulation variants with compressive sensing techniques. Delta modulation allows the transmission of one bit per TOA s le. The communication energy efficiency is increased by RⱮ, R ≥ 1, where R is the s le reduction ratio of compressive sensing, and Ɱ is the number of bits originally present in a TOA-s le word. It is found that the localization system involving sigma-delta modulation has a superior performance to that using delta-modulation or pure compressive s ling alone, in terms of both energy efficiency and localization error in the presence of TOA measurement noise and transmission noise, owing to the noise shaping property of sigma-delta modulation.
Publisher: IEEE
Date: 11-2007
Publisher: IEEE
Date: 12-2008
Publisher: IEEE
Date: 11-2007
Publisher: MDPI AG
Date: 31-07-2021
DOI: 10.3390/ELECTRONICS10151852
Abstract: Frequency estimation of a single sinusoid in colored noise has received a considerable amount of attention in the research community. Taking into account the recent emergence and advances in compressive covariance sensing (CCS), the aim of this work is to combine the two disciplines by studying the effects of compressed measurements of a single sinusoid in moving-average colored noise on its frequency estimation accuracy. CCS techniques can recover the second-order statistics of the original uncompressed signal from the compressed measurements, thereby enabling correlation-based frequency estimation of single tones in colored noise using higher order lags. Acceptable accuracy is achieved for moderate compression ratios and for a sufficiently large number of available compressed signal s les. It is expected that the proposed method would be advantageous in applications involving resource-limited systems such as wireless sensor networks.
Publisher: IEEE
Date: 2005
Publisher: IEEE
Date: 11-2005
Publisher: Institution of Engineering and Technology (IET)
Date: 06-2018
Publisher: IEEE
Date: 10-2008
Publisher: IEEE
Date: 12-2007
Publisher: Universitatea Stefan cel Mare din Suceava
Date: 2009
Publisher: MDPI AG
Date: 05-04-2021
DOI: 10.3390/JCM10071514
Abstract: The aim of this study was to explore sex-specific associations between systolic blood pressure (SBP), hypertension, and the risk of incident atrial fibrillation (AF) subtypes, including paroxysmal, persistent, and permanent AF, in a general population. A total of 13,137 women and 11,667 men who participated in the fourth survey of the Tromsø Study (1994–1995) were followed up for incident AF until the end of 2016. Cox proportional hazards regression analysis was conducted using fractional polynomials for SBP to provide sex- and AF-subtype-specific hazard ratios (HRs) for SBP. An SBP of 120 mmHg was used as the reference. Models were adjusted for other cardiovascular risk factors. Over a mean follow-up of 17.6 ± 6.6 years, incident AF occurred in 914 (7.0%) women (501 with paroxysmal ersistent AF and 413 with permanent AF) and 1104 (9.5%) men (606 with paroxysmal ersistent AF and 498 with permanent AF). In women, an SBP of 180 mmHg was associated with an HR of 2.10 (95% confidence interval [CI] 1.60–2.76) for paroxysmal ersistent AF and an HR of 1.80 (95% CI 1.33–2.44) for permanent AF. In men, an SBP of 180 mmHg was associated with an HR of 1.90 (95% CI 1.46–2.46) for paroxysmal ersistent AF, while there was no association with the risk of permanent AF. In conclusion, increasing SBP was associated with an increased risk of both paroxysmal ersistent AF and permanent AF in women, but only paroxysmal ersistent AF in men. Our findings highlight the importance of sex-specific risk stratification and optimizing blood pressure management for the prevention of AF subtypes in clinical practice.
Publisher: Springer Science and Business Media LLC
Date: 06-02-2019
Publisher: IEEE
Date: 12-2008
Publisher: IEEE
Date: 12-2008
Publisher: Springer Science and Business Media LLC
Date: 27-04-2006
Publisher: IEEE
Date: 12-2008
Publisher: IEEE
Date: 2004
Publisher: IEEE
Date: 10-2009
Publisher: Science Publications
Date: 09-2018
Publisher: IEEE
Date: 03-2009
Publisher: IEEE
Date: 2001
Publisher: IEEE
Date: 03-2009
Publisher: Institution of Engineering and Technology (IET)
Date: 2010
Publisher: Hindawi Limited
Date: 2013
DOI: 10.1155/2013/818696
Abstract: Images with subband coding and threshold wavelet compression are transmitted over a Rayleigh communication channel with additive white Gaussian noise (AWGN), after quantization and 16-QAM modulation. A comparison is made between these two types of compression using both mean square error (MSE) and structural similarity (SSIM) image quality assessment (IQA) criteria applied to the reconstructed image at the receiver. The two methods yielded comparable SSIM but different MSE measures. In this work, we justify our results which support previous findings in the literature that the MSE between two images is not indicative of structural similarity or the visibility of errors. It is found that it is difficult to reduce the pointwise errors in subband-compressed images (higher MSE). However, the compressed images provide comparable SSIM or perceived quality for both types of compression provided that the retained energy after compression is the same.
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: IEEE
Date: 12-2007
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Springer Science and Business Media LLC
Date: 15-10-2011
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: IEEE
Date: 12-2010
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Cold Spring Harbor Laboratory
Date: 30-09-2019
DOI: 10.1101/785345
Abstract: Social relationships can have important fitness consequences. Although there is increasing evidence that social relationships carry over across contexts, few studies have investigated whether relationships formed early in life are carried over to adulthood. For ex le, juveniles of monogamous species go through a major life-history stage transition—pair formation—during which the pair bond becomes a central unit of the social organization. At present, it remains unclear if pair members retain their early-life relationships after pair formation. We investigated whether same-sex associations formed early in life carry over into adulthood and whether carry-over was dependent on season, in a monogamous species. Moreover, we investigated the role of familiarity, genetic relatedness and aggression on the perseverance of social associations. We studied the social structure before and after pair formation in captive barnacle geese ( Branta leucopsis ), a highly social, long-lived, monogamous species. We constructed association networks of groups of geese before pair formation, during the subsequent breeding season, and in the following wintering season. Next, we studied how these associations carried over during seasonal changes. We found that early-life associations in females were lost during the breeding season, but resurfaced during the subsequent wintering season. In males, the early-life associations persisted across both seasons. Association persistence was not mediated by genetic relatedness or familiarity. The high level of aggressiveness of males, but not females, in the breeding season suggests that males may have played a key role in shaping both their own social environment and that of their partners. We show that early-life social relationships can be maintained well into later life. Such relationships can be sustained even if they are temporarily disrupted, for ex le due to reproductive behaviour. Our findings therefore highlight that the early-life social environment can have life-long consequences on in iduals’ social environment.
Publisher: IEEE
Date: 2000
Publisher: MDPI AG
Date: 28-12-2022
DOI: 10.3390/INFO14010018
Abstract: Deep learning (DL) and machine learning (ML) are widely used in many fields but rarely used in the frequency estimation (FE) and slope estimation (SE) of signals. Frequency and slope estimation for frequency-modulated (FM) and single-tone sinusoidal signals are essential in various applications, such as wireless communications, sound navigation and ranging (SONAR), and radio detection and ranging (RADAR) measurements. This work proposed a novel frequency estimation technique for instantaneous linear FM (LFM) sinusoidal wave using deep learning. Deep neural networks (DNN) and convolutional neural networks (CNN) are classes of artificial neural networks (ANNs) used for the frequency and slope estimation for LFM signals under additive white Gaussian noise (AWGN) and additive symmetric alpha stable noise (SαSN). DNN is composed of input, output, and two hidden layers, where several nodes in the first and second hidden layers are 25 and 8, respectively. CNN is the content input layer many hidden layers include convolution, batch normalization, ReLU, max pooling, fully connected, and dropout. The output layer consists of a fully connected softmax and classification layers. SαS distributions are impulsive noise disturbances found in many communication environments such as marine systems, their distribution lacks a closed-form probability density function (PDF), except for specific cases, and infinite second-order statistics, hence geometric SNR (GSNR) is used in this work to determine the effect of noise in a mixture of Gaussian and SαS noise processes. DNN is a machine learning classifier with few layers for reducing FE and SE complexity. CNN is a deep learning classifier, designed with many layers, and proved to be more accurate than DNN when dealing with big data and finding optimal features. Simulation results show that SαS noise can be much more harmful to the FE and SE of FM signals than Gaussian noise. DL and ML can significantly reduce FE complexity, memory cost, and power consumption as compared to the classical FE based on time–frequency analysis, which are important requirements for many systems, such as some Internet of Things (IoT) sensor applications. After training CNN for frequency and slope estimation of LFM signals, the performance of CNN (in terms of accuracy) can give good results at very low signal-to-noise ratios where time–frequency distribution (TFD) fails, giving more than 20 dB difference in the GSNR working range as compared to the classical spectrogram-based estimation, and over 15 dB difference with Viterbi-based estimate.
Publisher: IEEE
Date: 2001
Publisher: IEEE
Date: 2004
Publisher: IEEE
Publisher: IEEE
Date: 12-2018
Publisher: IEEE
Date: 2004
Publisher: IEEE
Date: 12-2008
Publisher: MDPI AG
Date: 24-05-2021
DOI: 10.3390/ELECTRONICS10111245
Abstract: Training data for a deep learning (DL) neural network (NN) controller are obtained from the input and output signals of a conventional digital controller that is designed to provide the suitable control signal to a specified plant within a feedback digital control system. It is found that if the DL controller is sufficiently deep (four hidden layers), it can outperform the conventional controller in terms of settling time of the system output transient response to a unit-step reference signal. That is, the DL controller introduces a d ing effect. Moreover, it does not need to be retrained to operate with a reference signal of different magnitude, or under system parameter change. Such properties make the DL control more attractive for applications that may undergo parameter variation, such as sensor networks. The promising results of robustness against parameter changes are calling for future research in the direction of robust DL control.
Publisher: MDPI AG
Date: 13-04-2021
DOI: 10.3390/S21082729
Abstract: Despite the increasing role of machine learning in various fields, very few works considered artificial intelligence for frequency estimation (FE). This work presents comprehensive analysis of a deep-learning (DL) approach for frequency estimation of single tones. A DL network with two layers having a few nodes can estimate frequency more accurately than well-known classical techniques can. While filling the gap in the existing literature, the study is comprehensive, analyzing errors under different signal-to-noise ratios (SNRs), numbers of nodes, and numbers of input s les under missing SNR information. DL-based FE is not significantly affected by SNR bias or number of nodes. A DL-based approach can properly work using a minimal number of input nodes N at which classical methods fail. DL could use as few as two layers while having two or three nodes for each, with the complexity of O{N} compared with discrete Fourier transform (DFT)-based FE with O{Nlog2 (N)} complexity. Furthermore, less N is required for DL. Therefore, DL can significantly reduce FE complexity, memory cost, and power consumption, which is attractive for resource-limited systems such as some Internet of Things (IoT) sensor applications. Reduced complexity also opens the door for hardware-efficient implementation using short-word-length (SWL) or time-efficient software-defined radio (SDR) communications.
Publisher: IEEE
Date: 10-2009
Publisher: IEEE
Date: 03-2009
Publisher: Institution of Engineering and Technology (IET)
Date: 2006
DOI: 10.1049/EL:20064257
Publisher: IEEE
Date: 03-2009
Publisher: IEEE
Date: 10-2009
Publisher: IEEE
Date: 08-2006
Publisher: IEEE
Date: 10-2009
Publisher: IEEE
Date: 2004
Publisher: IEEE
Date: 2005
Publisher: IEEE
Date: 03-2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2004
Publisher: IEEE
Date: 2002
Publisher: MDPI AG
Date: 11-04-2023
DOI: 10.20944/PREPRINTS202304.0172.V1
Abstract: Optimization using Genetic Algorithms (GA) is a well-known strategy in several scientific disciplines. The crossover is an essential operator of the Genetic Algorithm. It has been an active area of research to develop sustainable forms for this operand. In this work, a new crossover operand is proposed. This operand depends on giving an elicited description for the chromosome with a new structure for alleles of the parents. It is suggested that each allele has two attitudes, one attitude differs contrastingly with the other, and both of them complement the allele. Thus, in case of one attitude is good, the other should be bad. This is suitable for many systems which contain admired parameters and unadmired parameters. The proposed crossover would improve the desired attitudes and d the undesired attitudes. The proposed crossover can be achieved in two stages: the first stage is a mating method for both attitudes in one parent to improving one attitude at the expense of the other. The second stage comes after the first improvement stage for mating between different parents. Hence, two concurrent steps for improvement would be applied. Simulation experiments for the system shows improvement in the fitness function. The proposed crossover could be helpful in different fields, especially to optimize routing algorithms and network protocols, an application that has been tested as a case study in this work.
Publisher: IEEE
Date: 10-2009
Publisher: IEEE
Date: 08-2006
Publisher: Elsevier BV
Date: 2002
Publisher: IEEE
Date: 11-2007
Publisher: IEEE
Date: 2010
Publisher: IEEE
Date: 2004
Publisher: IEEE
Date: 2004
Publisher: IEEE
Date: 11-2005
Publisher: Springer Science and Business Media LLC
Date: 17-04-2019
Publisher: Elsevier BV
Date: 2022
Publisher: Springer Science and Business Media LLC
Date: 19-11-2007
DOI: 10.1155/2007/18361
Publisher: IEEE
Date: 2004
Publisher: IEEE
Date: 2005
Publisher: Springer US
Date: 2006
Publisher: IEEE
Date: 11-2005
Publisher: IEEE
Date: 12-2007
Publisher: IEEE
Date: 2006
Publisher: Springer Science and Business Media LLC
Date: 24-08-2012
Publisher: IEEE
Date: 11-2005
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Hindawi Limited
Date: 09-07-2018
DOI: 10.1155/2018/9801308
Abstract: Image similarity and image recognition are modern and rapidly growing technologies because of their wide use in the field of digital image processing. It is possible to recognize the face image of a specific person by finding the similarity between the images of the same person face and this is what we will address in detail in this paper. In this paper, we designed two new measures for image similarity and image recognition simultaneously. The proposed measures are based mainly on a combination of information theory and joint histogram. Information theory has a high capability to predict the relationship between image intensity values. The joint histogram is based mainly on selecting a set of local pixel features to construct a multidimensional histogram. The proposed approach incorporates the concepts of entropy and a modified 1D version of the 2D joint histogram of the two images under test. Two entropy measures were considered, Shannon and Renyi, giving a rise to two joint histogram-based, information-theoretic similarity measures: SHS and RSM. The proposed methods have been tested against powerful Zernike-moments approach with Euclidean and Minkowski distance metrics for image recognition and well-known statistical approaches for image similarity such as structural similarity index measure (SSIM), feature similarity index measure (FSIM) and feature-based structural measure (FSM). A comparison with a recent information-theoretic measure (ISSIM) has also been considered. A measure of recognition confidence is introduced in this work based on similarity distance between the best match and the second-best match in the face database during the face recognition process. Simulation results using AT& T and FEI face databases show that the proposed approaches outperform existing image recognition methods in terms of recognition confidence. TID2008 and IVC image databases show that SHS and RSM outperform existing similarity methods in terms of similarity confidence.
Publisher: MDPI AG
Date: 03-08-2017
DOI: 10.3390/APP7080786
Publisher: IEEE
Date: 10-2010
Publisher: Scientific Research Publishing, Inc.
Date: 2011
Publisher: IEEE
Date: 2005
Publisher: MDPI AG
Date: 30-08-2021
DOI: 10.20944/PREPRINTS202108.0547.V1
Abstract: This work combines compressive sensing and short word-length techniques to achieve localization and target tracking in wireless sensor networks with energy-efficient communication between the network anchors and the fusion center. Gradient descent localization is performed using time-of-arrival (TOA) data which are indicative of the distance between anchors and the target thereby achieving range-based localization. The short word-length techniques considered are delta modulation and sigma-delta modulation. The energy efficiency is due to the reduction of the data volume transmitted from anchors to the fusion center by employing any of the two delta modulation variants with compressive sensing techniques. Delta modulation allows the transmission of one bit per TOA s le. The communication energy efficiency is increased by RⱮ, R≥1, where R is the s le reduction ratio of compressive sensing and Ɱ is the number of bits originally present in a TOA-s le word. It is found that the localization system involving sigma-delta modulation has a superior performance to that using delta-modulation or pure compressive s ling alone, in terms of both energy efficiency and localization error in the presence of TOA measurement noise, owing to the noise shaping property of sigma-delta modulation.
Publisher: Elsevier BV
Date: 2010
Publisher: IEEE
Date: 10-2008
Publisher: IEEE
Date: 10-2008
Publisher: IEEE
Date: 12-2010
Publisher: IEEE
Date: 2001
Publisher: Science Publications
Date: 09-2017
Publisher: Frontiers Media SA
Date: 19-10-2020
Publisher: IEEE
Date: 11-2010
Publisher: IEEE
Date: 12-2009
Publisher: IEEE
Date: 2005
Publisher: InTech
Date: 29-08-2011
DOI: 10.5772/20260
Publisher: IEEE
Date: 2007
Publisher: IEEE
Date: 10-2009
Publisher: Science Publications
Date: 09-2016
Publisher: Informa UK Limited
Date: 2015
Publisher: IEEE
Date: 10-2009
Publisher: IEEE
Date: 05-2010
Publisher: Queensland Univ. Technol
Date: 1999
Publisher: Elsevier BV
Date: 12-2010
Publisher: IEEE
Date: 2005
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2002
DOI: 10.1109/78.984723
Publisher: Informa UK Limited
Date: 11-2008
Publisher: IEEE
Date: 04-0440
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2002
Publisher: MDPI AG
Date: 05-05-2023
DOI: 10.3390/INFO14050276
Abstract: Optimization using genetic algorithms (GA) is a well-known strategy in several scientific disciplines. The crossover is an essential operator of the genetic algorithm. It has been an active area of research to develop sustainable forms for this operand. In this work, a new crossover operand is proposed. This operand depends on giving an elicited description for the chromosome with a new structure for alleles of the parents. It is suggested that each allele has two attitudes, one attitude differs contrastingly with the other, and both of them complement the allele. Thus, in case where one attitude is good, the other should be bad. This is suitable for many systems which contain admired parameters and unadmired parameters. The proposed crossover would improve the desired attitudes and d en the undesired attitudes. The proposed crossover can be achieved in two stages: The first stage is a mating method for both attitudes in one parent to improving one attitude at the expense of the other. The second stage comes after the first improvement stage for mating between different parents. Hence, two concurrent steps for improvement would be applied. Simulation experiments for the system show improvement in the fitness function. The proposed crossover could be helpful in different fields, especially to optimize routing algorithms and network protocols, an application that has been tested as a case study in this work.
Publisher: InTech
Date: 04-10-2017
Publisher: IEEE
Date: 02-2007
Publisher: Institution of Engineering and Technology (IET)
Date: 2009
Publisher: IEEE
Date: 11-2005
Publisher: IEEE
Date: 11-2005
Publisher: IEEE
Date: 11-2006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2001
DOI: 10.1109/78.934151
Publisher: IEEE
Date: 11-2005
Publisher: Elsevier BV
Date: 11-2006
Publisher: IEEE
Date: 12-2008
Publisher: IEEE
Date: 2010
Publisher: Science Publications
Date: 11-2014
Publisher: Science Publications
Date: 08-2014
Publisher: IEEE
Date: 2001
Publisher: IEEE
Date: 07-2009
Publisher: IEEE
Date: 11-2005
Publisher: IEEE
Date: 11-2005
Publisher: IEEE
Date: 2004
Publisher: IEEE
Date: 09-2009
Publisher: MDPI AG
Date: 26-04-2021
DOI: 10.20944/PREPRINTS202104.0664.V1
Abstract: Training data for a deep learning (DL) neural network (NN) controller are obtained from the input and output signals of a conventional digital controller that is designed to provide the suitable control signal to a specified plant within a feedback digital control system. It is found that if the DL controller is sufficiently deep (four hidden layers), it can outperform the conventional controller in terms of settling time of the system output transient response to a unit-step reference signal. That is, the DL controller introduces a d ing effect. Moreover, it does not need to be retrained to operate with a reference signal of different magnitude, or under system parameter change. Such properties make the DL control more attractive for applications that may undergo parameter variation, like sensor networks.
Publisher: IEEE
Date: 2005
Publisher: Informa UK Limited
Date: 17-07-2019
Publisher: IEEE
Date: 09-2009
Publisher: IEEE
Date: 2005
Publisher: Science Publications
Date: 2019
Publisher: IEEE
Date: 11-2005
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2002
Publisher: IEEE
Date: 10-2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2004
Publisher: IEEE
Date: 08-2006
Publisher: IEEE
Date: 08-2006
Publisher: IEEE
Date: 12-2007
Location: Germany
Location: Germany
No related grants have been discovered for Zahir Hussain.