Asoke Nandi - Selected Publications#

1. “Experimental observation of lepton pairs of invariant mass around 95 GeV/Ce at the CERN SPS collider” (Carlo Rubbia, Simon Van der Meer, Asoke Nandi, et. al.), Physics Letters B, vol. 7, 1983 (Google Scholar citations: 2632).

The observation of four electron-positron pairs and one muon pair which have the signature of a two-body decay of a particle of mass∼ 95 GeV/c 2 is reported in this paper. These events fit well the hypothesis that they are produced by the process p ̄+ p→ Z 0+ X (with Z 0→ ℓ++ ℓ−), where Z 0 is the Intermediate Vector Boson postulated by the electroweak theories as the mediator of weak neutral currents. The two leaders of this work, Carlo Rubbia and Simon Van der Meer, were awarded the 1984 Nobel Prize for Physics for their decisive role in this project.

2. “Applications of machine learning to machine fault diagnosis: A review and roadmap” (with Y. Lei, B. Yang, X. Jiang, F. Jia and N. Li), Mechanical Systems and Signal Processing, vol. 138, 2020 (Google Scholar citations: 970).

Intelligent fault diagnosis (IFD) refers to applications of machine learning theories to machine fault diagnosis. This is a promising way to release the contribution from human labor and automatically recognize the health states of machines and has attracted much attention in the last two or three decades. Although IFD has achieved a considerable number of successes, a review still leaves a blank space to systematically cover the development of IFD from the cradle to the bloom, and rarely provides potential guidelines for the future development. To bridge the gap, this article presents a review and roadmap to systematically cover the development of IFD following the progress of machine learning theories and offer a future perspective.

3. “Algorithms for automatic modulation recognition of communication signals” (with E. Azzouz), IEEE Transactions on Communications, vol. 46, 1988 (Google Scholar citations: 932).

This paper introduces two algorithms for analog and digital modulations recognition, offering the best results at that time. The first algorithm utilizes the decision-theoretic approach in which a set of decision criteria for identifying different types of modulations is developed. In the second algorithm the artificial neural network (ANN) is used as a new approach for the modulation recognition process. Computer simulations of different types of band-limited analog and digitally modulated signals corrupted by band-limited Gaussian noise sequences have been carried out to measure the performance of the developed algorithms. In the decision-theoretic algorithm, it was found that the overall success rate is over 94% at the signal-to-noise ratio (SNR) of 15 dB, while in the ANN algorithm the overall success rate is over 96% at the SNR of 15 dB.

4. “Automatic modulation recognition of communication signals” (with E. Azzouz), Springer Science & Business Media, 2013 (Google Scholar citations: 558).

Automatic modulation recognition is a rapidly evolving area of signal analysis. In recent years, interest from the academic and military research institutes has focused around the research and development of modulation recognition algorithms. Any communication intelligence (COMINT) system comprises three main blocks: receiver front-end, modulation recogniser and output stage. Considerable work has been done in the area of receiver front-ends. The work at the output stage is concerned with information extraction, recording and exploitation and begins with signal demodulation, that requires accurate knowledge about the signal modulation type. There are, however, two main reasons for knowing the current modulation type of a signal; to preserve the signal information content and to decide upon the suitable counter action, such as jamming. Automatic Modulation Recognition of Communications Signals describes in depth this modulation recognition process. Drawing on several years of research, the authors provide a critical review of automatic modulation recognition. This includes techniques for recognising digitally modulated signals. The book also gives comprehensive treatment of using artificial neural networks for recognising modulation types. Automatic Modulation Recognition of Communications Signals is the first comprehensive book on automatic modulation recognition.

5. “Fault detection using support vector machines and artificial neural networks augmented by genetic algorithms” (with L.B. Jack), Mechanical Systems and Signal Processing, vol. 16, 2002 (Google Scholar citations: 531).

Artificial neural networks (ANNs) have been used to detect faults in rotating machinery for a number of years, using statistical methods to preprocess the vibration signals as input features. ANNs have been shown to be highly successful in this type of application; in comparison, support vector machines (SVMs) are a more recent development, and little use has been made of them in the condition monitoring arena. The availability of a limited amount of training data creates certain problems for the use of SVMs, and a strategy is advanced to improve the generalisation performance in cases where only limited training data is available. This paper examines the performance of both types of classifiers in two-class fault/no-fault recognition examples and the attempts to improve the overall generalisation performance of both techniques through the use of genetic algorithm based feature selection process. The authors were the first to introduce successfully genetic algorithm in this area of research, producing the best results at that time.

6. “Automatic identification of digital modulation types” (with E. Azzouz), Signal Processing, vol. 47, 1995 (Google Scholar citations: 460).

In both covert and overt operations, modulation identification plays an important role. In communication intelligence (COMINT) applications where the main objective is the perfect monitoring of the intercepted signals and one of the parameters that affect the perfect monitoring is the modulation type of the intercepted signal. In this paper, a set of decision criteria for identifying different types of digital modulation is developed. Also, all the key features used in the identification algorithm are calculated using the conventional signal processing methods. Computer simulations for different types of band-limited digitally modulated signals corrupted by band-limited Gaussian noise have been carried out. Expressions for the instantaneous amplitude, and phase of different types of digitally modulated signals are derived. Also, two software solutions for estimating the instantaneous phase in the weak segments of a signal are introduced and analyzed. Finally, it is found that all modulation types of interest have been classified with success rate ≥ 90% at SNR = 10 dB offering the best results among existing algorithms.

7. “Noninvasive fetal electrocardiogram extraction: Blind separation versus adaptive noise cancellation” (with V Zarzoso), IEEE Transactions on Biomedical Engineering, vol. 48, 2001 (Google Scholar citations: 451).

The problem of the fetal electrocardiogram (FECG) extraction from maternal skin electrode measurements can be modeled from the perspective of blind source separation (BSS). Since no comparison between BSS techniques and other signal processing methods has been made, the authors compare a BSS procedure based on higher-order statistics and Widrow’s multireference adaptive noise cancelling approach. As a best-case scenario for this latter method, optimal Wiener-Hopf solutions are considered. Both procedures are applied to real multichannel ECG recordings obtained from a pregnant woman. The experimental outcomes demonstrate the more robust performance of the blind technique and, in turn, verify the validity of the BSS model in this important biomedical application.

8. “Significantly fast and robust fuzzy c-means clustering algorithms based on morphological reconstruction and membership filtering: (with T Lei, X Jia, Y Zhang, L He and H Meng), IEEE Transactions on Fuzzy Systems, vol. 26, 2018 (Google Scholar citations: 337).

As fuzzy c-means clustering (FCM) algorithm is sensitive to noise, local spatial information is often introduced to an objective function to improve the robustness of the FCM algorithm for image segmentation. However, the introduction of local spatial information often leads to a high computational complexity, arising out of an iterative calculation of the distance between pixels within local spatial neighbors and clustering centers. To address this issue, an improved FCM algorithm based on morphological reconstruction and membership filtering (FRFCM) that is significantly faster and more robust than FCM is proposed in this paper. First, the local spatial information of images is incorporated into FRFCM by introducing morphological reconstruction operation to guarantee noise-immunity and image detail-preservation. Second, the modification of membership partition, based on the distance between pixels within local spatial neighbors and clustering centers, is replaced by local membership filtering that depends only on the spatial neighbors of membership partition. Compared with state-of-the-art algorithms, the proposed FRFCM algorithm is simpler and significantly faster, since it is unnecessary to compute the distance between pixels within local spatial neighbors and clustering centers. In addition, it is efficient for noisy image segmentation because membership filtering are able to improve membership partition matrix efficiently. Experiments performed on synthetic and real-world images demonstrate that the proposed algorithm not only achieves better results, but also requires less time than the state-of-the-art algorithms for image segmentation along with offering both best results and reduced computational complexity.

9. “Blind Estimation using Higher-Order Statistics” Springer, 1999 (Google Scholar citations: 308).

In the signal-processing research community, a great deal of progress in higher-order statistics (HOS) began in the mid-1980s. These last fifteen years have witnessed a large number of theoretical developments as well as real applications. This book focuses on the blind estimation area and records some of the major developments in this field as well as providing an introduction to HOS and goes on to illustrate its use in blind signal equalisation (which has many applications including mobile communications, blind system identification, and blind sources separation, a generic problem in signal processing with many applications including radar, sonar and communications).

10. “Automatic digital modulation recognition using artificial neural network and genetic algorithm” (with M.D. Wong), Signal Processing, vol. 84, 2012 (Google Scholar citations: 295).

Automatic recognition of digital modulation signals has seen increasing demand nowadays. The use of artificial neural networks for this purpose has been popular since the late 1990s. Here, we include a variety of modulation types for recognition, e.g., QAM16, V29, V32, QAM64 through the addition of a newly proposed statistical feature set. Two training algorithms for multi-layer perceptron (MLP) recogniser, namely Backpropagation with Momentum and Adaptive Learning Rate are investigated, while resilient backpropagation (RPROP) is proposed for this problem, are employed in this work. In particular, the RPROP algorithm is applied for the first time in this area. In conjunction with these algorithms, we use a separate data set as validation set during training cycle to improve generalisation. Genetic algorithm (GA) based feature selection is used to select the best feature subset from the combined statistical and spectral feature set. RPROP MLP recogniser achieves about 99% recognition performance on most SNR values with only six features selected using GA.

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