Papers by Saptarshi Sengupta
Journal of Sensor and Actuator Networks, 2019
The Quantum Double Delta Swarm (QDDS) Algorithm is a networked, fully-connected novel metaheurist... more The Quantum Double Delta Swarm (QDDS) Algorithm is a networked, fully-connected novel metaheuristic optimization algorithm inspired by the convergence mechanism to the center of potential generated within a single well of a spatially colocated double–delta well setup. It mimics the wave nature of candidate positions in solution spaces and draws upon quantum mechanical interpretations much like other quantum-inspired computational intelligence paradigms. In this work, we introduce a Chebyshev map driven chaotic perturbation in the optimization phase of the algorithm to diversify weights placed on contemporary and historical, socially-optimal agents’ solutions. We follow this up with a characterization of solution quality on a suite of 23 single– objective functions and carry out a comparative analysis with eight other related nature–inspired approaches. By comparing solution quality and successful runs over dynamic solution ranges, insights about the nature of convergence are obtained. A two-tailed t-test establishes the statistical significance of the solution data whereas Cohen’s d and Hedge’s g values provide a measure of effect sizes. We trace the trajectory of the fittest pseudo-agent over all iterations to comment on the dynamics of the system and prove that the proposed algorithm is theoretically globally convergent under the assumptions adopted for proofs of other closely-related random search algorithms.
Machine Learning and Knowledge Extraction, 2018
Particle Swarm Optimization (PSO) is a metaheuristic global optimization paradigm that has gained... more Particle Swarm Optimization (PSO) is a metaheuristic global optimization paradigm that has gained prominence in the last two decades due to its ease of application in unsupervised, complex multidimensional problems that cannot be solved using traditional deterministic algorithms. The canonical particle swarm optimizer is based on the flocking behavior and social co-operation of birds and fish schools and draws heavily from the evolutionary behavior of these organisms. This paper serves to provide a thorough survey of the PSO algorithm with special emphasis on the development, deployment, and improvements of its most basic as well as some of the very recent state-of-the-art implementations. Concepts and directions on choosing the inertia weight, constriction factor, cognition and social weights and perspectives on convergence, parallelization, elitism, niching and discrete optimization as well as neighborhood topologies are outlined. Hybridization attempts with other evolutionary and swarm paradigms in selected applications are covered and an up-to-date review is put forward for the interested reader.
This paper looks into efficient implementation of one dimensional low pass Finite Impulse Respons... more This paper looks into efficient implementation of one dimensional low pass Finite Impulse Response filters using certain commonly used and state-of-the-art optimization techniques. Methods like Parks-McClellan (PM) equiripple design, Quantumbehaved Particle Swarm Optimization (QPSO) and Cuckoo Search Algorithm (CSA) with Lévy Flight are employed and overall performance is further improved by hybridization and adaptive step size update. Various performance metrics are
analyzed with a focus on increasing convergence speed to reach global optima faster. It is seen that the improved search methods used in this work, i.e., Simulated Annealing based Weighted Mean Best QPSO (SAWQPSO) and Adaptive CSA (ACSA) effect significant reductions in convergence time with ACSA proving to
be the faster one. The designed filter is used in the receiver stage of a Frequency Modulated Radio Transmission model using a Quadrature-Phase Shift Keyed (QPSK) Modulator and Demodulator. Its efficiency is validated by obtaining near perfect
correlation between the message and recovered signals.
Quantum-behaved particle swarm optimization (QPSO) algorithm theoretically guarantees global conv... more Quantum-behaved particle swarm optimization (QPSO) algorithm theoretically guarantees global convergence and has been implemented on a wide suite of continuous optimization problems. In this paper, the nonlinear multimodal optimization problem of high pass FIR filter design is investigated using the weighted mean best QPSO algorithm (WQPSO). The results are compared with competitive techniques such as QPSO keeping PSO and PM as references. It is seen that WQPSO statistically outperforms QPSO in terms of convergence characteristics and ripple performance of the designed filter.
Digital Filtering often renders important simplifications in Communications Engineering. One such... more Digital Filtering often renders important simplifications in Communications Engineering. One such instance is when unwanted noise in a communication channel is to be eliminated and only a particular band of the spectrum is to be retained/transmitted. While there are a number of ways to implement digital filters according to a particular requirement, some techniques are better than others and meet the specifications more closely than the rest. This paper compares a few Window Methods of designing an FIR Bandpass Filter and comes to the conclusion that the Kaiser Windowing Method is the most convenient one to use as it provides some control over passband ripple, stopband attenuation and can effectively represent other window functions by varying the control parameter.
Drafts by Saptarshi Sengupta
Deep learning has taken over - both in problems beyond the realm of traditional, hand-crafted mac... more Deep learning has taken over - both in problems beyond the realm of traditional, hand-crafted machine learning paradigms as well as in capturing the imagination of the practitioner sitting on top of petabytes of data. While the public perception about the efficacy of deep neural architectures in complex pattern recognition tasks grows, sequentially up-to-date primers on the current state of affairs must follow. In this review, we seek to present a refresher of the many different stacked, connectionist networks that make up the deep learning architectures followed by automatic architecture optimization protocols using multiagent approaches. Further, since guaranteeing system uptime is
fast becoming an indispensable asset across multiple industrial modalities, we include an investigative section on testing neural networks for fault detection and subsequent mitigation. This is followed by an exploratory survey of several application areas where deep learning has emerged as a game-changing technology - be it anomalous behavior detection in financial applications or financial time-series forecasting, predictive and prescriptive analytics, medical imaging, natural language processing or power systems research. The thrust of this review is on outlining emerging areas of application-oriented research within the deep learning community as well as to provide a handy reference to researchers seeking to embrace deep learning in their work for what it is: statistical pattern recognizers with unparalleled hierarchical structure learning capacity with the ability to scale
with information.
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Papers by Saptarshi Sengupta
analyzed with a focus on increasing convergence speed to reach global optima faster. It is seen that the improved search methods used in this work, i.e., Simulated Annealing based Weighted Mean Best QPSO (SAWQPSO) and Adaptive CSA (ACSA) effect significant reductions in convergence time with ACSA proving to
be the faster one. The designed filter is used in the receiver stage of a Frequency Modulated Radio Transmission model using a Quadrature-Phase Shift Keyed (QPSK) Modulator and Demodulator. Its efficiency is validated by obtaining near perfect
correlation between the message and recovered signals.
Drafts by Saptarshi Sengupta
fast becoming an indispensable asset across multiple industrial modalities, we include an investigative section on testing neural networks for fault detection and subsequent mitigation. This is followed by an exploratory survey of several application areas where deep learning has emerged as a game-changing technology - be it anomalous behavior detection in financial applications or financial time-series forecasting, predictive and prescriptive analytics, medical imaging, natural language processing or power systems research. The thrust of this review is on outlining emerging areas of application-oriented research within the deep learning community as well as to provide a handy reference to researchers seeking to embrace deep learning in their work for what it is: statistical pattern recognizers with unparalleled hierarchical structure learning capacity with the ability to scale
with information.
analyzed with a focus on increasing convergence speed to reach global optima faster. It is seen that the improved search methods used in this work, i.e., Simulated Annealing based Weighted Mean Best QPSO (SAWQPSO) and Adaptive CSA (ACSA) effect significant reductions in convergence time with ACSA proving to
be the faster one. The designed filter is used in the receiver stage of a Frequency Modulated Radio Transmission model using a Quadrature-Phase Shift Keyed (QPSK) Modulator and Demodulator. Its efficiency is validated by obtaining near perfect
correlation between the message and recovered signals.
fast becoming an indispensable asset across multiple industrial modalities, we include an investigative section on testing neural networks for fault detection and subsequent mitigation. This is followed by an exploratory survey of several application areas where deep learning has emerged as a game-changing technology - be it anomalous behavior detection in financial applications or financial time-series forecasting, predictive and prescriptive analytics, medical imaging, natural language processing or power systems research. The thrust of this review is on outlining emerging areas of application-oriented research within the deep learning community as well as to provide a handy reference to researchers seeking to embrace deep learning in their work for what it is: statistical pattern recognizers with unparalleled hierarchical structure learning capacity with the ability to scale
with information.