A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Development of Efficient Intensity Based Registration Techniques for Multi-modal Brain Images

Recent advances in medical imaging have resulted in the development of many imaging techniques that capture various aspects of the patients anatomy and metabolism. These are accomplished with image registration: the task of transforming images on a common anatomical coordinate space. Image registration is one of the important task for multi-modal brain images, which has paramount importance in clinical diagnosis, leads to treatment of brain diseases. In many other applications, image registration characterizes anatomical variability, to detect changes in disease state over time, and by mapping functional information into anatomical space. This thesis is focused to explore intensity-based registration techniques to accomplish precise information with accurate transformation for multi-modal brain images. In this view, we addressed mainly three important issues of image registration both in the rigid and non-rigid framework, i.e. i) information theoretic based similarity measure for alignment measurement, ii) free form deformation (FFD) based transformation, and iii) evolutionary technique based optimization of the cost function. Mutual information (MI) is a widely used information theoretic similarity measure criterion for multi-modal brain image registration. MI only dense the quantitative aspects of information based on the probability of events. For rustication of the information of events, qualitative aspect i.e. utility or saliency is a necessitate factor for consideration. In this work, a novel similarity measure is proposed, which incorporates the utility information into mutual Information, known as Enhanced Mutual Information(EMI).It is found that the maximum information gain using EMI is higher as compared to that of other state of arts. The utility or saliency employed in EMI is a scale invariant parameter, and hence it may fail to register in case of projective and perspective transformations. To overcome this bottleneck, salient region (SR) based Enhance Mutual Information (SR-EMI)is proposed, a new similarity measure for robust and accurate registration. The proposed SR-EMI based registration technique is robust to register the multi-modal brain images at a faster rate with better alignment

Perception architecture exploration for automotive cyber-physical systems

2022 Spring.Includes bibliographical references.In emerging autonomous and semi-autonomous vehicles, accurate environmental perception by automotive cyber physical platforms are critical for achieving safety and driving performance goals. An efficient perception solution capable of high fidelity environment modeling can improve Advanced Driver Assistance System (ADAS) performance and reduce the number of lives lost to traffic accidents as a result of human driving errors. Enabling robust perception for vehicles with ADAS requires solving multiple complex problems related to the selection and placement of sensors, object detection, and sensor fusion. Current methods address these problems in isolation, which leads to inefficient solutions. For instance, there is an inherent accuracy versus latency trade-off between one stage and two stage object detectors which makes selecting an enhanced object detector from a diverse range of choices difficult. Further, even if a perception architecture was equipped with an ideal object detector performing high accuracy and low latency inference, the relative position and orientation of selected sensors (e.g., cameras, radars, lidars) determine whether static or dynamic targets are inside the field of view of each sensor or in the combined field of view of the sensor configuration. If the combined field of view is too small or contains redundant overlap between individual sensors, important events and obstacles can go undetected. Conversely, if the combined field of view is too large, the number of false positive detections will be high in real time and appropriate sensor fusion algorithms are required for filtering. Sensor fusion algorithms also enable tracking of non-ego vehicles in situations where traffic is highly dynamic or there are many obstacles on the road. Position and velocity estimation using sensor fusion algorithms have a lower margin for error when trajectories of other vehicles in traffic are in the vicinity of the ego vehicle, as incorrect measurement can cause accidents. Due to the various complex inter-dependencies between design decisions, constraints and optimization goals a framework capable of synthesizing perception solutions for automotive cyber physical platforms is not trivial. We present a novel perception architecture exploration framework for automotive cyber- physical platforms capable of global co-optimization of deep learning and sensing infrastructure. The framework is capable of exploring the synthesis of heterogeneous sensor configurations towards achieving vehicle autonomy goals. As our first contribution, we propose a novel optimization framework called VESPA that explores the design space of sensor placement locations and orientations to find the optimal sensor configuration for a vehicle. We demonstrate how our framework can obtain optimal sensor configurations for heterogeneous sensors deployed across two contemporary real vehicles. We then utilize VESPA to create a comprehensive perception architecture synthesis framework called PASTA. This framework enables robust perception for vehicles with ADAS requiring solutions to multiple complex problems related not only to the selection and placement of sensors but also object detection, and sensor fusion as well. Experimental results with the Audi-TT and BMW Minicooper vehicles show how PASTA can intelligently traverse the perception design space to find robust, vehicle-specific solutions

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Foundations of Trusted Autonomy

Trusted Autonomy; Automation Technology; Autonomous Systems; Self-Governance; Trusted Autonomous Systems; Design of Algorithms and Methodologie