Showing papers by "Anupam Shukla published in 2013"

Egyptian Vulture Optimization Algorithm – A New Nature Inspired Meta-heuristics for Knapsack Problem

Abstract: In this paper we have introduced for the first time a new nature inspired meta-heuristics algorithm called Egyptian Vulture Optimization Algorithm which primarily favors combinatorial optimization problems. The algorithm is derived from the nature, behavior and key skills of the Egyptian Vultures for acquiring food for leading their livelihood. These spectacular, innovative and adaptive acts make Egyptian Vultures as one of the most intelligent of its kind among birds. The details of the bird’s habit and the mathematical modeling steps of the algorithm are illustrated demonstrating how the meta-heuristics can be applied for global solutions of the combinatorial optimization problems and has been studied on the traditional 0/1 Knapsack Problem (KSP) and tested for several datasets of different dimensions. The results of application of the algorithm on KSP datasets show that the algorithm works well w.r.t optimal value and provide the scope of utilization in similar kind of problems like path planning and other combinatorial optimization problems.

Clock gated low power sequential circuit design

Abstract: In this work, our focus is on study and analysis of various clock gating technique and design and analysis of clock gating based low power sequential circuit at RTL level. Virtex-6 is 40-nm FPGA, on which we implement our circuit to re-assure power reduction in sequential circuit. Clock gating is implemented on smaller circuit called D flip-flop and on larger circuit called 16-bit register. The percentage of reduction in dynamic power especially clock power is verified for different device operating frequency. Here, we achieved 87.09%, 88.02%, 88.02%, and 88.01% clock power reduction in this work when clock period is 1ns, 0.1ns, 0.01ns and 0.001ns respectively. Design and implementation result shows that there is reduction in dynamic power especialy significant reduction in clock power We also achieved 15%, 14.22%, 14.58%, 14.57% and 14.57% dynamic power reduction when clock period is 10ns, 1ns, 0.1ns, 0.01ns, and 01ps respectively.

Communication constraints multi-agent territory exploration task

Abstract: A common assumption made in multi-robot research is the connectedness of the underlying network. Although this seems a valid assumption for static networks, it is not realistic for mobile robotic networks, where communication between robots usually is distance dependent. Motivated by this fact, we explicitly consider the communication limitations. This paper extends the LFIP based exploration framework previously developed by Pal et al. (Cogn. Comput. doi: 10.1007/s12559-012-9142-7 , 2012), to address the Multi-Agent Territory Exploration (MATE-n k ) task under severe communication constraints. In MATE-n k task agents have to explore their environment to find and visit n checkpoints, which only count as "visited" when k agents are present at the same time. In its simplest form, the architecture consists of two layers: an "Exploration layer" consisting of a selection of future locations for the team for further exploring the environment, and "Exploration and CheckpointVisit layer", consisting of visiting the detected checkpoints while continuing the exploration task. The connectivity maintenance objective is achieved via two ways: (1) The first layer employs a leader-follower concept, where a communication zone is constructed by the leader using a distance transforms method, and (2) In the second layer we make use of a graph theory for characterizing the communication, which employs the adjacency and Laplacian matrices of the graph and their spectral properties. The proposed approach has been implemented and evaluated in several simulated environments and with varying team sizes and communication ranges. Throughout the paper, our conclusions are corroborated by the results from extensive simulations.

Performance Analysis of Received Signal Strength Fingerprinting Based Distributed Location Estimation System for Indoor WLAN

Abstract: Location Estimation has become important for many applications of indoor wireless networks. Received Signal Strength (RSS) fingerprinting methods have been widely used for location estimation. Most of the location estimation system suffers with the problem of scalability and unavailability of all the access points at all the location for large site. The accuracy and response time of estimation are critical issues in location estimation system for large sites. In this paper, we have proposed a distributed location estimation method, which divide the location estimation system into subsystems. Our method partitions the input signal space and output location space into clusters on the basis of visibility of access points at various locations of the site area. Each cluster of input signal space together with output location subspace is used to learn the association between RSS fingerprint and their respective location in a subsystem. We have performed experimentation on two RSS dataset, which are gathered on different testbeds, and compared our results with benchmark RADAR method. Experimental results show that our method provide better results in terms of accuracy and response time in comparison to centralized systems, in which a single system is used for large site.

Solving Travelling Salesman Problem Using Egyptian Vulture Optimization Algorithm – A New Approach

Abstract: Travelling Salesman Problem (TSP) is a NP-Hard combinatorial optimization problem and many real life problems are constrained replica of it which possesses exponential time complexity and requires heavy combination capability. In this work a new nature inspired meta-heuristics called Egyptian Vulture Optimization Algorithm (EVOA) is being introduced and presented for the first time and illustrated with examples how it can be utilized for the constrained graph based problems and is utilized to solve the various dimensional datasets of the traditional travelling salesman problem. There are not many discrete optimization bio-inspired algorithms available in the literature and in that respect it is a novel one which can readily utilized for the graph based and assignment based problems. This EVOA is inspired by the natural and skilled phenomenal habits, unique perceptions and intelligence of the Egyptian Vulture bird for carry out the livelihood and acquisition of food which is inevitable for any kind of organisms. The Egyptian Vulture bird is one of the few birds who are known for showing dexterous capability when it comes to its confrontation with tough challenges and usage of tools with combinations of force and weakness finding ability. The results show that the Egyptian Vulture Optimization meta-heuristics has potential for deriving solutions for the TSP combinatorial problem and it is found that the quality and perfection of the solutions for the datasets depend mainly on the number of dimensions when considerable for the same number of iterations.

Multi-objective adaptive intelligent water drops algorithm for optimization & vehicle guidance in road graph network

Abstract: In this paper we have applied Multi-Objective Intelligent Water Drops Algorithm (MO-IWDA) for optimized route determination of the vehicle through all the underutilized paths available in a road graph exploiting optimization of dynamic parameter based path planning for the vehicle users. Due to the tendency of all the vehicles to follow the same path and also the shortest distance being the implied choice, there occurs tremendous increase in waiting time and high congestion which leads to pollution and stress. Hence it becomes necessary to revise the traffic management system and with the emergence of advanced smart gadgets it is now possible to locally investigate the network information and provide the best decisions for the users so that there is not only enhancement of time factor but also there is optimal usage of fuel and power. IWDA has emerged as a successful graph based meta-heuristics and has been used in NP-hard combinatorial problems like travelling salesman problem, knapsack problems etc. The success of IWDA lies more on probabilistic exploration as there is time linked cooperation and communication between the droplets in terms of its velocity and carried sand. Here we have involved multiobjective optimization of distance and waiting time minimization and used non-weighted fitness function for decision making and evaluation of the best combinations. Here another problem has been resolved of whether there is requirement of keeping different sand quality for both distance and waiting time or the same sand can be dealt with both. A comparative study of the two schemes will clearly reveal the individuality of the IWDA in handling multi-parameter optimization or there is requirement of different types of sand types for each kind of optimization. Analysis of results shows that the optimization level depends on sand investigation criteria which are dependent on how the parameter scaling is done. For multi-objective optimization the relative ratio of scaling is the prime factor. However due to the introduction of the exploration and adaptive parameters the algorithm will sought out the explored path and lead to global optimization.

Optimization of Focused Wave Front Algorithm in Unknown Dynamic Environment for Multi-Robot Navigation

Abstract: Expressions of Affect Alwin de Rooij, Joost Broekens and Maarten H. Lamers (2013). International Journal of Synthetic Emotions (pp. 1-31). www.igi-global.com/article/abstract-expressions-affect/77654?camid=4v1a Design of a Mobile Robot to Clean the External Walls of Oil Tanks Hernán González Acuña, Alfonso René Quintero Lara, Ricardo Ortiz Guerrero, Jairo de Jesús Montes Alvarez, Hernando González Acevedo and Elkin Yesid Veslin Diaz (2014). Robotics: Concepts, Methodologies, Tools, and Applications (pp. 743-753). www.igi-global.com/chapter/design-of-a-mobile-robot-to-clean-the-externalwalls-of-oil-tanks/84922?camid=4v1a

Adaptive a Discrete Real Bat Algorithms for Route Search Optimization of Graph Based Road Network

Abstract: Bat Algorithm (BA) has recently emerged as an efficient nature inspired meta-heuristics due to its added parameters and searching features. In this paper modified versions of discrete bat algorithm is being proposed for the first time which will suit the discrete domain problems. Here the BA has utilized the famous three variable dependent Weibull Cumulative Distribution Function as Weibull Coded Binary Bat Algorithm (WCBBA), another as Real Bat Algorithm (RBA) and the third as the hybrid of the two, for search process with its modeling according to a road network management system where it is being tried to optimize the travel route and produce a vehicle load balancing structure of the network through optimized path establishment. In bat algorithm apart from the search criteria where the virtual bats move, they also utilize their Echolocation property for further investigation of the search space for prey. This makes the heuristic more probabilistic, dynamic and adaptive and the bats can reach a better solution through continuous analysis and exploration. Here the bat algorithm is modeled to handle a multi-objective optimization model where each bat tries to optimize its own route criteria or rather tries to find its liking prey. The enhanced search criteria of bats helps in reducing the number of bats for the search process, but as the Echolocation process spreads out it increases the complexity of the search process that is parallelism is traded off with complexity. The results show that the algorithm has potential for better results and has been compared with the converging rate of Ant Colony Optimization (ACO) & Intelligent Water Drops (IWD) algorithms.

New Bio-inspired Meta-Heuristics - Green Herons Optimization Algorithm - for Optimization of Travelling Salesman Problem and Road Network

Abstract: Following the nature and its processes has been proved to be very fruitful when it comes to tackling the difficult hardships and making life easy Yet again the nature and its processes has been proven to be worthy of following, but this time the discrete family is being facilitated and another member is added to the bio-inspired computing family A new biological phenomenon following meta-heuristics called Green Heron Optimization Algorithm (GHOA) is being introduced for the first time which acquired its potential and habit from an intelligent bird called Green Heron whose diligence, skills, perception analysis capability and procedure for food acquisition has overwhelmed many zoologists This natural skillset of the bird has been transferred into operations which readily favor the graph based and discrete combinatorial optimization problems, both unconstrained and constraint though the latter requires safe guard and validation check so that the generated solutions are acceptable With proper modifications and modeling it can also be utilized for other wide variety of real world problems and can even optimize benchmark equations In this work we have mainly concentrated on the algorithm introduction with establishment, illustration with minute details of the steps and performance validation of the algorithm for a wide range of dimensions of the Travelling Salesman Problem combinatorial optimization problem datasets to clearly validate its scalability performance and also on a road network for optimized graph based path planning The result of the simulation clearly stated its capability for combination generation through randomization and converging global optimization and thus has contributed another important member of the bio-inspired computation family

Discrete Invasive Weed Optimization Algorithm for Graph Based Combinatorial Road Network Management Problem

Abstract: Invasive Weed Optimization (IWO) Algorithm is a nature inspired swarm based continuous domain optimization meta-heuristics which mimicry the expansion-cum-survival strategy of the weeds in favorable, rich and unwanted regions which happens to be the best solution in terms of optimization with respect to competition, growth and nutrition These unwanted plants are in consistent competition and opposition from the other members of the nature either directly or indirectly and as a result their way of living, foraging and sustaining are the most robust and challenging This optimization technique has been proven to be successful in many continuous parameter domains due to their unique spreading characteristics and optimization search methods In this work we have extended the utility of the invasive weed optimization algorithm for graph based combinatorial optimization for path search and planning for vehicle routing from a source to destination The problem can be viewed as a multimodal optimization problem where selection of a certain sequence of multimodal solutions would be best solution For this we have modified the classical IWO to suit the graph based situation and made necessary change in implications to cope up with the graph parameters The convergence rate of the Discrete Invasive Weed Optimization (DIWO) Algorithm is being compared with Ant Colony Optimization (ACO) and Intelligent Water Drop (IWD) algorithm with an application on a road graph model for route optimization for vehicles with respect to multi-objective of travelling and waiting time

Dealing QAP & KSP with Green Heron optimization algorithm — A new bio-inspired meta-heuristic

Abstract: In this paper a new biological phenomenon following meta-heuristics called Green Heron Optimization Algorithm (GHOA) is being discussed, for the first time, which acquired its inspiration from the Green Heron birds, their intelligence, perception analysis capability and technique for food acquisition. The natural phenomenon of the bird has been capped into some unique operations which favour the graph based and discrete combinatorial optimization problems but with slight modification can also be utilized for other wide variety of problems of the real world which have discrete representation of data and variables having several constraints. In this work we have mainly concentrated on the description, mathematical representations, presentations, features, limitations and performance analysis of the algorithm on the scattered dimensional datasets of the Quadratic Assignment Problem (QAP) & 0/1 Knapsack Problem (KSP) to clearly demarcate its performance with change in dimension that is scalability. The results of the simulation clearly reveal how the algorithm has worked optimally for the various datasets of the problem. GHOA is one of the few members in the discrete domain algorithms of the bio-inspired computation family which favours suitably the graph based problems like path planning, process scheduling etc and has the capability of recombination and local search for global optimization and refinement of the solutions.

RSS Fingerprints Based Distributed Semi-Supervised Locally Linear Embedding (DSSLLE) Location Estimation System for Indoor WLAN

Abstract: An important requirement for many novel location based services, is to determine the locations of people, equipment, animals, etc. The accuracy and response time of estimation are critical issues in location estimation system. Most of the location estimation system suffers with the problem of scalability and unavailability of all the access points at all the location for large site. In this paper, we have proposed a distributed semi-supervised location estimation method, which divide the location estimation system into subsystems. Our method partition the input signal space and output location space into clusters on the basis of visibility of access points at various locations of the site area. Each cluster of input signal space together with output location subspace is used to learn the association between Received Signal Strength fingerprint and their respective location in a subsystem. Previous methods for location estimation in indoor wireless networks require a large amount of labeled data for learning the radio map. However, labeled instances are often difficult, expensive, or time consuming to obtain, as they require great efforts, meanwhile unlabeled data may be relatively easy to collect. So, the use of semi-supervised learning is more feasible. On each subsystem at first, we use Locally Linear Embedding to reduce the dimensions of data, and then we use semi-supervised learning algorithm to learn the radio map. The algorithm performs nonlinear mapping between the received signal strengths from nearby access points and the user's location. It is shown that the proposed Distributed Semi-Supervised Locally Linear Embedding scheme has the advantage of robustness, scalability, useful in large site application and is easy in training and implementation. We have compared our results with Distributed Subtract on Negative Add on Positive (DSNAP) and benchmark method RADAR. Experimental results show that our method provide better results in terms of accuracy and response time in comparison to centralized systems, in which a single system is used for large site as well as with DSNAP and benchmark method RADAR.

Breast Cancer Diagnostic System using Hierarchical Learning Vector Quantization

Abstract: Breast cancer has become a common mortality factor in the world. Lesser availability of diagnostic facilities along with large time requirements in manual diagnosis emphasize on automatic diagnosis for early diagnosis of the disease. In this paper a computerized breast cancer diagnosis prototype has been developed to reduce the time taken and indirectly reducing the probability of death. The paper presents Hierarchical Learning Vector Quantization (HLVQ) as a classifier for the diagnosis. Hierarchical LVQ networks consist of multiple LVQ networks assembled in different level or cascade architecture. In this research two stage of LVQ network is used on WDBC datasets. The first level of LVQ reduces the feature space which is further worked over by the second stage for computing the output. The experiments confirm an effective detection of the disease by use of multiple networks. A comparative study of work carried in the field of breast cancer diagnosis using different ANN algorithm is also done.

Speaker recognition using multimodal biometric system

Abstract: We proposed a model to recognize speaker by face and voice signal. In this model we input voice into model and we get voice and face features corresponding to that voice and face by which we try to recognize the user. To achieve this we used a supervised learning model ANN (Artificial neural network). To train this model we input voice feature and face features in synchronous way into model. The ANN model tries to classify the input with respect to a set of users. The main problem in designing this model is synchronization between voice feature and face feature, extraction of face feature, deciding parameter of ANN like iteration value and the last most difficult is making a model to map the face along with voice features. It's very difficult to tackle with the above discussed problem but we have designed a model to achieve somewhat realistic model of speaker recognition.

A customized flocking algorithm for swarms of sensors tracking a swarm of targets

Abstract: Wireless mobile sensor networks (WMSNs) are groups of mobile sensing agents with multi-modal sensing capabilities that communicate over wireless networks. WMSNs have more flexibility in terms of deployment and exploration abilities over static sensor networks. Sensor networks have a wide range of applications in security and surveillance systems, environmental monitoring, data gathering for network-centric healthcare systems, monitoring seismic activities and atmospheric events, tracking traffic congestion and air pollution levels, localization of autonomous vehicles in intelligent transportation systems, and detecting failures of sensing, storage, and switching components of smart grids. The above applications require target tracking for processes and events of interest occurring in an environment. Various methods and approaches have been proposed in order to track one or more targets in a pre-defined area. Usually, this turns out to be a complicated job involving higher order mathematics coupled with artificial intelligence due to the dynamic nature of the targets. To optimize the resources we need to have an approach that works in a more straightforward manner while resulting in fairly satisfactory data. In this paper we have discussed the various cases that might arise while flocking a group of sensors to track targets in a given environment. The approach has been developed from scratch although some basic assumptions have been made keeping in mind some previous theories. This paper outlines a customized approach for feasibly tracking swarms of targets in a specific area so as to minimize the resources and optimize tracking efficiency.

A customized flocking algorithm for swarms of sensors tracking a swarm of targets

Abstract: Wireless mobile sensor networks (WMSNs) are groups of mobile sensing agents with multimodal sensing capabilities that communicate over wireless networks. WMSNs have more flexibility in terms of deployment and exploration abilities over static sensor networks. Sensor networks have a wide range of applications in security and surveillance systems, environmental monitoring, data gathering for network-centric healthcare systems, monitoring seismic activities and atmospheric events, tracking traffic congestion and air pollution levels, localization of autonomous vehicles in intelligent transportation systems, and detecting failures of sensing, storage, and switching components of smart grids. The above applications require target tracking for processes and events of interest occurring in an environment. Various methods and approaches have been proposed in order to track one or more targets in a pre-defined area. Usually, this turns out to be a complicated job involving higher order mathematics coupled with artificial intelligence due to the dynamic nature of the targets. To optimize the resources we need to have an approach that works in a more straightforward manner while resulting in fairly satisfactory data. In this paper we have discussed the various cases that might arise while flocking a group of sensors to track targets in a given environment. The approach has been developed from scratch although some basic assumptions have been made keeping in mind some previous theories. This paper outlines a customized approach for feasibly tracking swarms of targets in a specific area so as to minimize the resources and optimize tracking efficiency.

Multi-robot System Using Low-cost Infrared Sensors

Abstract: This paper presents a proposed set of the novel technique, methods, and algorithm for simultaneous path planning, area exploration, area retrieval, obstacle avoidance, object detection, and object retrieval autonomously by a multi-robot system. The proposed methods and algorithms are built considering the use of low cost infrared sensors with the ultimate function of efficiently exploring the given unknown area and simultaneously identifying desired objects by analyzing the physical characteristics of several of the objects that come across during exploration. In this paper, we have explained the scenario by building a coordinative multi-robot system consisting of two autonomously operated robots equipped with low-cost and low-range infrared sensors to perform the assigned task by analyzing some of the sudden changes in their environment. Along with identifying and retrieving the desired object, the proposed methodology also provide an inclusive analysis of the area being explored. The novelties presented in the paper may significantly provide a cost-effective solution to the problem of area exploration and finding a known object in an unknown environment by demonstrating an innovative approach of using the infrared sensors instead of high cost long range sensors and cameras. Additionally, the methodology provides a speedy and uncomplicated method of traversing a complicated arena while performing all the necessary and inter-related tasks of avoiding the obstacles, analyzing the area as well as objects, and reconstructing the area using all these information collected and interpreted for an unknown environment. The methods and algorithms proposed are simulated over a complex arena to depict the operations and manually tested over a physical environment which provided 78% correct results with respect to various complex parameters set randomly.

Structure stability analysis of Ninn = 2 - 22 using nature inspired algorithms: a performance study

Abstract: Using nature inspired algorithms NIAs with plane-wave self-consistent field PWSCF, density functional theory DFT and pseudo-potentials, the minimisation of potential energy and relative stability for Ni

Efficient Data Structure Based Smart Card Implementation

Abstract: To make smart card much faster, we need efficient data structure. Access time of on chip data depends on how and where we stored. Some Data Structure take maximum time and some take minimum time depending on the space and time complexity of data structure. In this work, we have taken some data structures and find that BST is the best suitable data structure for performing smart card operations in compare to other possible data structures.

Green Heron Swarm Optimization Algorithm - State-of-the-Art of a New Nature Inspired Discrete Meta-Heuristics.

Abstract: Many real world problems are NP-Hard problems are a very large part of them can be represented as graph based problems. This makes graph theory a very important and prevalent field of study. In this work a new bio-inspired meta-heuristics called Green Heron Swarm Optimization (GHOSA) Algorithm is being introduced which is inspired by the fishing skills of the bird. The algorithm basically suited for graph based problems like combinatorial optimization etc. However introduction of an adaptive mathematical variation operator called Location Based Neighbour Influenced Variation (LBNIV) makes it suitable for high dimensional continuous domain problems. The new algorithm is being operated on the traditional benchmark equations and the results are compared with Genetic Algorithm and Particle Swarm Optimization. The algorithm is also operated on Travelling Salesman Problem, Quadratic Assignment Problem, Knapsack Problem dataset. The procedure to operate the algorithm on the Resource Constraint Shortest Path and road network optimization is also discussed. The results clearly demarcates the GHOSA algorithm as an efficient algorithm specially considering that the number of algorithms for the discrete optimization is very low and robust and more explorative algorithm is required in this age of social networking and mostly graph based problem scenarios.