Baljit Singh Khehra

A Genetic Approach to Standard Cell placement using Various Genetic Operators

Genetic algorithm (GA) is a powerful optimization algorithm, which starts with an initial set of random configurations and uses a process similar to biological evolution to improve upon them [1]. As we know that there is a progression towards miniaturization. This paper describes the Genetic Algorithm for standard cell placement on a VLSI Chip for minimization of chip area. Unlike the other placement algorithms that apply transformations on the physical layout, the genetic algorithm applies transformations on the chromosomal representation of the physical layout.

CPU task scheduling using genetic algorithm

This paper addresses p-processes single processor scheduling problem with a common deadline, to minimize the total execution time and reduce the penalty costs. Process scheduling is one of the most essential factor on which the efficiency and the performance of the work done by the CPU depends. Earliness and tardiness of the processes degrades the efficiency of the processor as they carry penalty costs with them. Thus, the scheduling problem of minimizing the total sum of earliness and tardiness with a common deadline on a single processor is important and competitive. Scheduling is particularly one of the subset of combinatorial optimization problems, which are in fact NP-Hard problems. This problem can be solved using heuristic and meta-heuristic approach such as genetic algorithm for optimal results. In the experiment performed, the Genetic Algorithm outperforms the results in comparison with a simple heuristic method.

AUTOMATIC DETECTION OF MICROCALCIFICATIONS IN DIGITIZED MAMMOGRAMS USING FUZZY 2-PARTITION ENTROPY AND MATHEMATICAL MORPHOLOGY

Cancer is a leading cause of death among men and women nowadays all over the world. Breast cancer is a most common form of cancer originated from breast tissue among women. Most frequent type of breast cancer is ductal carcinoma in situ (DCIS) and most frequent symptoms of DCIS recognized by mammography are clusters of Microcalcifications (MCCs). Automatic detection of Microcalcifications is an important task to prevent and treat the disease. In this paper, an effective approach for automatic detection of Microcalcifications in digitized mammograms is proposed. The proposed approach is based on fuzzy 2-partition entropy and mathematical morphology. In the proposed approach, first phase uses fuzzy Gaussian membership function for mammogram fuzzification. In this phase, fuzzy 2-partition entropy approach is used to find bandwidth of the Gaussian function. After this, mathematical morphological enhancement approach is used to enhance the contrast of Microcalcifications in mammograms. Finally, Microcalcifications are located using Otsu threshold selection method. Experiments have been conducted on images of mini-MIAS database (Mammogram Image Analysis Society database (UK)). In order to validate the results, several different kinds of standard test images (fatty, fatty-glandular and denseglandular) of mini-MIAS database are considered. Experimental results demonstrate that the proposed approach has an ability to detect Microcalcifications even in dense mammograms. The results of proposed approach are quite promising. The proposed approach can be a part of developing a computer aided decision (CAD) system for early detection of breast cancer.

A Comparative Study for Optimization of Video File Compression in Cloud Environment

Many organizations like hospitals for telemedicine, journalism for live-telecast and academias are using a service video-on-demand for delivering the lectures and research contents to the remote locations across the globe. The videos to be broadcasted are time and resource consuming due to the large amount of data and due to these constraints, for getting fast access over Internet and mobile devices, such video applications need to be compressed into another format. The usage of videos is occasional so to save huge infrastructure cost and time, the Infrastructure as a Service (IaaS) Cloud systems can be leveraged. In this paper, an attempt has been made to design, implement and optimize the performance of Digital Video to MPEG4 transcoding in the Cloud environment using Meghdoot (an Open-Source Cloud stack). The classical MapReduce approach is used to rationalize the use of resources by exploring on demand computing and performs parallel video conversion thereby reducing the video encoding times. Experimental results point out to suitability of better performance that by varying the technique of splitting the video file size of fragments that is through Mencoder and through default Hadoop Splitting. The comparison of both the systems to get the best compression times will help us to optimize the Cloud resources that further helps in trade-off between time, cost and quality.

Performance evaluation of fuzzy 2-partition entropy and big bang big crunch optimization based object detection and tracking approach

Background subtraction (BS) is one of most commonly used methods for detection of moving objects in videos that works by subtracting current frame from a background frame. Effective background modeling and threshold plays a crucial role in BS and can govern accuracy and preciseness of object boundaries. This paper proposes a fuzzy entropy based approach modified BS algorithm for moving object detection with Kalman tracker. The standard BS method has been enhanced using concept of fuzzy 2-partition entropy and big bang big crunch optimization (BBBCO). BBBCO has been used to enhance standard BS algorithm for extracting various parameters required in BS algorithm by framing the problem of parameters detection as optimization problem which is solved using concept of fuzzy partition entropy. The proposed algorithm generates optimal threshold values along with various other measures for background modeling. The detected objects are further tracked using Kalman filter based tracker. The evaluation of proposed method has been done on videos from benchmark datasets and statistical parameters have been calculated. The method is also compared with standard BS and another recent study in the field. The results show promise of the proposed method.

BBBCO and fuzzy entropy based modified background subtraction algorithm for object detection in videos

Background subtraction (BS) is one of the most commonly used methods for detecting moving objects in videos. In this task, moving objectpixels are extracted by subtracting the current frame from a background frame. The obtained difference is compared against a threshold value to classify pixels as belonging to the foreground or background regions. The threshold plays a crucial role in this categorization and can impact the accuracy and preciseness of the object boundaries obtained by the BS algorithm. This paper proposes an approach for enhancing and optimizing the performance of the standard BS algorithm. This approach uses the concept of fuzzy 2-partition entropy and Big Bang–Big Crunch Optimization (BBBCO). BBBCO is a recently proposed evolutionary optimization approach for providing solutions to problems operating on multiple variables within prescribed constraints. BBBCO enhances the standard BS algorithm by framing the problem of parameter detection for BS as an optimization problem, which is solved using the concept of fuzzy 2-partition entropy. The proposed method is evaluated using videos from benchmark datasets and a number of statistical metrics. The method is also compared with standard BS and another recently proposed method. The results show the promise of the proposed method.

Image Segmentation Using Two-Dimensional Renyi Entropy

Segmentation of an image is used to separate the image into several significant parts based on properties of discontinuity and similarity. Segmentation of an image is generally done with the help of thresholding technique. Thresholding is used to turn an image from gray scale to binary. The selection of suitable threshold value in the image is a challenging task. Thresholding value depends upon the randomness of intensity distribution of the image. Entropy is a parameter that is used to measure the randomness of intensity distribution of the image. In this work, Shannon-entropy-based and Non-Shannon (Renyi, Collision and Min) entropy-based approaches are used to select suitable threshold value. After this, thresholding values obtained from different approaches are tested on 6 standard test images. For evaluating, peak signal-to-noise ratio (PSNR) and uniformity (U) parameters are used. From the results, it is observed that Renyi-entropy-based approach is a better approach than other approaches.

Soft Computing based object detection and tracking approaches: State-of-the-Art survey

Abstract In recent years, analysis and interpretation of video sequences to detect and track objects of interest had become an active research field in computer vision and image processing. Detection and tracking includes extraction of moving object from frames and continuous tracking it thereafter forming persistent object trajectories over time. There are some really smart techniques proposed by researchers for efficient and robust detection or tracking of objects in videos. A comprehensive coverage of such innovative techniques for which solutions have been motivated by theories of soft computing approaches is proposed. The main objective of this research investigation is to study and highlight efforts of researchers who had conducted some brilliant work on soft computing based detection and tracking approaches in video sequence. The study is novel as it traces rise of soft computing methods in field of object detection and tracking in videos which has been neglected over the years. The survey is compilation of studies on neural network, deep learning, fuzzy logic, evolutionary algorithms, hybrid and recent innovative approaches that have been applied to field of detection and tracking. The paper also highlights benchmark datasets available to researchers for experimentation and validation of their own algorithms. Major research challenges in the field of detection and tracking along with some recommendations are also provided. The paper provides number of analyses to guide future directions of research and advocates for more applications of soft computing approaches for object detection and tracking approaches in videos. The paper is targeted at young researchers who will like to see it as platform for introduction to a mature and relatively complex field. The study will be helpful in appropriate use of an existing method for systematically designing a new approach or improving performance of existing approaches.

Comparative Analysis of Various Soft Computing Techniques for Classification of Fruits

Fruit classification and recognition techniques play a significant role in various fruit based computer vision systems. Many techniques have been proposed so far to classify and recognize fruit images. However, these techniques not provide efficient results whenever fruit images have poor visibility due to poor environmental conditions. The overall objective of this paper is to study and explore various techniques which have been designed so far to classify and recognize fruit images. Comparisons have been drawn between some well-known fruit classification and recognition techniques based upon certain features. It has been observed that soft computing based fruit classification and recognition techniques performs efficiently than existing techniques.

Image Segmentation Using Teaching-Learning-Based Optimization Algorithm and Fuzzy Entropy

Thresholding is one of the most frequently used methods in image segmentation. Fuzzy entropy thresholding approach has been widely applied to image thresholding. Such thresholding approach used two parametric fuzzy membership functions for fuzzy partitioning of the image. In this paper, Teaching-Learning-based Optimization (TLBO) algorithm is used to search an optimal combination of parameters of the membership functions for maximizing the entropy of fuzzy 2-partition. The selected optimal parameters are used to find optimal image threshold value. This new proposed fuzzy thresholding algorithm is called the TLBO-based Fuzzy Entropy Thresholding (TLBO-based FET) algorithm. The proposed algorithm is tested on a number of standard test images. Three different approaches, Genetic Algorithm (GA), Biogeography-based Optimization (BBO), recursive approach, are also implemented for comparison with the results of the proposed approach. From experimental results, it is observed that the performance of the proposed algorithm is more effective than GA-based, BBO-based and recursive approaches.