In this paper, we extend classical results on unimodular elements and the completability of unimodular rows in torsion-free pure projective modules over commutative Noetherian rings. We establish a stable freeness criterion that characterises global completability in terms of module-theoretic properties, generalising Serre-Plumstead-type results for a broader class of modules that are not necessarily free. Applications to linear codes over rings are investigated, showing strong structural and distance properties. This work generalises the local-global principle for row completability beyond projective modules and provides a new framework for understanding module decompositions in the algebraic case. Applications include the construction of generator matrices in coding theory over rings, where unimodular rows encode critical structural and distance properties.

In this literature review the methods of control lower limb exoskeleton\nwith the EMG-based controller is being discussed. The focus of the most reference-es in this chapter is on EMG signals features and how to calibrate them to design.motion controller for knee prosthesis and or thosis. In the end non linear-based control design is mentioned to design PID and MRAC controllers.

Regression testing is a re-testing technique to test the changes, which is taken in the modified or enhanced application to ensure that the changes do not impairment the accessible behavior of the application. Modifications in the applications mainly focus on three types namely binding, process and interfaces. In order to accomplish the regression testing for a modified portion of an application, test cases are selected from a test suite. Selection and generation of the test cases are more important and also it is a tough process in regression testing. In this article, we proposed a technique to automatically generate the test cases for testing the changes of various versions of BPEL (Business Process Execution Language) dataset. We construct a hierarchical test tree (HTT) for both the new and old versions composite services that are modified for an application and also for the unmodified. The changes are tracked by analyzing the control flow of both trees constructed above using the BPEL dataset. We analyzed the performance of the proposed technique and the experimental results showed that our method performs well than the earlier techniques.

This paper proposes a different approach based on Metaheuristic Algorithm is presented for selecting region of interest in mammogram image. The process is carried out on the basis of image segmentation. The foraging behavior of monkey is optimized as Monkey Search Optimization (MSO) which is the subset of the metaheuristic algorithm. Pectoral region removed image is given as input for feature extraction. To solve complex problems by cooperation the behaviors are considered. Several algorithms based on population-based metaheuristic algorithms were introduced in the literatures to solve different problems like optimization problems; it is proven by result that the proposed approach has the potential to be an appropriate algorithm for image segmentation. Results are presented based on simulation made with the implementation in MATLAB which is tested on the images of MIAS database.

In this paper a different approach based on Metaheuristic Algorithm is presented for removing pectoral muscle region in mammogram image is carried out using image segmentation process. The foraging behavior of monkey is optimized as Monkey Search Optimization (MSO) which is the subset of metaheuristic algorithm. To solve complex problems by cooperation the behaviors of nature is considered. Several algorithms based on population-based metaheuristic algorithms were introduced in the literatures to solve different problems like optimization problems; it is proven by result that the proposed approach has the potential to be an appropriate algorithm for image segmentation. Results are presented based on simulation made with the implementation in MATLAB which is tested on the images of MIAS database

Starting system plays crucial role in vehicle operation and failure in which causes engine staring problem. Vehicle starting system failure detection is a challenging task, especially for the inexperienced mechanics and drivers. The success of finding the fault is extremely dependent on the expertise of the individual. To avoid unnecessary breakdown of the vehicle due to starting system problem which cause monetary loss to individuals and overcome the problem of dependent on the expertise individual, in this study, a fuzzy logic based fault detection and diagnosis system has been developed. For this purpose information of current and voltage values of starting system with various fault conditions are acquired by conducting set of tests on vehicles and practiced using fuzzy logic fault diagnosis system (FLFDS). A graphical user interface (GUI) software is also developed using MATLAB® GUI software and it is interfaced with PIC18F4520 controller for implementation on petrol and diesel cars. With the help of this electronic control unit, starting system faults like starter motor brush fault, armature fault, field winding fault, short circuit fault, battery fault and open circuit fault can be detected for further corrective action. The developed graphical user interface (GUI) software was tested (off-board) using sample data obtained from real time starting system and validated these results (on-board) using the developed electronic control unit by conducting field trials on petrol and diesel cars.

In an interconnected power system, low frequency electromechanical oscillations are initiated by normal small changes in system loads, and they become much worse following a large disturbance. Flexible AC Transmission System (FACTS) devices are widely recognized as powerful controllers for damping power system oscillations. The standard FACTS controllers are linear controllers which may not guarantee acceptable performance or stability in the event of a major disturbance. To overcome the drawbacks of conventional controllers, ANFIS (Adaptive Neuro-Fuzzy Inference System) control scheme has been developed in this paper, and it has been applied for the external coordinated control of series connected FACTS controllers known as Static Synchronous Series Compensators (SSSCs) employed in a multimachine power system. In neuro-fuzzy control method, the simplicity of fuzzy systems and the ability of training in neural networks have been combined. The training data set the parameters of membership functions in fuzzy controller. This ANFIS can track the given input-output data in order to conform to the desired controller. Simulation studies carried out in MATLAB/SIMULINK environment demonstrate that the proposed ANFIS based SSSC controller shows the improved damping performance as compared to conventional SSSC based damping controllers under different operating conditions.