The study proposed the classification and recognition of hand gestures using electromyography (EMG) signals for controlling the upper limb prosthesis. In this research, the EMG signals were measured through an embedded system by wearing a band of MYO gesture control. In order to observe the behavior of these change movements, the EMG data was acquired from 10 healthy subjects (five male and five females) performing four upper limb movements. After extracting EMG data from MYO, the supervised classification approach was applied to recognize the different hand movements. The classification was performed with a 5-fold cross-validation technique under the supervision of Quadratic discriminant analysis (QDA), support vector machine (SVM), random forest, gradient boosted, ensemble (bagged tree), and ensemble (subspace K-Nearest Neighbors) classifier. The execution of these classifiers shows the overall accuracy of 83.9% in the case of ensemble (bagged tree) which is higher than other classifiers. Additionally, in this research an embedded system-based classification approach of hand movement was used for designing an upper limb prosthesis. This approach is different than previous techniques as MYO is used with an external Bluetooth module and different libraries that make its movement and performance boundless. The results of this study also inferred the operations which were easy for hand recognition and can be used for developing a powerful, efficient, and flexible prosthetic design in the future.

The software organizations rapidly adopting global software development (GSD) to gain the economic and strategic benefits. Besides, GSD faces many challenges that mainly concerned with the requirements change management (RCM). This study aims to identify and empirically validate the factors that can negatively influence the RCM process in GSD. To this end, literature review and questionnaire survey were conducted for the investigation and validation of RCM challenges. A total of 31 RCM challenges were identified. We have further classified the identified challenges in organization types, organization size and based on experts’ opinions with the aim to provide a clear understanding of the RCM process and its challenges to the practitioners. Based on these identified challenges, we believe that this study can provide a framework for tackling problems associated with RCM activities in GSD environment, which is significant to success and progression of GSD organizations.

In this study, the energy management and switching control of plug-in hybrid electric vehicles (PHEVs) in a hybrid smart micro-grid system was designed. The charging station in this research consists of real market PHEVs of different companies with different sizes. The rate of charging of PHEVs is managed via switching control to receive maximum benefits from renewable energy sources and reduce the consumption of electricity from the grid. To support the optimum utilization of sustainable power, charging time and network stability, seven scenarios were developed for different interaction among the proposed micro-grid system and PHEVs. The proposed hybrid smart micro-grid system consists of three renewable energy sources: photovoltaic (PV) array controlled via an intelligent fuzzy control maximum power point subsystem, a fuel cell stack and a microturbine set controlled by proportional integral differential/proportional integral subsystems. A hybrid energy storage system (super-capacitor, battery storage bank and hydrogen) and residential load are also included in the proposed architecture. The hybrid smart micro-grid system is checked in terms of voltage regulation, frequency deviation and total harmonic distortion (THD). It was found that these are in limits according to the international standards. The simulations verify the feasibility of the proposed system and fulfill the requirement of vehicle-to-grid and grid-to-vehicle operations in a smart grid environment.

Wireless Android Based Home Automation System Volume 2, Issue 1, Page No 234-239, 2017 Muhammad Tanveer Riaz, Eman Manzoor Ahmed, Fariha Durrani

In this article, we introduce a cost-effective and real-time intelligent system tailored to Pakistan sign language (PSL) recognition, aimed at facilitating communication for hearing-impaired individuals. The system utilizes a specialized glove equipped with flex sensors and an MPU-6050 device to capture finger movements and hand orientation in three-dimensional space. A dataset comprising ten unique PSL signs, each performed by five participants for a total of 5000 samples, was used to train machine learning classifiers. These signs involve single-hand and single-movement gestures, optimizing the system for real-time PSL recognition. Machine learning classifiers, including decision tree, k-nearest neighbor, and support vector machine, achieved accuracy levels of 96%, 96.5%, and 97%, respectively. While direct quantitative comparisons with state-of-the-art systems are limited due to the uniqueness of PSL, we discuss our approach in the context of recent advancements in sign language recognition. Real-time testing underscores the system’s practical applicability and portability, demonstrating its potential for deployment in resource-constrained settings as an accessible initial step toward more comprehensive PSL recognition solutions.

The software organizations widely consider the cloud based global software development (CGSD) as it offer the quality projects with low cast. The adoption of CGSD is challenging due to the geographical distance between practitioners. This study aims to identify and analyses the motivators that could positively impact the implementation of CGSD paradigm. The systematic literature review approach was applied to identify the CGSD motivators reported in the literature, and were further validated with industry experts using questionnaire survey study. Moreover, the fuzzy-AHP approach was applied to prioritize the investigated motivators concerning their significance for the successful adoption of CGSD. The findings of the study provide the prioritization-based taxonomy of the investigated motivators that assists the software organizations to develop and revise their strategies for the successful implementation of CGSD.