Object detection is one of the most fundamental and challenging tasks to locate objects in images and videos. Over the past, it has gained much attention to do more research on computer vision tasks such as object classification, counting of objects, and object monitoring. This study provides a detailed literature review focusing on object detection and discusses the object detection techniques. A systematic review has been followed to summarize the current research work’s findings and discuss seven research questions related to object detection. Our contribution to the current research work is (i) analysis of traditional, two-stage, one-stage object detection techniques, (ii) Dataset preparation and available standard dataset, (iii) Annotation tools, and (iv) performance evaluation metrics. In addition, a comparative analysis has been performed and analyzed that the proposed techniques are different in their architecture, optimization function, and training strategies. With the remarkable success of deep neural networks in object detection, the performance of the detectors has improved. Various research challenges and future directions for object detection also has been discussed in this research paper.
Sign language (SL) is the best suited communication medium for hearing impaired people. Even with the advancement of technology, there is a communication gap between the hearing impaired and hearing people. The aim of this research work is to bridge this gap by developing an automatic system that translates the speech to Indian Sign Language using Avatar (SISLA). The whole system works in three phases: (i) The first phase includes the speech recognition (SR) of isolated words for English, Hindi and Punjabi in speaker independent environment (ii) The second phase translates the source language into Indian Sign Language (ISL) (iii) HamNoSys based 3D avatar represents the ISL gestures. The four major implementation modules for SISLA include: requirement analysis, data collection, technical development and evaluation. The multi-lingual feature makes the system more efficient. The training and testing speech sample files for English (12,660, 4218), Hindi (12,610, 4211) and Punjabi (12,600, 4193) have been used to train and test the SR models. Empirical results of automatic machine translation show that the proposed trained models have achieved the minimum accuracy of 91%, 89% and 89% for English, Punjabi and Hindi respectively. Sign language experts have also been used to evaluate the sign error rate through feedback. Future directions to enhance the proposed system using non-manual SL features along with the sentence level translation has been suggested. Usability testing based on survey results confirm that the proposed SISLA system is suitable for education as well as communication purpose for hearing impaired people.
Wireless communication has seen gigantic advancement all the way through past years. The maturity of newer generations of technology and boost up in user mobility has created the need and demand for wireless networks that has triggered considerable technological advances as well as the investigation of optimization algorithms to support design and planning decisions. Wireless system providers will be vital to enlarge their infrastructure rapidly in order to meet this swift escalation in wireless data demand. Demand for cheaper and better wireless communication services from customers are the key factors to optimally design the cell geometry and select the minimum number of cell sites to provide maximum possible coverage. In this paper, we consider how to optimally determine the cell site locations such that, number of base stations (N) is minimum while coverage is maximum so that best possible service is possible with minimum infrastructural costs. An optimized algorithm is presented here that determines the optimal locations of base stations without performing an exhaustive search. The algorithm simulates the network, uses the function to rank the cells and then applies screening criteria that removes the cell with the and repeats the process until k cells are removed.
Road Traffic Accidents (RTAs) are a major public health concern, resulting in an estimated 1.2 million deaths and 50 million injuries worldwide each year. In the developing world, RTAs are among the leading cause of death and injury. The objective of this study is to evaluate a set of variables that contribute to the degree of injury severity in traffic crashes. The issue of traffic safety has raised great concerns across the globe and it has become one of the key issues challenging the sustainable development of modern traffic and transportation. The study on road traffic accident causes can identify the key factors rapidly, efficiently and provide instructional methods to the traffic accidents prevention and road traffic accidents reduction, which could greatly reduce personal casualty and property loss caused by road traffic accidents. Using the method of traffic data analysis, can improve the road traffic safety management level effectively.
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