Current Issue : July - September Volume : 2017 Issue Number : 3 Articles : 5 Articles
The simultaneous localisation and mapping (SLAM) algorithm has drawn increasing interests in autonomous robotic\nsystems. However, SLAM has not been widely explored in embedded system design spaces yet due to the limitation of\nprocessing recourses in embedded systems. Especially when landmarks are not identifiable, the amount of computer\nprocessing will dramatically increase due to unknown data association. In this work, we propose an intelligible SLAM\nsolution for an embedded processing platform to reduce computer processing time using a low-variance resampling\ntechnique. Our prototype includes a low-cost pixy camera, a Robot kit with L298N motor board and Raspberry Pi V2.0.\nOur prototype is able to recognise artificial landmarks in a real environment with an average 75% of identified\nlandmarks in corner detection and corridor detection with only average 1.14 W....
Theconcept of the smart city is widely favored, as it enhances the quality of life of urban citizens, involvingmultiple disciplines, that\nis, smart community, smart transportation, smart healthcare, smart parking, and many more. Continuous growth of the complex\nurban networks is significantly challenged by real-time data processing and intelligent decision-making capabilities. Therefore, in\nthis paper, we propose a smart city framework based on Big Data analytics.The proposed framework operates on three levels: (1)\ndata generation and acquisition level collecting heterogeneous data related to city operations, (2) data management and processing\nlevel filtering, analyzing, and storing data to make decisions and events autonomously, and (3) application level initiating execution\nof the events corresponding to the received decisions. In order to validate the proposed architecture, we analyze a fewmajor types of\ndataset based on the proposed three-level architecture. Further, we tested authentic datasets onHadoop ecosystem to determine the\nthreshold and the analysis shows that the proposed architecture offers useful insights into the community development authorities\nto improve the existing smart city architecture...
With the advancement of mobile and embedded devices, many applications such as data mining have found their\nway into these devices. These devices consist of various design constraints including stringent area and power\nlimitations, high speed-performance, reduced cost, and time-to-market requirements. Also, applications running on\nmobile devices are becoming more complex requiring significant processing power. Our previous analysis illustrated\nthat FPGA-based dynamic reconfigurable systems are currently the best avenue to overcome these challenges. In this\nresearch work, we introduce efficient reconfigurable hardware architecture for principal component analysis (PCA), a\nwidely used dimensionality reduction technique in data mining. For mobile applications such as signature verification\nand handwritten analysis, PCA is applied initially to reduce the dimensionality of the data, followed by similarity\nmeasure. Experiments are performed, using a handwritten analysis application together with a benchmark dataset, to\nevaluate and illustrate the feasibility, efficiency, and flexibility of reconfigurable hardware for data mining applications.\nOur hardware designs are generic, parameterized, and scalable. Furthermore, our partial and dynamic reconfigurable\nhardware design achieved 79 times speedup compared to its software counterpart, and 71% space saving compared\nto its static reconfigurable hardware design....
Recently, embedded systems have become popular because of the rising demand\nfor portable, low-power devices. A common task for these devices is object tracking,\nwhich is an essential part of various applications. Until now, object tracking in video\nsequences remains a challenging problem because of the visual properties of objects\nand their surrounding environments. Among the common approaches, particle filter\nhas been proven effective in dealing with difficulties in object tracking. In this research,\nwe develop a particle filter based object tracking method using color distributions of\nvideo frames as features, and deploy it in an embedded system. Because particle filter\nis a high-complexity algorithm, we utilize computing power of embedded systems\nby implementing a parallel version of the algorithm. The experimental results show\nthat parallelization can enhance the performance of particle filter when deployed in\nembedded systems....
Shortening the marketing cycle of the product and accelerating its development efficiency have become a vital concern in the field of\nembedded system design. Therefore, hardware/software partitioning has become one of the mainstream technologies of embedded\nsystem development since it affects the overall system performance.Given today�s largest requirement for great efficiency necessarily\naccompanied by high speed, our new algorithm presents the best version that can meet such unpreceded levels. In fact, we describe\nin this paper an algorithm that is based on HW/SW partitioning which aims to find the best tradeoff between power and latency\nof a system taking into consideration the dark silicon problem.Moreover, it has been tested and has shown its efficiency compared\nto other existing heuristic well-known algorithms which are Simulated Annealing, Tabu search, and Genetic algorithms....
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