Current Issue : April - June Volume : 2020 Issue Number : 2 Articles : 5 Articles
Surveillance systems paradigm envisions the pervasive interconnection and cooperation of interactive devices over the Internet\ninfrastructure. Nevertheless, dissemination and processing of surveillance video amid the Internet of Things (IoT) applications\nbecome a susceptible issue due to the large volume and the significant information of these data. Moreover, surveillance devices on\nIoT have very limited resources such as memory and storage. The actual security methods are not quite appropriate for surveillance\nIoT systems. Thus, a particular cryptosystem technique is required for surveillance data security. In this paper, we\npropose an efficient cryptosystem to secure IoT-based surveillance systems. The proposed cryptosystem framework contains three\nparts. First, a lightweight automatic summarization technique based on a fast histogram-clustering approach is used to extract the\nkeyframes from the surveillance video. Then, we employ a discrete cosine transform (DCT) technique to compress the extracted\ndata size. Finally, the proposed framework performs an efficient image encryption algorithm by employing a discrete fractional\nrandom transform (DFRT). The testing results and analysis confirm the features of the proposed cryptosystem on surveillance\nsystems. The proposed framework is fast and ensures secure and efficient real-time processing by minimizing the transmission\ncost and storage....
Pitch shifting is a common voice editing technique in which the original pitch of a digital voice is raised or lowered. It is likely to\nbe abused by the malicious attacker to conceal his/her true identity. Existing forensic detection methods are no longer effective for\nweakly pitch-shifted voice. In this paper, we proposed a convolutional neural network (CNN) to detect not only strongly pitchshifted\nvoice but also weakly pitch-shifted voice of which the shifting factor is less than ±4 semitones. Specifically, linear frequency\ncepstral coefficients (LFCC) computed from power spectrums are considered and their dynamic coefficients are extracted as the\ndiscriminative features. And the CNN model is carefully designed with particular attention to the input feature map, the activation\nfunction and the network topology. We evaluated the algorithm on voices from two datasets with three pitch shifting software.\nExtensive results show that the algorithm achieves high detection rates for both binary and multiple classifications....
Graphics processing units (GPUs) are extensively used as accelerators across multiple\napplication domains, ranging from general purpose applications to neural networks, and\ncryptocurrency mining. The initial utilization paradigm for GPUs was one application accessing\nall the resources of the GPU. In recent years, time sharing is broadly used among applications of a\nGPU, nevertheless, spatial sharing is not fully explored. When concurrent applications share the\ncomputational resources of a GPU, performance can be improved by eliminating idle resources.\nAdditionally, the incorporation of GPUs in embedded and mobile devices increases the demand\nfor power efficient computation due to battery limitations. In this article, we present an allocation\nmethodology for streaming multiprocessors (SMs). The presented methodology works for two\nconcurrent applications on a GPU and determines an allocation scheme that will provide power\nefficient application execution, combined with improved GPU performance. Experimental results\nshow that the developed methodology yields higher throughput while achieving improved power\nefficiency, compared to other SM power-aware and performance-aware policies. If the presented\nmethodology is adopted, it will lead to higher performance of applications that are concurrently\nexecuting on a GPU. This will lead to a faster and more efficient acceleration of execution, even for\ndevices with restrained energy sources....
Research on the application of live streaming in ESL learning is still quite\nscarce among researchers especially in Malaysia. However, a number of studies\non live streaming in the field of marketing and branding as well as the\nculture of live streaming are evolving rapidly. A lot of researches also have\nbeen done on games streaming. As live streaming has becoming more significant\nin all field, a study on the impacts of live streaming in the education\nfield should become important, too. Therefore, this leads to the purpose of\nthe study which is to explore the possibility of live streaming as a new platform\nfor ESL learning. Since Gen Z requires teaching and learning process\nthat suits their learning preference. Hence, live streaming serves as a good\nplatform for them to indulge in the ESL learning process. A lot of reading has\nbeen done to give emphasis on the significance of the study. Live streaming\nwill be helpful in ESL learning to keep pace with globalization, produce independent\nlearners, provide authentic learning experiences as well as give\nrooms for real life communication and interactivity. The discussion of this\nstudy would hopefully shed lights to researcher to investigate further in this\narea....
This paper explores the objective of the present video quality analysis (VQA) and measures the full reference metrics keeping in\nview the quality degradation. During the research work, we conduct experiments on different social clouds (SCs) and low-quality\nvideos. Selected videos are uploaded to SC to assess differences in video service and quality. WeChat shows that the average of all\nvideos (Avg= 100), peak signal-to-noise ratio (PSNR), has no impact on other indicators. Therefore, we believe that WeChat\nprovides the best video quality and multimedia services to their users to meet Quality of Service (QoS)/Quality of\nExperience (QoE)....
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