Search Results - (( developing mobile compression algorithm ) OR ( java implication based algorithm ))

Enhancing unity-based AR with optimal lossless compression for digital twin assets by Hlayel, Mohammed, Hairulnizam, Mahdin, Hayajneh, Mohammad, AlDaajeh, Saleh H., Siti Salwani, Yaacob, Mazidah, Mat Rejab

“…This study also creates mathematical models for predicting resource utilization, like RAM and CPU time, required by AR mobile applications. Experimental results show a detailed comparison among these compression algorithms, which can give insights and help choose the best method according to the compression ratio, decompression speed, and resource usage. …”
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Speech Compression Using Discrete Wavelet Transform by M.Ali Najih, Abdulmawla

“…The reconstruction of the compressed signal as well as the detailed steps needed are discussed.The performance of wavelet compression is compared against linear Productive Coding and Global System for Mobile Communication (GSM) algorithms using SNR, PSNR, NRMSE and compression ratio. …”
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The implications for ahybrid detection technique against malicious sqlattacks on web applications by Bahjat Arif, Sarajaldeen Akram, Wani, Sharyar

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Optimize and deploy machine learning algorithms on embedded devices for manufacturing applications by Teoh, Ming Xue

“…In recent studies, we seen developers and researchers proposing solutions on deep learning algorithms like YOLO, EfficientNet, CNN, MobileNet etc. …”
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Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method by Ashraf Saleh Mohammad Alomoush

Enhancing geographic coordinates representation standard for reverse geocoding web services by Al-Habshi, Mohammed Mustafa Abdulrahman

“…Benchmarking the guidelines found the data is maintained within about 68% ± 1% of the original size without any algorithm compression. This study provides some basis for consideration when addressing geographic standards rather arbitrary.…”
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REGION-BASED ADAPTIVE DISTRIBUTED VIDEO CODING CODEC by ABDELRAHMAN ELAMIN, ABDELRAHMAN ELAMIN

“…The resulting regions map is compressed by employing quadtree algorithm and communicated to the encoder via the feedback channel. …”
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ReSTiNet: An efficient deep learning approach to improve human detection accuracy by Shahriar Shakir, Sumi, Dayang Rohaya, Awang Rambli, Mirjalili, Seyedali, Miah, M. Saef Ullah, Muhammad Mudassir, Ejaz

“…To improve the detection speed and accuracy of ReSTiNet, the residual block within the fire modules is carefully designed to increase the feature propagation and maximize the information flow in the network. The developed approach compresses the well-known Tiny-YOLO architecture while improving the following features: (i) small model size, (ii) faster detection speed, (iii) resolution of overfitting, and (iv) better performance than other compact networks such as SqueezeNet and MobileNet in terms of mAP on the Pascal VOC and MS COCO datasets. …”
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ReSTiNet: An Efficient Deep Learning Approach to Improve Human Detection Accuracy by Sumit, S.S., Rambli, D.R.A., Mirjalili, S., Miah, M.S.U., Ejaz, M.M.

“…To improve the detection speed and accuracy of ReSTiNet, the residual block within the fire modules is carefully designed to increase the feature propagation and maximize the information flow in the network. The developed approach compresses the well-known Tiny-YOLO architecture while improving the following features: (i) small model size, (ii) faster detection speed, (iii) resolution of overfitting, and (iv) better performance than other compact networks such as SqueezeNet and MobileNet in terms of mAP on the Pascal VOC and MS COCO datasets. …”
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ReSTiNet : An efficient deep learning approach to improve human detection accuracy by Sumit, Shahriar Shakir, Dayang Rohaya, Awang Rambli, Seyedali, Mirjalili, Miah, Md Saef Ullah, Muhammad Mudassir, Ejaz

“…To improve the detection speed and accuracy of ReSTiNet, the residual block within the fire modules is carefully designed to increase the feature propagation and maximize the information flow in the network. The developed approach compresses the well-known Tiny-YOLO architecture while improving the following features: (i) small model size, (ii) faster detection speed, (iii) resolution of overfitting, and (iv) better performance than other compact networks such as SqueezeNet and MobileNet in terms of mAP on the Pascal VOC and MS COCO datasets. …”
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Design and implementation of a private and public key crypto processor for next-generation it security applications by Hani, Mohamed Khalil, Wen, Hau Yuan, Paniandi, Arul

“…A demonstration application prototype in the form of a real-time secure e-document application has been developed to verify the functionality and validate the embedded system.…”
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A smart spectrum access technique for dynamic multi-hop routing in cognitive radio-based disaster response networks by Khaled F. I, Alaqad

“…Further, the deployment of Mobile Cognitive Base Stations (McBS) using the Dynamic Rule-Based Movement (DRUM) algorithm facilitates the effective transmission of data with low latency. …”
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ReSTiNet : On improving the performance of Tiny-YOLO-Based CNN architecture for applications in human detection by Sumit, Shahriar Shakir, Awang Rambli, Dayang Rohaya, Mirjalili, Seyedali, Ejaz, Muhammad Mudassir, Miah, Md Saef Ullah

“…In this article, we propose ReSTiNet, a novel compressed convolutional neural network that addresses the issues of size, detection speed, and accuracy. …”
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