scholarly journals Fast Thumbnail Extraction for H.264/AVC, HEVC and VP9

2021 ◽  
Vol 11 (4) ◽  
pp. 1844
Author(s):  
Joohyung Byeon ◽  
Seungchul Jang ◽  
Jongseok Lee ◽  
Kyungyong Kim ◽  
Donggyu Sim
Keyword(s):  
High Speed ◽  
Intra Prediction ◽  
Extraction Time ◽  
Memory Usage ◽  
Video Codecs ◽  
Limited Memory ◽  
Full Resolution ◽  
Inverse Transform ◽  
Partial Inverse

In this paper, we propose a partial decoding method with limited memory usage for high-speed thumbnail extraction. The proposed method performs a partial inverse transform and a partial intra prediction in order to reconstruct pixels for intra prediction and thumbnails. Thereafter, the reconstructed pixels at the bottom and right line of the block are stored in the line buffer and the thumbnail buffer without being stored in the decoded picture buffer with full resolution. H.264/AVC, HEVC and VP9 video codecs have different coding structures, prediction and transforms; however, the proposed algorithm can be applied to the corresponding codecs in the same manner. In order to evaluate the performance of the proposed method, we implemented the proposed algorithm for H.264/AVC, HEVC and VP9. We found that the thumbnail extraction time of the proposed method decreased by 66% in H.264/AVC, 52% in HEVC and 48% in VP9 as compared to the full decoding method.

scholarly journals Evaluation of speech signal features extraction methods

2020 ◽  
Vol 2 (1) ◽  
pp. 69-78
Author(s):  
Ismail Shayeb ◽  
Naseem Asad ◽  
Ziad Alqadi ◽  
Qazem Jaber
Keyword(s):  
Speech Signal ◽  
High Speed ◽  
Research Paper ◽  
Extraction Methods ◽  
Features Extraction ◽  
Experimental Results ◽  
Extraction Time ◽  
Digital Signals ◽  
Human Speech ◽  
Signal Features

Human speech digital signals are famous and important digital types, they are used in many vital applications which require a high speed processing, so creating a speech signal features is a needed issue. In this research paper we will study more widely used methods of features extraction, we will implement them, and the obtained experimental results will be compared, efficiency parameters such as extraction time and throughput will be obtained and a speedup of each method will be calculated. Speech signal histogram will be used to improve some methods efficiency.

High-Speed Planning and Reducing Memory Usage of a Precomputed Search Tree Using Pruning

2010 ◽  
Vol 24 (5-6) ◽  
pp. 903-920
Author(s):  
Yumiko Suzuki ◽  
Simon Thompson ◽  
Satoshi Kagami
Keyword(s):  
High Speed ◽  
Search Tree ◽  
Memory Usage

Coupling of Micro-Scale and Macro-Scale Eulerian-Lagrangian Models for the Computation of Shocked Particle-Laden Flows

2013 ◽  
Author(s):  
Sean Davis ◽  
Oishik Sen ◽  
Gustaaf Jacobs ◽  
H. S. Udaykumar
Keyword(s):  
Drag Coefficient ◽  
High Speed ◽  
Clustering Algorithm ◽  
Point Particles ◽  
Micro Scale ◽  
Full Resolution ◽  
Macro Scale ◽  
Comparable Accuracy ◽  
Artificial Neural Network Ann ◽  
Particle Laden Flow

The accuracy and efficiency of several algorithms that couple output from full resolution micro-scale Direct Numerical Simulation computations to input for macro-scale Eulerian-Lagrangian (EL) methods for the computation of high-speed, particle-laden flow are assessed. A Stochastic Collocation method, a Gaussian Radial Basis Function (RBF) Artificial Neural Network (ANN), and an improved RBF-ANN are compared for the fitting of an analytical drag coefficient formula that depends on Mach number and Reynolds number. The improved RBF-ANN uses a clustering algorithm to enhance conditioning of interpolation matrices. The fitted drag coefficient mantle, used to trace point particles in macro-scale computations, is in excellent agreement with the analytical drag formula. The SC method requires fewer micro-scale realizations to obtain comparable accuracy of the drag coefficient. The Gaussian RBF does not converge monotonically, while the improved RBF-ANN converges algebraically and has the potential to provide error estimates.

A green strategy for obtaining anthraquinones from Rheum tanguticum by subcritical water

2020 ◽  
Vol 18 (1) ◽  
pp. 702-710
Author(s):  
Guoying Zhang ◽  
Xiaofeng Chi
Keyword(s):  
Flow Rate ◽  
High Speed ◽  
Subcritical Water ◽  
Extraction Time ◽  
High Yield ◽  
Extraction Temperature ◽  
Subcritical Water Extraction ◽  
Traditional Chinese Herbal Medicine ◽  
Anthraquinone Glycosides ◽  
Rheum Tanguticum

AbstractRheum tanguticum is a traditional Chinese herbal medicine, which contains abundant anthraquinones. In this study, anthraquinones were efficiently extracted from Rheum tanguticum by subcritical water extraction (SWE). The parameters of extraction time (33–67 min), temperature (100–200°C), and SW flow rate (1.4–4.6 mL/min) were optimized so as to achieve a high yield of the target product. A high yield of the total anthraquinones was achieved under the optimized SWE conditions of extraction time 54 min, extraction temperature 170°C, and the flow rate 2.0 mL/min. The comparison between the SWE and traditional extraction techniques implied that the SWE is an efficient and green alternative method for the extraction of anthraquinones. Four anthraquinone glycosides were purified from the SWE extract by high-speed counter-current chromatography and identified as emodin-1-O-β-D-glucoside, physcion-8-O-β-D-glucopyranoside, chrysophanol-1-O-β-D-glucoside, and chrysophanol-8-O-β-D-glucoside.

scholarly journals Addressing limited weight resolution in a fully optical neuromorphic reservoir computing readout

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chonghuai Ma ◽  
Floris Laporte ◽  
Joni Dambre ◽  
Peter Bienstman
Keyword(s):  
Power Consumption ◽  
Bit Error Rate ◽  
Error Rate ◽  
High Speed ◽  
Low Power Consumption ◽  
Reservoir Computing ◽  
Training Procedure ◽  
Low Resolution ◽  
Full Resolution ◽  
High Speed Data

AbstractUsing optical hardware for neuromorphic computing has become more and more popular recently, due to its efficient high-speed data processing capabilities and low power consumption. However, there are still some remaining obstacles to realizing the vision of a completely optical neuromorphic computer. One of them is that, depending on the technology used, optical weighting elements may not share the same resolution as in the electrical domain. Moreover, noise of the weighting elements are important considerations as well. In this article, we investigate a new method for improving the performance of optical weighting components, even in the presence of noise and in the case of very low resolution. Our method utilizes an iterative training procedure and is able to select weight connections that are more robust to quantization and noise. As a result, even with only 8 to 32 levels of resolution, in noisy weighting environments, the method can outperform both nearest rounding low-resolution weighting and random rounding weighting by up to several orders of magnitude in terms of bit error rate and can deliver performance very close to full-resolution weighting elements.

scholarly journals Deteksi Covid-19 pada Citra Sinar-X Dada Menggunakan Deep Learning yang Efisien

2020 ◽  
Vol 7 (6) ◽  
pp. 1289
Author(s):  
Novanto Yudistira ◽  
Agus Wahyu Widodo ◽  
Bayu Rahayudi
Keyword(s):  
Real Time ◽  
Model Parameters ◽  
Memory Usage ◽  
Detection Capability ◽  
X Ray ◽  
Robust Detection ◽  
Limited Memory ◽  
Large Memory ◽  
Deep Cnn ◽  
Fold Cross Validation

<p><span lang="EN-GB">Deteksi Covid-19 merupakan tahapan penting untuk mengenali secara dini pasien terduga Covid-19 sehingga dapat dilakukan langkah lanjutan. Salah satu cara pendeteksian adalah melalui citra sinar-x paru. Namun demikian, selain dibutuhkan suatu model algoritma yang dapat menghasilkan akurasi tinggi, komputasi yang ringan merupakan hal yang dibutuhkan sehingga dapat diaplikasikan dalam alat pendeteksi. Model deep CNN dapat melakukan deteksi dengan akurat namun cenderung memerlukan penggunaan memori yang besar. CNN dengan parameter yang lebih sedikit dapat menghemat <em>storage </em></span><span lang="EN-GB">maupun penggunaan memori sehingga dapat berproses secara real time baik berupa alat pendeteksi maupun sistem pengambilan keputusan via <em>cloud</em>. Selain itu, CNN dengan parameter yang lebih kecil juga dapat untuk diaplikasikan pada FPGA dan perangkat keras lainnya yang mempunyai kapasitas memori terbatas. Untuk menghasilkan deteksi COVID-19 pada citra sinar-x paru yang akurat namun komputasinya juga ringan, kami mengusulkan arsitektur CNN kecil namun handal </span><span lang="EN-GB">dengan menggunakan teknik pertukaran <em>channel</em> yang disebut ShuffleNet. Dalam penelitian ini, kami menguji dan membandingkan kemampuan ShuffleNet, EfficientNet, dan ResNet50 karena mempunyai jumlah parameter yang lebih kecil dibanding CNN pada umumnya seperti VGGNet atau FullConv yang menggunakan lapisan konvolusi secara penuh namun mempunyai kemampuan deteksi yang mumpuni. Kami menggunakan 1125 citra sinar-x dan mencapai akurasi 86.93 % dengan jumlah parameter model yang 18.55 kali lebih sedikit dari EfficientNet dan 22.36 kali lebih sedikit dari ResNet50 untuk mendeteksi 3 kategori yaitu Covid-19, Pneumonia, dan normal melalui uji 5-<em>fold crossvalidation</em>. Memori yang diperlukan oleh masing-masing arsitektur CNN tersebut untuk melakukan sekali deteksi berhubungan secara linier dengan jumlah parameternya dimana ShuffleNet hanya memerlukan memori GPU sebesar 0.646 GB atau 0.43 kali dari ResNet50,  0.2 kali dari EfficientNet, dan 0.53 kali dari FullConv. Lebih lanjut, ShuffleNet melakukan deteksi paling cepat yaitu sebesar 0.0027 detik.</span></p><p><span lang="EN-GB"><br /></span></p><p><em><strong><span lang="EN-GB">Abstract</span></strong></em></p><p><em>Covid-19 detection is an important step in identifying early patients with suspected Covid-19 so that further steps can be taken. One way of detection is through pulmonary x-ray images. However, besides requiring an algorithm model that can produce high accuracy, lightweight computation is needed so that it can be applied in a detector. The deep CNN model can detect accurately but tends to require large memory usage. CNN with fewer parameters can save storage and memory usage so that it can process in real time both in the form of detection devices and decision-making systems via the cloud. In addition, CNN with smaller parameters can also be applied to FPGA and other hardware that have limited memory capacity. To produce accurate COVID-19 detection on x-ray images with lightweight computation, we propose a small but reliable CNN architecture using a channel shuffle technique called ShuffleNet. In this study, we tested and compared the capabilities of ShuffleNet, EfficientNet, and ResNet because they have a smaller number of parameters than usual deep CNN, such as VGGNet or FullConv which uses a full convolution layers with a robust detection capability. We used 1125 x-ray images and achieved an accuracy of 86.93% with a number of model parameters of 18.55 times less than EfficientNet and 22.36 times less than ResNet50 to detect 3 categories namely Covid-19, Pneumonia, and normal through the 5-fold cross validation. The memory required by each CNN architecture to perform one detection is linearly related to the number of parameters where ShuffleNet only requires GPU memory of 0.646 GB or 0.43 times that of ResNet50, 0.2 times of EfficientNet, and 0.53 times of FullConv. Furthermore, ShuffleNet performs the fastest detection at 0.0027 seconds. </em></p><p><em><strong><span lang="EN-GB"><br /></span></strong></em></p>

Proposal of Contour Line Model for High-Speed End Milling Simulation

2020 ◽  
Vol 14 (1) ◽  
pp. 38-45 ◽  
Author(s):  
Isamu Nishida ◽  
◽  
Keiichi Shirase
Keyword(s):  
Cutting Force ◽  
Cross Sections ◽  
High Speed ◽  
End Milling ◽  
Cutting Edge ◽  
Contour Line ◽  
Memory Usage ◽  
Line Model ◽  
Milling Simulation ◽  
Contour Lines

A contour line model for end milling simulation, which realizes high-speed arithmetic processing by reducing memory usage, is proposed. In this model, a 3-dimensional shape can be expressed by superimposing the contour lines of the cross-sections obtained by dividing the workpiece along any axial direction. Therefore, the memory usage is reduced compared to a Z-map model or a voxel model as the interior information of the object can be eliminated. The contour line model can also be applied to complicated shapes having overhangs. Furthermore, cutting volume can be calculated from the interference area enclosed by two contour lines of the workpiece and the tool cross-sections. The workpiece shape can be changed by eliminating the interference area. In the contour line model, cutting force can also be predicted with an instantaneous rigid force model using the uncut chip thickness for each cutting edge from the positional relationship between the interference area and the cutting edge. To validate the proposed model, cutting experiments were conducted, which confirmed that the predicted machining shape had good agreement with the actual machined shape. Furthermore, it was confirmed that the cutting force can be predicted accurately.

scholarly journals A CRC-Based Classifier Micro-Engine for Efficient Flow Processing in SDN-Based Internet of Things

2020 ◽  
Vol 2020 ◽  
pp. 1-8 ◽  
Author(s):  
Mahdi Abbasi ◽  
Navid Mousavi ◽  
Milad Rafiee ◽  
Mohammad R. Khosravi ◽  
Varun G. Menon
Keyword(s):  
Internet Of Things ◽  
High Speed ◽  
Packet Classification ◽  
Classification Systems ◽  
Mobile Systems ◽  
Memory Usage ◽  
High Speed Flow ◽  
Network Systems ◽  
Speed Flow ◽  
Network Speed

In the Internet of things (IoT), network devices and mobile systems should exchange a considerable amount of data with negligible delays. For this purpose, the community has used the software-defined networking (SDN), which has provided high-speed flow-based communication mechanisms. To satisfy the requirements of SDN in the classification of communicated packets, high-throughput packet classification systems are needed. A hardware-based method of Internet packet classification that could be simultaneously high-speed and memory-aware has been proved to be able to fill the gap between the network speed and the processing speed of the systems on the network in traffics higher than 100 Gbps. The current architectures, however, have not been successful in achieving these two goals. This paper proposes the architecture of a processing micro-core for packet classification in high-speed, flow-based network systems. By using the hashing technique, this classifying micro-core fixes the length of the rules field. As a result, with a combination of SRAM and BRAM memory cells and implementation of two ports on Virtex®6 FPGAs, the memory usage of 14.5 bytes per rule and a throughput of 324 Mpps were achieved in our experiments. Also, the performance per memory of the proposed design is the highest as compared to its major counterparts and is able to simultaneously meet the speed and memory-usage criteria.

scholarly journals Music Identification System Using MPEG-7 Audio Signature Descriptors

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Shingchern D. You ◽  
Wei-Hwa Chen ◽  
Woei-Kae Chen
Keyword(s):  
High Speed ◽  
High Accuracy ◽  
The Internet ◽  
Identification System ◽  
Decision Threshold ◽  
Low Resolution ◽  
Full Resolution ◽  
Music Identification ◽  
Simulation Results

This paper describes a multiresolution system based on MPEG-7 audio signature descriptors for music identification. Such an identification system may be used to detect illegally copied music circulated over the Internet. In the proposed system, low-resolution descriptors are used to search likely candidates, and then full-resolution descriptors are used to identify the unknown (query) audio. With this arrangement, the proposed system achieves both high speed and high accuracy. To deal with the problem that a piece of query audio may not be inside the system’s database, we suggest two different methods to find the decision threshold. Simulation results show that the proposed method II can achieve an accuracy of 99.4% for query inputs both inside and outside the database. Overall, it is highly possible to use the proposed system for copyright control.

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