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A simple and efficient automated diagnostic model for Schizophrenia detection based on Hjorth complexity

A simple and efficient automated diagnostic model for Schizo...

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International conference on Multimedia, Signal Processing and Communication Technologies, (IMPACT)

作者： Nausheen Ansari Yusuf Khan Omar Farooq Centre of Interdisciplinary Biomedical and Human Factor Engg. Aligarh Muslim University Aligarh India

ISBN: (纸本)9781665476485

Schizophrenia is a type of psychiatric disorder characterized by distortion of thoughts, speech and actions. Automatic detection of schizophrenia via Electroencephalography (EEG) has been a widely explored area of research. this work is based on developing a simple as well as an accurate model for automatic diagnosis of schizophrenia by analyzing the EEG patterns. Multivariate EEG data, collected by M.V. Lomonosov Moscow State University, is utilized. A sliding window of 0.5 s duration with 50% overlap is applied to extract Hjorth parameters (Activity, Mobility, complexity) from EEG of both the normal controls and those with schizophrenic symptoms. In addition, feature dimension reduction is applied to further optimize the model performance using minimum redundancy maximum relevance algorithm. this model performed better than the current state-of-the-art techniques by achieving an overall accuracy of 98.82% using Quadratic Discriminant Analysis (QDA) classifier.

关键词： Dimensionality reduction Analytical models Mental disorders Redundancy Signal processing algorithms Brain modeling Electroencephalography

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A Sequence Recommendation Algorithm Based on GCN Network and Self-Attention Mechanism

A Sequence Recommendation Algorithm Based on GCN Network and...

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Intelligent Computing and Human-Computer Interaction (ICHCI), International conference on

作者： Yuyuan Sun Tongyan Li Jiyong He Chengdu University of Information Technology Chengdu China hengdu University of Information Technology Chengdu China

ISBN: (数字)9798350368284

ISBN: (纸本)9798350368291

Traditional recommendation systems primarily rely on collaborative filtering, which struggles to effectively capture the dynamic changes in user interests. Sequential recommendation addresses this limitation by considering both user interests and social relationships to enhance recommendation outcomes. this paper proposes a sequential recommendation algorithm based on Graph Convolutional Networks (GCN) and self-attention mechanisms, termed GSRec. the algorithm utilizes GCN to extract features from both trusted and untrusted users, integrating these features with item features. Subsequently, self-attention mechanisms are employed to construct dynamic user interest features, taking into account the positive influence of trusted users and the negative influence of untrusted users. Finally, the dynamic user interest features are combined with the influence of social relationships to generate the user's future interest ***, the input to the GCN includes a feature matrix and an adjacency matrix. For trusted users, the feature matrix consists of the trust levels of the top n trusted users, and the adjacency matrix represents the trust relationships among these users. For untrusted users, the feature matrix and adjacency matrix incorporate the trust and distrust levels of the top n untrusted users. Experimental results on the Epinions dataset validate the accuracy of GSRec, demonstrating its effectiveness in improving recommendation efficiency.

关键词： Human computer interaction Accuracy Graph convolutional networks Heuristic algorithms Collaborative filtering Termination of employment Feature extraction Vectors Time complexity Recommender systems

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Student Behavior Detection Method Based on Improved YOLOv5 Algorithm

Student Behavior Detection Method Based on Improved YOLOv5 A...

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Computer Vision, Image and Deep Learning (CVIDL), International conference on

作者： Haitao Hu Meihong Yang Ying Guo Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350373820

ISBN: (纸本)9798350373837

When using computer vision technology to identify students in the classroom, it will be affected by the inconsistency of the seating layout in different classrooms, the complexity of the background, as well as the existence of back row students with smaller portraits, mutual occlusion and other problems resulting in poor recognition of student behavior and other problems. In order to solve this problem, this paper firstly takes the real classroom video of a university, summarizes five common student behaviors, intercepts the video frame by frame, labels the data, and constructs the dataset, and then proposes a target detection technique based on the improved YOLOv5 algorithm, which improves the YOLOv5 algorithm by replacing the SPP module in the original network with the BasicRFB module and introducing the SACA attention mechanism. algorithm, which enhances the feature extraction capability of the model and ensures the detection speed at the same time; in the post-processing stage of the model, Soft-NMS is used to replace the original NMS non-maximal value suppression method, which reduces the probability of the model leakage detection. Finally, through the ablation experiment, it is verified that the improved method proposed in this paper has better detection effect for real classroom scenarios, which can help teachers grasp the learning status of students in time and optimize the teaching methods accordingly, and at the same time, it provides solid support for the dynamic evaluation of classroom teaching.

关键词： YOLO Computer vision Attention mechanisms Accuracy Heuristic algorithms Education Layout

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Resource-Constrained Image Processing for Scientific CubeSats 6

Resource-Constrained Image Processing for Scientific CubeSat...

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6th International conference on algorithms, Computing and Systems, ICACS 2022

作者： Kozaris, Ioannis Gioulekas, Fotios Katsaros, Panagiotis School of Informatics Aristotle University of Thessaloniki Thessaloniki Greece Directorate of Informatics 5th Regional Health Authority Larissa Greece

ISBN: (纸本)9781450397407

CubeSat systems are preferred for short-term space missions, like those with a scientific objective, since they make it possible to complete them in an affordable cost. On the other hand, the resource-constrained nature of such systems poses significant challenges in software design, for the system payload. We focus on image compression and processing for scientific space missions, a vital functionality, due to the limited onboard storage capacity and the infrequent time periods, in which a satellite in orbit can send images to the ground, through a limited bandwidth connection. For CubeSat systems, the performance of image processing depends - even more than other space systems - on trade-offs, which are influenced by size, power and complexity constraints. In this context, we present the design challenges and the image compression/processing solution developed, for a university CubeSat built to carry on a biological experiment. All changes of the performance capability in the different parts of the payload image data chain (image resolution, onboard storage capacity, communication channel bandwidth and error characteristics) are taken into account. Moreover, any decision that increases the risk of incorrect reception of images from the experiment is weighted against the benefits of improved performance. We provide experimental results that show how our image processing solution resolves the associated trade-offs, while fulfilling the mission requirements. © 2022 ACM.

关键词： Image compression

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Interleave-Division Multiple Access Systems with Invert Tree Based Interleavers with Unequal Power Sharing Algorithm 5

Interleave-Division Multiple Access Systems with Invert Tree...

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5th International conference on Inventive Computation Technologies (ICICT)

作者： Patel, Arpita Charotar Univ Sci & Technol CHARUSAT Dept E&C Engn Changa Gujarat India

ISBN: (纸本)9781728146850

Power allocation is a major fear for practically coded Interleave division multiple access scheme. Various power allocation algorithms have been proposed for IDMA system. In this paper, the performance improvement of IDMA system using unequal power algorithm with comparison of various interleavers with coded and uncoded IDMA is presented. the simulation result shows that IDMA system gives better performance with invert tree based interleaver (ITBI) in comparison with other interleaver in terms of memory and complexity.

关键词： unequal power uncoded and coded IDMA memory and complexity

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Enhanced Deep Learning for Massive MIMO Detection Using Approximate Matrix Inversion

Enhanced Deep Learning for Massive MIMO Detection Using Appr...

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International conference on Communications, Signal Processing, and their Applications (ICCSPA)

作者： Ali J. Almasadeh Khawla A. Alnajjar Mahmoud A. Albreem Department of Electrical Engineering University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9781665482387

Massive multiple-input multiple-output (MIMO) is a crucial technology in fifth-generation (5G) and beyond 5G (B5G). However, the huge number of antennas used in massive MIMO systems causes a high computational complexity during signal detection. In this paper, we propose an efficient massive MIMO detection technique which is based on approximate matrix inversion methods and deep learning to enhance the system performance while keeping computational complexity low. three approximate methods which are Gauss–Seidel (GS), successive over-relaxation (SOR), and conjugate gradient (CG) are exploited for the initialization of a modified version of the MM network (MMNet) algorithm. the performance of the proposed technique is validated under both Gaussian and realistic channel scenarios, i.e., Quadriga channels models. Simulation results show that the proposed technique outperforms MMNet, minimum mean square estimation (MMSE), detection network (DetNet), and orthogonal approximate message passing deep net (OAMP-Net) in terms of symbol error rate (SER) during offline training. It also provides a significant SER improvement of up to 87% when compared to MMNet in the online training scenario.

关键词： Training Deep learning 5G mobile communication System performance Signal processing algorithms Symbols Massive MIMO

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On generic complexity of the problem of searching of isomorphism for finite semigroups 5

On generic complexity of the problem of searching of isomorp...

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5th International Scientific and Technical conference on Mechanical Science and Technology Update, MSTU 2021

作者： Rybalov, Alexander Sobolev Institute of Mathematics prospekt Koptyuga 4 Novosibirsk630090 Russia

this article is devoted to the investigation of generic complexity of the problem of searching of isomorphism for finite semigroups. that problem can be formulated as follows. For any pair of isomorphic finite semigroup Si and S2 with elements {1, 2,..., n}, represented by its multiplication tables, we need to find a bijection (permutation) of the set {1, 2,..., n}, which implements an isomorphism between S1 and S2. As for the classical isomorphism problem, any polynomial (even probabilistic) algorithms are unknown for the problem of searching of isomorphism for finite semigroups. We construct natural subproblems of this problem, for which there is no polynomial generic algorithm, provided that this problem is hard in the classical case. the study was carried out within the framework of the state contract of the Sobolev Institute of Mathematics (project no. 0314-2019-0004). © Published under licence by IOP Publishing Ltd.

关键词： Computer programming

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A Low-complexity 2D-DOA Estimation Algorithm with Massive URA 5

A Low-Complexity 2D-DOA Estimation Algorithm with Massive UR...

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5th Annual International conference on Information System and Artificial Intelligence (ISAI)

作者： Lu, Zhou Li, Baobao Lai, Xin Zeng, Haowei Nanjing Univ Aeronaut & Astronaut Coll Elect Informat Engn Nanjing 211106 Peoples R China Nanjing Univ Aeronaut & Astronaut Minist Ind & Informat Technol Key Lab Dynam Cognit Syst Electromagnet Spectrum Nanjing 211100 Peoples R China

Conventional Direction of Arrival (DOA) estimation algorithms, such as Multiple Signal Classification (MUSIC), Root-MUSIC, and Estimating Signal Parameters via Rotational Invariance Techniques (ESPRIT) algorithms, are difficult to apply to large-scale antenna arrays. Because these algorithms not only require a lot of snapshots of the received data, but also the hardware equipment cannot meet the algorithm complexity requirements. this paper presents a low-complexity DOA estimation algorithm for large-scale Uniform Rectangle Array (URA). We first obtain coarse initial DOA estimates via the two-dimensional fast Fourier transform and then search for the accurate estimates within a very small region. the proposed algorithm only needs one snapshot of the received data to obtain the estimation accuracy of the traditional algorithm. the algorithm also has good robustness, and it can estimate DOA accurately even under extremely low signal-to-noise ratio and many sources. theoretical results and numerical simulations are provided to assess the superiority of the proposed algorithm.

关键词： Direction of arrival

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Automatic Reference Counting Implementation and Optimization of the Octave JIT Compiler for High-Performance Computing Platforms

Automatic Reference Counting Implementation and Optimization...

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International conference on Computer Engineering and Applications (ICCEA)

作者： Shengyou Lu Yujie Hu Feipeng Li Dan Huang Chuxuan Zeng School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Computing Network Research and Operation Department Guangdong Unicom Guangzhou China

ISBN: (数字)9798350386776

ISBN: (纸本)9798350386783

Reference counting, a venerable technique, has seen a resurgence owing to recent advancements. this resurgence holds particular significance for dynamic languages, which have historically faced constraints linked to traditional reference counting issues, thus standing to greatly benefit from these advancements. However, implementing reference counting in Just-In-Time (JIT) compilers for dynamic languages presents significant challenges. these challenges originate from two main sources: the intrinsic properties of dynamic languages and the application requirements of high-performance computing (HPC) platforms. Taking Octave, a dynamic language celebrated for its matrix computation capabilities, as a case study, we delved into and refined the memory management mechanisms of its JIT compiler. By resolving the correctness issues in these mechanisms, we shed light on the complexities surrounding the integration of automatic reference counting (ARC) algorithms from static to dynamic languages. Moreover, within the realm of HPC platforms, we emphasize the critical role of timely garbage collection for dynamic languages. Consequently, we introduce an ARC algorithm tailor-made for HPC environments, demonstrating through experiments that this algorithm effectively enhances memory management, curtails memory utilization, expedites program execution, and significantly mitigates excessive memory access across Non-Uniform Memory Access (NUMA) nodes. Our findings unveil novel strategies for memory management and JIT compilation in dynamic languages, underscoring ARC's potential to streamline complex data structure management, ease the burden on compiler developers, and boost the execution speed of dynamic languages.

关键词： Productivity Scientific computing Heuristic algorithms High performance computing Memory management Aerodynamics complexity theory

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Pareto front optimization for enhanced model prediction accuracy

Pareto front optimization for enhanced model prediction accu...

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conference on DTCO and Computational Patterning

作者： Fryer, David Moskalenko, Ignat Wu, Tung-Yu Chiang, Meng-Shiun Liao, Chun Yen Lin, Tsung-Wei Wu, Chun-Sheng Huang, Chao-Yi Lo, Luke Fang, Xiang Fenger, Germain Shahidi, Farruh Siemens Digital Ind Software 8005 SW Boeckman Rd Wilsonville OR 97070 USA Siemens Digital Ind Software Shabolovka St Bldg 10Campus 2 Moscow 119049 Russia Winbond Elect Corp CTSP Site 8Keya 1st RdCent Taiwan Sci Pk Taichung 42881 Taiwan Siemens Digital Ind Software 120Sec 2Gongdao 5th Rd Hsinchu 30072 Taiwan

ISBN: (纸本)9781510649804;9781510649798

A multi-objective optimization flow is developed to identify balanced compact optical proximity correction (OPC) models with ideal calibration accuracy, runtime performance and prediction accuracy. We demonstrate a model selection process based on Pareto front optimization to meet multiple modeling requirements in a single optimization step. A genetic search algorithm determines the final population that offers the best trade-off in set model properties. As a demonstration, we co-optimize calibration accuracy, verification accuracy and term count in a mode developed for hot spot prediction for a line and space memory layer. the optimization determines the minimum number of model terms to meet the off-nominal dose and focus patterning accuracy requirements in verification. Multi-objective optimization provides better verification process window condition (PWC) accuracy because of the multi-objective trade-off built into the genetic algorithm (GA). the optimizer also provides better calibration accuracy (Rms Weighted) than compact models with a fixed configuration because model composition is optimized during GA search. the resulting champion model is 30% more predictive and 5% faster in simulation using this approach. Results for a negative tone develop hole layer with a model complexity of up to 44 terms are also analyzed based on nominal only measurement data. We further show the models selected by multi-objective optimization have a lesser tendency to over- fit the calibration data. the methodology can be applied to streamline complex models for optimum performance and target error rate. In many cases, for smaller data sets, we show that simplified models provide improved verification accuracy within metrology error limits.

关键词： Optimization (mathematics) Calibration Data modeling Binary data Optical proximity correction Performance modeling Genetic algorithms Process modeling Computer simulations Precision calibration

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