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International Bhurban conference on Applied Sciences & Technology, IBCAST

作者： Muhammad Farooq Muhammad Haris Bin Amir Usama Abdullah Ejaz Raza Ali Adeel Mumtaz Department of Computer Science National University of Science and Technology Department of Control and Signal Processing Center of Excellence in Science and Applied Technologies Islamabad Pakistan

ISBN: (数字)9798350308259

ISBN: (纸本)9798350308266

Autism spectrum disorder(ASD) poses challenges in recognizing and socializing with others very challenging. Early detection is crucial for improved outcomes in individuals with ASD. In this research we present a novel approach for autism classification using facial images. Our approach utilizes the GAN based super-resolution techniques in conjunction with a Pretrained ResNet18 and vision transformer based embedding by combining their features. Our study focuses on enhancing image quality through super resolution, enabling finer details to be captured for mode accurate analysis. CNN based ResNet18 captures the local features from image while ViT, attention based network captures more global details. By combining the features of both networks our propose model performs robust and discriminative feature extraction. The integration of super-resolution techniques and fine-tuned deep learning models provides a comprehensive framework for early detection of autism in children. To correctly detect the autism disorder, more robust ResNet18 features and vision transform based embedding are extracted from enhanced image details. The enhanced image quality aids in capturing subtle facial expressions, critical for accurate classification. Our findings demonstrate the effectiveness of our proposed model in achieving notable improvements in accuracy and reliability with test accuracy of 91% and AUC of 96.42%. This research contributes to the growing body of knowledge in autism classification, highlighting the potential of advanced computer vision techniques and deep learning algorithms. By enabling early detection, out approach provides a positive impact on the lives of children with ASD, facilitating timely interventions and support for better outcomes and increased independence.

关键词： Image quality Deep learning Autism Computer vision Accuracy Superresolution Feature extraction Transformers Generative adversarial networks Image enhancement

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A Tool for the Statistical Investigation of the Automotive Radar Interference

A Tool for the Statistical Investigation of the Automotive R...

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URSI Atlantic Radio Science conference (URSI AT-RASC)

作者： Alessandro Bazzi Francesco Miccoli Zhuofei Wu Fabrizio Cuccoli Vincent Martinez DEI Università di Bologna Bologna Italy National Laboratory of Wireless Communications (WiLab) CNIT Bologna Italy College of Computer Science and Technology Harbin Engineering University Harbin China National Laboratory of Radar and Surveillance Systems (RaSS) CNIT Pisa Italy NXP Semiconductors Toulouse France

ISBN: (数字)9789463968102

ISBN: (纸本)9798350360257

Vehicular radars, which play a key role within advanced driver-assistance systems (ADAS), sense the environment and provide distance and speed estimations about nearby vehicles and objects. However, the increasing penetration rate of such radar systems and the typically very open use of the radar bands is raising the possibility that radars’ performance is vastly degraded because of the interference from the neighboring vehicles. The risk is therefore that applications failures became unacceptably frequent and features like adaptive cruise-control or autopilot are often temporarily disabled. Driven by ongoing discussions on automotive radar spectrum expansion to other bands, we are evaluating the opportunity to exploit the 140 GHz band, and the use of orthogonal frequency division multiplexing (OFDM) in addition to frequency modulated continuous waveform (FMCW). It is therefore of utmost importance to analyze the impact of the expected interference on radar devices in the scenarios of interest and study possible countermeasures. In this work, we describe a tool to derive, in realistic scenarios such as highways or urban areas, the number and characteristics (such as direction and path length) of potentially interfering signals, which can be then used to evaluate the impact of the interference and the effectiveness of mitigation solutions. Results obtained in a dense highway scenario show that with 100% penetration, the signals transmitted will often enter the field of view (FOV) of the other sensors, either directly or after a single reflection.

关键词： Road transportation OFDM Prevention and mitigation Urban areas signal processing algorithms Interference Radar

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Development of BCI-Driven Robotic Hand Control System: A Preliminary Study

Development of BCI-Driven Robotic Hand Control System: A Pre...

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Control Technology and Applications (CCTA),

作者： Rohit Gupta Bijuni Charan Sutar Amit Bhongade Tapan K. Gandhi Department of of Biomedical Engineering SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Electrical Engineering Indian Institute of Technology Delhi New Delhi India Department of Electrical Engineering Indian Institute of Technology Delhi New Delhi India

ISBN: (数字)9798350370942

ISBN: (纸本)9798350370959

The sophisticated Brain-Computer Interface (BCI) utilizes discrete or model-based control techniques to manage external devices. A continuous control strategy is vital for achieving optimal and smooth operation. Achieving a continuous estimation of control parameters with minimal latency is crucial for enhancing the effectiveness and acceptance of mind-controlled prostheses, exoskeletons, and robotic arms by users. This research proposes a novel BCI model for controlling a robotic hand, incorporating Task Classification (TC) and Trajectory Estimation (TE) modules. The TC module interprets user intentions based on EEG signals, identifying tasks such as initiating, grasping, releasing, halting, or maintaining the current state. Upon detecting grasping (Task 2), the TE module estimates the 3D trajectory of hand movements, enabling precise control of the robotic hand. The control algorithm translates the estimated trajectory into commands for the robotic hand, facilitating object manipulation and task execution. Real-time feedback loops and error-handling mechanisms enhance user interaction and system robustness. Results demonstrate the efficacy of the integrated TC and TE modules in providing seamless control of the robotic hand, empowering individuals with motor disabilities to perform daily tasks independently. The implications of this work extend to the development of advanced BCIs, facilitating more natural and seamless communication between the human brain and technological devices.

关键词： Accuracy Robot kinematics Estimation signal processing algorithms Grasping Control systems Brain modeling

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Online Fault Diagnosis System for Electric Powertrains using advanced signal processing and Machine Learning

Online Fault Diagnosis System for Electric Powertrains using...

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International conference on Electrical Machines

作者： Jagath Sri Lal Senanayaka Huynh Van Khang Kjell G. Robbersmyr Department of Engineering Sciences University of Agder Grimstad Norway

Online condition monitoring and fault diagnosis systems are necessary to prevent unexpected downtimes in critical electric powertrains. The machine learning algorithms provide a better way to diagnose faults in complex cases, such as mixed faults and/or in variable speed conditions. Most of studies focus on training phases of the machine learning algorithms, but the development of the trained machine learning algorithms for an online diagnosis system is not detailed. In this study, a complete procedure of training and implementation of an online fault diagnosis system is presented and discussed. Aspects of the development of an online fault diagnosis based on machine learning algorithms are introduced. A developed fault diagnosis system based on the presented procedure is implemented on an in-house test setup and the reliably detected results suggest that such a system can be widely used to predict multiple faults in the power drivetrains under variable speeds online.

关键词： Online fault diagnosis Convolutional neural network Electrical fault detection Induction motors Electromechanical systems Gears Bearings

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Performance of multiple space-time coded MIMO in spatially correlated channels

Performance of multiple space-time coded MIMO in spatially c...

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IEEE conference on Wireless Communications and Networking

作者： Jung-Tao Liu Wireless Advanced Technology Lab Bell Laboratories Lucent Technologies Inc. Whippany NJ USA

In Jung-Tao Liu (November 2002), it is shown that multiple space-time coded streams can be transmitted simultaneously by labeling the transmit antennas into different transmission groups. Space-time codes are applied to the antennas within the same groups. A receiver structure using array processing and maximum likelihood (ML) detector was proposed. A multi-code wideband CDMA downlink high-speed packet data channel was simulated assuming the Alamouti code (S.M. Alamouti, October 1998) on all antenna groups. The results show performance gains over the VBLAST at data rates above 10 Mbps for both MMSE and zero-forcing optimized array processors. In this work, we further study the performance of the same scheme in the spatially correlated channels. Performance analysis shows that due to the built-in diversity of the space-time codes, the proposed scheme is robust even in the highly spatially correlated environments. Simulation results are provided only for MMSE under channels with different correlations. It is shown that when transmitting at 14.4 Mbps and at one percent packet error rate, the proposed scheme is 1.5dB worse than the full-blown maximum-likelihood detector in an i.i.d. (independent identically distributed) flat fading channel, and is about 5dB worse in a channel with medium-to-high correlations; while the VBLAST is about 10 dB worse than ML in i.i.d. and is about 17dB and at least 27dB worse than the ML in channels with medium and high correlations, respectively. The proposed scheme strikes a good balance between the complexity and the performance. It should be considered whenever the layered space-time architectures become too cumbersome to implement. Due to its robustness under correlations, it is also recommended for systems that cannot meet the i.i.d. fading assumption that has been imposed by many multi-input, multi-output transceiver algorithms.

关键词： MIMO Maximum likelihood detection Space time codes Detectors Robustness Fading Labeling Transmitting antennas Array signal processing Wideband

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Improved Decision Directed Carrier Phase Estimation for Nonlinearity Mitigation in 16-QAM Systems

Improved Decision Directed Carrier Phase Estimation for Nonl...

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International conference on Transparent Optical Networks

作者： Lalitha Pakala Bernhard Schmauss Institute of Microwaves and Photonics (LHFT) Erlangen Graduate School for Advanced Optical Technologies (SAOT) Fiber Optic Transmission and Sensing (FiTS) University of Erlangen-Nuremberg Erlangen Germany

ISBN: (纸本)9781479956029

We propose and numerically investigate an improved decision directed (IDD) carrier phase estimation (CPE) to mitigate fiber nonlinearity in 16 quadrature amplitude modulation (QAM) coherent transmission systems. The proposed IDD-CPE algorithm is derived based on a two stage approach and compared with other CPE algorithms namely, the pre-decision directed (PDD) CPE and the decision directed (DD) phase locked loop (PLL). By exploiting the symbol position information in the I-Q plane and a euclidean distance measure, the proposed IDD-CPE algorithm outperforms the PDD-CPE and DD-PLL methods, exhibiting an optimal performance at signal launch power of 0 dBm and at optical signal to noise ratios (OSNRs) greater than 28dB, where no bit errors are detected.

关键词： Carrier phase estimation digital signal processing nonlinear mitigation coherent receivers 16-QAM decision directed

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CS Based Wall Ringing and Reverberation Mitigation for Through-the-Wall Radar Imaging

CS Based Wall Ringing and Reverberation Mitigation for Throu...

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IEEE Radar conference

作者： Michael Leigsnering Fauzia Ahmad Moeness G. Amin Abdelhak M. Zoubir Signal Processing Group Institute of Telecommunications Technische Universitat Darmstadt Radar Imaging Laboratory Center for Advanced Communications Villanova University

ISBN: (纸本)9781467357920

Wall ringing and reverberation pose a challenging problem for through-the-wall radar image formation algorithms. We propose a novel method based on compressive sensing (CS) that enables high reconstruction quality combined with efficient data collection. A joint wall reverberation and target model is developed and the reconstructed scene is obtained based on a group sparse CS approach. Thus, the wall returns can be separated from the target returns resulting in a clean image. We demonstrate the performance using simulation results.

关键词： Image reconstruction Reverberation Radar imaging Arrays Compressed sensing Imaging Joints

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Fourier ptychographic microscopy based on Gaussian apodization coherent transfer function constraints 3

Fourier ptychographic microscopy based on Gaussian apodizati...

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3rd International Computing Imaging conference, CITA 2023

作者： Cheng, Hong Li, Ju Liu, Hong Zhang, Hongyi Li, Yang School of Electronic and Information Engineering Anhui University Anhui Hefei230601 China Key Laboratory of Intelligent Computing & Signal Processing Anhui University Anhui Hefei230601 China

ISBN: (纸本)9781510671409

Fourier ptychographic microscopy technique combines the concept of phase retrieval algorithm and synthetic aperture, which can solve the problem that large field of view and high resolution cannot coexist. However, describing the linear transfer process of the imaging system by coherent transfer function in conventional calculations leads to ringing artifacts. Gaussian apodization coherent transfer function constraints is proposed and embedded into the software platform, and a high-resolution Fourier ptychographic microscopy imaging system is constructed together with the designed hardware. The proposed algorithm can reduce the ringing artifacts and ensure the accuracy and resolution of the reconstructed results by simulating experiments and acquiring real images. © 2023 SPIE.

关键词： Imaging systems

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Application of signal processing and machine learning techniques for segmentation and spatial registration of material property data

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AIP conference Proceedings 2019年第1期2102卷

作者： Josiah Dierken Daniel Sparkman Sean Donegan Sarah Wallentine John Wertz David Zainey 2University of Dayton Research Institute Dayton OH 45469 USA 1Air Force Research Laboratory WPAFB OH 45433 USA

Errors introduced into data acquired for nondestructive evaluation due to suboptimal digitization rates, bandwidth, and signal processing settings can dominate the perceived noise in acquired data, leading to artifacts and erroneous interpretation. Furthermore, the presence of such errors incurred through the data acquisition process can also inhibit post-processing techniques utilized in multimodal data segmentation and registration efforts. This study illustrates the use of advanced signal processing techniques to limit the effects of quantization errors in normal-incidence ultrasonic inspection data, thereby optimizing the signals for further processing while maintaining the integrity of the data. In conjunction with signal processing methods, K-means and Expectation-Maximization algorithms are investigated for applications in automated data segmentation and multimodal spatial registration. Using results from segmented and registered data, techniques in constructing computer aided design (CAD) models are investigated for importing measured material property and flaw information into various modeling software platforms.

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Design and Implementation of an advanced Radar signal Processor with Waveform Generator and BIST Unit on a Single FPGA Chip

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IOP conference Series: Materials Science and Engineering 2019年第1期610卷

作者： Ahmed M. Abdel Razek Fathy M. Ahmed Hazem Kamel Wael Mahfouz K. H. Moustafa Radar Department Military Technical College Cairo Egypt

Field Programmable Gate Arrays (FPGAs) are best suited for signal processing applications that require real-time processing. Therefore, it is preferred over DSP processors in implementing radar receivers that processes incoming continuous stream of data. In the past, implementing complex arithmetic operations in floating point representation was a monopoly on DSP processors and the designers had to work around the sequential nature of the DSP processor to make it suitable for real-time applications by using buffered and multi-clocking designs. But nowadays, the great advances in FPGA design technology minimized this design effort since it is capable of performing these complex algorithms in real time. This paper represents the design and implementation of an advanced radar signal processor for binary phase-coded pulsed radar incorporating a time range side lobes suppression technique on a single FPGA chip. The proposed hardware design includes a waveform generator, an advanced signal processing unit, and a built-in self-test (BIST). Design aspects and hardware details for each part is introduced thoroughly.

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