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Electron beam lithography on nonplanar and irregular surfaces

Microsystems & Nanoengineering 2024年第2期10卷 1-23页

作者： Chenxu Zhu Huseyin Ekinci Aixi Pan Bo Cui Xiaoli Zhu Department of Electrical and Computer Engineering and Waterloo Institute for Nanotechnology(WIN) University of WaterlooWaterlooONCanada

E-beam lithography is a powerful tool for generating nanostructures and fabricating nanodevices with fine features approaching a few nanometers in ***,alternative approaches to conventional spin coating and development processes are required to optimize the lithography procedure on irregular *** this review,we summarize the state of the art in nanofabrication on irregular substrates using e-beam *** overcome these challenges,unconventional methods have been *** instance,polymeric and nonpolymeric materials can be sprayed or evaporated to form uniform layers of electron-sensitive materials on irregular ***,chemical bonds can be applied to help form polymer brushes or self-assembled monolayers on these *** addition,thermal oxides can serve as resists,as the etching rate in solution changes after e-beam ***,e-beam lithography tools can be combined with cryostages,evaporation systems,and metal deposition chambers for sample development and lift-off while maintaining low *** nanopyramids can be fabricated on an AFM tip by utilizing ice as a positive ***,Ti/Au caps can be patterned around a carbon ***,3D nanostructures can be formed on irregular surfaces by exposing layers of anisole on organic ice surfaces with a focused *** advances in e-beam lithography on irregular substrates,including uniform film coating,instrumentation improvement,and new pattern transferring method development,substantially extend its capabilities in the fabrication and application of nanoscale structures.

关键词： lithography beam coating

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Maximally Flat Negative Group Delay Prototype Filter Based on Capped Reciprocal Transfer Function of Classical Bessel Filter

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Progress in Electromagnetics Research B 2025年 110卷 91-105页

作者： Kandic, Miodrag Bridges, Greg E. Department of Electrical and Computer Engineering University of Manitoba WinnipegMB Canada

A prototype filter design exhibiting Negative Group Delay (NGD) is presented, based on the ratio of two low-pass classical Bessel filter transfer functions of the same order, but with different 3 dB-bandwidths. The resulting design is a reciprocal-Bessel filter transfer function, capped at a finite out-of-band gain. The proposed capped reciprocal-Bessel design is based on a similar concept applied to previously reported capped reciprocal-Butterworth and reciprocal-Chebyshev NGD designs, which use ratios of corresponding classical low-pass filter transfer functions. It is shown that within the in-band frequency range, the synthesized NGD transfer function exhibits a maximally flat group delay characteristic (Bessel-like property). Due to its near-flat in-band group delay characteristic, the design is suitable for constant phase shifter applications. For high design orders, it is shown that the achieved NGD-bandwidth product has an upper asymptotic limit, given by the square root of the out-of-band gain in decibels. When the prototype baseband transfer function is translated to a non-zero center frequency, it is demonstrated that resonator-based implementations are feasible via Sallen-Key, as well as all-passive ladder topologies. A combined in-band magnitude/phase distortion metric is evaluated for selected design examples and applied Gaussian and sinc input waveforms, and it is shown to be proportional to the design order and out-of-band gain. The proposed design's distortion metric is also shown to be generally lower than the previously reported capped reciprocal-Butterworth and reciprocal-Chebyshev designs. © (2025), (Electromagnetics Academy). All rights reserved.

关键词： Group delay

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Motion Planning and Control for Improved Ride Comfort in Urban Autonomous Driving

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Journal of Institute of Control, Robotics and Systems 2024年第3期30卷 206-213页

作者： Jang, Kyeongjun Kim, Hakil Department of Electrical and Computer Engineering Inha University Korea Republic of

Self-driving shuttle services frequently provide a less comfortable experience for passengers than human drivers. This observation emphasizes the importance of motion planning and control methods. This paper proposes a method to improve ride comfort in urban autonomous driving. The path information is utilized for velocity planning to minimize lateral acceleration and limiting the acceleration when controlling the PID to avoid rapid deceleration. Additionally, the hybrid lateral controller increases path following performance. The proposed method proved excellent performance compared to the existing geometric lateral control method. A decision tree was employed to evaluate ride comfort to meet ISO standards. The acceleration and jerk were assessed to determine their impact on ride comfort. Furthermore, the lateral error and the heading error were analyzed to evaluate the stability of the route during autonomous driving. An experiment was conducted and verified in the actual urban environment of Songdo New Town, Incheon, to ascertain the efficacy of the proposed method in enhancing ride comfort. © ICROS 2024.

关键词： Decision trees

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Knowledge distillation on spatial-temporal graph convolutional network for traffic prediction

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International Journal of computers and Applications 2025年第1期47卷 45-56页

作者： Izadi, Mohammad Safayani, Mehran Mirzaei, Abdolreza Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran

Efficient real-time traffic prediction is crucial for reducing transportation time. To predict traffic conditions, we employ a spatio-temporal graph neural network (ST-GNN) to model our real-time traffic data as temporal graphs. Despite its capabilities, it often encounters challenges in delivering efficient real-time predictions for real-world traffic data. Recognizing the significance of timely prediction due to the dynamic nature of real-time data, we employ knowledge distillation (KD) as a solution to enhance the execution time of ST-GNNs for traffic prediction. In this paper, We introduce a cost function designed to train a network with fewer parameters (the student) using distilled data from a complex network (the teacher) while maintaining its accuracy close to that of the teacher. We use knowledge distillation, incorporating spatial-temporal correlations from the teacher network to enable the student to learn the complex patterns perceived by the teacher. However, a challenge arises in determining the student network architecture rather than considering it inadvertently. To address this challenge, we propose an algorithm that utilizes the cost function to calculate pruning scores, addressing small network architecture search issues, and jointly fine-tunes the network resulting from each pruning stage using KD. Ultimately, we evaluate our proposed ideas on two real-world datasets, PeMSD7 and PeMSD8. The results indicate that our method can maintain the student's accuracy close to that of the teacher, even with the retention of only (Formula presented.) of network parameters. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Cost functions

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Predicting Distant Metastatic Sites in Cancer Using miRNA and mRNA Expression Data

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Current Bioinformatics 2025年第5期20卷 473-485页

作者： Mamatkarimov, Dostonjon Kang, Jiahui Han, Kyungsook Department of Electrical and Computer Engineering Inha University Incheon Korea Republic of

Background: Cancer patients with metastasis face a much lower survival rate and a higher risk of recurrence than those without metastasis. So far, several learning methods have been proposed to predict cancer metastasis, but most of these methods are intended to predict lymph node metastasis rather than distant metastasis. Distant metastasis is more difficult to predict than lymph node metastasis because distant metastasis is detected after a comprehensive examination of the entire body, and there are not enough publicly available tumor samples with distant metastasis that can be used for training learning methods. Predicting distant metastatic sites is even more challenging than predicting whether distant metastasis will occur or not. Methods: The problem of predicting distant metastatic sites is a multi‐class and multi‐label classification problem;there are more than two classes for distant metastatic sites (bone, brain, liver, lung, and other organs), and a single sample can have multiple labels for multiple metastatic sites. We transformed the multi‐label and multi‐class problem into multiple single‐label binary problems. For each metastatic site, we built a random forest model that deals with binary classification and linked the models along a chain. Results: Testing the model on miRNA and mRNA expression datasets of several cancer types showed a high performance in all performance measures. In the comparison of our model with other methods, our method outperformed the others. Conclusion: We developed a new method for predicting multiple metastatic sites using miRNA and mRNA expression data. The technique will be useful in predicting distant metastatic sites before distant metastasis occurs, which in turn will help clinicians determine treatment options for cancer patients. © 2025 Bentham science Publishers.

关键词： Diseases

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Local Search-Based Anytime Algorithms for Continuous Distributed Constraint Optimization Problems

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 288-290页

作者： Xin Liao Khoi Hoang Xin Luo the College of Computer and Information Science Southwest University the Department of Computer Science and Engineering Washington University in St.Louis IEEE

Dear Editor,The distributed constraint optimization problems(DCOPs) [1]-[3]provide an efficient model for solving the cooperative problems of multi-agent systems, which has been successfully applied to model the real-world problems like the distributed scheduling [4], sensor network management [5], [6], multi-robot coordination [7], and smart grid [8]. However, DCOPs were not well suited to solve the problems with continuous variables and constraint cost in functional form, such as the target tracking sensor orientation [9], the air and ground cooperative surveillance [10], and the sensor network coverage [11].

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Resource Allocation in a RIS-Assisted TDMA Wireless Powered Sensor Network using UAV

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IEEE Communications Letters 2025年第6期29卷 1375-1379页

作者： Ghasemi, Omid Abachian Amirani, Mehdi Chehel Urmia University Electrical and Computer Engineering Department Urmia Iran

This paper studies an RIS-assisted wireless powered sensor network (WPSN) where sensors harvest energy from a UAV and transmit data to the fusion center (FC) using TDMA, aided by an RIS mounted on the UAV. In the proposed model, the wireless power transfer (WPT) time is predetermined as a fraction of the total data transmission time and is excluded from the optimization variables. The goal is to maximize sum throughput by efficiently allocating sensor time and adjusting the phase shifts of the RIS. The resulting non-convex optimization problem is solved by decomposing it into two subproblems: first, determining the optimal phase shifts;and second, developing an algorithm for optimal time allocation using the Lagrange dual method. Excluding the WPT time from the optimization variables enables us to develop an efficient algorithm for time allocation with significantly reduced execution time compared to scenarios where WPT time is also an optimization variable, while maintaining comparable performance. Numerical results demonstrate that the proposed method significantly improves the network's performance. © 1997-2012 IEEE.

关键词： Convex optimization

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Motion Signal-based Recognition of Human Activity from Video Stream Dataset Using Deep Learning Approach

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Recent Advances in computer science and Communications 2024年第3期17卷 77-91页

作者： Yadav, Ram Kumar Arockiam, Daniel Semwal, Vijay Bhaskar Department of Computer Science and Engineering Amity University Madhya Pradesh Gwalior India Department of Computer Science and Engineering MANIT Bhopal India

Background: Human physical activity recognition is challenging in various research eras, such as healthcare, surveillance, senior monitoring, athletics, and rehabilitation. The use of various sensors has attracted outstanding research attention due to the implementation of machine learning and deep learning approaches. Aim: This paper proposes a unique deep learning framework based on motion signals to recognize human activity to handle these constraints and challenges through deep learning (e.g., enhance CNN, LR, RF, DT, KNN, and SVM) approaches. Method: This research article uses the BML (Biological Motion Library) dataset gathered from thirty volunteers with four various activities to analyze the performance metrics. It compares the evaluated results with existing results, which are found by machine learning and deep learning methods to identify human activity. Result: This framework was successfully investigated with the help of laboratory metrics with convolutional neural networks (CNN) and achieved 89.0% accuracy compared to machine learning methods. Conclusion: The novel work of this research is to increase classification accuracy with a lower error rate and faster execution. Moreover, it introduces a novel approach to human activity recognition in the BML dataset using the CNN with Adam optimizer approach. © 2024 Bentham science Publishers.

关键词： Support vector machines

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Occupancy Map Guided Attributes Artifacts Removal for Video-Based Point Cloud Compression

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ACM Transactions on Multimedia Computing, Communications and Applications 2024年第12期20卷 1-20页

作者： Chen, Peilin Wang, Shiqi Li, Zhu Department of Computer Science City University of Hong Kong Kowloon Hong Kong Department of Computer Science and Electrical Engineering University of Missouri-Kansas City KansasMO United States

Point clouds offer realistic 3D representations of objects and scenes at the expense of large data volumes. To represent such data compactly in real-world applications, Video-Based Point Cloud Compression (V-PCC) converts their texture into 2D attributes and occupancy maps before applying lossy video compression. Unfortunately, the coding artifacts introduced in the decoded attribute maps eventually degrade the quality of the reconstructed point cloud, thereby influencing its immersive experience. This article proposes a deep learning-based attribute map enhancement method that fully leverages the occupancy map's guidance. The design philosophy is that the cross-modality guidance from occupancy can be leveraged as critical information to enhance the attribute. Therefore, instead of treating attribute and occupancy as two separate sources of signals, occupancy serves as an indispensable auxiliary, such that the proposed framework explicitly provides the model with abundant clues by conducting local feature modification and global dependencies aggregation. In particular, the proposed framework is compatible with existing V-PCC bitstreams and can be feasibly incorporated into the standardized decoder pipeline. Extensive evaluations show the effectiveness of the proposed framework in attribute enhancement, with equivalently 6.0% Bjontegaard Delta-rate (BD-rate) savings obtained. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Image compression

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Counting vehicles types using deep learning algorithm in video surveillance systems

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Multimedia Tools and Applications 2024年 1-24页

作者： Akoushideh, Alireza Sadat, Seyed Shafiullah Shahbahrami, Asadollah Electrical and Computer Engineering Department National University of Skill Tehran Iran Computer Engineering Department Kabul Polytechnic University Kabul Afghanistan Computer Engineering Department University of Guilan Rasht Iran

Detection, identification, and automatic counting of vehicles using video surveillance cameras plays an essential role in intelligent transportation management. Despite the progress that researchers have made in these cases, its operational implementation still faces challenges such as various environmental conditions, unbalanced data sets, accuracy, and speed. Therefore, research is fundamental in solving these issues. This research investigates the use of deep convolutional neural networks (CNNs) for vehicle detection, classification, and counting in video surveillance applications. We analyze recent research trends and challenges, examining the suitability of popular datasets like MSCOCO, PASCAL, ImageNet, DAWN, DETRAC, and MIO-TCD for validating proposed algorithms. Our study focuses on overcoming challenges like poor lighting conditions and unbalanced traffic scenarios. We propose a novel approach for counting vehicle types based on the YOLO model. The proposed method leverages the YOLO model for object detection and recognition, the ByteTrack algorithm for tracking vehicles across frames, and the CLAHE pre-processing and the Multiple-Zone-Line approach for addressing video surveillance challenges. Experimental results on three benchmark videos demonstrate the superiority of YOLOv8 over YOLOv9 in our benchmarks. Moreover, the application of CLAHE pre-processing algorithms further enhances performance. This research offers a promising solution for accurate and robust vehicle detection, classification, and counting in real-world video surveillance scenarios. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

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