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Integrated Word2Vec-based Speech Annotations for Enhanced EEG Decoding of Speech Intentions 13

Integrated Word2Vec-based Speech Annotations for Enhanced EE...

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13th International Winter Conference on Brain-computer Interface, BCI 2025

作者： Jang, Se-Na Park, Hyeong-Yeong Kim, Seong-Wook Yu, Seong-Hyun Lee, Minji Jeong, Ji-Hoon Dept. Computer Science Chungbuk National University Cheongju Korea Republic of Dept. Biomedical Software Engineering The Catholic University of Korea Bucheon Korea Republic of

ISBN: (纸本)9798331521929

Brain-computer interface (BCI) technology has promising applications as an intuitive communication tool and in fields such as language rehabilitation. This study aims to decode human speech intentions by analyzing EEG signals recorded during actual and imagined speech. EEG data were collected using a 64 channels system, with preprocessing to remove artifacts. Automatic speech recognition (ASR) was used to extract precise speech onset times, generating time-specific speech annotations corresponding with EEG data. Pretrained Word2Vec embeddings were integrated to provide semantic context, combining neural signals with high-level linguistic features. Support vector machine (SVM), linear discriminant analysis (LDA) were employed for decoding. The results demonstrate that integrating speech annotations improves decoding accuracy, even for imagined speech, highlighting the potential of BCI technology for advanced applications in communication and rehabilitation. © 2025 IEEE.

关键词： Semantics

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A New Method for One-Pedal Driving With Fuzzy-Based Efficiency-Optimization Algorithm

A New Method for One-Pedal Driving With Fuzzy-Based Efficien...

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2025 IEEE International Conference on Mechatronics, ICM 2025

作者： Heydrich, Marius Hammer, Tim Mitsching, Thomas Ivanov, Valentin Technische Universitä t Ilmenau Dept. of Computer Science and Automation Ilmenau Germany Technische Universitä t Ilmenau Dept. of Mechanical Engineering Ilmenau Germany

ISBN: (纸本)9798331533892

This paper presents a case study on adaptive one pedal driving for a battery electric sport utility vehicle with in wheel based rear-wheel drive that is able to adjust the drive pedal curve automatically. In addition, a fuzzy logic observes the state of the electric machines to adapt the motor torque to the most efficient region. The control method was tested in a co-simulation using a validated vehicle model and a driver-in-the-loop strategy. Within the experiments, an improvement in energy recovery of up to 4.8 % was achieved compared to the classic brake blending. In addition, the total number of brake pedal applications is ten times lower, which translates into less driving stress and greater driving comfort, especially on long distance trips, proving the effectiveness of the controller. © 2025 IEEE.

关键词： Vehicle wheels

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Injecting Conflict Situations in Autonomous Driving Simulation Using CARLA 20

Injecting Conflict Situations in Autonomous Driving Simulati...

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20th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2025

作者： Mihaylova, Tsvetomila Reitmann, Stefan Topp, Elin A. Kyrki, Ville School of Electrical Engineering Aalto University Espoo Finland Lund University Dept. of Computer Science Lund Sweden

ISBN: (纸本)9798350378931

Simulation of conflict situations for autonomous driving research is crucial for understanding and managing interactions between Automated Vehicles (AVs) and human drivers. This paper presents a set of exemplary conflict scenarios in CARLA that arise in shared autonomy settings, where both AVs and human drivers must navigate complex traffic environments. We explore various conflict situations, focusing on the impact of driver behavior and decision-making processes on overall traffic safety and efficiency. We build a simple extendable toolkit for situation awareness research, in which the implemented conflicts can be demonstrated. © 2025 IEEE.

关键词： Automobile driver simulators

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Position Information Emerges in Causal Transformers Without Positional Encodings via Similarity of Nearby Embeddings 31

Position Information Emerges in Causal Transformers Without ...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Zuo, Chunsheng Guerzhoy, Pavel Guerzhoy, Michael Dept. of Computer Science Johns Hopkins University United States Dept. of Mathematics University of Hawai'i at Mānoa United States Division of Engineering Science University of Toronto Canada

ISBN: (纸本)9798891761964

Transformers with causal attention can solve tasks that require positional information without using positional encodings. In this work, we propose and investigate a new hypothesis about how positional information can be stored without using explicit positional encoding. We observe that nearby embeddings are more similar to each other than faraway embeddings, allowing the transformer to potentially reconstruct the positions of tokens. We show that this pattern can occur in both the trained and the randomly initialized Transformer models with causal attention and no positional encodings over a common range of hyperparameters. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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An electromagnetic indirect-driving scanning mirror for wide-field coaxial LiDAR applications 24

An electromagnetic indirect-driving scanning mirror for wide...

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MOEMS and Miniaturized Systems XXIV 2025

作者： Li, Shuangliang Wang, Di Song, Dezhen Zou, Jun Dept. of Electrical and Computer Engineering Texas A&M University College StationTX77843 United States Dept. of Computer Science and Engineering Texas A&M University College StationTX77843 United States

ISBN: (数字)9781510685130

ISBN: (纸本)9781510685123

This paper reports an electromagnetic indirect-driving scanning mirror with an enlarged mirror plate (17mm × 17mm) supported by high-strength polymer hinges for wide-field coaxial LiDAR (Light Detection and Ranging) applications. An indirect-driving mechanism was developed to achieve large tilting angle through mechanical amplification, while maintaining a relatively high resonance frequency of the enlarged mirror plate. A prototype mirror was designed, fabricated, and tested. A Hall scan position sensor was integrated to monitor the pose of the mirror in real time. The testing results show a coupled resonance frequency of 54.9 Hz with an optical tilting angle of ±60°, corresponding to a field of view (FoV) of 120°. A wide-field coaxial LiDAR setup was also built based on the indirect-driving scanning mirror, and 2D imaging was demonstrated. © 2025 SPIE.

关键词： Mirrors

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Energy Efficient LEDCA Analysis for Improved Throughput in Wireless Sensor Networks

Energy Efficient LEDCA Analysis for Improved Throughput in W...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Padmageetha B. G Pramod Kumar Naik Mallanagouda Patil Dept.of Computer Applications Dayananda Sagar University Bengaluru India Dept. of Computer science and Engineering Dayananda Sagar University Bangalore India Dept. of Computer science and Engineering RVITM Bengaluru India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Wireless Sensor Networks (WSNs) face significant challenges in terms of energy conservation, scalability, and data throughput owing to their limited node energy resources. This paper presents a low-energy data-centric algorithm (LEDCA), a novel energy-aware clustering and routing protocol designed to optimize energy efficiency and improve throughput in WSNs. Through simulations, LEDCA demonstrated a Packet Delivery Ratio (PDR) of up to 90%, throughput of 255,217 bps, and average latency of 159 ms, validating its efficiency in resource-constrained environments. The algorithm integrates residual energy awareness by prioritizing high-energy nodes for Cluster Head (CH) roles, dynamically adjusting routing paths, and balancing the network load to prevent premature node failure. By applying an Energy Mapping Node and a real-time Energy Mapping Table, LEDCA minimizes re-clustering, reduces data redundancy, and ensures stable data transmission even under high-density networks. Simulation results also show LEDCA's scalability of LEDCA, with A9_T_200 achieving the highest throughput under extended transmission ranges and A3_T_200 achieving the lowest latency, making it suitable for energy-sensitive and latency-critical applications. Future enhancements, such as integrating Non-Orthogonal Multiple Access (NOMA) and dynamic resource allocation, could further improve LEDCA's interference management and scalability of LEDCA for IoT and 5G/6G networks, addressing the needs of next-generation applications.

关键词： Wireless sensor networks NOMA Scalability Simulation Clustering algorithms Throughput Routing Energy efficiency Routing protocols Resource management

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Diversity Seeking Techniques for Red-Teaming Large Language Models

Diversity Seeking Techniques for Red-Teaming Large Language ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Lee, Seokhan Ku, Bonhwa Ko, Hanseok Dept. Computer Science and Engineering Korea University Seoul Korea Republic of Machine Learning Big Data Institute Korea University Seoul Korea Republic of Dept. Electrical and Computer Engineering Korea University Seoul Korea Republic of

ISBN: (纸本)9798350368741

In this paper, we present new techniques for increasing the diversity of red-teaming prompts generated by automated machine learning-based methods, thereby enabling the discovery of more vulnerabilities in large language models. Using reinforcement learning to train models to output effective prompts for this task results in the models converging deterministically to a single output. Our first technique, which we term Defender, acts by blocking the reward signal for prompts that have already been discovered, thus making what was a stationary problem into a non-stationary problem that compels the reward maximizing algorithm to continually seek new prompts. Our second technique, Teamplay, trains two prompt generation models in tandem and adds the KL divergence between them to the reward in order to make them search in disparate regions of the space of prompts. Our techniques are shown experimentally to increase the effectiveness and diversity of prompts generated by existing reinforcement learning baselines. © 2025 IEEE.

关键词： AI safety large language models red-teaming reinforcement learning toxicity

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A 200/400 GHz Cascode SiGe Voltage-Controlled Oscillator Exploiting Harmonic Optimization Technique for mmWave applications

A 200/400 GHz Cascode SiGe Voltage-Controlled Oscillator Exp...

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2025 International Workshop on Integrated Nonlinear Microwave and Millimetre-Wave Circuits, INMMiC 2025

作者： Khanehbeygi, Mehran Tatangelo, Giorgio Pantoli, Leonardo Leuzzi, Giorgio University of L'Aquila Dept. of Industrial and Information Engineering & Economics L'Aquila Italy University of L'Aquila Computer Science and Mathematics Dept. of Information Engineering L'Aquila Italy

ISBN: (纸本)9798331519605

This brief presents a Voltage-Controlled Oscillator (VCO) for operation in the mmWave frequency range. The VCO is based on the Colpitts topology, utilising SiGe:C 130 nm bipolar devices. Unlike traditional configurations, the proposed VCO simultaneously provides both a differential output at the fundamental frequency (200 GHz) and a push-push output at the second harmonic (400 GHz). The overall performance is significantly enhanced through the implementation of an innovative Second Harmonic Extraction Network (SHEN), which efficiently captures and utilizes the second harmonic for improved output power and efficiency. © 2025 IEEE.

关键词： Colpitts MMIC mmWave Oscillator SiGe

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An Overview of Machine Learning applications in Blockchain for Green Energy Exchanges 33

An Overview of Machine Learning Applications in Blockchain f...

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33rd Southern African Universities Power engineering Conference, SAUPEC 2025

作者： Nemakonde, Pfano Nemangwele, Fhulufhelo Ratshitanga, Mukovhe University of Venda Faculty Science Engineering and Agriculture Dept. of Physics Thohoyandou South Africa Cape Peninsula University of Technology Dept. of Electrical Electronic and Computer Engineering Bellville South Africa

ISBN: (纸本)9798331535162

There is an increasing demand for affordable, decentralised and distributed electricity, which is one of the key motivating factors that has incentivised this review article's writing. Industrial 4.0 has been a conduit through which the conventional grids shift towards a self-autonomous system, and blockchain platforms are emerging as viable solutions to facilitate that paradigm. With the advent of peer-to-peer (P2P) energy trading, community-based energy exchanges have significantly contributed to reduced grid dependency. Machine learning (ML) is being implemented in predictive analytics for energy forecasting (EF), thereby aiding both distribution network operators (DNOs) and aggregators to precisely and predictably monitor, optimise and stabilise grid operations through smart agents. The main aim of this review paper is to provide a detailed analysis of the existing research and implementation approaches on blockchain-based P2P energy trading and a comparative overview of how ML applications aid predictive analytics in community-based energy exchanges. © 2025 IEEE.

关键词： Predictive analytics

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Electric Vehicle Charging System Using RFID and IoT for Enhanced User Experience 5

Electric Vehicle Charging System Using RFID and IoT for Enha...

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5th IEEE Middle East and North Africa COMMunications Conference, MENACOMM 2025

作者： Hassain, Md Mehedi Haque Jamil, Injamul Hoque, Md Mahmudul Hasan, Md. Jahid International Islamic University Chittagong Dept. of Electrical and Electronics Engineering Chittagong Bangladesh University of Greenwich Dept. of Computing and Mathematical Sciences London United Kingdom CCN University of Science and Technology Dept. of Computer Science and Engineering Cumilla Bangladesh Comilla University Dept. of Information and Communication Technology Cumilla Bangladesh

ISBN: (纸本)9798331519957

Electric vehicles (EVs) are surging in popularity globally, offering a greener and more energy-efficient alternative to traditional cars. Our research aims to develop an RFID based system designed to automate EV charging with minimal user input. This system integrates RFID readers, tags, a microcontroller, and a relay module to manage and monitor charging sessions efficiently. An IoT platform supports real time monitoring and remote control, while a secure payment mechanism handles transactions seamlessly. RFID tags identify vehicles for automatic charging initiation, and the relay module prevents overcharging by disconnecting power after a set time. The system uses MQTT for data transmission and offers real-Time insights into energy use and account balances. All information is updated and displayed in real-Time on the server. MySQL ensures scalability, enabling effective handling of large volumes of data. The mobile application dashboard provides a view of the charging and discharging processes for user monitoring, and an email notification is sent after the recharge. EV battery charging status is monitored through visual graphs. Key benefits include a streamlined user experience, improved charging management, and support for EV adoption. This innovation advances sustainable mobility with a user friendly, efficient charging solution. © 2025 IEEE.

关键词： Radio frequency identification (RFID)

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