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20th International Conference on Frontiers of Information Technology, FIT 2023

作者： Qazi, Muhammad Hasham Khan, Farhan Kim, Jeeeun Rojas-Munoz, Edgar J. Habib University Electrical Engineering Karachi Pakistan Habib University Electrical and Computer Engineering Karachi Pakistan Texas A&m University Computer Science & Engineering College Station United States Texas A&m University Performance Visualization & Fine Arts College Station United States

ISBN: (纸本)9798350395785

The rising incidence of fires calls for advanced training methodologies that surpass the limitations of traditional firefighter training, both in scale and scope. Virtual Reality (VR) emerges as a potent solution, offering a wide range of realistic scenarios and cost-effective, safe training environments. This paper presents a novel VR-based training platform tailored for firefighters, which leverages Unity 3D and state-of-the-art fire simulation techniques to deliver high-fidelity experiences that closely mimic real-world dynamics. Trainees engage in an immersive VR setting where they experience full autonomy and multisensory feedback, heightening the educational impact through a procedural fire spreading mechanism that emulates actual fire behavior. Our system excels in providing a comprehensive framework, modular design for customizable scenarios, and integration of varied training modules to prepare trainees for an array of firefighting emergencies. Future work aims to enhance realism through advanced features such as Flashover and Backdraft simulation, real-time environmental controls for trainers, team-based exercises with human-agent interaction, etc. The paper concludes by emphasizing the platform's alignment with the set design standards for VR-based firefighter training and outlines prospective user testing with professional firefighters to further refine the VR experience. © 2023 IEEE.

关键词： Virtual reality

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An autonomous robotic system for collecting garbage over small water bodies 6

An autonomous robotic system for collecting garbage over sma...

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6th International Conference on Automation, Control and Robotics engineering, CACRE 2021

作者： Hasany, Syed Nouman Zaidi, Saniya Shah Sohail, Syed Ahmad Farhan, Muhammad Electrical Engineering Program Habib University Karachi Pakistan Computer Science Program Habib University Karachi Pakistan Electrical and Computer Engineering Program Habib University Karachi Pakistan

ISBN: (纸本)9781665435765

Water pollution is a grave problem requiring utmost attention as it directly affects marine lives. In freshwater ecosystem, for example, lakes and ponds, a major chunk of water garbage is plastic floating on the surface of water which is labor intensive to collect manually. In this paper, we present a novel autonomous robotic system capable of navigating small water bodies and is equipped with computer vision that helps it to detect the garbage. The detection is followed by focusing on an area of interest to determine whether the garbage lies in the scope of robot for collection. Additionally, if WiFi communication is available, the robot has the provision of tracking the detected garbage to determine its path. The design of garbage collection unit of the robot ensures the garbage does not move outward once collected. The robot is tested in two different pools and with plastic bottles as main type of garbage. The results manifest the high degree of control of its locomotion as well as of detection and collection of the bottles. The deep neural network based detector onboard the robot can be retrained after self-collection of appropriate data to detect other types of garbage as well. © 2021 IEEE.

关键词： Plastic bottles

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Compact Model Build Upon Dynamic Weighting Artificial Neural Network Approach for Complementary Field Effect Transistors

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IEEE Transactions on Electron Devices 2024年第1期71卷 246-253页

作者： Butola, Rajat Li, Yiming Kola, Sekhar Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation The Institute of Artificial Intelligence Innovation National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program The Institute of Communications Engineering the Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan

In this work, a dynamic weighting-artificial neural network (DW-ANN) methodology is presented for quick and automated compact model (CM) generation. It takes advantage of both TCAD simulations for high accuracy and SPICE simulations for cost-effectiveness. This methodology is developed for gate-all-around (GAA) silicon (Si) nanosheet (NS) complementary field effect transistor (CFET), a potential candidate for future CMOS technology due to its innate properties. The critical process variation (PV) sources that severely degrade the CFET performance are estimated using DW-ANN. It reduces the computation cost and predicts the effects of PV sources with less than 1% error. Furthermore, a compact DW-ANN-based Verilog-A model has been developed that captures the dc as well as transient behavior accurately for circuit-level analysis. CFET-based circuits such as inverter, 6T-static random access memory (SRAM), and ring oscillator (RO) are characterized and implemented using DW-ANN-based CM. The overall average error of the model is reported as less than 2%. Therefore, the proposed device and circuit modeling approach provides a feasible solution for the rapid compact modeling of new emerging devices with good convergence and high accuracy. © 1963-2012 IEEE.

关键词： Neural networks

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LLM-Guided Speech Processing for 3D Human Motion Generation

LLM-Guided Speech Processing for 3D Human Motion Generation

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IEEE International Conference on Multi Topic

作者： Muhammad Hasham Qazi Sandesh Kumar Farhan Khan Electrical Engineering Habib University Karachi Pakistan Computer Science Habib University Karachi Pakistan Electrical and Computer Engineering Habib University Karachi Pakistan

ISBN: (数字)9798331507213

ISBN: (纸本)9798331507220

This paper proposes a novel approach for generating 3D human motion from speech input using Large Language Models (LLMs). The system processes spoken language to derive motion generation prompts, which are then used by a 3D human motion generation engine to generate the corresponding 3D animations. This method eliminates the need for predefined action sequence text prompts and instead offers a dynamic approach towards motion generation. By utilizing a GPT model, the system produces body motions aligned with speech. Results demonstrate the system’s capability to generate various types of motions in response to diverse speech inputs. Despite challenges in handling complex sentences, motion continuity, and real-time performance, this approach lays the groundwork for applications in education, virtual reality-based training, and gaming environments, where naturalistic agent interactions are essential. Future improvements include optimizing the system for real-time applications and addressing challenges related to complex sentences and motion continuity.

关键词： Training Human computer interaction Three-dimensional displays Translation Large language models Semantics Animation Real-time systems Tokenization Speech processing

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Correntropy-Based Data Selective Adaptive Filtering

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IEEE Transactions on Circuits and Systems I: Regular Papers 2024年第2期71卷 754-766页

作者： Chien, Ying-Ren Wu, Sheng-Teng Tsao, Hen-Wai Diniz, Paulo S.R. Adaptive and Autonomous Communication Laboratory Department of Electrical Engineering National Ilan University Yilan City260007 Taiwan College of Electrical Engineering and Computer Science Graduate Institute of Communication Engineering National Taiwan University Taipei10617 Taiwan Program of Electrical Engineering Department of Electronics and Computer Engineering COPPE/Poli Universidade Federal do Rio de Janeiro Rio de Janeiro68504 Brazil

Data selection can be used in conjunction with adaptive filtering algorithms to avoid unnecessary weight updating and thereby reduce computational overhead. This paper presents a novel correntropy-based data selection method as an alternative to conventional data selection mechanisms based on squared error values. We developed a variable correntropy sensing algorithm to maximize the instantaneous correntropy function for the Gaussian kernel function to mitigate the impact of impulse noise and other forms of noise that can be disregarded in data selection. The proposed data selection mechanism can be implemented with any adaptive filtering algorithm. In simulations, the proposed method (implemented with the least mean squared algorithm) outperformed comparable error-based data selection schemes in terms of hit rate and miss rate, and the resulting weight updating ratio was close to the expected weight updating ratio. © 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.

关键词： Adaptive filters

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Reconfigurable Cryptoprocessor Design for RSA and Rabin-p Cryptosystems

Reconfigurable Cryptoprocessor Design for RSA and Rabin-p Cr...

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IEEE International Conference on Multi Topic

作者： Zain Ahmed Usmani Mohit Rai Farhan Khan Computer Science Habib University Karachi Pakistan Computer Engineering Habib University Karachi Pakistan Electrical & Computer Engineering Habib University Karachi Pakistan

ISBN: (数字)9798331507213

ISBN: (纸本)9798331507220

With recent advances in computing and the accessibility of hardware design, there has been an increase in cryptoprocessors being used as hardware accelerators for encryption and other security measures. However, as demands increase so do the strains on the hardware production supply and the need to be sustainable, coupled with the rising popularity of reconfigurable systems. This paper introduces a reconfigurable cryptoprocessor that provides encryption and decryption using the RSA cryptosystem and a more efficient, but less widely adopted Rabin-p cryptosystem. The proposed design is more efficient than other Rabin cryptosystems as it leverages the Rabin-p variant, enabling decryption without the additional step of identifying correct quadratic residues. This reconfigurable cryptoprocessor can perform operations for RSA and Rabin-p cryptosystems within one piece of hardware, enabling compatibility with RSA and providing access to the improved security of Rabin-p. In this paper, we share the implementation signatures and features of this cryptosystem.

关键词： program processors Production Encryption Hardware acceleration Strain

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Context Aware Mamba-based Reinforcement Learning for Social Robot Navigation

Context Aware Mamba-based Reinforcement Learning for Social ...

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International Conference on Control, Mechatronics and Automation (ICCMA)

作者： Syed Muhammad Mustafa Zain Ahmed Usmani Omema Rizvi Abdul Basit Memon Muhammad Mobeen Movania Computer Engineering Habib University Karachi Pakistan Electrical & Computer Engineering Habib University Karachi Pakistan Computer Science Habib University Karachi Pakistan

ISBN: (数字)9798331517519

ISBN: (纸本)9798331517526

Social robot navigation (SRN) is a relevant problem that involves navigating a pedestrian-rich environment in a socially acceptable manner. It is an essential part of making social robots effective in pedestrian-rich settings. The use cases of such robots could vary from companion robots to warehouse robots to autonomous wheelchairs. In recent years, deep reinforcement learning has been increasingly used in research on social robot navigation. Our work introduces CAMRL (Context-Aware Mamba-based Reinforcement Learning). Mamba is a new deep learning-based State Space Model (SSM) that has achieved results comparable to transformers in sequencing tasks. CAMRL uses Mamba to determine the robot’s next action, which maximizes the value of the next state predicted by the neural network, enabling the robot to navigate effectively based on the rewards assigned. We evaluate CAMRL alongside existing solutions (CADRL, LSTM-RL, SARL) using a rigorous testing dataset which involves a variety of densities and environment behaviors based on ORCA and SFM, thus, demonstrating that CAMRL achieves higher success rates, minimizes collisions, and maintains safer distances from pedestrians. This work introduces a new SRN planner, showcasing the potential for deep-state space models for robot navigation.

关键词： Sequential analysis Navigation Wheelchairs Social robots Aerospace electronics Transformers Path planning Mobile robots Collision avoidance Testing

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Context Aware Mamba-based Reinforcement Learning for Social Robot Navigation

arXiv

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arXiv 2024年

作者： Mustafa, Syed Muhammad Usmani, Zain Ahmed Rizvi, Omema Memon, Abdul Basit Movania, Muhammad Mobeen Computer Engineering Habib University Karachi Pakistan Computer Science Habib University Karachi Pakistan Electrical & Computer Engineering Habib University Karachi Pakistan

关键词： Deep reinforcement learning

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An Ant Colony Algorithm Approach to the Vehicle Routing Problem and Variants

An Ant Colony Algorithm Approach to the Vehicle Routing Prob...

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2024 Systems and Information engineering Design Symposium, SIEDS 2024

作者： Tablada, Tulio Davis, Cleon Palumbo, Neil Johns Hopkins University Electrical and Computer Engineering Program Johns Hopkins University Applied and Computational Mathematics Program United States

ISBN: (纸本)9798350385144

In the basic vehicle routing problem (VRP), a vehicle must deliver goods from one centralized warehouse to multiple customers efficiently. Several VRP variants and constraints exist, including different product types, specific delivery times, multiple warehouses, vehicle fuel constraints, and pick-up from one location and delivery to another. This work proposes and demonstrates a flexible algorithm for solving the VRP via ant colony optimization (ACO) that can address many of the variants discussed above. ACO algorithms mimic the behavior of ants, learning optimal paths to a food source and back to the nest based on stigmergic behavior. This work compares a proposed, more flexible ACO algorithm to a traditional optimization algorithm that is implemented in the Google VRP solver. Readily available data were used to test and demonstrate results. Several VRP variants were implemented in both the (proposed) flexible ACO algorithm and the Google VRP solver. The flexible ACO algorithm performed better in terms of distance traveled versus the Google VRP solver for two variants and worse for three other cases. However, the Google VRP solver was not able to solve some of the VRP variant combinations considered here and failed when solving some backhaul datasets. Because the flexible ACO algorithm was able to better handle many case variations, it may be an attractive alternative optimization tool. © 2024 IEEE.

关键词： Ant colony optimization

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Impact Analysis of Time-of-Use pricing enabled Electric Vehicle charging to the uncoordinated charging on a Distribution Network

Impact Analysis of Time-of-Use pricing enabled Electric Vehi...

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IEEE General Meeting Power& Energy Society

作者： Muhammad Abeer Sohail Rashna Khan Syed Hammad Mukhtar Ahmad Usman Electrical and Computer Engineering Program Habib University Karachi Pakistan

Increased adoption of Electric Vehicles (EVs) result in appearance of high energy demand clusters in a distribution network. This has adverse effects on the distribution network resulting in voltage drop, increased losses, overloading of distribution feeders, transformers, and critical components. This results in degradation of power quality and network stability. In this work, a modified IEEE 13 bus system has been used to model and simulate 11 kV distribution grid using the actual network parameters, network topology, and an identified cluster region, based on the information from a distribution company in Pakistan. A scheduling strategy for EV charging using the the time-of-use pricing (ToUP) mechanism is devised and compared to the uncoordinated EV charging approach. The performance evaluation of these strategies for various degrees of EV penetration has been conducted and new challenges are identified.

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