Edward Delp

Excited to share that our paper titled " Exploiting Cross-Dimensional Dependency Using Vision-LSTM for Efficient Medical Image Segmentation " has been accepted for presentation at the IEEE International Symposium on Biomedical Imaging (ISBI) 2025. Our paper introduces Rot-UViL, a novel computationally efficient approach to medical image segmentation. By rotating input tensors and using Vision-LSTM blocks, we capture crucial cross-dimensional dependencies. This leads to more accurate and efficient segmentation, especially for complex structures spanning multiple slices. Thanks to my co-author Sushmita Mitra for her contributions. #ISBI2025 #biomedicalimaging

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I am happy to announce the launch of "Quantum Mechanics in the Single Photon Laboratory (Second Edition)," a book that I co-authored with Muhammad Hamza Waseem, FAIZAN ILAHI, and Muhammad Sabieh Anwar. Book link: https://lnkd.in/dWyi6XqA The book has been published by IOP Publishing in the "IOP Series in Quantum Technology." Series link: https://lnkd.in/dZwc8GtF Here’s some information from the About section of the book: "This book is valuable for laboratory developers, researchers, and teachers who would like to recreate a similar suite of experiments for students and early career researchers. These experiments enliven theoretical descriptions of key ideas from quantum information and quantum computing, providing accessible means to generate, manipulate, and measure quantum states and understand their implications for the foundations of quantum mechanics. This new edition features additional experiments related to quantum entanglement and non-local quantum erasure and has restructured previous descriptions. Data processing in an open-source language is described. The appendix on programming FGPAs is considerably enhanced."

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Taking advantage of the weekend to post :-) ECE research addresses some of the most diverse, vibrant, and contemporary issues. #Technology #AI Lawrence Technological University Robotic Automation &Intelligent Equipment Robotics Lawrence Technological University

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Excellent work in Medical Engineering

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Join us for an enlightening workshop titled "New Approaches in Robotic Grasping: From Deep Learning, Reinforcement Learning to Graph Neural Network" at the upcoming ICCKE 2024 conference. In this workshop, we present the culmination of six years of intensive research conducted at the Human and Robot Interaction Laboratory, University of Tehran, about Robotics Grasping. I will present this workshop alongside two of my Ph.D. students, Hamed Ghasemi and Hamed Hosseini Ghasemi. Together, we will unravel the complexities of robot grasping from a deep learning perspective, shedding light on cutting-edge techniques, challenges, and future prospects. Date: 19th Nov, 13:00 Online https://lnkd.in/dqcGb8KE https://lnkd.in/dzXKb-ES Abstract: The workshop titled "New Approaches in Robotic Grasping: From Deep Learning, Reinforcement Learning to Graph Neural Networks" delves into the cutting-edge advancements in robotic grasp detection, a critical component for the automation of industrial tasks. Over the past six years, the Human-Robot Interaction Laboratory has focused on developing innovative solutions for grasp detection, utilizing a combination of geometric approaches and intelligent methods. This workshop will showcase the practical applications of these approaches through the development of a 3-DOF Delta robot and an advanced gripper. Attendees will gain insights into the integration of deep learning models for accurate and adaptive grasp detection, the application of reinforcement learning to enhance the decision-making capabilities of robotic systems, and the utilization of graph theory for optimizing grasp strategies and robot movements. In the workshop, we will also discuss how Transformers are revolutionizing robotic grasping and manipulation. These models, like GPT-4V and Gemini, enable complex robotic tasks with minimal training, using just input prompts. We'll review the latest developments in this area, including models like RT-1 and RT-2. By exploring these new approaches, participants will be equipped with the knowledge and tools necessary to advance the field of robotic grasping, contributing to the evolution of more autonomous and efficient robotic systems in industrial settings. Workshop Outlines: 1. Introduction to Robotic Grasping 2. Geometric Methods and Integration with Intelligent Methods 3. Reinforcement Learning (RL) Based Methods for Data Insufficiency 4. Graph Analysis and Modeling for Compatible Problems 5. Popular Simulators Overview 6. Practical Setup and Results Presentation 7. Applications of Grasping Over the Developed Gripper and Delta Robot 8. Latest Researches in Robotic Manipulation #robot #robotics #grasp #deep_learning #RL #reinforcement_learning

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Check out the poster presented by PhD student, Ishfaq Aziz, at the 2nd USACM Thematic Conference on Uncertainty Quantification for Machine Learning Integrated Physics Modeling (UQ-MLIP 2024). This poster was titled "PHYSICS-INFORMED MACHINE LEARNING (PIML) FOR ELECTROMAGNETIC WAVE PROPAGATION AND MATERIAL PROPERTY ESTIMATION" and discusses a solution that integrates machine learning with physics to analyze EM wave propagation for microwave radar sensing. #PIML #PINN #Microwave_sensing #radar_sensing #parameter_estimation #physics_informed #nondestructive_Evaluation

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Another acceptance of my recent research work, titled “Histopathological Image-Based Classification of Lung and Colon Cancer Using Deep Learning Architecture with Preprocessing Enhancements” will be presented at the 27th International Conference on Computer and Information Technology. #medical_image #image_processing #computer_vision #Deep_Learning

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#article_introduce Title: SA-based Approach to Implement Digital Systems on 3D Integrated Circuits Author: #Hemin_Rahimi #Hadi_jahanirad Journal: #Computational_Intelligence_in_Electrical_Engineering Publisher: #University_of_Isfahan Keywords: #3D_IC #Evolutionary_algorithms #SA_algorithm #Partitioning #Placement #and_Routing Access link: https://lnkd.in/dRj5uESG

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If you are in Regions 1-7&9, please circulate the workshop information to the students who are studying Electrical and Electronic Engineering majors. The workshop is jointly organised by IEEE AP-S and MTT-S Education Committees. We appreciate your kind support in promoting the workshop with our undergraduate students. The workshop will help the students shape their careers. #aps #mtts #microwave #antennas #radiofrequency #rf #wireless #communication #education #electronics #electrical #engineering

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I'm thrilled to announce the publication of my Research Scholar Anughna N published a paper titled "Multiplicative Basis Function Based Adaptive DOA Estimation in Optimal MIMO Sparse Antenna Reconfiguration Model" in the IETE Journal of Research! 🔍 Summary: This paper presents a novel approach for Direction of Arrival (DOA) estimation in optimal Multiple-Input Multiple-Output (MIMO) systems using Multiplicative Basis Functions. The proposed method offers enhanced accuracy and efficiency in sparse antenna reconfiguration scenarios. 🔬 Methodology: We introduce a sophisticated methodology that leverages Multiplicative Basis Functions to adaptively estimate DOA in MIMO setups, particularly in scenarios where antenna configurations are sparse. 💡 Key Findings: Our research unveils promising results, showcasing the effectiveness of our approach in accurately estimating DOA even in challenging environments. These findings hold significant implications for optimizing MIMO systems for various applications. 🔗 Read the full paper here: [https://lnkd.in/gzdFvCsS] I'm excited to share this work with my network and welcome any discussions or feedback on the topic. Let's continue pushing the boundaries of knowledge together!

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Thanks to almighty ALLAH, another research paper published in MDPI-Sensors. https://lnkd.in/dfmBZPjh

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Research Topic: Autonomous Vehicles and Advancements in Object Detection Technology Dr. Mohammad ElYabroudi is one of our new faculty members who has already embarked on collecting data for his current research on autonomous vehicles. One of his research projects involves advancements in object detection technology, focusing specifically on traffic signs in the USA under challenging conditions. This research addresses complex scenarios such as severe weather conditions, shadows, and sun glare to improve the recognition of traffic signs, which are crucial for understanding traffic laws and directions. The initial phase involves extensive data collection, labeling, and preprocessing. The upcoming phase will see the release of a comprehensive public dataset uniquely centered on USA traffic signs. This dataset features meticulous pixel-level labels and includes various environmental attributes, such as: *** Weather Conditions: Sunny, foggy, snowy, and rainy *** Types of Roads: Highway, suburban *** Contextual Factors: Construction zones, school zones, residential zones *** Complexity Levels: Easy, moderate, complex These detailed attributes, combined with improved object detection algorithms, promise to significantly enhance the accuracy and reliability of traffic sign detection systems. Dr. ElYabroudi’s research gate profile: https://lnkd.in/enQeR8Jt #autonomous #ADAS #artificialintelligence #AI Lawrence Technological University Lawrence Tech Electrical and Computer Engineering (ECE)

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Pleased to share that our new research paper from my PhD student Furkan Hasan SAKACI's thesis, entitled “Conducted Emission signal-based identification and real-time hardware security with deep learning” has been published in Engineering Applications of Artificial Intelligence. 50 days' free access to the paper. Anyone clicking on this link before September 9, 2024 will be taken directly to the final version of this paper on ScienceDirect, which they are welcome to read or download. No sign up, registration or fees are required. https://lnkd.in/dh53PRpF Abstract: In advancing electronic systems, the replicable nature of devices used in critical environments raises concerns regarding system security. It is of great importance to establish security by identifying electronic devices and computers based on their unique electrical signals, rather than assigning an artificial identity number for security purposes. This paper presents a methodology for identifying electronic devices by capturing their Conducted Emission signal over cables using a Line Impedance Stabilization Network (LISN), followed by applying deep learning techniques. After the extraction of the identities specific to each device based on their electronic characteristics, the establishment of security has been accomplished through these characteristics. The identification process was analyzed through theoretical approaches, and signals were examined by acquiring simulation-based data. After performing the analyses, the data was converted into spectrograms for training the deep learning model using the Conducted Emission signals, and a Convolutional Neural Network (CNN) was used due to its recognized efficacy in handling two-dimensional data formats like spectrogram for the deep learning algorithm. It was observed that with an accuracy of 99.9%, identification was achieved in deep learning results utilizing the data obtained through simulations. Real-time data from 15 devices was collected after training the system with simulation-based data, demonstrating a 99.8% accuracy in accurately identifying the devices. Furthermore, the developed deep learning model was deployed on an Embedded Linux edge device, tested within the system for one day, and achieved an average accuracy of 99.54% in accurately identifying registered devices.

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We are thankful to Prof. Alireza Mohammadi for delivering a research seminar at NJIT on “Vehicle Lateral Motion Dynamics Under Braking/ABS Cyber Physical Attacks”. If you’re interested in this topic or some fancy control design methodology for snake robots, check out his recent work!

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Mehmet Savasci presented our paper titled "PADS: Power Budgeting with Diagonal Scaling for Performance-Aware Cloud Workloads" at the recently held IEEE Conference on Green and Sustainable Computing (IGSC) conference in Austin, TX. This paper presents a new hardware-agnostic diagonal scaling technique for power management and power capping in compute clusters. Read it here: https://lnkd.in/eyeKsrz8 Abel S. Ahmed Ali El Din David Irwin #energyefficiency #cloudcomputing

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Meeting one of the researchers I respect in the language testing field Vahid Aryadoust, at AILA 2024

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Another research topic: the field of medical robotics and intelligent systems. A list of research areas conducted by our ECE faculty is included at the end of this post. Assistant Professor Dr. Mostafa Rahbar's most recent paper, titled 'Enhanced U-Net with GridMask (EUGNet): A Novel Approach for Robotic Surgical Tool Segmentation,' can be found at the link below. Great work Dr. Rahbar! Mostafa Daneshgar Rahbar, Ph.D. is also currently working on another project with George P Pappas Ph.D. and Nabih Jaber, Ph.D. on an intraoperative visual intelligence system, specifically “Real-Time Surgical Instrument Segmentation for Enhanced Surgical Monitoring.” https://lnkd.in/eiSmfJDA This study proposed enhanced U-Net with GridMask (EUGNet) image augmentation techniques focused on pixel manipulation, emphasizing GridMask augmentation. This study introduces EUGNet, which incorporates GridMask augmentation to address U-Net’s limitations. EUGNet features a deep contextual encoder, residual connections, class-balancing loss, adaptive feature fusion, GridMask augmentation module, efficient implementation, and multi-modal fusion. These innovations enhance segmentation accuracy and robustness, making it well-suited for medical image analysis. The GridMask algorithm is detailed, demonstrating its distinct approach to pixel elimination, enhancing model adaptability to occlusions and local features. A comprehensive dataset of robotic surgical scenarios and instruments is used for evaluation, showcasing the framework’s robustness. Specifically, there are improvements of 1.6 percentage points in balanced accuracy for the foreground, 1.7 points in intersection over union (IoU), and 1.7 points in mean Dice similarity coefficient (DSC). These improvements are highly significant and have a substantial impact on inference speed. The inference speed, which is a critical factor in real-time applications, has seen a noteworthy reduction. It decreased from 0.163 milliseconds for the U-Net without GridMask to 0.097 milliseconds for the U-Net with GridMask. ECE faculty research areas include: . Intelligent Transportation Systems . Smart Grid Power Line Communications . Artificial Intelligence . Intelligent Systems . Medical Robotics . Wireless Communication, Vehicle Communication Network, Sensor Networks . Speech Enhancement Techniques . Internet of Things . Digital Signal Processing . Embedded System and Software . Non-Destructive Optical Detection, Ultrasound Generation and imaging, Photoacoustic Imaging and Sensing . Augmented reality #technology #robotics #smart #AI

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