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Design and In-training Optimization of Binary Search ADC for Flexible Classifiers 25

Design and In-training Optimization of Binary Search ADC for...

引用

30th Asia and South Pacific Design Automation Conference, ASP-DAC 2025

作者： Lozano Duarte, Paula Carolina Afentaki, Florentia Zervakis, Georgios Tahoori, Mehdi Department of Computer Science Karlsruhe Inst. of Tech. Karlsruhe Germany Computer Engineering and Informatics Department Univ. of Patras Patras Greece

ISBN: (纸本)9798400706356

Flexible Electronics (FE) offer distinct advantages, including mechanical flexibility and low process temperatures, enabling extremely low-cost production. To address the demands of applications such as smart sensors and wearables, flexible devices must be small and operate at low supply voltages. Additionally, target applications often require classifiers to operate directly on analog sensory input, necessitating the use of Analog to Digital Converters (ADCs) to process the sensory data. However, ADCs present serious challenges, particularly in terms of high area and power consumption, especially when considering stringent area and energy budget. In this work, we target common classifiers in this domain such as MLPs and SVMs and present a holistic approach to mitigate the elevated overhead of analog to digital interfacing in FE. First, we propose a novel design for Binary Search ADC that reduces area overhead 2× compared with the state-of-the-art Binary design and up to 5.4× compared with Flash ADC. Next, we present an in-training ADC optimization in which we keep the bare-minimum representations required and simplifying ADCs by removing unnecessary components. Our in-training optimization further reduces on average the area in terms of transistor count of the required ADCs by 5× for less than 1% accuracy loss. © 2025 inst.tute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Budget control

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Decoding the Skin with AI: A Review of Cutting-Edge Technologies and Applications 2

Decoding the Skin with AI: A Review of Cutting-Edge Technolo...

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Kumari, Sejal Umrao, Sandhya Kushwaha, Dileep Noida Inst. of Engg. and Technology Computer Science & Engineering Greater Noida India Greater Noida India

ISBN: (纸本)9798350371055

The skin of humans can tell stories through its textures and colors, providing insights into both health and disease. Dermatologists have been interpreting these stories for years, using their expertise to diagnose and treat conditions. They have an ally in this pursuit: artificial intelligence and machine learning (AI/ML). In this review, we will delve into the meeting point of AI/ML and dermatology, exploring the applications, challenges and the promising future. Looking ahead, we can imagine a future where emerging technologies drive advancements. In this future, AI will work hand in hand with dermatologists in procedures guided by images, speed up the process of discovering drugs, and empower individuals to keep track of their skin health using AI-powered sensors. This goes beyond upgrading technology. It signifies a revolution in how we perceive and approach skin health. It promises a future where early detection, personalized treatment and preventive measures become the standard. Join us on our journey at the intersection of skin and technology as we witness the beginning of an era in dermatology. With AI shedding light on the secrets of our skin we pave the way for a healthier tomorrow. © 2024 IEEE.

关键词： Artificial intelligence Derma-tology Diagnosis Machine learning Personalized medicine Skin cancer Treatment Wearable sensors

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Enhancing Hyperspectral Image Compression Through Stacked Autoencoder Approach 6

Enhancing Hyperspectral Image Compression Through Stacked Au...

引用

6th International Conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Afrin, Afsana Haque, Md. Rakibul Mamun, Md. Al Inst. of Information and Communication Technology Rajshahi University of Engineering Technology Rajshahi6204 Bangladesh Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350385779

Hyperspectral Image (HSI) consisting of numerous high-resolution spectral bands creates challenges of the high dimensionality problem in HSI classification which hinder real-life applications despite their abundance of information. We present a lossy compression approach using stacked autoencoders to reduce high dimensionality problem in this paper. The pro-posed method utilizes stacked autoencoders to extract features from HSIs, allowing compression and subsequent reconstruction. The study demonstrates improved Peak Signal-to-Noise Ratio (PSNR) of 70.43, 60.87 and 61.36 on three distinct HSI datasets-Salinas, Botswana, and KSC respectively compared to previous autoencoder-based compression method. Additionally, the impact of compression on classification accuracy is assessed using a 3D-2D CNN model, achieving an average accuracy of 99 % across the datasets. Improved compression along with high classification accuracy shows our proposed method's usefulness. © 2024 IEEE.

关键词： Image enhancement

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A Robust Cold-Start Approach Using Phylogram 10

A Robust Cold-Start Approach Using Phylogram

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10th International Conference on Optimization and Applications, ICOA 2024

作者： Da Silva, Breno Caetano Franco, Jorge Luiz Branco, Hermes Manoel Galvão Castelo Tokuda, Eric Delbem, Alexandre Claudio Botazzo Federal Institute of Maranhão Computer Science Department Teresina Brazil Inst. of Math. and Computer Science University of São Paulo São Carlos Brazil Federal University of Piaui Electrical Engineering Department Teresina Brazil

ISBN: (纸本)9798350387353

In Active Learning (AL) applications, it is crucial to develop a model that efficiently represents data using a limited number of labeled samples. In this context, an oracle is often employed to label samples that are deemed representative and informative. However, one of the primary limitations of AL lies in the need for an initial set of labeled samples, which may not be representative of the entire data space. To address this issue, cold-start strategies are implemented with the goal of selecting an appropriate initial set of samples for labeling, ensuring that the initial model is robust and effective. We propose a novel approach - PhyIL - that harness the advantages of phylogram analysis and community detection techniques to smartly perform the cold-start selection in AL. The method facilitates the choice of samples that best represent the diversity and complexity of the data, which is fundamental for the subsequent success of the active learning process. We evaluated the proposed approach using nine real-world databases and nine traditional ML models. The findings indicate that PhyIL offers an effective and viable solution for the initial sample selection, when compared to the random selection. © 2024 IEEE.

关键词： Active learning

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How to Construct Pseudorandom Bit Sequences from Mazes by a Method Digging Out Walls

How to Construct Pseudorandom Bit Sequences from Mazes by a ...

引用

2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Miyazaki, Takeru Araki, Shunsuke Uehara, Satoshi Kyushu Inst. of Information Sciences Faculity of Management and Information Sciences Fukuoka Japan Kyushu Inst. of Technology Faculty of Computer Science and Systems Engineering Fukuoka Japan The Univ. of Kitakyushu Faculty of Environmental Engineering Fukuoka Japan

ISBN: (纸本)9798350324174

In this paper, we propose a new pseudorandom bit sequence generator, which is based on a method digging out walls. Although each of these sequences lacks enough randomness, synthesized sequences of them can pass all of the NIST statistical test suite. We also describe a conjectural relation between the number of types on the square mazes and their sizes. © 2023 IEEE.

关键词： Digging Out the Walls Maze Generator Pseudorandom Bit Sequence

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Chinese Medical Record Entity Recognition Based on Lexicon and Self-attention

Chinese Medical Record Entity Recognition Based on Lexicon a...

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2023 IEEE International Conference on Sensors, Electronics and computer engineering, ICSECE 2023

作者： Wang, Le Zhao, Yahui Cui, Rongyi Jin, Guozhe Wang, Jinxiang Yanbian University Inst. of Intelligent Information Processing Yanji133002 China Yanbian University Computer Science and Technology College of Engineering Yanji China

ISBN: (纸本)9798350313734

Named Entity Recognition (NER) is a pivotal challenge in the field of natural language processing. In the medical field, NER plays a crucial role in identifying medical entities, such as diagnoses and drugs, from electronic medical records. To address the issue of incomplete and inaccurate word recognition in Chinese electronic medical records, we propose a BERT-BiLSTM-CRF model that incorporates both a lexicon and self-attention *** our approach, we first utilize the BERT model for preprocessing to generate word vectors. These vectors capture important semantic information about the words. Next, we employ a BiLSTM network to further capture contextual information from the input sequence. This helps to enhance the understanding of the text's meaning. To reinforce the significance of the sequence information, we introduce a self-attention layer that learns to prioritize relevant contextual details. Subsequently,the extracted features are fed into a CRF model for final training. The CRF layer takes into account the dependencies among the labels and improves the overall sequence labeling accuracy. Given the specialized nature of the medical domain, we integrate a medical lexicon, incorporating external knowledge features into the model. This inclusion enables better recognition of medical terms and enhances performance on medical-related *** extensive experiments on the CCKS2019 dataset, our proposed model demonstrates favorable performance. It get relatively excellent results in identifying medical entities accurately and effectively from Chinese electronic medical records. © 2023 IEEE.

关键词： Diagnosis

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Using Object Detection to Select Grasp Type & Control Functional Electrical Stimulation for Hand Rehabilitation 10

Using Object Detection to Select Grasp Type & Control Functi...

引用

10th International Conference on Signal Processing and Integrated Networks, SPIN 2023

作者： Bhagat, Nikunj Arunkumar Ruppa, Manikanta Indian Institute of Science Dept. of Electrical Engineering Bengaluru India Indian Inst. of Inf. Tech. & Mgmt Dept. of Computer Science Thiruvananthapuram India

ISBN: (纸本)9781665490993

Unilateral muscle weakness and hand paralysis is the most common outcome after a stroke. Functional electrical stimulation (FES) is effective in assisting hand prehension, but conventional stimulators require users to manually select the grasp type (e.g. by pressing a button), which is challenging for patients with severe paralysis. Also, patients need to frequently divert attention from their task to operate the stimulator, which is cumbersome and reduces their engagement in the therapy. In this study, we develop a novel deep learning-based object detection approach to select multiple grasp types and control an electrical stimulator, in order to assist grasping. Object detection was performed using a state-of-the-art YOLOv5 algorithm, which achieved above 93% mean average precision. The algorithm tracked the positions of the hand and objects and selected a grasp type based on the object nearest to the hand. Once the grasp type was selected, a custom-built FES stimulator was activated to execute pre-defined stimulation sequences and allow a person to grasp the nearest object. This contactless, vision-based solution is beneficial for patients opting for homebased rehabilitation since it doesn't require additional setup time or help from caregivers. The future scope of this work includes testing the object detection-based FES on stroke patients and determining its efficacy in restoring hand movements. © 2023 IEEE.

关键词： Object detection

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Decoding the Skin with AI: A Review of Cutting-Edge Technologies and Applications*

Decoding the Skin with AI: A Review of Cutting-Edge Technolo...

引用

Disruptive Technologies (ICDT), International Conference on

作者： Sejal Kumari Sandhya Umrao Dileep Kushwaha Computer Science & Engineering Noida Inst. of Engg. and Technology Greater Noida India Computer Science & Engineering(AI) Noida Inst. of Engg. and Technology Greater Noida India

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Optimizing Machine Learning Algorithms for Temperature Forecasting and Energy Management Systems

Optimizing Machine Learning Algorithms for Temperature Forec...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Chandra Sekhar, G. Kedhari Sailaja, P. MadhusudhanaRao, G. Srinivas, N. Sai Thrinath, B.V. Akash, G. Dept. of Computer Science Engg Aeronautical Engineering Hyd Visakhapatnam Dept of Computer Science Engg Institute Visakha Inst. of Engg. & Tech Dept of Electrical and Electronics Engg Vardhaman College of Engg Hyd. Dept of Electrical and Electronics Engg Mohan Babu University Tirupati Dept of Computer Science Engg Woxsen University Hyderabad

ISBN: (纸本)9798350373783

Cloud data centers strive to provide efficient, scalable, and reliable computing resources while addressing challenges related to security, privacy, cost, and more. Their objectives include delivering value to users through flexibility, availability, innovation, and environmental responsibility. These Data centers have observed tremendous growth, utilizing massive energy for computer resources and cooling systems. A combination of time series regression models, machine learning, and other optimization techniques may be used to develop more robust and adaptive resource management policies for complex data center environments. Additionally, continuous monitoring and feedback loops are essential to ensure that the models remain accurate and effective as conditions evolve. This paper studied existing machine learning algorithms and methods for temperature forecasting and adequate energy consumption for data centers. The investigations have been analyzed that the impact of energy consumption on computer resource workloads significantly improved, and Virtual Machines scheduling algorithms will optimize the forecasting of thermal effects. This paper also compares the regression model with the Machine Learning algorithms, which will provide the best methodology for forecasting thermal effects and improving efficient energy. © 2024 IEEE.

关键词： Cloud Computing Data Centres Index: Energy Efficiency Privacy Security Thermal Forecast Virtual Machines

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Multi-Agent Deep Reinforcement Learning for Hybrid Motion Planning in Dynamic Environments 4

Multi-Agent Deep Reinforcement Learning for Hybrid Motion Pl...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Chopra, Jatin Avhad, Priyanka Ekatpure, Shubham Rajendra Rallapalli, Monesh Kumari, Supriya Upreti, Kamal SEAS GWU WashingtonDC United States Computer Engineering Veermata Jijabai Technological Inst. Mumbai India Industrial Engineering Kulicke and Soffa PA United States CSE - AI JAIN University Bengaluru India HSBC Maharashtra India Computer Science Engineering CHRIST University Delhi Ncr India

ISBN: (纸本)9798350353266

This research presents a novel approach to addressing the challenges of gesture forecasting in impenetrable and dynamic atmospheres by integrating a hybrid algorithm within a multi-agent system framework. Traditional methods such as Force-based motion planning (FMP) & deep reinforcement learning (RL) often struggle to handle complex scenarios involving multiple autonomous agents due to their inherent limitations. To overcome these challenges, we propose a hybrid algorithm that seamlessly combines the strengths of RL and FMP while leveraging the coordination capabilities of a multi-agent system. By integrating this hybrid algorithm into a multi-agent framework, we demonstrate its effectiveness in enabling multiple agents to navigate densely populated environments with dynamic obstacles. Through extensive simulation studies, we illustrate the superior performance of our approach compared to traditional methods, achieving higher success rates and improved efficiency in scenarios involving simultaneous motion planning for multiple agents. A hybrid motion planning algorithm is also introduced in this very research. Performance Comparison of Hybrid Algorithm, Deep RL, and FMP are also discussed in the result section. This research paves the way for the development of robust and scalable solutions for motion planning in real-world applications such as collaborative robotics, autonomous vehicle fleets, and intelligent transportation systems. © 2024 IEEE.

关键词： Multi agent systems

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