检索结果-内蒙古大学图书馆

Ultra8T: A Sub-Threshold 8T SRAM with Leakage Detection

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Shen, Shan Xu, Hao Zhou, Yongliang Ling, Ming Yu, Wenjian Dept. Computer Science & Tech. BNRist Tsinghua University Beijing China Nation ASIC System Engineering Technology Research Center Southeast University Nanjing210096 China School of Integrated Circuits Anhui University Hefei230601 China

In energy-constrained scenarios such as IoT applications, the primary requirement for System-on-Chips (SoCs) is to increase battery life. However, when performing sub/near-threshold operations, the relatively large leakage current hinders Static Random Access Memory (SRAM) from normal read/write functionalities at the lowest possible voltage (VDDMIN). In this work, we first put forward a model that describes a specific relationship between read current and leakage noise in a given column. Based on the model, Ultra8T SRAM is designed to aggressively reduce VDDMIN by using a leakage detection strategy where the safety sensing time on bitlines is quantified without any additional hardware overhead. We validate the proposed Ultra8T using a 256×64 array in 28nm CMOS tech.ology. Post-simulations show successful read operation at 0.25V with 1.11µs read delay, and the minimum energy required is 1.69pJ at 0.4V. Copyright © 2023, The Authors. All rights reserved.

关键词： System-on-chip

Intra-Guided Dual-Energy Material Decomposition Method for Dual-Source CBCT Systems

学校读者我要写书评

暂无评论

Intra-Guided Dual-Energy Material Decomposition Method for D...

Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Xinyun Zhong Tianling Lyu Zhan Wu Wei Gao Yan Xi Wei Zhao Wentao Zhu Yang Chen Laboratory of Image Science and Technology School of Computer Science and Engineering Southeast University Nanjing China Zhejiang Lab Hangzhou China First-Imaging Tech. Shanghai China Beihang University Beijing China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Nanjing China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Dual-energy CBCT imaging plays a crucial role in advanced imaging applications due to its ability to quantify material components. Although there are multiple established systems for dual-energy imaging, they often come with high deployment costs. This study investigates the feasibility of dual-energy material decomposition on a less expensive dual-source CBCT system. Due to the specific geometry of dual-source CBCT, cone-beam (CB) artifacts seriously degrade the material decomposition quality when the direct inversion method is adopted. In this paper, a novel dual-source intra-guided material decomposition framework is proposed to suppress CB artifacts in the material maps. Firstly, a dual-source two-pass algorithm is developed for artifact pre-correction during image reconstruction. Then, the structure of small cone-angle regions from one X-ray source is utilized to guide cone beam artifact removal in large cone-angle regions from another source. Finally, direct matrix inverse material decomposition is applied to the filtered image. Extensive experiments have been conducted on the authentic human phantom and demonstrate that the proposed framework achieves excellent performance in cone beam artifact reduction compared to other methods, and significantly enhances the quality of the material maps.

关键词： Geometry Head Costs Imaging Phantoms Filtering algorithms Matrix decomposition engineering in medicine and biology Image reconstruction X-ray imaging

IoT-Enabled Water Management: A Low-Cost Framework for Sustainable Irrigation in Agricultural Farms and Home Gardens

学校读者我要写书评

暂无评论

IoT-Enabled Water Management: A Low-Cost Framework for Susta...

Advances in Computing,Communication, Electrical, and Smart Systems (iCACCESS), International Conference on

作者： Md Zweel Rana A S Md. Mostafizur Rahaman Md Osman Ali M A Parvez Mahmud Dept. of Research and Innovation Walton Hi-Tech Ind. PLC Gazipur Bangladesh Dept. of Computer Science and Engineering Jahangirnagar University Savar Dhaka Bangladesh Dept. of Electrical and Electronic Engineering Noakhali Sci. and Tech. University Noakhali Bangladesh School of Mathematical and Physical Sciences University of Technology Sydney Australia

Agriculture is a major contributor to Bangladesh's economy, with many people depending on it for their livelihoods. Farming and gardening are considered a key initiative in mitigating global warming as well as providing economic support for many families in Bangladesh. However, the vulnerability of gardens to damage during long weekends and vacations necessitates smart solutions. Also, proper irrigation is crucial for agriculture-based farms to thrive, as it is highly dependent on environmental conditions. This paper explores the application of Internet of Things (IoT)-based smart tech.ologies as a promising solution to address this issue. A user-friendly, low-cost system facilitates the commercialization of such solutions on a large scale. In this work, we have implemented an IoT-based solution for the irrigation and watering system for agricultural firms and home gardens in Bangladesh, respectively. Considering the diverse weather conditions of Bangladesh, three key parameters, such as moisture, temperature, and humidity; have been monitored continuously and analyzed in real-time. The proposed system consists of two parts: the watering system, and the mobile app interface. The watering system incorporates an ESP32 Node MCU, sensors, two servo motors and a water pump. In automatic mode the water supply inside the garden or farm is connected to a water pump, that will be activated when the soil moisture sensor detects low moisture level, and the ambient sensor detects low humidity or high temperature. Alternatively, the garden or farm owners can remotely control the pump motor through the mobile app to water in the farm leveraging sensor data. Also, it can control the water flow angle according to their preferences. Additionally, this proposed system has the potential to save 25% to 40% of water and energy resources than traditional irrigation methods. The overall cost of the system is approximately 2476 taka, which is around the cost of 20 USD. The system is design

关键词：

Multi-objective Discrete Brainstorming Optimizer for Multiple-product Partial U-shaped Disassembly Line Balancing Problem

学校读者我要写书评

暂无评论

Multi-objective Discrete Brainstorming Optimizer for Multipl...

第33届中国控制与决策会议

作者： Kun Wu Xiwang Guo Shixin Liu Liang Qi Jian Zhao Ziyan Zhao Xu Wang College of Computer and Communication Engineering Liaoning Petrochemical University College of Information Science and Engineering Northeastern University Department of Computer Intelligent Sci.& Tech. Shandong University of Science and Technology School of Science University of Science and Technology Liaoning School of Economics Hebei University of Environmental Engineering

To promote the development of disassembly industrialization,reasonable disassembly line layout and disassembly process planning are *** to the concept of lean production,this paper presents a multiple-product partial U-shaped disassembly line balancing(MP-UDLB) *** this MP-UDLB problem,different types of end-of-life products are disassembled on the same U-shaped disassembly line at the same time.A mathematical model is constructed for the MP-UDLB problem to maximize the disassembly profit and minimize disassembly energy consumption simultaneously.A multi-objective discrete brainstorming optimizer(MDBO) is proposed to solve the MP-UDLB *** proposed MDBO is applied to real-world cases,i.e.,a ballpoint pen and washing machine,and compared with other multi-objective *** experimental results show that the proposed MDBO outperforms peer algorithms.

关键词： Multiple product disassembly U-shaped disassembly line balancing problem Energy consumption Multi-objective discrete brainstorming optimizer

Understanding the Robustness of 3D Object Detection with Bird'View Representations in Autonomous Driving

学校读者我要写书评

暂无评论

Understanding the Robustness of 3D Object Detection with Bir...

2023 IEEE/CVF Conference on computer Vision and Pattern Recognition, CVPR 2023

作者： Zhu, Zijian Zhang, Yichi Chen, Hai Dong, Yinpeng Zhao, Shu Ding, Wenbo Zhong, Jiachen Zheng, Shibao Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China Institute for Ai Tsinghua University BNRist Center Thbi Lab Dept. of Comp. Sci. and Tech. China School of Computer Science and Technology Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Information Materials and Intelligent Sensing Laboratory of Anhui Province China Saic Motor Ai Lab Zhongguancun Laboratory China

ISBN: (纸本)9798350301298

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird's-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations;2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features;3) Camera-LiDARfusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models. © 2023 IEEE.

关键词： Autonomous driving

CoSpace: Benchmarking Continuous Space Perception Ability for Vision-Language Models

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Zhu, Yiqi Wang, Ziyue Zhang, Can Li, Peng Liu, Yang Dept. of Comp. Sci. & Tech. Institute for AI Tsinghua University Beijing China Tsinghua University Beijing China School of Computer and Communication Engineering University of Science and Technology Beijing China Shanghai Artificial Intelligence Laboratory Shanghai China Jiangsu Collaborative Innovation Center for Language Competence Jiangsu China

Vision-Language Models (VLMs) have recently witnessed significant progress in visual comprehension. As the permitting length of image context grows, VLMs can now comprehend a broader range of views and spaces. Current benchmarks provide insightful analysis of VLMs in tasks involving complex visual instructions following, multi-image understanding and spatial reasoning. However, they usually focus on spatially irrelevant images or discrete images captured from varied viewpoints. The compositional characteristic of images captured from a static viewpoint remains underestimated. We term this characteristic as Continuous Space Perception. When observing a scene from a static viewpoint while shifting orientations, it produces a series of spatially continuous images, enabling the reconstruction of the entire space. In this paper, we present CoSpace, a multi-image visual understanding benchmark designed to assess the Continuous Space perception ability for VLMs. CoSpace contains 2,918 images and 1,626 question-answer pairs, covering seven types of tasks. We conduct evaluation across 19 proprietary and open-source VLMs. Results reveal that there exist pitfalls on the continuous space perception ability for most of the evaluated models, including proprietary ones. Interestingly, we find that the main discrepancy between open-source and proprietary models lies not in accuracy but in the consistency of responses. We believe that enhancing the ability of continuous space perception is essential for VLMs to perform effectively in real-world tasks and encourage further research to advance this capability. © 2025, CC BY.

关键词： Visual languages

CODE-MVP: Learning to Represent Source Code from Multiple Views with Contrastive Pre-Training

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Wang, Xin Wang, Yasheng Wan, Yao Wang, Jiawei Zhou, Pingyi Li, Li Wu, Hao Liu, Jin School of Computer Science Wuhan University China Huawei Noah’s Ark Lab. School of Computer Sci. & Tech. Huazhong University of Science and Technology China Faculty of Information Technology Monash University Australia School of Information Science and Engineering Yunnan University China

Recent years have witnessed increasing interest in code representation learning, which aims to represent the semantics of source code into distributed vectors. Currently, various works have been proposed to represent the complex semantics of source code from different views, including plain text, Abstract Syntax Tree (AST), and several kinds of code graphs (e.g., Control/Data Flow Graph). However, most of them only consider a single view of source code independently, ignoring the correspondences among different views. In this paper, we propose to integrate different views with the natural-language description of source code into a unified framework with Multi-View contrastive Pre-training, and name our model as CODE-MVP. Specifically, we first extract multiple code views using compiler tools, and learn the complementary information among them under a contrastive learning framework. Inspired by the type checking in compilation, we also design a fine-grained type inference objective in the pretraining. Experiments on three downstream tasks over five datasets demonstrate the superiority of CODE-MVP when compared with several state-of-the-art baselines. For example, we achieve 2.4/2.3/1.1 gain in terms of MRR/MAP/Accuracy metrics on natural language code retrieval, code similarity, and code defect detection tasks, respectively. Copyright © 2022, The Authors. All rights reserved.

关键词： computer programming languages

Learning superconductivity from ordered and disordered material structures 24

学校读者我要写书评

暂无评论

Learning superconductivity from ordered and disordered mater...

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Pin Chen Luoxuan Peng Rui Jiao Qing Mo Zhen Wang Wenbing Huang Yang Liu Yutong Lu National Supercomputer Center in Guangzhou School of Computer Science and Engineering Sun Yat-sen University Dept. of Comp. Sci. & Tech. Institute for AI BNRist Center Tsinghua University and Institute for AIR Tsinghua University Gaoling School of Artificial Intelligence Renmin University of China and Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China

ISBN: (纸本)9798331314385

Superconductivity is a fascinating phenomenon observed in certain materials under certain conditions. However, some critical aspects of it, such as the relationship between superconductivity and materials' chemical/structural features, still need to be understood. Recent successes of data-driven approaches in material science strongly inspire researchers to study this relationship with them, but a corresponding dataset is still lacking. Hence, we present a new dataset for data-driven approaches, namely SuperCon3D, containing both 3D crystal structures and experimental superconducting transition temperature (Tc) for the first time. Based on SuperCon3D, we propose two deep learning methods for designing high Tc superconductors. The first is SODNet, a novel equivariant graph attention model for screening known structures, which differs from existing models in incorporating both ordered and disordered geometric content. The second is a diffusion generative model DiffCSP-SC for creating new structures, which enables high Tc-targeted generation. Extensive experiments demonstrate that both our proposed dataset and models are advantageous for designing new high Tc superconducting candidates.

关键词：

Compilable Neural Code Generation with Compiler Feedback

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Wang, Xin Wang, Yasheng Wan, Yao Mi, Fei Li, Yitong Zhou, Pingyi Liu, Jin Wu, Hao Jiang, Xin Liu, Qun School of Computer Science Wuhan University China Huawei Noah’s Ark Lab Huawei Technologies Co. Ltd. China School of Computer Sci. & Tech. Huazhong University of Science and Technology China School of Information Science and Engineering Yunnan University China

Automatically generating compilable programs with (or without) natural language descriptions has always been a touchstone problem for computational linguistics and automated software engineering. Existing deep-learning approaches model code generation as text generation, either constrained by grammar structures in decoder, or driven by pre-trained language models on large-scale code corpus (e.g., CodeGPT, PLBART, and CodeT5). However, few of them account for compilability of the generated programs. To improve compilability of the generated programs, this paper proposes COMPCODER, a three-stage pipeline utilizing compiler feedback for compilable code generation, including language model fine-tuning, compilability reinforcement, and compilability discrimination. Comprehensive experiments on two code generation tasks demonstrate the effectiveness of our proposed approach, improving the success rate of compilation from 44.18 to 89.18 in code completion on average and from 70.3 to 96.2 in text-to-code generation, respectively, when comparing with the state-of-the-art CodeGPT. Copyright © 2022, The Authors. All rights reserved.

关键词： Program compilers