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Artificial Intelligence in Innovation: How to Spot Emerging Trends and Technologies Published in IEEE Transactions on Engineering Management

Abstract: Firms apply strategic foresight in technology and innovation management to detect discontinuous changes early, to assess their expected consequences, and to develop a future course of action enabling superior company performance. For this purpose, an ever-increasing amount of data has to be collected, analyzed, and interpreted. Still, a major part of these activities is performed manually, which requires high investments in various resources. To support these processes more efficiently, this article presents an artificial-intelligence-based data mining model that helps firms spot emerging topics and trends at a higher level of automation than before. 

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It’s quite obvious that in today’s information age, access to research papers is crucial for academic and professional success and growth.

However, many research papers, especially those published in IEEE (Institute of Electrical and Electronics Engineers) journals and conferences, often come with subscription that requires one to pay.

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In this article, we will look at how to download IEEE research papers for free.

Table of Contents ↬

Steps on how to download ieee research papers for free, step 1: visit the ieee explore site library.

To kick-start your journey to accessing IEEE research papers for free, the first step is to visit the IEEE Explore Site Library.

Step 2: Search for the Paper Name

Once you’re on the IEEE Explore site, use the search bar to look for the paper you’re interested in.

You can search by the paper’s title, keywords, or the author’s name.

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After finding the paper you need, open it and scroll down to locate the DOI (Digital Object Identifier) section.

⚠️ As you can see, access is needed to download the paper - but don’t worry we are going to get it for free.

The DOI is a unique alphanumeric code assigned to each academic paper, making it easy to access the paper online.

Now that you’ve found the DOI, highlight and copy the code.

💡 Though, instead of copying the DOI code, you can also copy the site URL. But we recommend copying the DOI code, as it’s a more direct method.

Step 4: Visit Sci-Hub

To access the IEEE research paper for free, you’ll need to head over to Sci-Hub .

Sci-Hub is a platform that provides free access to millions of research papers, bypassing paywalls and subscription requirements.

It allows one to download not only IEEE research papers for free but also research papers from Wiley, ScienceDirect, Springer and others.

Step 5: Paste the DOI Code

Once you’re on Sci-Hub’s website, you’ll find an available box.

Paste the DOI code you copied from the IEEE Explore site library into this box.

Then, click the “Open” button.

Step 6: Access and Download the Paper

To download the paper, simply click on the “Save” button located on the left side of the screen.

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⚠️ While Sci-Hub provides access to research papers without a fee, it’s important to be aware of the ethical considerations surrounding this practice. Researchers and institutions rely on subscription fees to support their work. If you find a paper particularly useful, consider supporting the authors and journals through legal means.

• Is it legal to use Sci-Hub to access research papers for free? While Sci-Hub provides free access to research papers, it operates in a legal gray area. It’s essential to be aware of the legal implications and ethical considerations in your region.
• Can I access all IEEE research papers for free using this method? You can access many IEEE research papers through Sci-Hub, but not all papers may be available. Availability depends on various factors, including copyright restrictions.
• Is there an alternative to Sci-Hub for accessing research papers for free? Yes, some universities and institutions offer open-access repositories where you can find research papers for free. Additionally, you can explore preprint servers and open-access journals.
• How can I ensure the downloaded papers are of high quality and reliable? To ensure the quality of downloaded papers, check the source and verify the DOI. Additionally, consider using other academic databases and resources for a comprehensive review.
• Can I share the downloaded papers with others? Sharing downloaded papers should be done in accordance with copyright laws and licensing agreements. Be cautious and respectful of intellectual property rights.

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What are the methods to access IEEE papers without any cost?

To download IEEE papers for free, you can try the following methods:

Method 1: IEEE Member Digital Library Trial [1]

• If you are an IEEE member, you can sign up for a free trial of the IEEE Member Digital Library.
• During the trial, you will have access to all IEEE journal articles and conference proceedings.
• You can download up to three IEEE articles for free during the trial period.
• After the trial, you can subscribe to the IEEE Member Digital Library for a monthly fee to continue accessing articles.

Method 2: Sci-Hub [2]

• Sci-Hub is a website that provides free access to research papers, including IEEE papers.
• You can access Sci-Hub using different URLs such as http://sci-hub.ee , https://sci-hub.ee , https://sci-hub.st , https://sci-hub.ai , or https://sci-hub.se .
• Simply visit one of these URLs and enter the title or DOI of the IEEE paper you want to download.
• Sci-Hub will retrieve the paper for you, and you can download it for free.

Method 3: Using Links [3]

• For IEEE papers, you can visit the IEEE Xplore website at http://ieeexplore.ieee.org .
• Search for the paper you want to download and copy the URL of the paper's page.
• You can then use websites like http://sci-hub.se to paste the paper's URL and download it for free.
• This method can also be used for papers from other publishers like ScienceDirect, Springer, ACM, and Wiley.

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Join the community, search results for author: ieee, found 68 papers, 11 papers with code, variational neuron shifting for few-shot image classification across domains.

no code implementations • journal 2024 • Liyun Zuo , Baoyan Wang , Lei Zhang , Jun Xu , Member , IEEE , and Xiantong Zhen

Existing meta-learning models learn the ability of learning good representation or model parameters, in order to adapt to new tasks with a few training samples.

Instance Paradigm Contrastive Learning for Domain Generalization

no code implementations • IEEE Transactions on Circuits and Systems for Video Technology 2024 • Zining Chen , Weiqiu Wang , Zhicheng Zhao , Fei Su , Member , IEEE , Aidong Men , and Yuan Dong

In this paper, we propose an instance paradigm contrastive learning framework, introducing contrast between original features and novel paradigms to alleviate domain-specific distractions.

An Ultralightweight Hybrid CNN Based on Redundancy Removal for Hyperspectral Image Classification

no code implementations • IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2024 • Xiaohu Ma , Wuli Wang , Member , IEEE

Simultaneously, for PW-Conv, we design a spectral convolution with redundancy removal (R2Spectral-Conv).

Meta Reinforcement Learning for Multi-Task Offloading in Vehicular Edge Computing

no code implementations • TMC 2024 • Penglin Dai , Yaorong Huang , Kaiwen Hu , Xiao Wu , Huanlai Xing , and Zhaofei Yu , Member , IEEE

The objective is to design a unified solution to minimize task execution time under different MTO scenarios.

Ultra-Robust Real-Time Estimation of Gait Phase

no code implementations • IEEE Transactions on Neural Systems and Rehabilitation Engineering 2023 • Mohammad Shushtari , Hannah Dinovitzer , Jiacheng Weng , and Arash Arami , Member , IEEE

The estimator is finally tested on a participant walking with an active exoskeleton, demonstrating the robustness of D67 in interaction with an exoskeleton without being trained on any data from the test subject with or without an exoskeleton.

Interaction-Aware Planning With Deep Inverse Reinforcement Learning for Human-Like Autonomous Driving in Merge Scenarios

1 code implementation • journal 2023 • Jiangfeng Nan , Weiwen Deng , Member , IEEE , Ruzheng Zhang , Ying Wang , Rui Zhao , Juan Ding

To consider the interaction factor, the reward function for planning is utilized to evaluate the joint trajectories of the autonomous driving vehicle (ADV) and traffic vehicles.

Spoof Trace Disentanglement for generic face antispoofing

no code implementations • journal 2023 • Yaojie Liu and Xiaoming Liu , Member , IEEE

Yet, it is a challenging task due to the diversity of spoof attacks and the lack of ground truth for spoof traces.

Bio-Inspired Feature Selection in Brain Disease Detection via an Improved Sparrow Search Algorithm

no code implementations • IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2022 • Wenyu Yu , Hui Kang , Geng Sun , Member , Shuang Liang , and Jiahui Li , Student Member , IEEE

Finally, the proposed ISSA is utilized to solve the objective function.

VCI-LSTM: Vector Choquet Integral-based Long Short-Term Memory

no code implementations • IEEE 2022 • Mikel Ferrero-Jaurrieta , Zdenko Taka ́cˇ , Javier Ferna ́ndez , Member , IEEE , Lˇubom ́ıra Horanska ́ , Grac ̧aliz Pereira Dimuro , Susana Montes , Irene D ́ıaz and Humberto Bustince , Fellow , IEEE.

Choquet integral is a widely used aggregation oper- ator on one-dimensional and interval-valued information, since it is able to take into account the possible interaction among data.

Lightweight Deep Neural Network for Joint Learning of Underwater Object Detection and Color Conversion

no code implementations • journal 2022 • Chia-Hung Yeh , Chu-Han Lin , Li-Wei Kang , Member , Chih-Hsiang Huang , Min-Hui Lin , Chuan-Yu Chang , and Chua-Chin Wang , Senior Member , IEEE

Li-Wei Kang is with the Department of Electrical Engineering, National Taiwan Normal University, Taipei 106, Taiwan (e-mail: lwkang@ntnu. edu. tw).

Topology Change Aware Data-Driven Probabilistic Distribution State Estimation Based on Gaussian Process

no code implementations • IEEE Transactions on Smart Grid 2022 • Di Cao , Member , Junbo Zhao , Weihao Hu , Senior Member , Qishu Liao , Qi Huang , Zhe Chen , Fellow , IEEE

Abstract—This paper addresses the distribution system state estimation (DSSE) with unknown topology change.

STMGCN: Mobile Edge Computing-Empowered Vessel Trajectory Prediction Using Spatio-Temporal Multigraph Convolutional Network

no code implementations • IEEE Transactions on Industrial Informatics 2022 • Ryan Wen Liu , Maohan Liang , Jiangtian Nie , Yanli Yuan , Zehui Xiong , Member , IEEE , Han Yu

—The revolutionary advances in machine learning and data mining techniques have contributed greatly to the rapid developments of maritime Internet of Things (IoT).

Coverage Control Algorithm for DSNs Based on Improved Gravitational Search

no code implementations • IEEE Sensors Journal 2022 • Yindi Yao , Huanmin Liao , Xiong Li , Student Member , IEEE , Feng Zhao , Xuan Yang , and Shanshan Hu

—In directional sensor networks (DSNs), coverage control is an important way to ensure efficient communication and reliable data transmission.

High-order Correlation Preserved Incomplete Multi-view Subspace Clustering

3 code implementations • IEEE Transactions on Image Processing 2022 • Zhenglai Li , Chang Tang , Xiao Zheng , Xinwang Liu , Senior Member , Wei zhang , Member , IEEE , and En Zhu

Specifically, multiple affinity matrices constructed from the incomplete multi-view data are treated as a thirdorder low rank tensor with a tensor factorization regularization which preserves the high-order view correlation and sample correlation.

A GAN-Based Short-Term Link Traffic Prediction Approach for Urban Road Networks Under a Parallel Learning Framework

no code implementations • IEEE Transactions on Intelligent Transportation Systems 2022 • Junchen Jin , Member , IEEE , Dingding Rong , Tong Zhang , Qingyuan Ji , Haifeng Guo , Yisheng Lv , Xiaoliang Ma , and Fei-Yue Wang

This paper proposes a short-term traffic speed prediction approach, called PL-WGAN, for urban road networks, which is considered an important part of a novel parallel learning framework for traffic control and operation.

Shallow Network Based on Depthwise Over-Parameterized Convolution for Hyperspectral Image Classification

no code implementations • 1 Dec 2021 • Hongmin Gao , Member , Zhonghao Chen , Student Member , IEEE , Chenming Li

Therefore, this letter proposes a shallow model for HSIC, which is called depthwise over-parameterized convolutional neural network (DOCNN).

Distributed Differential Evolution Based on Adaptive Mergence and Split for Large-Scale Optimization

1 code implementation • IEEE Transactions on Evolutionary Computation 2021 • Yinglan Feng , Liang Feng , Senior Member , Sam Kwong , and Kay Chen Tan , Fellow , IEEE

In this way, the number of subpopulations is adaptively adjusted and better performing subpopulations obtain more individuals.

Double Deep Q-learning Based Real-Time Optimization Strategy for Microgrids

no code implementations • 27 Jul 2021 • Hang Shuai , Xiaomeng Ai , Jiakun Fang , Wei Yao , Senior Member , Jinyu Wen , Member , IEEE

It is challenging to solve this kind of stochastic nonlinear optimization problem.

A Novel Deep Learning Method for Thermal to Annotated Thermal-Optical Fused Images

no code implementations • 13 Jul 2021 • Suranjan Goswami , IEEE Student Member , Satish Kumar Singh , Senior Member , Bidyut B. Chaudhuri , Life Fellow , IEEE

As a part of this work, we also present a new and unique database for obtaining the region of interest in thermal images based on an existing thermal visual paired database, containing the Region of Interest on 5 different classes of data.

Deep Learning Based Autonomous Vehicle Super Resolution DOA Estimation for Safety Driving

no code implementations • IEEE Transactions on Intelligent Transportation Systems 2021 • Liangtian Wan , Yuchen Sun , Lu Sun , Member , Zhaolong Ning , Senior Member , and Joel J. P. C. Rodrigues , Fellow , IEEE

Abstract— In this paper, a novel system architecture including a massive multi-input multi-output (MIMO) or a reconfigurable intelligent surface (RIS) and multiple autonomous vehicles is considered in vehicle location systems.

Content-Preserving Image Stitching with Piecewise Rectangular Boundary Constraints

no code implementations • IEEE Transactions on Visualization and Computer Graphics 2021 • Yun Zhang , Yu-Kun Lai , and Fang-Lue Zhang , Member , IEEE

By analyzing the irregular boundary, we construct a piecewise rectangular boundary.

Deep Reinforcement Learning Based Optimization for IRS Based UAV-NOMA Downlink Networks

no code implementations • 17 Jun 2021 • Shiyu Jiao , Ximing Xie , Zhiguo Ding , Fellow , IEEE

This paper investigates the application of deep deterministic policy gradient (DDPG) to intelligent reflecting surface (IRS) based unmanned aerial vehicles (UAV) assisted non-orthogonal multiple access (NOMA) downlink networks.

Detailed Primary and Secondary Distribution System Model Enhancement Using AMI Data

no code implementations • 29 May 2021 • Karen Montano-Martinez , Sushrut Thakar , Shanshan Ma , Zahra Soltani , Student Member , Vijay Vittal , Life Fellow , Mojdeh Khorsand , Raja Ayyanar , Senior Member , Cynthia Rojas , Member , IEEE

Reliable and accurate distribution system modeling, including the secondary network, is essential in examining distribution system performance with high penetration of distributed energy resources (DERs).

Context-aware taxi dispatching at city-scale using deep reinforcement learning

no code implementations • IEEE Transactions on Intelligent Transportation Systems 2021 • Zhidan Liu , Jiangzhou Li , and Kaishun Wu , Member , IEEE

Abstract— Proactive taxi dispatching is of great importance to balance taxi demand-supply gaps among different locations in a city.

Low-Complexity Symbol Detection and Interference Cancellation for OTFS System

no code implementations • 期刊 2021 • Huiyang Qu , Guanghui Liu , Lei Zhang , Shan Wen , Graduate Student Member , and Muhammad Ali Imran , Senior Member , IEEE

Orthogonal time frequency space (OTFS) is a two-dimensional modulation scheme realized in the delay- Doppler domain, which targets the robust wireless transmissions in high-mobility environments.

Multi-Scale and Multi-Direction GAN for CNN-Based Single Palm-V ein Identification

no code implementations • IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2021 • Huafeng Qin , Mounim A. El-Y acoubi , Y a n t a o L i , Member , IEEE , and Chongwen Liu

Despite recent advances of deep neural networks in hand vein identification, the existing solutions assume the availability of a large and rich set of training image samples.

Joint Trajectory and Power Allocation Design for Secure Artificial Noise Aided UAV Communications

no code implementations • journals 2021 • Milad Tatar Mamaghani , Graduate Student Member , and Yi Hong , Senior Member , IEEE

This paper investigates an average secrecy rate (ASR) maximization problem for an unmanned aerial vehicle (UAV) enabled wireless communication system, wherein a UAV is employed to deliver confidential information to a ground destination in the presence of a terrestrial passive eavesdropper.

A 510-nW Wake-Up Keyword-Spotting Chip Using Serial-FFT-Based MFCC and Binarized Depthwise Separable CNN in 28-nm CMOS

no code implementations • journal 2021 • Weiwei Shan , Minhao Yang , Tao Wang , Yicheng Lu , Hao Cai , Lixuan Zhu , Jiaming Xu , Chengjun Wu , Longxing Shi , Senior Member , and Jun Yang , Member , IEEE

We propose a sub-µW always-ON keyword spotting (µKWS) chip for audio wake-up systems.

Data-Driven Assisted Chance-Constrained Energy and Reserve Scheduling with Wind Curtailment

no code implementations • 2 Nov 2020 • Xingyu Lei , Student Member , Zhifang Yang , Member , Junbo Zhao , Juan Yu , Senior Member , IEEE

Case studies performed on the PJM 5-bus and IEEE 118-bus systems demonstrate that the proposed method is capable of accurately accounting the influence of wind curtailment dispatch in CCO.

Systems and Control Systems and Control

CRPN-SFNet: A High-Performance Object Detector on Large-Scale Remote Sensing Images

no code implementations • 28 Oct 2020 • QiFeng Lin , Jianhui Zhao , Gang Fu , and Zhiyong Yuan , Member , IEEE

Extensive experiments on the public Dataset for Object deTection in Aerial images data set indicate that our CRPN can help our detector deal the larger image faster with the limited GPU memory; meanwhile, the SFNet is beneficial to achieve more accurate detection of geospatial objects with wide-scale range.

Frame-wise Cross-modal Matching for Video Moment Retrieval

1 code implementation • 22 Sep 2020 • Haoyu Tang , Jihua Zhu , Meng Liu , Member , IEEE , Zan Gao , Zhiyong Cheng

Another contribution is that we propose an additional predictor to utilize the internal frames in the model training to improve the localization accuracy.

Attention Transfer Network for Nature Image Matting

1 code implementation • IEEE Transactions on Circuits and Systems for Video Technology 2020 • Fenfen Zhou , Yingjie Tian , Member , IEEE , and Zhiquan Qi

Then, we introduce a scale transfer block to magnify the feature maps without adding extra information.

A New Multiple Source Domain Adaptation Fault Diagnosis Method between Different Rotating Machines

no code implementations • TRANSACTIONS ON INDUSTRIAL INFORMA TICS 2020 • un Zhu , Nan Chen , Member , IEEE , and Changqing Shen

To solve this issue, transfer learning is proposed by leveraging knowl- edge learned from source domain to target domain.

Learning Person Re-identification Models from Videos with Weak Supervision

no code implementations • 21 Jul 2020 • Xueping Wang , Sujoy Paul , Dripta S. Raychaudhuri , Min Liu , Yaonan Wang , Amit K. Roy-Chowdhury , Fellow , IEEE

In order to cope with this issue, we introduce the problem of learning person re-identification models from videos with weak supervision.

Obstacle Avoidance and Tracking Control of Redundant Robotic Manipulator: An RNN-Based Metaheuristic Approach

no code implementations • IEEE Transactions on Industrial Informatics 2020 • Ameer Hamza Khan , Student Member , Shuai Li , and Xin Luo , Senior Member , IEEE

In this article, we present a metaheuristic-based control framework, called beetle antennae olfactory recurrent neural network, for simultaneous tracking control and obstacle avoidance of a redundant manipulator.

Edge server deployment scheme of blockchain in IoVs

no code implementations • 16 Jun 2020 • Liya Xu , Mingzhu Ge , Weili Wu , Member , IEEE

In fact, the application of blockchain in IoVs can be implemented by employing edge computing.

Service Provisioning Framework for RAN Slicing: User Admissibility, Slice Association and Bandwidth Allocation

no code implementations • IEEE Transactions on Mobile Computing 2020 • Yao Sun , Shuang Qin , Member , Gang Feng , Lei Zhang , and Muhammad Ali Imran , SeniorMember , IEEE

Network slicing (NS) has been identified as one of the most promising architectural technologies for future mobile network systems to meet the extremely diversified service requirements of users.

A Simplified 2D-3D CNN Architecture for Hyperspectral Image Classification Based on Spatial–Spectral Fusion

no code implementations • 5 Jun 2020 • Chunyan Yu , Rui Han , Meiping Song , Caiyu Liu , and Chein-I Chang , Life Fellow , IEEE

Abstract—Convolutional neural networks (CNN) have led to a successful breakthrough for hyperspectral image classification (HSIC).

Decision Fusion in Space-Time Spreading aided Distributed MIMO WSNs

no code implementations • 16 May 2020 • I. Dey , H. Joshi , Member , N. Marchetti , Senior Member , IEEE

In this letter, we propose space-time spreading (STS) of local sensor decisions before reporting them over a wireless multiple access channel (MAC), in order to achieve flexible balance between diversity and multiplexing gain as well as eliminate any chance of intrinsic interference inherent in MAC scenarios.

Energy-Efficient Over-the-Air Computation Scheme for Densely Deployed IoT Networks

no code implementations • IEEE 2020 • Semiha Tedik Basaran , Student Member , Gunes Karabulut Kurt , and Periklis Chatzimisios , Senior Member , IEEE

The proposed MMSE estimator provides a signif- icant mean squared error improvement with reducing en- ergy consumption compared to the conventional estimator.

A Lightweight and Privacy-Preserving Authentication Protocol for Mobile Edge Computing

no code implementations • 27 Feb 2020 • Kuljeet Kaur∗ , Sahil Garg∗ , Georges Kaddoum∗ , Member , Mohsen Guizani† , Fellow , IEEE , and Dushantha Nalin K. Jayakody‡ , Senior Member , IEEE.

With the advent of the Internet-of-Things (IoT), vehicular networks and cyber-physical systems, the need for realtime data processing and analysis has emerged as an essential pre-requite for customers’ satisfaction.

Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning

1 code implementation • 24 Feb 2020 • Chongwen Huang , Member , IEEE , Ronghong Mo , Chau Yuen , Senior Member

In this paper, we investigate the joint design of transmit beamforming matrix at the base station and the phase shift matrix at the RIS, by leveraging recent advances in deep reinforcement learning (DRL).

Reinforcement Learning Tracking Control for Robotic Manipulator With Kernel-Based Dynamic Model

no code implementations • TRANSACTION 2020 • Yazhou Hu , Wenxue Wang , Hao liu , and Lianqing Liu , Member , IEEE

In this algorithm, a reward function is defined according to the features of tracking control in order to speed up the learning process, and then an RL tracking controller with a kernel-based transition dynamic model is proposed.

Broad Learning System Based on Maximum Correntropy Criterion

no code implementations • 24 Dec 2019 • Yunfei Zheng , Badong Chen , Shiyuan Wang , Senior Member , Weiqun Wang , Member , IEEE

As an effective and efficient discriminative learning method, Broad Learning System (BLS) has received increasing attention due to its outstanding performance in various regression and classification problems.

Localization and Clustering Based on Swarm Intelligence in UAV Networks for Emergency Communications

no code implementations • IEEE Internet of Things Journal 2019 • Muhammad Yeasir Arafat , Sangman Moh , Member , IEEE

Second, we propose an energy-efficient swarm-intelligence-based clustering (SIC) algorithm based on PSO, in which the particle fitness function is exploited for inter-cluster distance, intra-cluster distance, residual energy, and geographic location.

GETNET: A General End-to-end Two-dimensional CNN Framework for Hyperspectral Image Change Detection

1 code implementation • 5 May 2019 • Qi. Wang , Senior Member , Zhenghang Yuan , Qian Du , Xuelong. Li , Fellow , IEEE

In order to better handle high dimension problem and explore abundance information, this paper presents a General End-to-end Two-dimensional CNN (GETNET) framework for hyperspectral image change detection (HSI-CD).

VSSA-NET: Vertical Spatial Sequence Attention Network for Traffic Sign Detection

no code implementations • 5 May 2019 • Yuan Yuan , Zhitong Xiong , Student Member , Qi. Wang , Senior Member , IEEE

Our contributions are as follows: 1) We propose a multi-resolution feature fusion network architecture which exploits densely connected deconvolution layers with skip connections, and can learn more effective features for the small size object; 2) We frame the traffic sign detection as a spatial sequence classification and regression task, and propose a vertical spatial sequence attention (VSSA) module to gain more context information for better detection performance.

Discrete-Time Impulsive Adaptive Dynamic Programming

no code implementations • IEEE Transactions on Cybernetics 2019 • Qinglai Wei , Ruizhuo Song , Member , IEEE , Zehua Liao , Benkai Li , and Frank L. Lewis

Abstract—In this paper, a new iterative adaptive dynamic programming (ADP) algorithm is developed to solve optimal impulsive control problems for infinite horizon discrete-time nonlinear systems.

Generalization of the Dark Channel Prior for Single Image Restoration

no code implementations • IEEE Transactions on Image Processing 2019 • Yan-Tsung Peng , Keming Cao , and Pamela C. Cosman , Fellow , IEEE

Abstract— Images degraded by light scattering and absorption, such as hazy, sandstorm, and underwater images, often suffer color distortion and low contrast because of light traveling through turbid media.

A MIP Model for Risk Constrained Switch Placement in Distribution Networks

no code implementations • IEEE 2019 • Milad Izadi , Student Member , IEEE and Amir Safdarian , Member , IEEE

The model is applied to the RBTS-Bus4 and a real distribution network.

PEA265: Perceptual Assessment of Video Compression Artifacts

no code implementations • 1 Mar 2019 • Liqun Lin , Shiqi Yu , Tiesong Zhao , Member , Zhou Wang , Fellow , IEEE

To monitor and improve visual QoE, it is crucial to develop subjective and objective measures that can identify and quantify various types of PEAs.

A Provably Secure and Efficient Identity-Based Anonymous Authentication Scheme for Mobile Edge Computing

no code implementations • 22 Feb 2019 • Xiaoying Jia，Debiao He , Neeraj Kumar , and Kim-Kwang Raymond Choo , Senior Member , IEEE

Mobile edge computing (MEC) allows one to overcome a number of limitations inherent in cloud computing, although achieving the broad range of security requirements in MEC settings remains challenging.

Location-Centered House Price Prediction: A Multi-Task Learning Approach

no code implementations • 7 Jan 2019 • Guangliang Gao , Zhifeng Bao , Jie Cao , A. K. Qin , Timos Sellis , Fellow , IEEE , Zhiang Wu

Regarding the choice of prediction model, we observe that a variety of approaches either consider the entire house data for modeling, or split the entire data and model each partition independently.

DATS: Dispersive Stable Task Scheduling in Heterogeneous Fog Networks

no code implementations • Conference 2018 • Zening Liu , Xiumei Yang , Yang Yang , Kunlun Wang , and Guoqiang Mao , Fellow , IEEE

Abstract—Fog computing has risen as a promising architecture for future Internet of Things (IoT), 5G and embedded artificial intelligence (AI) applications with stringent service delay requirements along the cloud to things continuum.

Blockchain for Secure and Efficient Data Sharing in Vehicular Edge Computing and Networks

no code implementations • IEEE INTERNET OF THINGS JOURNAL, VOL. 6, NO. 3 2018 • Jiawen Kang , Rong Y u , Xumin Huang , Maoqiang Wu , Sabita Maharjan , Member , Shengli Xie , and Y an Zhang , Senior Member , IEEE

Due to limited resources with vehicles, vehicular edge computing and networks (VECONs) i. e., the integration of mobile edge computing and vehicular networks, can provide powerful computing and massive storage resources.

Optimal Training for Residual Self-Interference for Full-Duplex One-Way Relays

no code implementations • 13 Aug 2018 • Xiaofeng Li , Cihan Tepedelenlio˘glu , and Habib ¸Senol , Member , IEEE

For the former, we propose a training scheme to estimate the overall channel, and for the latter the CRB and the optimal number of relays are derived when the distance between the source and the destination is fixed.

Medical Image Synthesis with Deep Convolutional Adversarial Networks

1 code implementation • IEEE Transactions on Biomedical Engineering 2018 • Dong Nie , Roger Trullo , Jun Lian , Li Wang , Caroline Petitjean , Su Ruan , Qian Wang , and Dinggang Shen , Fellow , IEEE

To better model a nonlinear mapping from source to target and to produce more realistic target images, we propose to use the adversarial learning strategy to better model the FCN.

Single Image Dehazing Using Color Ellipsoid Prior

1 code implementation • IEEE Transactions on Image Processing 2018 • Trung Minh Bui , Student Member , and Wonha Kim , Senior Member , IEEE

The proposed method constructs color ellipsoids that are statistically fitted to haze pixel clusters in RGB space and then calculates the transmission values through color ellipsoid geometry.

Significantly Fast and Robust Fuzzy C-MeansClustering Algorithm Based on MorphologicalReconstruction and Membership Filtering

no code implementations • IEEE 2018 • Tao Lei , Xiaohong Jia , Yanning Zhang , Lifeng He , Hongy-ing Meng , Senior Member , and Asoke K. Nandi , Fellow , IEEE

However, the introduction oflocal spatial information often leads to a high computationalcomplexity, arising out of an iterative calculation of the distancebetween pixels within local spatial neighbors and clusteringcenters.

An Integrated Platform for Live 3D Human Reconstruction and Motion Capturing

no code implementations • 8 Dec 2017 • Dimitrios S. Alexiadis , Anargyros Chatzitofis , Nikolaos Zioulis , Olga Zoidi , Georgios Louizis , Dimitrios Zarpalas , Petros Daras , Senior Member , IEEE

The latest developments in 3D capturing, processing, and rendering provide means to unlock novel 3D application pathways.

Robust Single Image Super-Resolution via Deep Networks With Sparse Prior

1 code implementation • journals 2016 • Ding Liu , Zhaowen Wang , Bihan Wen , Student Member , Jianchao Yang , Member , Wei Han , and Thomas S. Huang , Fellow , IEEE

We demonstrate that a sparse coding model particularly designed for SR can be incarnated as a neural network with the merit of end-to-end optimization over training data.

A Decentralized Cooperative Control Scheme With Obstacle Avoidance for a Team of Mobile Robots

no code implementations • journal 2013 • Hamed Rezaee , Student Member , and Farzaneh Abdollahi , Member , IEEE

The problem of formation control of a team of mobile robots based on the virtual and behavioral structures is considered in this paper.

A Grid-Based Evolutionary Algorithm for Many-Objective Optimization

1 code implementation • IEEE Transactions on Evolutionary Computation 2013 • Shengxiang Yang , Member , IEEE , Miqing Li , Xiaohui Liu , and Jinhua Zheng

Balancing convergence and diversity plays a key role in evolutionary multiobjective optimization (EMO).

Physiological Parameter Monitoring from Optical Recordings with a Mobile Phone

no code implementations • 29 Jul 2011 • Christopher G. Scully , Student Member , Jinseok Lee , Joseph Meyer , Alexander M. Gorbach , Domhnull Granquist-Fraser , Yitzhak Mendelson , Member , and Ki H. Chon , Senior Member , IEEE

We show that a mobile phone can serve as an accurate monitor for several physiological variables, based on its ability to record and analyze the varying color signals of a fingertip placed in contact with its optical sensor.

Performance Analysis of Two Hop Amplify-and-Forward Systems with Interference at the Relay

no code implementations • journal 2010 • Himal A. Suraweera , Member , HariK.Garg , and A. Nallanathan , Senior Member , IEEE

Abstract—We analyze the performance of a two hop channel state information (CSI)-assisted amplify-and-forward system, with co-channel interference at the relay.

Efficiently Indexing Large Sparse Graphs for Similarity Search

no code implementations • 18 Feb 2010 • Guoren Wang , Bin Wang , Xiaochun Yang , IEEE Computer Society , and Ge Yu , Member , IEEE

Abstract—The graph structure is a very important means to model schemaless data with complicated structures, such as protein- protein interaction networks, chemical compounds, knowledge query inferring systems, and road networks.

ANALYSIS OF CALIBRATED SEA CLUTTER AND BOAT REFLECTIVITY DATA AT C- AND X-BAND IN SOUTH AFRICAN COASTAL WATERS

no code implementations • IEEE 2007 • Ron Rubinstein , Member , Tomer Peleg , Student Member , and Michael Elad , Fellow , IEEE

Abstract—The synthesis-based sparse representation model for signals has drawn considerable interest in the past decade.

Parameter-free Geometric Document Layout Analysis

no code implementations • IEEE Transactions on Pattern Analysis and Machine Intelligence 2001 • Seong-Whan Lee , Senior Member , IEEE , and Dae-Seok Ryu

Based on the proposed periodicity measure, multiscale analysis, and confirmation procedure, we could develop a robust method for geometric document layout analysis independent of character font sizes, text line spacing, and document layout structures.

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Download papers from IEEExplore with wget

I am now out of institute, but need to download paper from IEEExplore.

I can ssh to the institute server. I think I should be able to access IEEExplore there, but I don't know how to download.

Suppose I am out of institute, and want to download this paper:

I have tried:

but mypaper.pdf turns out to be a broken pdf file.

Could anyone give some suggestions?

• just to complete the solution. step1: ssh into the server, step2: use wget to download the paper, step3: use scp to copy the paper from the remote server to the local disk. –  ulyssis2 Commented Mar 4, 2017 at 22:21

2 Answers 2

I found http://techqe.blogspot.com/2009/09/quick-download-ieee-paper.html , which provides the following solution:

Identify the "arnumber" for the paper, in the hyperlink you found while browsing. In your example, it's 5738219

Enter the following command at your shell:

Your paper will now be saved in "paper.pdf"

• 2 It seems not working any more, the command line returns an html page instead of a pdf. –  tong Commented Dec 26, 2017 at 23:39
• 3 tested - still works as of the date of this comment. –  vrleboss Commented Feb 7, 2018 at 19:05

You can use your institute server as a socks proxy and download as usual using your favourite method.

ssh -D <portNumber> username@serverip

Now the socks proxy has been setup on localhost:portNumber . You can apply the proxy system wide based on your operating system and download as usual.

Not the answer you're looking for? Browse other questions tagged ssh download wget ieee or ask your own question .

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Download full journals from IEEE as pdf (ebook)

I was wonder if anyone knew if it is possible (and how) to download an entire journal (as if it would be printed) from ieeeXplore ?

2 Answers 2

I believe that violates the terms of use for IEEE Xplore. I don't know if you are an IEEE member, institutional subscriber, etc., but the terms of use for institutional subscribers states the following:

Institutional subscribers are NOT permitted to do the following: [...] Download or attempt to download an entire issue or issues of a publication contained in IEEE Xplore.

You should first view the terms of use for IEEE Xplore at http://ieeexplore.ieee.org/xpl/termsOfUse.jsp , and if you have questions contact Xplore support via the contact form http://ieeexplore.ieee.org/xpl/techform.jsp .

Well, to put legality aside merging pdfs into single file is rather easy (I assume that you can download individual articles as pdfs).

You need ghostscript program (avilable on any modern linux, and I guess also for windows) and then issue command:

which will merge 1.pdf , 2.pdf , 3.pdf into combined.pdf .

If you want to mass download articles you can use this firefox plugin: https://addons.mozilla.org/pl/firefox/addon/downthemall/

Anyways: please mind that you might be bending or violating terms and conditions using these techinques.

• I don't think that the problem that the OP wants solving is that he has all the articles in different PDF files ... –  xLeitix Commented Mar 20, 2014 at 8:40
• @xLeitix There is also information how to mass download the files ;) –  jb. Commented Jul 5, 2014 at 15:38

You must log in to answer this question.

Not the answer you're looking for browse other questions tagged journals ieee ..

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IEEE Paper Format | Template & Guidelines

Published on August 24, 2022 by Jack Caulfield . Revised on April 6, 2023.

IEEE provides guidelines for formatting your paper. These guidelines must be followed when you’re submitting a manuscript for publication in an IEEE journal. Some of the key guidelines are:

• Formatting the text as two columns, in Times New Roman, 10 pt.
• Including a byline, an abstract , and a set of keywords at the start of the research paper
• Placing any figures, tables, and equations at the top or bottom of a column, not in the middle
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• Not including page numbers

To learn more about the specifics of IEEE paper format, check out the free template below. Note that you may not need to follow these rules if you’ve only been told to use IEEE citation format for a student paper. But you do need to follow them to submit to IEEE publications.

Table of contents

Ieee format template, ieee heading styles, frequently asked questions about ieee.

The template below can be used to make sure that your paper follows IEEE format. It’s set up with custom Word styles for all the different parts of the text, with the right fonts and formatting and with further explanation of key points.

Make sure to remove all the explanatory text in the template when you insert your own.

Download IEEE paper format template

Prevent plagiarism. Run a free check.

IEEE recommends specific heading styles to distinguish the title and different levels of heading in your paper from each other. Styles for each of these are built into the template.

The paper title is written in 24 pt. Times New Roman, centered at the top of the first page. Other headings are all written in 10 pt. Times New Roman:

• Level 1 text headings begin with a roman numeral followed by a period. They are written in small caps, in title case, and centered.
• Level 2 text headings begin with a capital letter followed by a period. They are italicized, left-aligned, and written in title case.
• Level 3 text headings begin with a number followed by a closing parenthesis . They are italicized, written in sentence case, and indented like a regular paragraph. The text of the section follows the heading immediately, after a colon .
• Level 4 text headings begin with a lowercase letter followed by a closing parenthesis. They are italicized, written in sentence case, and indented slightly further than a normal paragraph. The text of the section follows the heading immediately, after a colon.
• Component headings are used for the different components of your paper outside of the main text, such as the acknowledgments and references. They are written in small caps, in title case, centered, and without any numbering.

You should use 10 pt. Times New Roman font in your IEEE format paper .

For the paper title, 26 pt. Times New Roman is used. For some other paper elements like table footnotes, the font can be slightly smaller. All the correct stylings are available in our free IEEE format template .

No, page numbers are not included in an IEEE format paper . If you’re submitting to an IEEE publication, page numbers will be added in the final publication but aren’t needed in the manuscript.

IEEE paper format requires you to include an abstract summarizing the content of your paper. It appears at the start of the paper, right after you list your name and affiliation.

The abstract begins with the word “Abstract,” italicized and followed by an em dash. The abstract itself follows immediately on the same line. The entire section is written in bold font. For example: “ Abstract —This paper discusses … ”

You can find the correct format for your IEEE abstract and other parts of the paper in our free IEEE paper format template .

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Caulfield, J. (2023, April 06). IEEE Paper Format | Template & Guidelines. Scribbr. Retrieved July 22, 2024, from https://www.scribbr.com/ieee/ieee-paper-format/

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Ieee spectrum, follow ieee spectrum, support ieee spectrum, enjoy more free content and benefits by creating an account, saving articles to read later requires an ieee spectrum account, the institute content is only available for members, downloading full pdf issues is exclusive for ieee members, downloading this e-book is exclusive for ieee members, access to spectrum 's digital edition is exclusive for ieee members, following topics is a feature exclusive for ieee members, adding your response to an article requires an ieee spectrum account, create an account to access more content and features on ieee spectrum , including the ability to save articles to read later, download spectrum collections, and participate in conversations with readers and editors. for more exclusive content and features, consider joining ieee ., join the world’s largest professional organization devoted to engineering and applied sciences and get access to all of spectrum’s articles, archives, pdf downloads, and other benefits. learn more about ieee →, join the world’s largest professional organization devoted to engineering and applied sciences and get access to this e-book plus all of ieee spectrum’s articles, archives, pdf downloads, and other benefits. learn more about ieee →, access thousands of articles — completely free, create an account and get exclusive content and features: save articles, download collections, and talk to tech insiders — all free for full access and benefits, join ieee as a paying member., how good is chatgpt at coding, really, study finds that while ai can be great, it also struggles due to training limitations.

This article is part of our exclusive IEEE Journal Watch series in partnership with IEEE Xplore.

Programmers have spent decades writing code for AI models , and now, in a full circle moment, AI is being used to write code. But how does an AI code generator compare to a human programmer?

A study published in the June issue of IEEE Transactions on Software Engineering evaluated the code produced by OpenAI’s ChatGPT in terms of functionality, complexity and security. The results show that ChatGPT has an extremely broad range of success when it comes to producing functional code—with a success rate ranging from anywhere as poor as 0.66 percent and as good as 89 percent—depending on the difficulty of the task, the programming language, and a number of other factors.

While in some cases the AI generator could produce better code than humans, the analysis also reveals some security concerns with AI-generated code.

Yutian Tang is a lecturer at the University of Glasgow who was involved in the study. He notes that AI-based code generation could provide some advantages in terms of enhancing productivity and automating software development tasks—but it’s important to understand the strengths and limitations of these models.

“By conducting a comprehensive analysis, we can uncover potential issues and limitations that arise in the ChatGPT-based code generation... [and] improve generation techniques,” Tang explains.

To explore these limitations in more detail, his team sought to test GPT-3.5’s ability to address 728 coding problems from the LeetCode testing platform in five programming languages: C, C++, Java, JavaScript, and Python .

“A reasonable hypothesis for why ChatGPT can do better with algorithm problems before 2021 is that these problems are frequently seen in the training dataset.” —Yutian Tang, University of Glasgow

Overall, ChatGPT was fairly good at solving problems in the different coding languages—but especially when attempting to solve coding problems that existed on LeetCode before 2021. For instance, it was able to produce functional code for easy, medium, and hard problems with success rates of about 89, 71, and 40 percent, respectively.

“However, when it comes to the algorithm problems after 2021, ChatGPT’s ability to generate functionally correct code is affected. It sometimes fails to understand the meaning of questions, even for easy level problems,” Tang notes.

For example, ChatGPT’s ability to produce functional code for “easy” coding problems dropped from 89 percent to 52 percent after 2021. And its ability to generate functional code for “hard” problems dropped from 40 percent to 0.66 percent after this time as well.

“A reasonable hypothesis for why ChatGPT can do better with algorithm problems before 2021 is that these problems are frequently seen in the training dataset,” Tang says.

Essentially, as coding evolves, ChatGPT has not been exposed yet to new problems and solutions. It lacks the critical thinking skills of a human and can only address problems it has previously encountered. This could explain why it is so much better at addressing older coding problems than newer ones.

“ChatGPT may generate incorrect code because it does not understand the meaning of algorithm problems.” —Yutian Tang, University of Glasgow

Interestingly, ChatGPT is able to generate code with smaller runtime and memory overheads than at least 50 percent of human solutions to the same LeetCode problems.

The researchers also explored the ability of ChatGPT to fix its own coding errors after receiving feedback from LeetCode. They randomly selected 50 coding scenarios where ChatGPT initially generated incorrect coding, either because it didn’t understand the content or problem at hand.

While ChatGPT was good at fixing compiling errors, it generally was not good at correcting its own mistakes.

“ChatGPT may generate incorrect code because it does not understand the meaning of algorithm problems, thus, this simple error feedback information is not enough,” Tang explains.

The researchers also found that ChatGPT-generated code did have a fair amount of vulnerabilities, such as a missing null test, but many of these were easily fixable. Their results also show that generated code in C was the most complex, followed by C++ and Python, which has a similar complexity to the human-written code.

Tangs says, based on these results, it’s important that developers using ChatGPT provide additional information to help ChatGPT better understand problems or avoid vulnerabilities.

“For example, when encountering more complex programming problems, developers can provide relevant knowledge as much as possible, and tell ChatGPT in the prompt which potential vulnerabilities to be aware of,” Tang says.

• What to Do When the Ghost in the Machine Is You ›
• How Coders Can Survive—and Thrive—in a ChatGPT World ›
• Coding Assistant - ChatGPT ›

Michelle Hampson is a freelance writer based in Halifax. She frequently contributes to Spectrum's Journal Watch coverage, which highlights newsworthy studies published in IEEE journals.

That's yesterday's news, try it with version 4o, it's free.

"struggles due to training limitations" isn't that EVERYONE's problem with EVERYTHING.

"I could be an awesome guitar playing, but I struggle due to training limitations."

"I could be a great Opera singer, but I struggle due to training limitations."

"I could be a great jockey, but I am 6'4"...." Ok, well maybe not everything.

ChatGPT sucks at coding because it's not an AI - it's a big ass word predictor.

I actually think the key here is writing good test suits to ensure AI does the right thing...

Here is the full argument: https://medium.com/@samuel.sperling/software-2-1-ai-is-coding-now-why-test-mastery-is-your-new-job-security-31a65e792f7f

How Amazon’s New CPU Fights Cybersecurity Threats

Long-sought tech helps scientists see the weakest stars, the rise of groupware, related stories, what to do when the ghost in the machine is you, chatgpt’s new upgrade teases ai’s multimodal future, chatgpt may be a better improviser than you.

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A review of precision irrigation water-saving technology under changing climate for enhancing water use efficiency, crop yield, and environmental footprints.

1. Introduction

2. review methodology, 3. results and discussion, 3.1. traditional irrigation method and its modernization for enhancing water use efficiency, 3.1.1. precision irrigation scheduling (ps) for efficient water management, 3.1.2. why do we need precision agriculture and irrigation water-saving systems, 3.2. related work on the precision irrigation water-saving systems, 3.3. precision irrigation water-saving monitoring and control systems, 3.3.1. smart irrigation water-saving controlling tools, 3.3.2. smart irrigation water-saving monitoring techniques.

3.4. Smart Irrigation Water-Saving Architecture and Data-Sharing Communication Technologies

3.4.1. iot architecture, 3.4.2. wireless sensor network architecture, 3.4.3. data sharing and communication technologies (dsct), 3.5. role of artificial intelligence (ai) in irrigation water saving, 3.6. future prospects of piss/siss, 4. conclusions, author contributions, data availability statement, conflicts of interest.

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Lakhiar, I.A.; Yan, H.; Zhang, C.; Wang, G.; He, B.; Hao, B.; Han, Y.; Wang, B.; Bao, R.; Syed, T.N.; et al. A Review of Precision Irrigation Water-Saving Technology under Changing Climate for Enhancing Water Use Efficiency, Crop Yield, and Environmental Footprints. Agriculture 2024 , 14 , 1141. https://doi.org/10.3390/agriculture14071141

Lakhiar IA, Yan H, Zhang C, Wang G, He B, Hao B, Han Y, Wang B, Bao R, Syed TN, et al. A Review of Precision Irrigation Water-Saving Technology under Changing Climate for Enhancing Water Use Efficiency, Crop Yield, and Environmental Footprints. Agriculture . 2024; 14(7):1141. https://doi.org/10.3390/agriculture14071141

Lakhiar, Imran Ali, Haofang Yan, Chuan Zhang, Guoqing Wang, Bin He, Beibei Hao, Yujing Han, Biyu Wang, Rongxuan Bao, Tabinda Naz Syed, and et al. 2024. "A Review of Precision Irrigation Water-Saving Technology under Changing Climate for Enhancing Water Use Efficiency, Crop Yield, and Environmental Footprints" Agriculture 14, no. 7: 1141. https://doi.org/10.3390/agriculture14071141

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