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Internet of Things (The Netherlands) 2025年 32卷

作者： Khoshvaght, Parisa Tanveer, Jawad Rahmani, Amir Masoud Mohammadi, Mohammad Mehranzadeh, Amin Lansky, Jan Hosseinzadeh, Mehdi Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Engineering & Technology Duy Tan University Da Nang Viet Nam Department of Computer Science and Engineering Sejong University Seoul 05006 South Korea Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Computer Engineering Dezful Branch Islamic Azad University Dezful Iran Department of Computer Science and Mathematics Faculty of Economic Studies University of Finance and Administration Prague Czech Republic Pattern Recognition and Machine Learning Laboratory School of Computing Gachon University Seongnam South Korea

The Internet of Things (IoT) technology today has grown rapidly compared to the last few years, and the use of this technology has increased the quality of service to users day by day. The various applications of IoT have caused the attention of this innovation to enhance among different organizations. One of the important challenges of the IoT is routing, which can affect having a stable network. In this research, a hybrid approach called H-TERF (Hybrid TOPSIS and Enhanced Random Forest) is proposed for achieving efficient routing in IoT networks, specifically in Wireless Body Area Networks (WBAN). This method initially cluster nodes by using the DBSCAN clustering algorithm to optimize intra-cluster communication. Then, for routing, the nodes are ranked using the Fuzzy TOPSIS and Fuzzy AHP. This ranking is determined by several criteria, including the remaining energy of nodes, node memory, and throughput. Additionally, to manage more complex criteria such as node historical records and traffic rate, the initial ranking by the TOPSIS approach, along with the other mentioned criteria, is fed into an enhanced random forest model to identify the optimal path. This hybrid method enhances network performance in terms of lifespan, efficiency, delay, and packet delivery ratio. The outcomes of the simulation show that the suggested method surpasses existing approaches and is highly effective for application in IoT and WBAN networks. For example, the performance improvement of the proposed approach over the F-EVM, DECR, and DHH-EFO approaches in energy consumption was 20.62%, 25.85%, and 32.57%, respectively. © 2025 Elsevier B.V.

关键词： DBSCAN clustering Enhanced random forest Fuzzy AHP Fuzzy TOPSIS Internet of Things

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An efficient algorithm for detection of road-like structures in satellite images

An efficient algorithm for detection of road-like structures...

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13th International Conference on pattern recognition, ICPR 1996

作者： Mukherjee, A. Parui, S.K. Chaudhuri, D. Chaudhuri, B.B. Krishnan, R. Computer Vision and Pattern Recognition Unit Indian Statistical Institute Calcutta India Advanced Data Processing Research Institute Secunderabad India

ISBN: (纸本)081867282X

Road networks are important features of satellite imagery. The main contribution of the present road detection method consists of an effective enhancement technique and an efficient segmentation technique that removes non-road pixels step by step from the image where parameters involved: in each step images are determined by the sensor characteristics (like spatial resolution and spectral range) of the satellite. Also, the segmentation process depends not only on the road contrast but also on the road length. Thus, a low contrast but long road segment does not get removed. We have tested the algorithm on a number of images from IRS and SPOT satellites and the results are satisfactory. © 1996 IEEE.

关键词： Satellite imagery

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Human-focused computer vision applications

Human-focused computer vision applications

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International Conference on computer Graphics, Imaging and Visualisation, CGIV'06

作者： Piccardi, Massimo Department of Computer Systems Faculty of IT University of Technology Sydney Australia UTS Computer Vision Research Group IEEE IEEE Computer Society International Association for Pattern Recognition

ISBN: (纸本)0769526063

Recent years have seen an increasing number of computer vision applications focusing on humans as their objects of interest. Such applications include video surveillance, domotics, multimedia semantic annotation and indexing, human-computer interfaces, affective computing, just to cite a few. What exactly are their "objects of interest"? A broad range of human-related features: motion, gestures, actions, interactions, activities, attitudes, behaviours, identity. This keynote will offer a survey of this field and present some current work from the speaker and his collaborators in the areas of emotion recognition and people tracking within camera networks. © 2006 IEEE.

关键词： computer vision

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A new method for handwritten scene text detection in video

A new method for handwritten scene text detection in video

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International Conference on Frontiers in Handwriting recognition

作者： Shivakumara, Palaiahnakote Dutta, Anjan Pal, Umapada Tan, Chew Lim School of Computing National University of Singapore Singapore Singapore Computer Vision Center Universitat Autònoma de Barcelona Barcelona Spain Computer Vision and Pattern Recognition Unit Indian Statistical Institute India

ISBN: (纸本)9780769542218

There are many video images where hand written text may appear. Therefore handwritten scene text detection in video is essential and useful for many applications for efficient indexing, retrieval etc. Also there are many video frames where text line may be multi-oriented in nature. To the best of our knowledge there is no work on handwritten text detection in video, which is multi-oriented in nature. In this paper, we present a new method based on maximum color difference and boundary growing method for detection of multi-oriented handwritten scene text in video. The method computes maximum color difference for the average of R, G and B channels of the original frame to enhance the text information. The output of maximum color difference is fed to a K-means algorithm with K=2 to separate text and non-text clusters. Text candidates are obtained by intersecting the text cluster with the Sobel output of the original frame. To tackle the fundamental problem of different orientations and skews of handwritten text, boundary growing method based on a nearest neighbor concept is employed. We evaluate the proposed method by testing on our own handwritten text database and publicly available video data (Hua's data). Experimental results obtained from the proposed method are promising. © 2010 IEEE.

关键词： Color

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Auxiliary Demographic Information Assisted Age Estimation With Cascaded Structure

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IEEE TRANSACTIONS ON CYBERNETICS 2018年第9期48卷 2531-2541页

作者： Wan, Jun Tan, Zichang Lei, Zhen Guo, Guodong Li, Stan Z. Center for Biometrics and Security Research and the National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China Lane Department of Computer Science and Electrical Engineering West Virginia University Morgantown WV USA

Owing to the variations including both intrinsic and extrinsic factors, age estimation remains a challenging problem. In this paper, five cascaded structure frameworks are proposed for age estimation based on convolutional neural networks. All frameworks are learned and guided by auxiliary demographic information, since other demographic information (i.e., gender and race) is beneficial for age prediction. Each cascaded structure framework is embodied in a parent network and several subnetworks. For example, one of the applied framework is a gender classifier trained by gender information, and then two subnetworks are trained by the male and female samples, respectively. Furthermore, we use the features extracted from the cascaded structure frameworks with Gaussian process regression that can boost the performance further for age estimation. Experimental results on the MORPH II and CACD datasets have gained superior performances compared to the state-of-the-art methods. The mean absolute error is significantly reduced from 3.63 to 2.93 years under the same test protocol on the MORPH II dataset.

关键词： Age estimation convolutional neural networks (CNNs) demographic Gaussian process regression (GPR)

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6G-Enabled Internet of Things:vision,Techniques,and Open Issues

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computer Modeling in Engineering & Sciences 2022年第12期133卷 509-556页

作者： Mehdi Hosseinzadeh Atefeh Hemmati Amir Masoud Rahmani Pattern Recognition and Machine Learning Lab Gachon UniversitySeongnam13120Korea Department of Computer Engineering Science and Research BranchIslamic Azad UniversityTehran***Iran Future Technology Research Center National Yunlin University of Science and TechnologyYunlin64002Taiwan

There are changes in the development of wireless technology systems every decade.6G(sixth generation)wireless networks improve on previous generations by increasing dependability,accelerating networks,increasing available bandwidth,decreasing latency,and increasing data transmission speed to standardize communication *** purpose of this article is to comprehend the current directions in 6G studies and their relationship to the Internet of Things(IoT).Also,this paper discusses the impacts of 6G on IoT,critical requirements and trends for 6G-enabled IoT,new service classes of 6G and IoT technologies,and current 6G-enabled IoT studies selected by the systematic literature review(SLR)method published from 2018 to *** addition,we present a technical taxonomy for the classification of 6G-enabled IoT,which includes self-organization systems,energy efficiency,channel assessment,and ***,according to the articles reviewed,we consider the evaluation factors in this domain,including data transmission,delay,energy consumption,and ***,we focus on open issues and future research challenges in 6G-enabled *** mention important future challenges and directions,we can point to migration,data storage,data resource,data security,data sharing,data offloading,availability,scalability,portability,user experience,reliability,authentication,and authorization.

关键词： Internet of Things 6G wireless network machine learning artificial intelligence

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Non-deterministic Behavior of Ranking-Based Metrics When Evaluating Embeddings 2nd

Non-deterministic Behavior of Ranking-Based Metrics When Eva...

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2nd International Workshop on Reproducible research in pattern recognition, RRPR 2018

作者： Nicolaou, Anguelos Dey, Sounak Christlein, Vincent Maier, Andreas Karatzas, Dimosthenis Computer Vision Center Edificio O Campus UAB Bellaterra08193 Spain Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany

ISBN: (纸本)9783030239862

Embedding data into vector spaces is a very popular strategy of pattern recognition methods. When distances between embeddings are quantized, performance metrics become ambiguous. In this paper, we present an analysis of the ambiguity quantized distances introduce and provide bounds on the effect. We demonstrate that it can have a measurable effect in empirical data in state-of-the-art systems. We also approach the phenomenon from a computer security perspective and demonstrate how someone being evaluated by a third party can exploit this ambiguity and greatly outperform a random predictor without even access to the input data. We also suggest a simple solution making the performance metrics, which rely on ranking, totally deterministic and impervious to such exploits. © Springer Nature Switzerland AG 2019.

关键词： Embeddings

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A Holistic Approach for recognition of Complete Urdu Ligatures Using Hidden Markov Models

A Holistic Approach for Recognition of Complete Urdu Ligatur...

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Frontiers of Information Technology (FIT)

作者： Israr Uddin Imran Siddiqi Shehzad Khalid Center of Computer Vision and Pattern Recognition Bahria University Islamabad Pakistan

Optical Character recognition (OCR) is one of the continuously explored problems. Presently, commercial character recognizers are available reporting near to 100% recognition rates on text in a number of scripts. Despite these advancements, OCR systems however, have yet to mature for cursive scripts like Urdu. This study presents a holistic technique for recognition of Urdu text in Nastaliq font using "complete" ligatures as recognition units. The term "complete" refers to a partial word including its main body and secondary components (dots and diacritic marks). Discrete Wavelet Transform (DWT) is employed as feature extractor while a separate Hidden Markov Model (HMM) is trained for each ligature considered in our study. More than 2000 frequently used unique Urdu ligatures from the standard CLE (center of Language Engineering) dataset are considered in our evaluations. The system reads a promising accuracy of 88.87% on more than 10,000 partial words.

关键词： Character recognition Feature extraction Hidden Markov models Text recognition Discrete wavelet transforms Training Optical character recognition software

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A fire hawk optimizer-based energy-efficient clustering scheme in underwater acoustic sensor networks (UASNs)

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Ad Hoc Networks 2025年 176卷

作者： Sang-Woong Lee Musaed Alhussein Khursheed Aurangzeb Mohammad Sadegh Yousefpoor Efat Yousefpoor Mehdi Hosseinzadeh Pattern Recognition and Machine Learning Lab School of Computing Gachon University Seongnam 13120 Republic of Korea Department of Computer Engineering College of Computer and Information Sciences King Saud University P.O. Box 51178 Riyadh 11543 Saudi Arabia Center of Research and Strategic Studies Lebanese French University Kurdistan Region Iraq School of Computer Science Duy Tan University Da Nang Vietnam Jadara Research Center Jadara University Irbid 21110 Jordan

An underwater acoustic sensor network (UASN) is suitable for gathering data from aquatic environments, including lakes, rivers, seas, and oceans. This network faces several issues due to the distinct features of underwater environments and the limitations of acoustic channels. These challenges include energy limitations, unreliable communication links, and dynamic network topologies. Additionally, the difficulty of recharging or replacing batteries in underwater conditions makes energy optimization essential for prolonging the network lifespan. Currently, many energy-efficient approaches in UASNs emphasize node clustering and multi-hop communication, but most of these methods rely on distributed algorithms. This paper introduces a novel energy-efficient clustering framework called FHOEEC ( F ire H awk O ptimization-based E nergy- E fficient C lustering), which integrates both distributed and centralized strategies. The clustering process is divided into three stages: (1) cluster formation, (2) selection of cluster heads, and (3) cluster maintenance. During the periodic neighbor discovery phase, FHOEEC examines two key aspects: the format of the hello packet and its propagation process. FHOEEC aims to create an energy-efficient, cluster-based network structure. To achieve this, the sink node utilizes the fire hawk optimization (FHO) algorithm to decide on the optimal range and number of clusters. To establish these clusters, a fitness function considers a weighted combination of three sub-functions: intra-cluster and inter-cluster distances, the proportion of isolated clusters compared to others, and cluster density. In the final stage, intra-cluster and inter-cluster communication paths are established by focusing on energy balance. This ensures that nodes with energy levels below a specified threshold are excluded from serving as intermediate nodes. Simulation results and performance evaluations show that FHOEEC outperforms three existing clustering methods–CCCS, G

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3-D non-rigid motion estimation from image sequence based on Makov random field [Makov read Markov]

3-D non-rigid motion estimation from image sequence based on...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Ya-Ming Wang Wen-Qing Huang Kai Zheng Research Center for Computer Vision and Pattern Recognition Zhejiang University of Science and Technology Hangzhou China

ISBN: (纸本)0780384032

We propose an approach to 3-D non-rigid motion estimation from image sequence in this paper. First, with the establishment of feature point correspondence between consecutive image frames, the affine motion model and the central projection model are presented for local non-rigid motion. Then, in order to obtain the global motion parameters and overcome the ill-posed 3-D estimation problem, a framework of Markov random field (MRF) is proposed. By incorporating the motion prior constrains into the MRF, the motion smoothness feature between local regions is reflected. This converts the ill-posed problem into a well-posed one and guarantees a robust solution. Experimental results from a sequence of synthetic image sequence demonstrate the feasibility of the proposed approach.

关键词： Motion estimation Image sequences Shape Deformable models computer vision Parametric statistics Robustness pattern recognition Markov random fields Image converters

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