Zeyi Wang¹, Mingxi Jiao¹, Daliang Wang¹, Minxu Liu¹, Minglei Jiang², He Wang³, Shiqiang Li^3,*

Energy Engineering, Vol.121, No.8, pp. 2275-2291, 2024, DOI:10.32604/ee.2024.028223

Abstract This paper addresses the problem of complex and challenging disturbance localization in the current power system operation environment by proposing a disturbance localization method for power systems based on group sparse representation and entropy weight method. Three different electrical quantities are selected as observations in the compressed sensing algorithm. The entropy weighting method is employed to calculate the weights of different observations based on their relative disturbance levels. Subsequently, by leveraging the topological information of the power system and pre-designing an overcomplete dictionary of disturbances based on the corresponding system parameter variations caused by disturbances,… More >

Muhammad S. Alam^1,5,*, Farhan B. Mohamed^1,3, Ali Selamat², Faruk Ahmed⁴, AKM B. Hossain^6,7

Intelligent Automation & Soft Computing, Vol.39, No.3, pp. 417-436, 2024, DOI:10.32604/iasc.2024.051999

Abstract Real-time indoor camera localization is a significant problem in indoor robot navigation and surveillance systems. The scene can change during the image sequence and plays a vital role in the localization performance of robotic applications in terms of accuracy and speed. This research proposed a real-time indoor camera localization system based on a recurrent neural network that detects scene change during the image sequence. An annotated image dataset trains the proposed system and predicts the camera pose in real-time. The system mainly improved the localization performance of indoor cameras by more accurately predicting the camera More >

Xiaobin Hou^*1, Tinghui Li^†1, Zhipeng Ren^*, Yang Liu^*

Oncology Research, Vol.24, No.4, pp. 247-253, 2016, DOI:10.3727/096504016X14652175055765

Abstract Mutation of breast cancer 2, early onset (BRCA2) has been identified as a vital risk factor for esophageal cancer (EC). To date, several proteins have been reported as BRCA2-interacting proteins and are associated with multiple biological processes. This study's aim was to identify a novel interactive protein of BRCA2 and to explore its functional roles in EC. A yeast two-hybrid screening was performed to identify a novel BRCA2-interacting protein. Glutathione-S-transferase (GST) pull-down analysis was performed to find out how the binding domain of BRCA2 interacts with LIM domains containing 1 (LIMD1). The interaction between LIMD1… More >

Hela Elmannai^1,*, Monia Hamdi¹, Souham Meshoul¹, Amel Ali Alhussan², Manel Ayadi³, Amel Ksibi³

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1429-1457, 2024, DOI:10.32604/cmes.2024.048557

Abstract Glaucoma disease causes irreversible damage to the optical nerve and it has the potential to cause permanent loss of vision. Glaucoma ranks as the second most prevalent cause of permanent blindness. Traditional glaucoma diagnosis requires a highly experienced specialist, costly equipment, and a lengthy wait time. For automatic glaucoma detection, state-of-the-art glaucoma detection methods include a segmentation-based method to calculate the cup-to-disc ratio. Other methods include multi-label segmentation networks and learning-based methods and rely on hand-crafted features. Localizing the optic disc (OD) is one of the key features in retinal images for detecting retinal diseases,… More >

Mingze Li, Diwen Zheng, Shuhua Lu^*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2105-2122, 2024, DOI:10.32604/cmc.2024.048928

Abstract Crowd counting is a promising hotspot of computer vision involving crowd intelligence analysis, achieving tremendous success recently with the development of deep learning. However, there have been still many challenges including crowd multi-scale variations and high network complexity, etc. To tackle these issues, a lightweight Res-connection multi-branch network (LRMBNet) for highly accurate crowd counting and localization is proposed. Specifically, using improved ShuffleNet V2 as the backbone, a lightweight shallow extractor has been designed by employing the channel compression mechanism to reduce enormously the number of network parameters. A light multi-branch structure with different expansion rate… More >

Syed Ayaz Ali Shah^1,*, Aamir Shahzad^1,*, Musaed Alhussein², Chuan Meng Goh³, Khursheed Aurangzeb², Tong Boon Tang⁴, Muhammad Awais⁵

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2565-2583, 2024, DOI:10.32604/cmc.2024.047597

Abstract Diagnosing various diseases such as glaucoma, age-related macular degeneration, cardiovascular conditions, and diabetic retinopathy involves segmenting retinal blood vessels. The task is particularly challenging when dealing with color fundus images due to issues like non-uniform illumination, low contrast, and variations in vessel appearance, especially in the presence of different pathologies. Furthermore, the speed of the retinal vessel segmentation system is of utmost importance. With the surge of now available big data, the speed of the algorithm becomes increasingly important, carrying almost equivalent weightage to the accuracy of the algorithm. To address these challenges, we present… More > Graphic Abstract

Khaldon Azzam Kordi¹, Mardeni Roslee^1,*, Mohamad Yusoff Alias¹, Abdulraqeb Alhammadi², Athar Waseem³, Anwar Faizd Osman⁴

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3261-3298, 2024, DOI:10.32604/cmc.2024.044890

Abstract This study comprehensively examines the current state of deep learning (DL) usage in indoor positioning. It emphasizes the significance and efficiency of convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Unlike prior studies focused on single sensor modalities like Wi-Fi or Bluetooth, this research explores the integration of multiple sensor modalities (e.g., Wi-Fi, Bluetooth, Ultra-Wideband, ZigBee) to expand indoor localization methods, particularly in obstructed environments. It addresses the challenge of precise object localization, introducing a novel hybrid DL approach using received signal information (RSI), Received Signal Strength (RSS), and Channel State Information (CSI) data… More >

Kranthi Alluri, S. Gopikrishnan^*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 943-961, 2024, DOI:10.32604/cmc.2024.048621

Abstract Secure authentication and accurate localization among Internet of Things (IoT) sensors are pivotal for the functionality and integrity of IoT networks. IoT authentication and localization are intricate and symbiotic, impacting both the security and operational functionality of IoT systems. Hence, accurate localization and lightweight authentication on resource-constrained IoT devices pose several challenges. To overcome these challenges, recent approaches have used encryption techniques with well-known key infrastructures. However, these methods are inefficient due to the increasing number of data breaches in their localization approaches. This proposed research efficiently integrates authentication and localization processes in such a… More >

Zhuoqun Xia¹, Hangyu Hu¹, Wenjing Li^2,3, Qisheng Jiang¹, Lan Pu¹, Yicong Shu¹, Arun Kumar Sangaiah^4,5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 409-430, 2024, DOI:10.32604/cmes.2024.030052

Abstract Early screening of diabetes retinopathy (DR) plays an important role in preventing irreversible blindness. Existing research has failed to fully explore effective DR lesion information in fundus maps. Besides, traditional attention schemes have not considered the impact of lesion type differences on grading, resulting in unreasonable extraction of important lesion features. Therefore, this paper proposes a DR diagnosis scheme that integrates a multi-level patch attention generator (MPAG) and a lesion localization module (LLM). Firstly, MPAG is used to predict patches of different sizes and generate a weighted attention map based on the prediction score and… More >

Peng Geng^1,*, Annan Yang², Yan Liu³

Journal on Artificial Intelligence, Vol.6, pp. 43-52, 2024, DOI:10.32604/jai.2024.047642

Abstract This article presents a real-time localization method for Unmanned Aerial Vehicles (UAVs) based on continuous image processing. The proposed method employs the Scale Invariant Feature Transform (SIFT) algorithm to identify key points in multi-scale space and generate descriptor vectors to match identical objects across multiple images. These corresponding points in the image provide pixel positions, which can be combined with transformation equations, allow for the calculation of the UAV’s actual ground position. Additionally, the physical coordinates of matching points in the image can be obtained, corresponding to the UAV’s physical coordinates. The method achieves real-time More >