Cloud storage is an emerging archetype used by businesses for data storage. Clients require easy access to data in the cloud, which helps clients with limited computing power move their high-value, high-risk principle...
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With the rapid development of the distributed network and communication, the Internet of Things (IoT) systems have been applied to all walks of life. Blockchain technology is one of the most popular research fields re...
With the rapid development of the distributed network and communication, the Internet of Things (IoT) systems have been applied to all walks of life. Blockchain technology is one of the most popular research fields recently, which can provide a reliable storage solution and solve information security issues for IoT systems. Transaction information is the most critical fundamental data in the blockchain systems, which needs to be verified to avoid being tempered with by malicious nodes in the process of transmission. With the exponential growth of the number of IoT devices, the limitations of the current storage structure put too much pressure on the system nodes. How to improve verification efficiency has become a key challenge. To alleviate this problem, this paper proposes a novel high-performance verification mechanism for data security protection in blockchain-based IoT systems. We design a new storage structure based on Huffman trunk tree (HTT), and conduct the quantitative analysis of transaction weights. Transactions are stored in full-featured devices in the form of Huffman Merkle tree (HMT), and only the content of HTT is saved in lightweight devices. Finally, the performance superiority of our mechanism is proved through theoretical analysis and experimental evaluation. In blockchain-based IoT systems, our mechanism significantly reduces the data transmission cost and computation overhead, effectively improving the efficiency of data verification.
Fingerprint matching,spoof mitigation and liveness detection are the trendiest biometric techniques,mostly because of their stability through life,uniqueness and their least risk of *** recent decade,several technique...
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Fingerprint matching,spoof mitigation and liveness detection are the trendiest biometric techniques,mostly because of their stability through life,uniqueness and their least risk of *** recent decade,several techniques are presented to address these challenges over well-known *** study provides a comprehensive review on the fingerprint algorithms and techniques which have been published in the last few *** divides the research on fingerprint into nine different approaches including feature based,fuzzy logic,holistic,image enhancement,latent,conventional machine learning,deep learning,template matching and miscellaneous *** these,deep learning approach has outperformed other approaches and gained significant attention for future *** reviewing fingerprint literature,it is historically divided into four eras based on 106 referred papers and their cumulative citations.
The increasing use of high-resolution displays and the demand for interactive frame rates presents a major challenge to widespread adoption of virtual reality. Foveated rendering address this issue by lowering pixel s...
The increasing use of high-resolution displays and the demand for interactive frame rates presents a major challenge to widespread adoption of virtual reality. Foveated rendering address this issue by lowering pixel sampling rate at the periphery of the display. How-ever, existing techniques do not fully exploit the feature of human binocular vision, i.e., the dominant eye. In this paper, we propose a Dominant-Eye-Aware foveated rendering method optimized with Multi-Parameter foveation (DEAMP). Specifically, we control the level of foveation for both eyes with two distinct sets of foveation parameters. To achieve this, each eye’s visual field is divided into three nested layers based on eccentricity. Multiple parameters govern the level of foveation of each layer, respectively. We conduct user studies to evaluate our method. Experimental results demonstrate that DEAMP is superior in terms of rendering time and reduces the disparity between pixel sampling rate and the visual acuity fall-off model while maintaining the perceptual quality.
Moving object segmentation(MOS),aiming at segmenting moving objects from video frames,is an important and challenging task in computer vision and with various *** the development of deep learning(DL),MOS has also ente...
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Moving object segmentation(MOS),aiming at segmenting moving objects from video frames,is an important and challenging task in computer vision and with various *** the development of deep learning(DL),MOS has also entered the era of deep models toward spatiotemporal feature *** paper aims to provide the latest review of recent DL-based MOS methods proposed during the past three ***,we present a more up-to-date categorization based on model characteristics,then compare and discuss each category from feature learning(FL),and model training and evaluation *** FL,the methods reviewed are divided into three types:spatial FL,temporal FL,and spatiotemporal FL,then analyzed from input and model architectures aspects,three input types,and four typical preprocessing subnetworks are *** terms of training,we discuss ideas for enhancing model *** terms of evaluation,based on a previous categorization of scene dependent evaluation and scene independent evaluation,and combined with whether used videos are recorded with static or moving cameras,we further provide four subdivided evaluation setups and analyze that of reviewed *** also show performance comparisons of some reviewed MOS methods and analyze the advantages and disadvantages of reviewed MOS methods in terms of ***,based on the above comparisons and discussions,we present research prospects and future directions.
In precision poultry farming, detecting multiple objects is challenging. While Convolutional Neural Networks (CNNs) excel in single-object detection, addressing the complexity of multi-object detection, requires advan...
In precision poultry farming, detecting multiple objects is challenging. While Convolutional Neural Networks (CNNs) excel in single-object detection, addressing the complexity of multi-object detection, requires advanced approaches. To overcome this challenge, we implement advanced algorithms like YOLOv8, SSD, and Faster RCNN. The primary goal is to analyze their performance, focusing on accuracy, speed, and adaptability. We aim to balance computational efficiency with optimal resource utilization, considering hardware constraints. Integrating these models into precision farming systems and adapting to environmental variations are key challenges. This project specifically aims to validate YOLOv8's effectiveness, and yields an accuracy around 98% in poultry farming scenarios. This research contributes insights to advance precision poultry farming practices.
Unspent Transaction Output (UTXO) is part of the transaction data set, which represents the digital cryptocurrency asset in transaction-based blockchain systems. The data management capability, storage method and occu...
Unspent Transaction Output (UTXO) is part of the transaction data set, which represents the digital cryptocurrency asset in transaction-based blockchain systems. The data management capability, storage method and occupied space of UTXOs will greatly affect the running efficiency and the verification performance of blockchain systems. Especially, with the popularity of blockchain technology, the relevant UTXO data sets have been growing, and all the stored data can no longer be almost completely stored in memory. How should the UTXO transaction data be stored and managed at this time, it is an urgent issue to be solved in bitcoin-like blockchain systems. This paper provides a blockchain transaction data management optimization mechanism based on multi-partitioning. First, we analyze the influencing factors of transactions through real blockchain data. The proposed method can evaluate the time interval and transaction frequency factors, and use the received information to realize the efficient transaction data storage. In our design, UTXOs with lower likelihood to be used in new transactions will be stored in the disk, and the other UTXOs with higher likelihood to be used in new generated transactions should be stored in the cache. This approach aims to minimize memory consumption for the transaction data sets, accelerate UTXO access time during block verification, and ultimately decrease the overall time required for verification, leading to efficient UTXO transaction data management. Finally, the effectiveness of the proposed optimization mechanism is verified through theoretical analysis and simulation experiments, and the UXTO access time has been reduced compared with state-of-the-art methods.
Non-overlapping codes are a set of codewords such that the prefix of each codeword is not a suffix of any codeword in the set, including itself. If the lengths of the codewords are variable, it is additionally require...
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Testing of GUI is crucial for assessing software reliability, usability, and functionality; however, classical approaches are not sustainable in contemporary applications. It proposes the Quasi-Oppositional Genetic Sp...
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ISBN:
(数字)9798331543624
ISBN:
(纸本)9798331543631
Testing of GUI is crucial for assessing software reliability, usability, and functionality; however, classical approaches are not sustainable in contemporary applications. It proposes the Quasi-Oppositional Genetic Sparrow Search Algorithm (OOGSSA) to auto-generate efficient GUI test cases. It combines genetic algorithms, sparrow search dynamics, and quasi-oppositional learning to optimize test suites, minimize redundant test cases, and improve the testing fault. The result obtained for the model OOGSSA is 95.8 % in test coverage, 85.3 % in redundancy, and 92.6 % in fault detection. Furthermore, test generation time was minimized to forty minutes and the overall testing time to seventy-five minutes, which is more efficient than conventional genetic or model-based testing. The outcomes presented above show that OOGSSA is highly efficient and effective in solving the issues of the current GUI testing and improving the overall quality and reliability of software. More enhancements to the algorithm will be done in future work as well as leveraging the algorithm to cover a wider GUI space including mobile and web.
This article introduces a novel and accurate automatic classifier for Power Signal (PS) alterations. The efficient detection and accurate classification represent crucial steps in developing an automatic power quality...
This article introduces a novel and accurate automatic classifier for Power Signal (PS) alterations. The efficient detection and accurate classification represent crucial steps in developing an automatic power quality (PQ) measurement system, which has become essential in contemporary settings. The objective of this article is to enhance alteration classification accuracy. This is accomplished by combining the Hilbert–Huang transform (HHT) and convolutional neural network (CNN). The HHT is employed to extract PS features and is resilient to the non-stationarity introduced by alterations. Meanwhile, the CNN is adept at extracting information from the bidimensional characteristics of PS features and is robust against noise. Numerical tests demonstrate promising results showing high classification accuracy also in the case of PSs affected by high level of noise.
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