Side-scan sonar image target detection is of great significance in seabed resource exploration and other fields. However, affected by the complex underwater environment, side-scan sonar images have the problems of few...
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Side-scan sonar image target detection is of great significance in seabed resource exploration and other fields. However, affected by the complex underwater environment, side-scan sonar images have the problems of few target samples and large differences in the scale of each type of target. In addition, the computational complexity of high-performance models based on deep learning is too high to be applied on platforms with limited computational resources. To solve these problems, this paper proposes a lightweight algorithm for target detection in side-scan sonar images based on multi-scale feature fusion and attention mechanism (MAL-YOLO). Firstly, a lightweight feature extraction module is used. This module combines depthwise separable convolution and efficient multi-scale attention (EMA) module to improve the feature extraction capability of the model while reducing the computational volume. Secondly, a multi-scale feature fusion network combining asymptotic feature pyramid network and EMA module is used to enhance the fusion and representation of multi-scale features in the model. Finally, the MPDIoU loss function is used to provide more accurate bounding box regression. The experimental results show that the algorithm has significant advantages in both detection accuracy and model lightweighting compared with the current state-of-the-art algorithms such as YOLOv7 and YOLOv8.
Internet of things(IoT)is used in various fields such as smart cities,smart home,manufacturing industries,and *** application in healthcare has many advantages and *** of its most common protocols is Message Queue Tel...
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Internet of things(IoT)is used in various fields such as smart cities,smart home,manufacturing industries,and *** application in healthcare has many advantages and *** of its most common protocols is Message Queue Telemetry Transport(MQTT).MQTT protocol works as a publisher/subscriber which is suitable for IoT devices with limited *** of the drawbacks of MQTT is that it is easy to *** default security provided by MQTT during user authentication,through username and password,does not provide any type of data encryption,to ensure confidentiality or *** paper focuses on the security of IoT healthcare over the MQTT protocol,through the implementation of lightweight generating and key exchange *** research contribution of this paper is *** first one is to implement a lightweight generating and key exchange algorithm for MQTT protocol,with the key length of 64 bits through OMNET++*** second one is to obtain lower power consumption from some existing ***,the power consumption through using the proposed algorithm is 0.78%,1.16%,and 1.93% of power for 256 bits,512 bits,and 1024 *** the other hand,the power consumption without using the encryption is 0.25%,0.51%,and 1.03% for the same three payloads length.
The web of things is one of the most important innovations of modern technology. It aims to connect billions of devices, resulting in a vast number of contacts between devices and a huge volume of data. On the other h...
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ISBN:
(纸本)9783030972554;9783030972547
The web of things is one of the most important innovations of modern technology. It aims to connect billions of devices, resulting in a vast number of contacts between devices and a huge volume of data. On the other hand, there are many security challenges to protect this information from risk exposure. It's crucial to note that these devices are compact and use very little power. As a consequence, using several rounds of data encryption would be exceedingly difficult and costly. Moreover, when fewer complex computations are used, integrity may be compromised. So, in this paper, a lightweight encryption algorithm called (L.W.A.E.S) is proposed. The proposed algorithm aims to achieve the highest speed in Cryptography (Encryption/Decryption) and reducing computational complexity. The MixColumns stage of the A.E.S algorithm is the most computationally challenging. So, it takes up the bulk of the time spent encrypting and decrypting data. The stage of MixColumns has been replaced by simple SHIFT processes in the proposed algorithm. It took just 1-8 s from the starting of the sensors' reading to the moment they were collected for the customer. The experimental results show that the modified algorithm (L.W.A.E.S) provides suitable security, the encryption techniques speed, low computational complexity, and light-weight in the manner of storage.
The accuracy of insulators and their defect identification by UAVs (unmanned aerial vehicles) in transmission-line inspection needs to be further improved, and the model size of the detection algorithm is significantl...
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The accuracy of insulators and their defect identification by UAVs (unmanned aerial vehicles) in transmission-line inspection needs to be further improved, and the model size of the detection algorithm is significantly reduced to make it more suitable for edge-end deployment. In this paper, the algorithm uses a lightweight GhostNet module to reconstruct the backbone feature extraction network of the YOLOv4 model and employs depthwise separable convolution in the feature fusion layer. The model is lighter on the premise of ensuring the effect of image information extraction. Meanwhile, the ECA-Net channel attention mechanism is embedded into the feature extraction layer and PANet (Path Aggregation Network) to improve the recognition accuracy of the model for small targets. The experimental results show that the size of the improved model is reduced from 244 MB to 42 MB, which is only 17.3% of the original model. At the same time, the mAp of the improved model is 0.77% higher than that of the original model, reaching 95.4%. Moreover, the mAP compared with YOLOv5-s and YOLOX-s, respectively, is improved by 1.98% and 1.29%. Finally, the improved model is deployed into Jetson Xavier NX and run at a speed of 8.8 FPS, which is 4.3 FPS faster than the original model.
The existing seal ring surface defect detection methods for aerospace applications have the problems of low detection efficiency, strong specificity, large fine-grained classification errors, and unstable detection re...
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The existing seal ring surface defect detection methods for aerospace applications have the problems of low detection efficiency, strong specificity, large fine-grained classification errors, and unstable detection results. Considering these problems, a fine-grained seal ring surface defect detection algorithm for aerospace applications is proposed. Based on analysis of the stacking process of standard convolution, heat maps of original pixels in the receptive field participating in the convolution operation are quantified and generated. According to the generated heat map, the feature extraction optimization method of convolution combinations with different dilation rates is proposed, and an efficient convolution feature extraction network containing three kinds of dilated convolutions is designed. Combined with the O-ring surface defect features, a multiscale defect detection network is designed. Before the head of multiscale classification and position regression, feature fusion tree modules are added to ensure the reuse and compression of the responsive features of different receptive fields on the same scale feature maps. Experimental results show that on the O-rings-3000 testing dataset, the mean condition accuracy of the proposed algorithm reaches 95.10% for 5 types of surface defects of aerospace O-rings. Compared with RefineDet, the mean condition accuracy of the proposed algorithm is only reduced by 1.79%, while the parameters and FLOPs are reduced by 35.29% and 64.90%, respectively. Moreover, the proposed algorithm has good adaptability to image blur and light changes caused by the cutting of imaging hardware, thus saving the cost.
Objectives: Cracked tooth syndrome (CTS) is one of the major causes of tooth loss, presents the problem of early microcrack symptoms that are difficult to distinguish. This paper aims to investigate the practicality a...
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Data has been pivotal to all facets of human life in the last decades. In recent years, the massive growth of data as a result of the development of various applications. This data needs to be secured and stored in se...
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Data has been pivotal to all facets of human life in the last decades. In recent years, the massive growth of data as a result of the development of various applications. This data needs to be secured and stored in secure sites. Cloud computing is the technology can be used to store those massive amounts of data. . The rapid development of this technology makes it more critical. Therefore, it has become urgent to secure data from attackers to preserve its integrity, confidentiality, protection, privacy and procedures required for handling it. This paper proposed a New lightweight Cryptographic algorithm for Enhancing Data Security that can be used to secure applications on cloud computing. The algorithm is a 16 bytes (128-bit) block cipher and wants 16 bytes (128-bit) key to encrypt the data. It is inspired by feistal and substitution permutation architectural methods to improve the complexity of the encryption. The algorithm achieves Shannon's theory of diffusion and confusion by the involvement of logical operations, such as (XOR, XNOR, shifting, swapping). It also features flexibility in the length of the secret key and the number of turns. The experimental results of the proposed algorithm presented a strong security level and an apparent enhancement in measures of cipher execution time and security forces compared to the cryptographic systems widely used in cloud computing.
The Internet of Things (IoT) is a promising field that will connect billions of devices to communicate and share information in the future. The increased amount of information sharing causes a threat to data security ...
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ISBN:
(纸本)9781728160405
The Internet of Things (IoT) is a promising field that will connect billions of devices to communicate and share information in the future. The increased amount of information sharing causes a threat to data security and privacy. However, IoT devices are fundamentally resource-constrained, thus, traditional encryption algorithms are computationally expensive for this small size and low power devices. Therefore, a lightweight security algorithm is needed. In this paper, resource-friendly, energy-saving data encryption along with an efficient key generation mechanism is proposed namely Generalized Triangle Based Security algorithm (G-TBSA). The proposed G-TBSA is applied in wireless sensor networks (WSNs) with low power WiFi. The key generation process is the heart of the algorithm because of its usage of fewer resources to generate keys and thus, minimize the complexity of the algorithm and achieving its energy efficiency. It outperforms state-of-the-art methods in terms of power consumption while achieving all necessary security requirements.
Data confidentiality and security are important issues due to the sensitivity of the data and its relationship with users' privacy. Sensitive data includes images and texts that can be transmitted over the Interne...
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Data confidentiality and security are important issues due to the sensitivity of the data and its relationship with users' privacy. Sensitive data includes images and texts that can be transmitted over the Internet, Internet of Things devices and edge-fog-cloud system. These devices require speed and accuracy responses, and they are vulnerable to hacking. To solve these problems, encryption algorithms provide necessary solution to meet these requirements. Advanced Encryption Standard represents the best development in data encryption;however, it is computational expensive. In this research, an improved advanced encryption standard algorithm is proposed with advanced security and lightweight computation utilized for encrypting of images and texts. The algorithm is improved using various steps including key generation which is performed in two steps. First, using an innovative, proven chaotic function distinguished by its sensitivity to any change in its variables. Second, using three-dimensional Lorenzo function. In our research, a unique key was used for all rounds, and round key then used like advanced encryption standard. Two new dynamic substitution boxes are used one for odd rounds and the other for even rounds in which the speed does not exceeding a millisecond. The mix column function was replaced by a circular permutation function at the bit level, which improved the speed and performance of the algorithm, Our extensive simulation results indicated enhanced speed, randomness, and high efficiency in encrypting Internet of Things data. The algorithm was evaluated using the National Institute of Standards and Technology tests.
Health monitoring enabled by wearable device aids in the early warning of potential health issues, but wearable devices still face technical bottlenecks in multiple physiology acquisition, low-power consumption, intel...
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ISBN:
(纸本)9798350363524;9798350363517
Health monitoring enabled by wearable device aids in the early warning of potential health issues, but wearable devices still face technical bottlenecks in multiple physiology acquisition, low-power consumption, intelligent data processing. To address these challenges in a holistic manner, this paper proposes a multi-sensor, intelligent and low-power wearable system, integrating both multi-sensor data acquisition and lightweight machine learning algorithm into a computation-limited wearable device. A one-dimensional CNN model, with a motion status recognition accuracy of 99%, is constructed and optimized for lightweight deployment on the wearable device, used for realtime data processing of multi-channel foot pressure and 3-axis acceleration signals. To further reduce the system power consumption, an event-triggered mechanism is developed based on human motion characteristics. The system ultimately realizes the low-power intelligent perception of five motion states, achieving an optimized power consumption of 6.3 mA in the active mode and 652 mu A in the low-power mode. This study provides a potential solution for real-time intelligent monitoring of remote personalized healthcare.
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