In the process of digital transformation of power grids, the 5th generation mobile communication technology (5G) plays a key role in supporting technological innovation in power systems and promoting industry reform d...
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This paper examines Quantum image Representation Algorithms (QIRA), which have significantly advanced the encoding and manipulation of visual data using quantum computing. It explores key developments in image represe...
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As a promising technology, Orthogonal Frequency Division Multiplexing-Index Modulation (OFDM-IM) has received significant attention in wireless communication systems. However, as the number of subcarriers increases, t...
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ISBN:
(纸本)9798350333398
As a promising technology, Orthogonal Frequency Division Multiplexing-Index Modulation (OFDM-IM) has received significant attention in wireless communication systems. However, as the number of subcarriers increases, the complexity of the Maximum Likelihood (ML) detector grows exponentially. In this paper, we propose a novel detection method, referred to as the Maximum Subcarrier Power (MSP) detection algorithm. The MSP algorithm leverages the power information of each subcarrier to detect its activation status and employs a power threshold to determine the appropriate modulation method. Specifically, the MSP algorithm switches from SIPM-OOFDM to OFDM-IM in low signal-to-noise ratio (SNR) scenarios. In comparison to ML detection and Log-Likelihood Ratio (LLR) detection techniques based on optimum transceiver design in OFDM-IM, the proposed MSP detection algorithm has lower complexity and is more robust in terms of Bit Error Rate (BER) performance at varying noise levels. Additionally, the MSP algorithm effectively reduces the cost of Digital signal Processing (DSP) and the detection time. This consequently leads to enhanced communication efficiency, demonstrating great potential for future low-latency B5G/6G wireless networks.
internet of Things (IoT) is a network to connect anything with the internet. While its purpose is to provide efficient and effective solutions, security of the network devices is a challenging issue. Therefore, securi...
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Radio Frequency Fingerprinting (RFF) is an effective physical layer identification technique that makes internet of Things (IoT) systems more secure. It uses the deviation of the parameters of each electronic componen...
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A large number of patient images are generated every day in medical applications such as ultrasound, Computer Tomography scans, X-Ray, and so on. Medical data is related to patient privacy and personal rights. Thus, t...
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In wireless sensor networks (WSN), flow transmission is one of the factors which is responsible of nodes batteries drain. This issue becomes more important in the context of internet of Things (IoT) with massive conne...
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ISBN:
(纸本)9781665464956
In wireless sensor networks (WSN), flow transmission is one of the factors which is responsible of nodes batteries drain. This issue becomes more important in the context of internet of Things (IoT) with massive connections. In this paper, we propose a path allocation strategy for flows which considers the network energy availability. To accomplish this, we have proposed a linear programming formulation of the path planning problem that includes multiple types of flows. For each flow we have assigned a threshold energy requirement. In order to avoid energy imbalance in the network during flow transmission, we compute the maximum percentage energy reduction along path that allows us to select a path with a residual energy that meets the flow requirement. The results demonstrated the effectiveness of the approach.
Remote Sensing Scene Classification (RSSC) is essential for applications such as environmental monitoring and catastrophe management, which often have stringent time constraints requiring real-time processing. On-boar...
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Resting State Functional Magnetic Resonance Imaging (rs-fMRI) technique is gaining more attention among medical practitioners because, it allows recognition of functional brain networks and is very suitable for comple...
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ISBN:
(纸本)9781665464956
Resting State Functional Magnetic Resonance Imaging (rs-fMRI) technique is gaining more attention among medical practitioners because, it allows recognition of functional brain networks and is very suitable for complex situations where the participation of the patients is not required. This approach is also interesting for non-invasive medical imaging where healthy subjects can be enrolled very easily during the data acquisition process. However, one of its limitations is that the clinicians must manually annotate the image data. While no clinical use of this annotation is needed at any stage of neurosurgical procedure, this process is often time consuming and can only be carried our by domain experts. We investigate the possibility to perform self-supervision from healthy subject data without the need of image annotation, followed by transfer learning from the models trained on some pretext task. The result of self-supervision is shown to bring about 3% increase in performance without the effort and time of manual annotation of fMRI data by expert.
High-squint synthetic aperture radar (SAR) imaging with accelerated trajectory is significant for target detection and precise guidance. However, seriously space-variant Doppler parameters caused by high-squint angle ...
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