In continuous variable quantum key distribution (CV-QKD) systems, low-density parity check (LDPC) decoders have gained widespread adoption due to their robust error correction capabilities and good adaptability to par...
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ISBN:
(数字)9798350356656
ISBN:
(纸本)9798350356663
In continuous variable quantum key distribution (CV-QKD) systems, low-density parity check (LDPC) decoders have gained widespread adoption due to their robust error correction capabilities and good adaptability to parallel processing hardware designs. We introduce a syndrome-based normalized min-sum algorithm (S-NMSA) and implement the decoder on FPGAs for high throughput and minimal resource consumption. The decoder utilizes a semi-parallel architecture. The proposed rapid cyclic shifter (RCS) can complete the shift of any dimension matrix in one clock cycle and realizes a significant reduction in hardware resource consumption. The semi-parallel decoding structure attains a 68.5 Mbps processing throughput on the condition that the system clock of the quasi-cyclic (QC) LDPC decoder is 250 Mhz.
Speech enhancement algorithms and machine learning can play a fundamental role in signalprocessing to improve speech quality. These techniques can be used to reduce noise and distortions in speech signals, hence ensu...
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ISBN:
(数字)9798350368697
ISBN:
(纸本)9798350368703
Speech enhancement algorithms and machine learning can play a fundamental role in signalprocessing to improve speech quality. These techniques can be used to reduce noise and distortions in speech signals, hence ensuring clearer and more intelligible speech. By leveraging advanced machine learning, speech enhancement algorithms not only improve the listener’s auditory system, but also increase the efficacy of speech recognition systems. In particular, deep learning is a class of machine learning techniques, which have recently been used in speech enhancement. This paper proposes the use of Discrete Hahn polynomials (DHPs) o extract spectral features from noisy signals using fully connected neural networks and convolutional neural network. Deep learning can efficiently capture the contextual information of speech signals, resulting in superior improvements in speech quality and intelligibility properties. The results are evaluated based on the well-known TIMIT database. The results show that the presented model is able to enhance the speech signal for different conditions.
With the proliferation of sophisticated AI techniques, the creation and dissemination of deep fake images and videos have emerged as a pressing concern in today's digital landscape. Deep fake technology employs ad...
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ISBN:
(数字)9798331540685
ISBN:
(纸本)9798331540692
With the proliferation of sophisticated AI techniques, the creation and dissemination of deep fake images and videos have emerged as a pressing concern in today's digital landscape. Deep fake technology employs advanced machine learning algorithms to manipulate or synthesize realistic-looking media, often with malicious intent. This study evaluates the effectiveness of various pre-trained deep learning models using transfer learning for detecting deep fake images on the Face Forensics++ dataset. The models considered include MobileN etV2, ResN et50, Inception V3, EfficientN et, Xception, NASNetMobile, and a Custom CNN. Accuracies obtained from these models are compared to assess their performance in distinguishing between real and fake images. With the highest performance in MobileNetV2 with 89% followed by ResNet50 with 83 % and other models.
Radar observation of group targets has recently received much attention in the flight mechanism research and many other applications. Group targets are usually closely spaced with lots of missed detections and false a...
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In this work, we conduct a subjective video quality assessment (VQA) experiment to evaluate the performance of modern video codecs when displayed on a large (30sqm) ultra-high-definition (UHD) video wall. Our comparat...
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ISBN:
(数字)9798350353235
ISBN:
(纸本)9798350353242
In this work, we conduct a subjective video quality assessment (VQA) experiment to evaluate the performance of modern video codecs when displayed on a large (30sqm) ultra-high-definition (UHD) video wall. Our comparative study involves four encoding standards: advanced Video Coding (AVC) / H.264, High-Efficiency Video Coding (HEVC) / H.265, Versatile Video Coding (VVC) / H.266, and AOMedia Video 1 (AV1). Moreover, the study includes the evaluation in terms of correlation performance of a set of widely used full-reference objective VQA metrics against the subjective scores. Our results showcase the perceptual superiority of the VVC over rival encoding standards, aligning with the objective quality assessment and relevant literature.
This study investigates the integration of advancedsignalprocessing techniques with the AdaBoost classification algorithm for the purpose of identifying malfunctions in brushless DC motors through audio analysis. Gi...
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ISBN:
(数字)9798350359299
ISBN:
(纸本)9798350359305
This study investigates the integration of advancedsignalprocessing techniques with the AdaBoost classification algorithm for the purpose of identifying malfunctions in brushless DC motors through audio analysis. Given the increasing significance of brushless DC motors in modern manufacturing and transportation, the timely detection of anomalies becomes crucial for maintaining consistent operational efficiency and preventing potential injuries. Despite regular maintenance practices, the occurrence of unexpected mechanical and electrical failures often eludes operators, leading to the potential escalation of minor malfunctions into catastrophic failures. Consequently, the implementation of a reliable and consistent monitoring system is imperative in practical applications. The proposed solution leverages audio analysis as an effective approach. Through the amalgamation of machine learning and advancedsignalprocessing techniques, this research employs audio signal classification to discern malfunctions in brushless DC motors from audio recordings of their operations. The extraction of dominant frequencies using the Fast Fourier Transform (FFT) enhances the analysis, and the AdaBoost classifier is subsequently employed to assess the proper functioning of the motors. Recognizing the substantial impact of hyperparameter selections on classifier performance, a modified version of the Red Fox Optimization (RFO) algorithm is introduced to meet the specific demands of this study. Results demonstrate that the optimizers, when appropriately constructed, achieve perfect accuracy when applied to a real-world dataset, highlighting the potential practical applicability of the proposed methodology.
In recent decades, medical imaging has emerged as a vital field in medicine, playing a crucial role in diagnosis. Computer Assisted Diagnosis (CAD) systems have become instrumental in this arena, employing sophisticat...
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ISBN:
(数字)9798350309249
ISBN:
(纸本)9798350309256
In recent decades, medical imaging has emerged as a vital field in medicine, playing a crucial role in diagnosis. Computer Assisted Diagnosis (CAD) systems have become instrumental in this arena, employing sophisticated algorithms to extract crucial information from medical images. This study presents an innovative brain cancer detection system utilizing statistical classification methods. The approach involves three key stages: firstly, the identification of regions of interest through Gradient Vector Flow (GVF) Snake models and mathematical morphology techniques; secondly, the characterization of these regions using morphological and textural parameters; and finally, employing this characterization as inputs for a Bayesian network to classify malignant and benign cancer cases. Experimental validation of the proposed approach yielded impressive results, including a 100% sensitivity rate and a classification accuracy exceeding 98% for tumor segmentation. These findings underscore the high efficacy of the proposed CAD system, showcasing its potential in enhancing cancer diagnosis and patient care.
Multi-function radar can flexibly adjust their working modes and carry out multiple tasks in parallel, posing great challenges to radar intelligence reconnaissance. Identifying the working mode of multi-functional rad...
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ISBN:
(数字)9798331535087
ISBN:
(纸本)9798331535094
Multi-function radar can flexibly adjust their working modes and carry out multiple tasks in parallel, posing great challenges to radar intelligence reconnaissance. Identifying the working mode of multi-functional radar is the foundation of subsequent threat assessment, adaptive confrontation and guided attack, which directly determines the effectiveness of radar countermeasures. In this paper, a new method of multi-function radar working mode recognition based on Relational Graph Attention Network (RGAT) is proposed. The simulation results show that the accuracy of this paper's method for multi-functional radar working mode identification reaches 98.40% under 5% estimation error, which is higher than that of GAT model and GCN model, and it can effectively identify different working modes; in the case of 10% of spurious pulses and 40% of missing pulses, the accuracy of the identification is more than 94%, which is highly robust.
One of the most frequently diagnosed cancers in women is breast cancer. Mitotic cells in breast histopathological images are a very important biomarker to diagnose breast cancer. Mitotic scores help m...
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ISBN:
(数字)9798350350845
ISBN:
(纸本)9798350350852
One of the most frequently diagnosed cancers in women is breast cancer. Mitotic cells in breast histopathological images are a very important biomarker to diagnose breast cancer. Mitotic scores help medical professionals to grade breast cancer appropriately. The procedure of identifying mitotic cells is quite time-consuming. To speed up and improve the process, automated deep learning methods can be used. The suggested study aims to conduct analysis on the detection of mitotic cells using U-Net and modified VGG16 technique. In this study, pre-processing of the input images is done using stain normalization and enhancement processes. A modified VGG16 classifier is used to classify the segmented results after the altered image has been segmented using U-Net technology. The suggested method's robustness is evaluated using data from the MITOSIS 2012 dataset. The proposed strategy performed better with a precision of 86%,recall of 75% and F1-Score of 80%.
The rapid expansion of online platforms necessitates sophisticated recommendation systems to enhance user engagement. Leveraging user preferences and social interactions, the system aims to provide dynamic and tailore...
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ISBN:
(数字)9798350353068
ISBN:
(纸本)9798350353075
The rapid expansion of online platforms necessitates sophisticated recommendation systems to enhance user engagement. Leveraging user preferences and social interactions, the system aims to provide dynamic and tailored recommendations. Traditional recommendation systems face challenges in accuracy and personalization. Collaborative filtering struggles with the cold start problem, and social-based approaches may overlook individual preferences. Addressing these drawbacks, this paper proposes a hybrid model that combines collaborative filtering and social media analytics. This paper introduces an advanced recommendation system seamlessly integrating collaborative filtering and social media analytics to deliver real-time personalized suggestions. The novelty lies in assigning appropriate weights to recommendations based on both collaborative filtering and social influence, offering a comprehensive and accurate approach to personalized suggestions. The methodology involves defining objectives, collecting and pre-processing data, implementing the hybrid recommendation system, incorporating personalization techniques, and implementing a real-time engine. Evaluation includes key metrics such as accuracy, precision as $75 \%$, recall as $80 \%$, and user engagement. $A / B$ testing and continuous optimization based on user feedback contribute to a comprehensive assessment, showcasing the hybrid model’s effectiveness. In conclusion, this paper presents an innovative hybrid recommendation system, addressing existing drawbacks through integrated collaborative filtering and social media analytics.
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