There has been a huge increase in the number of mobile devices over the past few years. Many mobile users own more than one mobile device. Compared to conventional laptops, mobile devices are constantly active and the...
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作者：
Fang, PietNihtianov, StoyanTU Delft
Faculty of Electrical Engineering Mathematics and Computer Science Department of Microelectronics Mekelweg 4 Delft Netherlands
Photodiodes based on the Boron on Silicon junction (B-Si) show excellent responsivity to DUV and VUV photons, radiation hardness, and impressive electrical characteristics. However, the proposed models describing the ...
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Speech is a fundamental means of human interaction. Speaker Identification (SI) plays a crucial role in various applications, such as authentication systems, forensic investigation, and personal voice assistance. Howe...
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Speech is a fundamental means of human interaction. Speaker Identification (SI) plays a crucial role in various applications, such as authentication systems, forensic investigation, and personal voice assistance. However, achieving robust and secure SI in both open and closed environments remains challenging. To address this issue, researchers have explored new techniques that enable computers to better understand and interact with humans. Smart systems leverage Artificial Neural Networks (ANNs) to mimic the human brain in identifying speakers. However, speech signals often suffer from interference, leading to signal degradation. The performance of a Speaker Identification System (SIS) is influenced by various environmental factors, such as noise and reverberation in open and closed environments, respectively. This research paper is concerned with the investigation of SI using Mel-Frequency Cepstral Coefficients (MFCCs) and polynomial coefficients, with an ANN serving as the classifier. To tackle the challenges posed by environmental interference, we propose a novel approach that depends on symmetric comb filters for modeling. In closed environments, we study the effect of reverberation on speech signals, as it occurs due to multiple reflections. To address this issue, we model the reverberation effect with comb filters. We explore different domains, including time, Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Discrete Sine Transform (DST) domains for feature extraction to determine the best combination for SI in case of reverberation environments. Simulation results reveal that DWT outperforms other transforms, leading to a recognition rate of 93.75% at a Signal-to-Noise Ratio (SNR) of 15 dB. Additionally, we investigate the concept of cancelable SI to ensure user privacy, while maintaining high recognition rates. Our simulation results show a recognition rate of 97.5% at 0 dB using features extracted from speech signals and their DCTs. Fo
Particles in the atmosphere, such as dust and smoke, can cause visual clarity problems in both images and videos. Haze is the result of the interaction between airborne particles and light, which is scattered and atte...
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Particles in the atmosphere, such as dust and smoke, can cause visual clarity problems in both images and videos. Haze is the result of the interaction between airborne particles and light, which is scattered and attenuated. Hazy media present difficulties in a variety of applications due to the reduced contrast and loss of essential information. In response, dehazing techniques have been introduced to bring hazy videos and images back to clarity. Here, we provide a novel technique for eliminating haze. It comprises preprocessing steps before dehazing. Preprocessing is applied to hazy images through homomorphic processing and Contrast Limited Adaptive Histogram Equalization (CLAHE). We present a dehazing technique referred to as the pre-trained Feature Fusion Attention Network (FFA-Net) that directly lets dehazed images be restored from hazy or preprocessed hazy inputs without requiring the determination of atmospheric factors, such as air light and transmission maps. The FFA-Net architecture incorporates a Feature Attention (FA) method to do this task. We assess the proposed technique in a variety of circumstances, including visible frames, Near-Infrared (NIR) frames, and real-world hazy images. Evaluation criteria like entropy, correlation, and Peak Signal-to-Noise Ratio (PSNR) are used to compare the quality of dehazed frames or images to their hazy counterparts. Furthermore, histogram analysis and spectral entropy are adopted to determine the effectiveness of the proposed technique in comparison to existing dehazing techniques. Comparative results are presented for both real-world and simulated environments. The benefits of the proposed technique are demonstrated by a comparison of the results obtained from the standalone pre-trained FFA-Net and the proposed comprehensive methodology. Moreover, a thorough assessment is carried out for comparing the effectiveness of the proposed FFA-Net technique to those of some current dehazing techniques on real hazy images. T
Convolutional Neural Networks (CNNs) have revolutionized the field of computer vision, offering significant advancements in image and video recognition, classification, and segmentation tasks. Leveraging hierarchical ...
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Accurate segmentation of skin lesions is critical for dermatological diagnosis and treatment. This study investigates the impact of label errors on the performance of convolutional neural networks in skin lesion segme...
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This paper reports on ongoing and innovative research in the area of eXplainable Artificial Intelligence (XAI). A classical XAI task is considered as finding an explanation of the model generated via Machine Learning ...
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In this paper, a high-precision three-dimensional (3-D) near-field (NF) localization method is proposed under an underdetermined case based on a symmetric enhanced nested array (SENA). Firstly, the symmetry of the arr...
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Despite its promising advancements, Web 3.0 solutions still encounter significant challenges, particularly in the realm of software testing and security testing. This paper introduces a literature review of existing r...
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Low inertia of the power system combined with low damping impairs the power system's ability to cope with disturbances that can significantly affect system stability. In such a case, in addition to speed or freque...
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