Purpose: Heart disease is one of the most prevalent and critical diseases that endangers the lives of human beings. In addition to clinical diagnosis, machine learning and deep learning-based approaches are vital...
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Capacitive pressure sensors have attracted considerable interest due to their high sensitivity, low energy consumption, and potential for miniaturization, making them suitable for applications in automotive systems, c...
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Phase noise measurement of Phase Locked Loop (PLL) in automated test equipment (ATE) is expensive and time-consuming. Indirect phase noise measurement relies on a few correlated low-cost measurements called signatures...
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This research paper presents a distributed architecture for optimized route planning and precise circle detection in unmanned aerial vehicle with machine learning. The architecture focuses on three key areas: motion p...
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Photonics technology is critical in a variety of industries, including manufacturing, medicine, alternative energy, aircraft, and communications. It is concerned with the manipulation and control of light, which manif...
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Mode division multiplexing (MDM) technology represents a significant advancement in high-capacity optical data transmission in photonics integrated circuits (PICs). Among the critical components in MDM architecture ar...
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Automated speaker recognition has recently emerged as a hot and demanding area of study. In this work, we provide a new approach to speaker detection and verification that makes use of attention mechanisms in conjunct...
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Automated speaker recognition has recently emerged as a hot and demanding area of study. In this work, we provide a new approach to speaker detection and verification that makes use of attention mechanisms in conjunction with deep multidimensional acoustic feature acquisition. This approach is based on several deep convolutional neural networks (CNN). In order to get deep, segment-level speaker-specific characteristics from the source speech signal, the suggested method first creates three separate audio components for multi-CNNs: one for training with an unprocessed waveform in one dimension, one for training with a time–frequency Mel-spectrogram in two dimensions, and one for training with dynamic time–space features. The utterance-level analysis outcomes are generated by applying global average pooling to the segment-level outputs acquired from 1D, 2D, and 3D CNN models. Finally, for speaker identification, an attention-based approach skillfully combines features from the three streams to incorporate various outcomes from utterance-level categorization. The extracted deep multimodal speaker properties are demonstrated to be mutually beneficial, allowing for their integration in an attention-based fusion network to yield substantially enhanced performance. In order to test the suggested scheme, we used a number of conventional and real-time audio datasets. The suggested attention-based multi-dimensional fused-feature convolutional neural network (AMDF-CNN) reduces the speaker misclassification error rate by 2.52% when tested against baseline approaches, according to the experimental results. With an impressive identification rate of 97.59%, the AMDF-CNN speaker identification model performed well in the experiments. All the while, we put the model through its paces under different kinds of noise to see how reliable it is. Experimental results show that the suggested strategy outperforms state-of-the-art schemes with a reliability of more than 85%. Relevance of the
A novel modified cylindrical dielectric resonator-based MIMO antenna is presented for WLAN and X-band applications. On the above Rogers substrate (RT / duroid 5880), alumina or ceramics-based two-element dielectric re...
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The welding characteristics of 5052 aluminum alloy and Q235 low-carbon steel sheet were systematically studied by the refilled friction stir spot *** effects of rotation speed and pressure speed on weld forming,tensil...
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The welding characteristics of 5052 aluminum alloy and Q235 low-carbon steel sheet were systematically studied by the refilled friction stir spot *** effects of rotation speed and pressure speed on weld forming,tensile strength,and welded joint structure were analyzed in different welding *** results indicated two different connection modes:the chimeric mode and the non-chimeric *** corresponding depression depth are 2 and 2.4 mm,*** the non-chimeric connection mode,the steel/aluminum metallurgical interface is a smooth transition,the hook structure is obvious,and the welding mechanism mainly depends on the mutual diffusion between ***,in the chimeric mode,a hook structure will be formed at the metallurgical interface of steel and *** connection mechanism is determined by mechanical interlocking and mutual *** maximum strength value is 7.48 kN in non-chimeric *** this time,the spindle speed is 1300 r/min and the pressure speed is 1 mm/*** were two types of fractures:the button fracture mode and the peel fracture mode.
The Internet of Things (IoT), which enables seamless connectivity and effective data exchange between physical items and digital systems, has completely changed the way we interact with our surroundings. This study ev...
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