An electrocardiogram (ECG) is a basic and quick test for evaluating cardiac disorders and is crucial for remote patient monitoring equipment. An accurate ECG signal classification is critical for real-time measurement, analysis, archiving, and transmission of clinical data. Numerous studies have focused on accurate heartbeat classification, and deep neural networks have been suggested for better accuracy and simplicity. We investigated a new model for ECG heartbeat classification and found that it surpasses state-of-the-art models, achieving remarkable accuracy scores of 98.5% on the Physionet MIT-BIH dataset and 98.28% on the PTB database. Furthermore, our model achieves an impressive F1-score of approximately 86.71%, outperforming other models, such as MINA, CRNN, and EXpertRF on the PhysioNet Challenge 2017 dataset.

Knowledge distillation, which is a process of transferring complex knowledge learned by a heavy network, i.e., a teacher, to a lightweight network, i.e., a student, has emerged as an effective technique for compressing neural networks. To reduce the necessity of training a large teacher network, this paper leverages the recent self-knowledge distillation approach to train a student network progressively by distilling its own knowledge without a pre-trained teacher network. Far from the existing self-knowledge distillation methods, which mainly focus on still images, our proposed Teaching Yourself is a self-knowledge distillation technique that targets at videos for human action recognition. Our proposed Teaching Yourself is not only designed as an effective lightweight network but also a high generalization capability model. In our approach, the network is able to update itself using the best past model, termed the preceding model, which is then utilized to guide the training process to update the present model. Inspired by consistency training in state-of-the-art semi-supervised learning methods, we also introduce an effective augmentation strategy to increase data diversity and improve network generalization and consistent predictions for our proposed Teaching Yourself approach. Our benchmark has been conducted on both the 3D Resnet-18 and 3D ResNet-50 backbone networks and evaluated on various standard datasets such as UCF101, HMDB51, and Kinetics400 datasets. The experimental results have shown that our teaching yourself method significantly improves the action recognition performance in terms of accuracy compared to existing supervised learning and knowledge distillation methods. We also have conducted an expensive ablation study to demonstrate that our approach mitigates overconfident predictions on dark knowledge and generates more consistent predictions in input variations of the same data point. The code is available at https://github.com/vdquang1991/Self-KD.

This brief presents the chip implementation of a support vector machine (SVM)-based speaker verification system. The proposed chip comprises a speaker feature extraction (SFE) module, an SVM module, and a decision module. The SFE module performs autocorrelation analysis, linear predictive coefficient (LPC) extraction, and LPC-to-cepstrum conversion. The SVM module includes a Gaussian kernel unit and a scaling unit. The purpose of the Gaussian kernel unit is first to evaluate the kernel value of a test vector and a support vector. Four Gaussian kernel processing elements (GK-PEs) are designed to process four support vectors simultaneously. Each GK-PE is designed in the pipeline fashion and is capable of performing 2-norm and exponential operations. An enhanced CORDIC architecture is proposed to calculate the exponential value. As well as the Gaussian kernel unit, a scaling unit is also developed for use in the SVM module. The scaling unit is used to perform scaling multiplications and the remaining operations of SVM decision value evaluation. Finally, the decision module accumulates the frame scores that are generated by all of the test frames, and then compare it with a threshold to see if the test utterance is spoken by the claimed speaker. This designed chip is characterized by its high speed and its ability to handle a large number of support vectors in the SVM. The prototype chip is a semicustom chip that is fabricated using Taiwan Semiconductor Manufacturing Company 0.90-nm CMOS technology on a die with a size of ~7.9 × 7.9 mm ² .

The need for a lightweight and reliable segmentation algorithm is critical in various biomedical image-prediction applications. However, the limited quantity of data presents a significant challenge for image segmentation. Additionally, low image quality negatively impacts the efficiency of segmentation, and previous deep learning models for image segmentation require large parameters with hundreds of millions of computations, resulting in high costs and processing times. In this study, we introduce a new lightweight segmentation model, the mobile anti-aliasing attention u-net model (MAAU), which features both encoder and decoder paths. The encoder incorporates an anti-aliasing layer and convolutional blocks to reduce the spatial resolution of input images while avoiding shift equivariance. The decoder uses an attention block and decoder module to capture prominent features in each channel. To address data-related problems, we implemented data augmentation methods such as flip, rotation, shear, translate, and color distortions, which enhanced segmentation efficiency in the international Skin Image Collaboration (ISIC) 2018 and PH2 datasets. Our experimental results demonstrated that our approach had fewer parameters, only 4.2 million, while it outperformed various state-of-the-art segmentation methods.

Automatic sound event recognition (SER) has recently attracted renewed interest. Although practical SER system has many useful applications in everyday life, SER is challenging owing to the variations among sounds and noises in the real-world environment. This work presents a novel feature extraction and classification method to solve the problem of sound event recognition. An audio-visual descriptor, called the auditory-receptive-field binary pattern (ARFBP), is designed based on the spectrogram image feature (SIF), the cepstral features, and the human auditory receptive field model. The extracted features are then fed into a classifier to perform event classification. The proposed classifier, called the hierarchical-diving deep belief network (HDDBN), is a deep neural network (DNN) system that hierarchically learns the discriminative characteristics from physical feature representation to the abstract concept. The performance of our proposed system was verified using several experiments under various conditions. Using the RWCP sound scene dataset, the proposed system achieved a recognition rate of 99.27\% for real-world sound data in 105 categories. Under noisy conditions, the developed system is very robust, with which it achieved 95.06\% recognition rate with 0 dB signal-to-noise ratio (SNR). Using the TUT sound event dataset, the proposed system achieves an error of 0.81 and 0.73 in sound event detection in home and residential area scenes. The experimental results reveal that the proposed system outperformed the other systems in this field.