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Monitoring heart conditions through electrocardiography (ECG) has been the cornerstone of identifying cardiac irregularities. Cardiologists often rely on a detailed analysis of ECG recordings to pinpoint deviations that are indicative of heart anomalies. This traditional method, while effective, demands significant expertise and is susceptible to inaccuracies due to its manual nature. In the realm of computational analysis, Artificial Neural Networks (ANNs) have gained prominence across various domains, which can be attributed to their superior analytical capabilities. Conversely, Spiking Neural Networks (SNNs), which mimic the neural activity of the brain more closely through impulse-based processing, have not seen widespread adoption. The challenge lies primarily in the complexity of their training methodologies. Despite this, SNNs offer a promising avenue for energy-efficient computational models capable of displaying a high-level performance. This paper introduces an innovative approach employing SNNs augmented with an attention mechanism to enhance feature recognition in ECG signals. By leveraging the inherent efficiency of SNNs, coupled with the precision of attention modules, this model aims to refine the analysis of cardiac signals. The novel aspect of our methodology involves adapting the learned parameters from ANNs to SNNs using leaky integrate-and-fire (LIF) neurons. This transfer learning strategy not only capitalizes on the strengths of both neural network models but also addresses the training challenges associated with SNNs. The proposed method is evaluated through extensive experiments on two publicly available benchmark ECG datasets. The results show that our model achieves an overall accuracy of 93.8% on the MIT-BIH Arrhythmia dataset and 85.8% on the 2017 PhysioNet Challenge dataset. This advancement underscores the potential of SNNs in the field of medical diagnostics, offering a path towards more accurate, efficient, and less resource-intensive analyses of heart diseases.

In nanometer technology, accurate aging prediction of MOSFET digital circuits is one of the most critical issues for more reliable adaptive system design. This paper proposes a new on-chip aging prediction circuit to monitor BTI and HCI aging effects on digital circuits. The proposed circuit deploys a flip-flop based delay detector for monitoring a guardband violation of sequential logics. The outputs of the proposed circuit can be used as a control signal in reliable self-adaptive systems. A 0.11µm CMOS technology has been used to implement and evaluate the proposed circuits. [ABSTRACT FROM AUTHOR]

This paper presents a jitter decomposition method in the test of serial data channels. It is difficult to detect minor adjacent spectral jitter components due to frequency leakage in the jitter decomposition. Especially, when spectral jitter decomposition in random jitter (RJ) and periodic jitter (PJ) is performed, some degree of the PJ leakage can occur. The PJ leakage can cause serious error in separation PJ and RJ. Therefore, in this paper, a new method to avoid PJ leakage has been proposed. The proposed method shows significant improvement in PJ leakage control. [ABSTRACT FROM AUTHOR]