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Nonvolatile memory (NVM)‐based neuromorphic computing has been attracting considerable attention from academia and the industry. Although it is not completely successful yet, remarkable achievements have been reported pertaining to synaptic devices that can leverage NVM capable of storing multiple states. The analog synaptic devices performing computation similar to biological nerve systems are crucial in energy‐efficient analog neuromorphic computing systems. To use NVM as an analog synaptic device, researchers focus on improving device characteristics. Among various characteristics, the most challenging one is linearity and symmetry of synaptic weight update that is required for on‐chip training. In this regard, this review paper discusses recent synaptic device improvements focusing on novel schemes tailored for each NVM device to improve the linearity and symmetry. In addition to device‐level studies, recent research achievements are reviewed expanded up to chip‐level studies because in realizing neuromorphic hardware systems beyond a single synaptic device, several considerations and requirements are needed to confirm for high‐level design, and accordingly, cooptimize among synaptic devices, synapse arrays, electrical circuits, neural networks, algorithms, and implementation. Also, this review introduces various circuit and algorithmic approaches to compensate for the non‐ideality of the analog synaptic device. [ABSTRACT FROM AUTHOR]

Background: Computed tomography (CT)-guided transthoracic needle biopsy (TNB) could damage lung structures and may disseminate tumor cells into the airway, blood vessels, and pleural cavity, affecting post-operative outcomes. Several studies have investigated the effects of TNB on the prognosis of patients, but the effects remain unclear. This study aimed to investigate whether TNB increases the risk of recurrence of resected stage IA non-small cell lung cancer (NSCLC)., Methods: In this retrospective study, we enrolled 1,077 patients with stage IA NSCLC who underwent curative resection from 2010 to 2020. Recurrence risk factors were evaluated using Cox regression analyses. A multiple logistic regression model, including age, sex, smoking history, total tumor size, invasive tumor size, histology, histologic differentiation, lymphatic invasion, vascular invasion, perineural invasion, and the number of harvested lymph nodes (LNs), was used to calculate the propensity score., Results: According to the pre-operative TNB, patients were classified into the no-TNB (n=823) and TNB (n=190) groups. After propensity score matching analysis, 380 patients were included in the no-TNB group (1:2 matching). Multivariable Cox analysis revealed that pre-operative TNB was a negative prognostic factor in patients with surgically resected stage IA NSCLC [hazard ratio (HR), 3.15; 95% confidence interval (CI): 1.49-6.67; P=0.003]. The 5-year locoregional and overall recurrence-free survival (RFS) rates were significantly lower in the TNB group than in the no-TNB group (88.3% vs . 96.8%, P=0.001; and 84.2% vs . 93.7%, P=0.02, respectively)., Conclusions: For patients with stage IA NSCLC, pre-operative TNB was a negative prognostic factor for recurrence. Surgical diagnosis and treatment without pre-operative tissue diagnosis may be considered first in patients with clinically early lung cancer., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-24-419/coif). The authors have no conflicts of interest to declare., (2024 Journal of Thoracic Disease. All rights reserved.)

This paper proposes a novel signal quality assessment method for quasi-periodic cardiovascular signals, chiefly focus on the photoplethysmogram (PPG). The proposed method utilizes the fact that most cardiovascular signals are slowly time varying and thus morphological aspects of the two adjacent beats are almost identical. In order to implement this idea, the method first identifies pulse onset to divide the signal into several segments each of which contains one period of the signal. The segmented pulse signals having different pulse durations are then temporarily normalized by resampling them at a specific rate. Finally, the quality of the signals is evaluated as the signal similarity between the two adjacent segments. Optimal thresholds for the classification between high-and low-quality PPG signals are determined using the equal training sensitivity and specificity criterion. The proposed method is evaluated using a database where PPG signals are collected during a variety of activities such as cycling exercise. It attains a sensitivity of 97.9%, a specificity of 85.3%, and an accuracy of 93.8%, compared to manually annotated results. The promising results indicate that the proposed method is affordable to simply determine the quality of quasi-periodic cardiovascular signals, particularly PPG signals. In addition, based on the quasi-periodic characteristics of cardiovascular signals, the proposed method can also be used to indicate the reliability and the availability of the collected signals.

Wearable devices for body-status monitoring require various signal processing such as ambient noise filtering and signal quality evaluation. While wearable devices are very limited in resource, conventional noise filtering and signal quality evaluating methods consume considerable amount of processing power. Moreover, these conventional methods are not suitable for processing bio-signals in terms of its performance. In this paper, we propose a novel method of preprocessing bio-signals. This preprocessing method includes distortionless noise filtering and signal quality estimation, where both parts are basically based on a simple combination of multiple low pass IIR filters.

Wearable devices are becoming a natural and economic means to gather biometric data from end users. The massive amount of information that they will provide, unimaginable until a few years ago, owns an immense potential for applications such as continuous monitoring for personalized healthcare and use within fitness applications. Wearables are however heavily constrained in terms of amount of memory, transmission capability and energy reserve. This calls for dedicated, lightweight but still effective algorithms for data management. This paper is centered around lossy data compression techniques, whose aim is to minimize the amount of information that is to be stored on their onboard memory and subsequently transmitted over wireless interfaces. Specifically, we analyze selected compression techniques for biometric signals, quantifying their complexity (energy consumption) and compression performance. Hence, we propose a new class of codebook-based (CB) compression algorithms, designed to be energy efficient, online and amenable to any type of signal exhibiting recurrent patterns. Finally, the performance of the selected and the new algorithm is assessed, underlining the advantages offered by CB schemes in terms of memory savings and classification algorithms.