The verification of analog mixed-signal (AMS) functionalities is paramount to the development of modern systems on a chip (SoCs). The AMS verification process benefits from automation in many areas, with only the generation of stimuli relying on manual procedures. As a result, it is a daunting and time-consuming endeavor. In conclusion, automation is a vital necessity. In order to create stimuli, the subcircuits or sub-blocks of a defined analog circuit module must be recognized and categorized. Still, an industrial tool is urgently needed to automate the identification and classification of analog sub-circuits (integrated into the circuit design process) or the classification of a provided analog circuit. For analog circuit modules, which may exist at various design levels, a robust and reliable automated classification model would significantly improve efficiency, especially when considering the verification process and others. This paper introduces a Graph Convolutional Network (GCN) model and a new data augmentation technique, both of which enable the automatic classification of analog circuits at a specific level. By design, the method can be developed to larger implementations or incorporated into a multifaceted functional block (useful for structural analysis of complex analog circuits), seeking to identify individual sub-circuits contained within the larger analog circuit. Considering the typical scarcity of analog circuit schematic datasets (i.e., sample architectures) in real-world settings, an integrated and novel data augmentation approach is of particular importance. By employing a comprehensive ontology, we initially delineate a graph representation structure for circuit schematics. This involves the transformation of the circuit's pertinent netlists into graphical forms. We then leverage a robust classifier, composed of a GCN processor, to determine the label associated with the input analog circuit's schematic diagram. Furthermore, the classification's performance benefits from the introduction of a novel data augmentation method, resulting in greater robustness. Through the augmentation of the feature matrix, the classification accuracy increased from 482% to 766%. Dataset augmentation, accomplished by flipping, concurrently enhanced accuracy, improving it from 72% to 92%. The combined effect of multi-stage augmentation or hyperphysical augmentation produced a remarkable 100% accuracy. To ensure high accuracy, a range of analog circuit classification tests were rigorously developed and executed for the concept. The viability of future automated analog circuit structure detection, essential for both analog mixed-signal stimulus generation and other crucial initiatives in AMS circuit engineering, is significantly bolstered by this solid support.
The advent of more affordable virtual reality (VR) and augmented reality (AR) technologies has significantly boosted researchers' drive to uncover practical applications, from entertainment and healthcare to rehabilitation sectors and beyond. This study seeks to present a comprehensive review of existing research on VR, AR, and physical activity. The Web of Science (WoS) served as the source for a bibliometric analysis of publications between 1994 and 2022. The analysis incorporated standard bibliometric principles, processed using VOSviewer software for data and metadata. The period from 2009 to 2021 saw a substantial, exponential rise in scientific publications, as evidenced by the data (R2 = 94%). The United States (USA) demonstrated the strongest co-authorship networks among all countries/regions, evident in 72 publications; Kerstin Witte was the most prolific author, and Richard Kulpa, the most influential figure. The most effective journals were centered on a core of high-impact and open-access publications. The keywords frequently used by co-authors pointed to a substantial diversity of thematic foci, ranging from rehabilitation and cognitive enhancement to training methods and the implications of obesity. This subject's investigation is currently undergoing an exponential expansion, attracting notable interest from the rehabilitation and sports science communities.
A theoretical examination of the acousto-electric (AE) effect, involving Rayleigh and Sezawa surface acoustic waves (SAWs) in ZnO/fused silica, predicated the hypothesis of an exponentially decaying electrical conductivity within the piezoelectric layer, mirroring the photoconductivity observed in wide-band-gap ZnO under ultraviolet illumination. The calculated waves' velocity and attenuation exhibit a double-relaxation pattern when plotted against ZnO conductivity, diverging from the single-relaxation response typically seen in AE effects related to surface conductivity. Two configurations, featuring UV illumination on the top or bottom of the ZnO/fused silica substrate, provided insights. First, inhomogeneity in ZnO conductivity starts from the surface of the layer and diminishes exponentially with depth. Second, conductivity inhomogeneity originates at the ZnO/fused silica interface. The author's research suggests that this is the first theoretical investigation of the double-relaxation AE effect in bi-layered architectural designs.
The article showcases the digital multimeter calibration process using multi-criteria optimization methods. Calibration, at the moment, hinges upon a single determination of a particular numerical value. We endeavored, in this study, to validate the capacity of a series of measurements to diminish measurement uncertainty without noticeably increasing the calibration duration. Safe biomedical applications Results confirming the thesis were achieved thanks to the automatic measurement loading laboratory stand used throughout the experimental process. The article elucidates the implemented optimization methods and the calibrated results of the sample digital multimeters. From the research, it was ascertained that a series of measurements enhanced calibration precision, lessened measurement error, and abridged the calibration time relative to conventional practices.
UAV target tracking has seen a surge in the use of DCF-based methods, leveraging the advantages of discriminative correlation filters in terms of accuracy and computational speed. The task of tracking UAVs, however, frequently presents significant challenges stemming from a variety of factors, including background congestion, visually similar objects, partial or complete obscuration, and rapid target velocity. These difficulties typically result in multiple peaks of interference on the response map, causing the target to wander or even vanish. In order to track UAVs, this proposal introduces a correlation filter that is consistent in its response and suppresses the background, thus addressing the problem. A module is built for consistent responses, where two response maps are synthesized through the utilization of the filter and the features extracted from frames positioned next to one another. hepatitis C virus infection In the next step, these two answers are kept consistent with the prior frame's answer. The L2-norm constraint, implemented within this module, guarantees consistent target response, effectively preventing volatility stemming from background disturbances. Concurrently, it empowers the learned filter to uphold the distinguishing properties of the prior filter. Secondly, a novel background-suppressed module is presented, leveraging an attention mask matrix to enhance the learned filter's awareness of contextual background information. The proposed method, enhanced by the addition of this module to the DCF framework, can further lessen the response interference stemming from distractors situated in the background. In conclusion, extensive comparative trials were executed across three rigorous UAV benchmarks: UAV123@10fps, DTB70, and UAVDT. The experimental findings unequivocally indicate that our tracker's tracking performance surpasses that of 22 other cutting-edge trackers. The proposed tracker can achieve real-time UAV tracking at a rate of 36 frames per second using a single CPU.
An efficient method for determining the shortest distance between a robot and its environment is presented in this paper, coupled with a framework for verifying robotic system safety. A critical safety issue in robotic systems is the potential for collisions. Accordingly, the software of robotic systems must be validated to prevent any risks of collision during the creation and integration processes. System software safety is evaluated by the online distance tracker (ODT), which establishes minimum distances between robots and their environment to prevent collisions. This method incorporates cylinder models of the robot and its environment, and further utilizes an occupancy map. In addition, the bounding box method enhances the computational efficiency of the minimum distance calculation. The methodology's concluding application is on a realistically modeled simulation of the ROKOS, a robotic inspection system used for quality control of automotive body-in-white, and currently utilized in the bus manufacturing industry. Simulation results highlight the potential and efficacy of the proposed method in practice.
This paper introduces a compact water quality detector for swiftly and precisely assessing drinking water, focusing on the detection of permanganate index and total dissolved solids (TDS). read more Using laser spectroscopy, the permanganate index can estimate the presence of organic material in water, just as TDS measurements obtained through conductivity methods offer an approximate assessment of inorganic matter in water. The paper introduces a percentage-scoring system for evaluating water quality, with the aim of promoting its civilian applications. The water quality results are seen on the screen of the instrument. Water quality parameters were measured in the experiment, encompassing tap water and post-primary and secondary filtration samples, all collected in Weihai City, Shandong Province, China.