The verification of analog mixed-signal (AMS) functionalities is paramount to the development of modern systems on a chip (SoCs). Automation encompasses most stages of the AMS verification flow, but stimulus generation persists as a manual process. As a result, it is a daunting and time-consuming endeavor. Henceforth, automation is a critical requirement. Stimuli creation necessitates the identification and classification of the subcircuits or sub-blocks inherent within a given analog circuit module. Although there is a need, a robust and dependable industrial tool is absent for automatically identifying/categorizing analog sub-circuits (eventually used in designing circuits) or categorizing a given analog circuit at hand. The potential of an automated classification model for analog circuit modules, spanning various levels, would be pivotal in improving numerous procedures, extending beyond the confines of verification. Utilizing a Graph Convolutional Network (GCN) model, this paper describes a novel data augmentation strategy for the automatic classification of analog circuits at a given level of design. Ultimately, this methodology can be adapted to a broader context or incorporated within a more complex operational module (for understanding circuit layouts in intricate analog circuits), thereby pinpointing sub-circuits within the more comprehensive analog circuit structure. A novel, integrated approach to data augmentation is essential given the stark reality of limited datasets of analog circuit schematics (i.e., sample architectures) in real-world situations. A comprehensive ontology facilitates the initial presentation of a graph framework for circuit schematics, which is developed by converting the relevant netlists of the circuit into graphs. For the input analog circuit's schematic, a robust classifier, utilizing a GCN processor, is used to derive the corresponding label. By incorporating a novel data augmentation method, the classification's performance is both improved and more robust. Classification accuracy experienced a remarkable jump from 482% to 766% due to the implementation of feature matrix augmentation. Furthermore, flipping the dataset during augmentation resulted in a corresponding improvement, raising accuracy from 72% to 92%. A 100% accuracy was obtained after the application of multi-stage augmentation or the utilization of hyperphysical augmentation. Demonstrating high accuracy in the classification of the analog circuit, extensive tests were designed and implemented 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.
Researchers are increasingly motivated to discover real-world applications for virtual reality (VR) and augmented reality (AR) technologies, driven by the growing accessibility and lower costs of these devices, including their utilization in sectors like entertainment, healthcare, and rehabilitation. This study's focus is on providing a summary of the existing scientific literature dedicated to VR, AR, and physical activity. In a study applying conventional bibliometric laws, a bibliometric analysis of publications spanning from 1994 to 2022 and recorded in The Web of Science (WoS) was undertaken. This process used VOSviewer for data and metadata management. Scientific output experienced an exponential surge between 2009 and 2021, as demonstrated by the results (R2 = 94%). The United States (USA) boasted the largest and most influential co-authorship networks, with 72 publications; Kerstin Witte emerged as the most prolific author, while Richard Kulpa was the most prominent. The most productive journals were built upon a central core of high-impact and open-access journals. The co-authorship's dominant keywords showcased a broad array of thematic interests, highlighting concepts such as rehabilitation, cognitive improvement, physical training, and the impact of obesity. Moving forward, the investigation of this subject is progressing exponentially, prompting significant engagement within rehabilitation and sports science circles.
Considering Rayleigh and Sezawa surface acoustic waves (SAWs) in ZnO/fused silica, the theoretical analysis of the acousto-electric (AE) effect examined the hypothesis of an exponentially decaying electrical conductivity in the piezoelectric layer, drawing parallels to the photoconductivity effect induced by ultraviolet light in wide-band-gap ZnO. In contrast to the single-relaxation response characterizing the AE effect, the ZnO conductivity curves, correlated with calculated wave velocities and attenuation, show a double-relaxation response pattern. Two configurations of UV light illumination, from either the top or bottom of the ZnO/fused silica substrate, were analyzed to elucidate the effects. First, ZnO's conductivity inhomogeneities originate at the external surface and decrease exponentially with depth; second, conductivity inhomogeneities initiate at the interface of the ZnO layer and the fused silica substrate. From the author's perspective, a theoretical analysis of the double-relaxation AE effect in bi-layered systems has been undertaken for the first time.
The article elucidates how multi-criteria optimization methods are implemented during the calibration of digital multimeters. Calibration, at the moment, hinges upon a single determination of a particular numerical value. This investigation aimed to confirm the practicality of using a series of measurements to reduce measurement uncertainty without extending the calibration timeframe to a considerable degree. Calcitriol The automatic measurement loading laboratory stand employed during the experiments was essential for generating the results necessary to verify the thesis. This article details the optimization techniques employed and the resultant calibration outcomes for the sample digital multimeters. The research findings indicated that employing a progression of measurements yielded an increase in calibration accuracy, a decrease in measurement error, and a reduction in the overall calibration time relative to customary techniques.
Discriminative correlation filters (DCFs) provide the accuracy and efficiency that make DCF-based methods popular for target tracking within the realm of unmanned aerial vehicles (UAVs). UAV tracking, unfortunately, is consistently confronted with a variety of demanding situations, such as background interference, comparable targets, partial or complete blockage, and high-speed movement. These challenges usually manifest as multi-peaked interference in the response map, thus leading to target deviation or even its total loss. 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. In the construction of a response-consistent module, two response maps are formed using the filter and the characteristics gleaned from surrounding frames. Insect immunity Thereafter, these two replies are held constant, mirroring the previous frame's response. This module's incorporation of the L2-norm constraint ensures a consistent target response, thereby warding off abrupt fluctuations due to background interference. The learned filter is thus empowered to retain the distinguishing characteristics of the previous filter. Presented is a novel background-suppression module, in which the learned filter's awareness of background data is improved via an attention mask matrix. The proposed methodology benefits from the incorporation of this module into the DCF framework, thereby further reducing the disruptive effect of background distractor responses. Subsequent to earlier investigations, extensive comparative tests were conducted to evaluate performance on three challenging UAV benchmarks, UAV123@10fps, DTB70, and UAVDT. Empirical testing has shown that our tracker outperforms 22 other state-of-the-art trackers in terms of tracking performance. For real-time monitoring of UAVs, our proposed tracking system can operate at 36 frames per second on a single CPU.
For the purpose of verifying robotic system safety, this paper presents a computationally efficient approach for calculating the minimum distance between a robot and its surrounding environment, including the supporting implementation framework. Within robotic systems, collisions stand as the most fundamental safety predicament. Therefore, a validation procedure is crucial for robotic system software, to mitigate any collision risks during the developmental and applicational phases. To ascertain the absence of collision risks within the system software, the online distance tracker (ODT) is designed to pinpoint the minimum distances between robots and their surroundings. The representations of the robot and its environment, using cylinders and an occupancy map, are integral to the proposed method. In addition, the bounding box method enhances the computational efficiency of the minimum distance calculation. To conclude, the method is applied to a realistically simulated twin of the ROKOS, an automated robotic inspection cell designed for quality control of automotive body-in-white and employed within the bus manufacturing industry. The proposed method's feasibility and effectiveness are showcased by the simulation results.
For the purpose of quick and precise evaluation of drinking water quality, a miniaturized instrument is proposed in this paper, capable of measuring both permanganate index and total dissolved solids (TDS). Microbiota functional profile prediction Water's organic content can be roughly determined by the permanganate index, which is measured using laser spectroscopy, while the conductivity method allows for a similar estimation of inorganic components by measuring TDS. This paper proposes and details a novel percentage-based method for evaluating water quality, supporting the proliferation of civilian applications. The instrument screen provides a visual representation of water quality results. During the Weihai City, Shandong Province, China experiment, we evaluated the water quality parameters of tap water, along with those of water following primary and secondary filtration processes.