The Design of efficient, safe, and reliable circuits is a prime objective in high-voltage (HV) electronic systems, such as switched-mode power amplifiers (PAs). One of the main causes of efficiency degradation and reliability problems, in these amplifiers, is the shoot-through current from the HV power supply to the ground. To eliminate such current, a dead time generator (DTG) is used to modify the signals propagating through the high-side and low-side gate drivers by adding a fixed dead time between them. However, any delay mismatch between these gate drivers can reduce the dead time to the point that it becomes negative. In this paper, an HV-DTG architecture is introduced. The architecture mitigates the effects of delay mismatch variations in gate drivers, which can result from parameters mismatch, fabrication process variations, and temperature variations. An HV switched-mode class-D power amplifier is used to illustrate the performance of the DTG. The amplifier is implemented in a low-cost$0.35~\mu m$HV CMOS process. The total area of the PA is$0.5~mm^{2}$, where the DTG covers an area of$0.066~mm^{2}$. A measured system’s efficiency of 95.14% is achieved with the shortest dead time of 10.8 ns, which is 1.38x smaller than the generated dead time in comparable state-of-the-art HV dead time generators.

This paper proposes a methodology based on machine learning to find apparent causal relations between performance targets and design variables in analog circuits. Diversified filtering and wrapping variable selection algorithms are utilized to construct a causal graph that identifies the major circuit design parameters that can be used to optimize the performance of analog circuits. Based on the constructed causal graph, a sequence of design procedures can be extracted and followed to optimize the performance of a design. The proposed methodology is validated using a two-stage op-amp. The obtained causal graph agrees with analytical design equations published in the literature for the selected two-stage op-amp. The results also show that the proposed methodology can accelerate the circuit design process and effectively help designers understand the reasoning behind different design decisions.

This paper presents the design and analysis of combining input-voltage feedforward and proportional-integral (PI) controllers to regulate the output voltage of DC-DC Buck converter subject to input line disturbances. Non-idealities of the Buck converter such as passive and active components parasitics are included in the mathematical model obtained by the statespace averaging (SSA) technique for accurate control. The stability boundary locus approach is used to graphically analyze the system stability. It guides the design of the PI controller gains and the feedforward scaling factor to achieve desired phase and gain margins. Analysis shows that the feedforward scaling factor affects the stability regions of the closed-loop system and can limit the possible PI controller gains for certain phase and gain margins; 75oand 9.54 dB in our case. The results are verified by a Simulink model developed for the Buck converter system.

This article proposes a multiagent-based methodology for the real-time fault diagnosis in industrial processes. This articles aims to build a decision support tool that helps process operators identify and better manage abnormal situations. The supervised and semisupervised machine learning methods are widely used to develop such tools. Despite their accuracy in classifying faults, supervised methods have a major limitation: they cannot diagnose novel faults. The semisupervised methods can detect and isolate novel faults but cannot disclose their root causes. The proposed methodology combines both supervised and semisupervised methods in a parallel-serial structure, exploiting their respective strengths. Moreover, it provides the process expert with the meaningful explanations of the detected novel faults or otherwise. Two case studies are used in this article to demonstrate the effectiveness of the proposed methodology. The first case is the Tennessee Eastman process benchmark. The second one uses the real data collected from a heat recovery system in a thermomechanical pulp mill.

The work in this paper introduces a platform that is able to visualize eye gaze data. The platform introduces two different directions or algorithms. The first is for videos with heat-map to convert the heat map data into raw numbers. The algorithm takes the video containing the heat map and cuts it into frames. In each frame, it locates the position of the heat-map by searching for the specific colour values and then displaying the position of heat-map in each frame. The algorithm also calculates the fixation points and how long each fixation was for the entirety of the video. The second algorithm is to do the opposite of the first and that is by converting the raw data of eye-movement to a heat map. We are also providing visualization platform for eye-tracking data.

Existing research on vulnerability discovery models shows that the existence of vulnerabilities inside an application may be linked to certain features, e.g., size or complexity, of that application. However, the applicability of such features to demonstrate the relative security between two applications is not well studied, which may depend on multiple factors in a complex way. In this paper, we perform the first large-scale empirical study of the correlation between various features of applications and the abundance of vulnerabilities. Unlike existing work, which typically focuses on one particular application, resulting in limited successes, we focus on the more realistic issue of assessing the relative security level among different applications. To the best of our knowledge, this is the most comprehensive study of 780 real-world applications involving 6498 vulnerabilities. We apply seven feature selection methods to nine feature subsets selected among 34 collected features, which are then fed into six types of machine learning models, producing 523 estimations. The predictive power of important features is evaluated using four different performance measures. This paper reflects that the complexity of applications is not the only factor in vulnerability discovery and the human-related factors contribute to explaining the number of discovered vulnerabilities in an application.

We propose in this paper a high-temperature non-linear modeling for the I-V characteristics of GaN150 HEMT. Three different data-driven models were developed for a temperature range varying from 25°C to 250°C, by using three machine learning regression techniques namely: The Artificial Neural Network (ANN), the Support Vector Machine (SVM) and the Decision Tree (DT). Experiments were conducted on a GaN150 device with a width of 40 μm and accordingly, a set of measurements were obtained and exploited to build the device model. The three models were evaluated based on their ability to predict the I-V characteristics outside the temperature range (greater than 250°C) and their mean square error. The obtained results show that the models predict the device characteristics correctly based on the calculated mean squared error between the actual and predicted characteristics.

We describe in this paper a model for the I-V characteristics of AlGaN/GaN high electron mobility transistors (HEMTs) working in high-temperature environments up to 250°C. Modeling of this emerging technology is a very significant step toward incorporating the technology in harsh environment applications. An extended version of the Angelov model is modified in this paper to consider the temperature as a variable. The developed model is fitted to the experimental I-V data using MATLAB. The reported experimental data are in good agreement with the model outputs over the specified temperature range. Moreover, the model was validated using the Spectre circuit simulator.