Miracle-dz/UniFAR-FTUs huggingface dataset:
A Structure-Based Methodology for Analog Layout Generation

In order to speed up analog design cycles, analog layout automation is a popular research in recent years. However, most previous works assume that the required design constraints are given by users manually. Designers still take a lot of time to fill-in the required design information. Template-based layout generation is another approach to consider the design constraints, but considerable development efforts are required for each new design and each new technology. In this paper, we propose a structure-based methodology for analog layout generation. This methodology starts from a structure analysis that divides the circuit netlist into several building blocks automatically. It can help to reduce the dependence on users' input and generate corresponding design constraints for the succeeding layout steps. With the help from structure analysis, the layouts of those analog structures are generated, placed, and routed automatically with proper constraints. As shown in the demo cases, the proposed flow is able to generate the required layout accurately without users' intervention and still keeps the post-layout performance within specifications.
Bayesian Identification of Distributed Vector AutoRegressive Processes

The identification of vector autoregressive (VAR) processes from partial samples is a relevant problem motivated by several applications in finance, econometrics, and networked systems (including social networks). The literature proposes several estimation algorithms, leveraging on the fact that these models can be interpreted as random Markov processes with covariance matrices satisfying Yule-Walker equations. In this paper, we address the problem of identification of distributed vector autoregressive (DVAR) processes from partial samples. The DVAR theory builds on the assumption that several processes are evolving in time, and the transition matrices of each process share some common characteristics. First, we discuss different models for describing the coupling among single processes. Subsequently, we propose an estimator for the transition matrices of the DVAR processes adopting an Empirical Bayes approach. More precisely, the local parameters are treated as random variables with a partially-unknown a priori density function, chosen as the conjugate family of distributions defined over symmetric, nonnegative-definite matrix-valued random variables and parameterized by suitable unknown hyperparameters. We develop an optimization algorithm to obtain the maximum likelihood estimates of the hyperparameters. The main feature of the proposed approach is that it does not require exact knowledge of the model describing the coupling between the different VAR processes, and it proves particularly well suited in scenarios in which the number of samples are allowed to be highly inhomogeneous or incomplete. The proposed techniques are validated on a numerical problem arising in social networks estimation.
MTCNN with Weighted Loss Penalty and Adaptive Threshold Learning for Facial Attribute Prediction

Facial attribute prediction is a meaningful but challenging task in computer vision, due to the demand of recognizing many attributes of a face image at a time. In this paper, we propose a novel multi-task convolutional neural network(MTCNN) with a weighted loss penalty scheme and an adaptive threshold learning algorithm to predict facial attributes by groups. In particular, the way of grouping leverages the spatial relationships among attributes and the design of MTCNN greatly decreases model complexities. The core idea of the weighted loss penalty is to make the prediction probability of an attribute appearing on a face close to ground truth. During validation stage, the adaptive threshold algorithm learns the threshold for every attribute prediction adaptively. Experiments on two benchmark datasets, show that our method achieves superior performance to the prior state-of-the-art methods under two different evaluation protocols.
Automatic Localization of Optic Disc in Fundus Image Using Iterative Background Removal

Diabetic retinopathy (DR) is leading to cause of blood clot stimulating the formation of abnormal new blood vessels. hemorrhage and protein secretion from blood vessels to retinal tissue. The retinal damage causes the loss of sight thus the localization of Optic Disc is necessary for analysis of the abnormal retinal image. However, the data located at the Optic Disc image are similar to Hard Exudates in the retinal image. Therefore, the aim of the present study is to detect Optic Disc using the technique of background removal. The principle of image processing is also applied for the retinal image. The image data from Fundus camera are recorded in RGB color model separated into 3 channels: red, green and blue channel. The data of all channels are evaluated by the preprocessing algorithm to make a more clear appearance of Optic Disc. Subsequently, the preprocessing image is analyzed by iterative background removal using entropy evaluation of the image data. Finally. , the variance analysis of data intensity is performed in both the horizontal and vertical axis to determine the localization and size of Optic Disc. The result shows that the accuracy of localization and size of Optic Disc is approximately 98%. It indicates that this method is useful for retinal image analysis of diabetic retinopathy.
Modeling of Parasitic Light Sensitivity in Global Shutter CMOS Image Sensors

This paper aims to model Parasitic Light Sensitivity (PLS) in Global Shutter CMOS Image Sensors (GS-CIS) through separation of the optical problem from the carriers motion one, reducing required simulation time. The optical problem is solved thanks to Finite-Differences Time-Domain (FDTD) simulations. Solution to the carriers motion problem is shown in two ways: through use of Boltzmann Transport Equation (BTE) and through a straight line type of motion. The results show good reproduction of experimental data behavior, though fitting has still to be improved.
Design of a Magnetoelectric Dipole Antenna for Wideband Wide-Scanning Phased Array

A magnetoelectric dipole (MED) antenna using substrate integrated waveguide (SIW) technology has been proposed for wideband phased array application. The proposed antenna is composed of a pair of arc-shaped patches as an electric dipole and a group of circular vertical shorted vias that are equivalent to a magnetic dipole. A Γ-shaped probe feed is employed to excite the MED antenna, and some planar metal reflectors with shorted via holes are located at the corner of the antenna to improve matching of the antenna. The simulated operating band is 2.75-5.94 GHz (relative bandwidth is 73.4%) for |S11|<-10 dB. Besides, a unit cell in an infinite array environment is simulated, which achieves more than 40.1% bandwidth with VSWR⩽2.5 when scanning to -75°both in the E- and H-planes.
Building Towards "Invisible Cloak": Robust Physical Adversarial Attack on YOLO Object Detector

Deep learning based object detection algorithms like R-CNN, SSD, YOLO have been applied to many scenarios, including video surveillance, autonomous vehicle, intelligent robotics et al. With more and more application and autonomy left to deep learning based artificial intelligence, humans want to ensure that the machine does the best for them under their control. However, deep learning algorithms are known to be vulnerable to carefully crafted input known as adversarial examples which makes it possible for an attacker to fool an AI system. In this work, we explored the mechanism behind the YOLO object detector and proposed an optimization method to craft adversarial examples to attack the YOLO model. The experiment shows that this white box attack method is effective and has a success rate of 100% in crafting digital adversarial examples to fool the YOLO model. We also proposed a robust physical adversarial sticker generation method based on an extended Expectation Over Transformation (EOT) method(a method to craft adversarial example in the physical world). We conduct experiments to find the most effective approach to generate adversarial stickers. We tested the stickers both digitally as a watermark and physically showing it on an electronic screen on the front surface of a person. Our result shows that the sticker attack as a watermark has a success rate of 90% and 45% on photos taken indoors and on random 318 pictures from ImageNet. Our physical attack also has a success rate of 72% on photos taken indoors. We shared our project source code on the Github and our work is reproducible.
Profiling the Mobility of Tourists Exploring Social Sensing

Tourism has become a global economic force; for this reason, offer better services for tourists is indispensable. We explore social media data to study how tourists move through time and space and the factors that influence their movements in four major cities worldwide. In addition, we propose a new approach, based on a topic model, that enables the automatic identification of mobility pattern themes, which, ultimately, leads to the better understanding of the profile of users. Our results have implications in several segments, enabling better services for tourists in different locations that can be culturally distinct.
A Novel Double-Mode Dielectric Resonator filter Using Patch Coupling Method

This paper proposes a novel dual-mode dielectric resonator filter, which is characterized by a simple structure and easy processing. The resonator consists mainly of a rectangular metal cavity and a dielectric rod. The rod is mounted inside the cavity and short circuited to the top and bottom surfaces of metallic cavity. With such a resonator setting, the dielectric rod can be used to implement a dual TM11 degenerate modes. Instead of using conventional coupling method, such as chamfer and through-hole, the required internal coupling is realized by adding a patch on dielectric rod. Moreover, the patch can be used to realize a pair of transmission zeros, by changing the orientation of polarization of the TM11 mode. To demonstrate the feasibility of the new coupling method, A four-pole filter with a pair of transmission zeros is designed, simulated and tested. The measurement and simulation result agree well.
Integrated Current Balancing Transformer Based Input-Parallel Output-Parallel LLC Resonant Converter Modules

Input-parallel output-parallel (IPOP) connected converter system allows the use of low power converter modules for high power applications. In this paper, a reliable integrated current balancing transformer (ICBT) is proposed for the current balancing requirement of IPOP LLC resonant converter modules. The proposed ICBT based IPOP LLC resonant converter modules ensures both input current sharing (ICS) and output current sharing (OCS) in constituent modules, without dedicated controller. The proposed ICBT is basically the resonant inductors of LLC resonant converter modules, coupled to perform the function of current balancing, and also have enough leakage inductance for resonance operation simultaneously. Moreover, the proposed ICBT can significantly reduce the overall magnetic volume, and increase the power density of IPOP LLC resonant converter modules. Magnetic and electrical models of the proposed ICBT have been presented in this paper. To validate the performance of the proposed ICBT based IPOP LLC resonant converter modules, a 2.5 kW hardware prototype has been designed, fabricated and tested successfully.
Performance Evaluation of Silicon Photomultipliers for Frequency-Domain Fluorescence Lifetime Measurement Based on a Comprehensive Circuit Model

The presented models for silicon photomultiplier (SiPM) detectors in literature have been mainly used to study fast light pulses. In this paper, a comprehensive dynamic model is proposed to investigate the output signal of a SiPM exposed to intensity-modulated lights (with sine-wave modulation). A procedure is also proposed to simulate this model. Using the proposed model, the SiPM behavior is examined under various frequencies and intensities of lights in simulation. Then, fluorescence lifetime (FLT) measurement in the frequency domain is discussed. Finally, the simulation results are compared with the experimental results which are extracted from an experiment set up employing two SiPMs exposed by excitation and emission lights.
Design and Implementation of Ozone Generators with Area-graph Method to Model DBD Chamber

The most well-known models of dielectric barrier discharge (DBD) ozone generator chambers are nonlinear model and linear model. And the Lissajous method is usually applied for obtaining the key parameters of these two equivalent models. However, it requires an extra large capacitor and an inductanceless voltage divider, resulting in loading effect to the cell. An area-graph method that can overcome the limitations of Lissajous method is proposed in this paper. A comparison between simulated and experimental results with both Lissajous method and the proposed graphing method is presented.
Pulse Electric Field by Half Bridge Modular Multilevel Inverter for Liquid Food Sterilization

The design and implementation of the half-bridge modular multilevel inverter (HB-MMI) for liquid food sterilization is proposed here. This inverter is able to generate both unipolar and bipolar rectangular high voltage pulses without using the transformer. Rise time, pulse width, frequency, and voltage level are controlled using a control switch. The simulation shows that the proposed pulsed electric field (PEF)system is able to generate alternate negative and positive pulses with 10 us and 3 us pulse width respectively, 3 us unipolar pulses, and instant-reverse pulses. The experimental results are present for unipolar pulse, bipolar pulse and difference duration pulse
Implementation and Analysis of Nonlinear Model Predictive Controller on Embedded Systems for Real-Time Applications

Today, various nonlinear programming problem (NLP) solvers and C/C++ code generation frameworks are available as open source for solving nonlinear model predictive control (NMPC). Almost all the solvers are written in C/C++ code which are more compatible for the PC-based simulation environment. These codes are not directly compatible for embedded implementation and real-time control. An attempt has been made to address this shortcoming by creating a customized framework on top of the C code generated from ACADO Toolkit to make it directly compatible with ARM based embedded platforms. The study also analyzes the embedded implementation aspects using C code generated for qpOASES, qpDUNES, and HPMPC solvers from ACADO Toolkit. We show the results of hardware-in-loop (HIL) simulations with detailed analysis and comparison of memory requirement and achievable sampling time for three benchmark dynamical systems on different embedded platforms viz ARM Cortex M3, PYNQ FPGA and Raspberry Pi. The results show that qpOASES outperforms as compared to the other two solvers when the computational time is of prime importance for small prediction horizon. Similarly, when there are limited on-chip memory resources, qpDUNES can prove beneficial.
Evaluation of DC-link Capacitor RMS Current in Switched Reluctance Motor Drive

In this paper a method to evaluate the DC-link capacitor RMS current of a switched reluctance machine (SRM) drive under different operating conditions is presented. Square current reference is considered. Methods are developed for both soft chopping and hard chopping method of current control. A newly proposed simplified flux linkage model is used to evaluate converter switching functions and phase currents which are then used to evaluate the capacitor current stress. The capacitor current is found to be lower for soft chopping than for hard chopping. While the capacitor current is maximum at lowest operating speed with hard chopping, the maximum occurs at an intermediate speed for soft chopping for a given magnitude of current reference. The proposed method is useful to size the DC-link capacitor during the design of SRM drive.
Divergence Measure of Belief Function and Its Application in Data Fusion

Divergence measure is widely used in many applications. To efficiently deal with uncertainty in real applications, basic probability assignment (<inline-formula> <tex-math notation="LaTeX">$BPA$ </tex-math></inline-formula>) in Dempster-Shafer evidence theory, instead of probability distribution, is adopted. As a result, an open issue is that how to measure the divergence of <inline-formula> <tex-math notation="LaTeX">$BPA$ </tex-math></inline-formula>. In this paper, a new divergence measure of two <inline-formula> <tex-math notation="LaTeX">$BPA\text{s}$ </tex-math></inline-formula> is proposed. The proposed divergence measure is the generalization of Kullback-Leibler divergence since when the <inline-formula> <tex-math notation="LaTeX">$BPA$ </tex-math></inline-formula> is degenerated as probability distribution, the proposed belief divergence is equal to Kullback-Leibler divergence. Furthermore, compared with existing belief divergence measure, the new method has a better performance under the situation with a great degree of uncertainty and ambiguity. Numerical examples are used to illustrate the efficiency of the proposed divergence measure. In addition, based on the proposed belief divergence measure, a combination model is proposed to address data fusion. Finally, an example in target recognition is shown to illustrate the advantage of the new belief divergence in handling not only extreme uncertainty, but also highly conflicting data.
The Study on Position Sensor Fault Detection and Algorithm Transition from Sensored to Sensorless Control for IPMSM

This paper presents a study on the algorithm transition from sensored to sensorless control when the position sensor fault of interior permanent magnet synchronous motor (IPMSM) occurs. The determination of threshold values related to the difference between estimated value and actual value is necessary to decide the fault condition of speed and position sensor. This threshold value should be selected by a higher value than the maximum overshoot caused by acceleration or deceleration of motor as well as steady state error. The overshoot values are depend on the gain of speed and position estimator for sensorless control. In this paper, the relation between the overshoot values and estimator gains is studied to define the stable selection of threshold value. The designed estimator gain and threshold value are verified by experimental results in order to obtain the stable algorithm transition.
Study of Some Operational Degrees of Freedom for Cascaded AC-DC Converters in Solid State Transformer

Cascaded ac-dc converter is widely used in the first stage of solid state transformer (SST). The study of various operation modes of the cascaded ac-dc converter will help to understand the operation possibilities of the SST system. This paper studies the effect of an undetected fault in medium voltage (MV) side of a solid state transformer in terms of grid voltage, grid current, power factor and efficiency. The fault scenarios on ac-dc converter is studied and the controller response is evaluated using emulation result on a real-time emulator. An alternative operation mode of the ac-dc converter when the fault occurs is also proposed and demonstrated using emulation results.
Modeling and Stability Analysis of the Cascaded Dual Active Bridge Converter System with Considerations of the Controller Parameters

The dual active bridge (DAB) converter is attracting more and more attention nowadays for its advantages like higher power density and soft switching properties. However, the LC input filter which is often cascaded with DAB to improve input current quality sometimes leads to instability of the whole cascaded system. To analyze stability of this cascaded system with an LC input filter, this paper builds the full-order DAB closed-loop impedance model impedance with generalized state-space averaging method. The derived model is used in the minor loop gain of the cascaded system for stability assessment with Nyquist Criteria. And the effects of different PI controller parameters are analyzed. Experimental results validate the theoretical analysis.
Restart Strategy for Scalar Controlled Synchronous Reluctance Machine Driving a High-Inertia Load with a Single DC-link Current Sensor

This paper proposes an approach for implementing the restart for Synchronous Reluctance Machines (SynRMs) driving a high inertia load. The proposed method uses a simple identification algorithm to estimate the rotor speed and position of the machine so that the appropriate voltage vector can be applied to the machine, thus minimizing the inrush current at the instant of the restart. The novelty is that its implementation only requires nameplate machine parameters and a single DC-link current sensor. The proposed method is suitable for implementation with both high-inertia vector and scalar-controlled SynRMs.
Control Design and Analysis of An Epidemic SEIV Model upon Adaptive Network

This paper focuses on the control design and stability analysis of a Susceptible-Exposed-Infected-Vigilant (SEIV) epidemic model via adaptive complex networks. The network is designed empirically as a state-dependent network, where the network structure keeps changing to inhibit the epidemic propagation. The recovery rate and the disease prevention rate are chosen as the control scheme in the epidemic system, both of which are closely associated with medical resources allocation. People may cut the connection with an infected neighbor and reduce the frequency to go out when an epidemic occurs. In order to formulate this behavior, an adaptive network structure is presented which is designed to be consistent with real human contact behaviors under epidemic prevalence. A candidate Lyapunov function is employed to analyze the system stability and guarantee the extinction of the epidemic. Simulation results are shown to illustrate the high efficiency and validity of the parameter control and the adaptive network design.
Unimanual Assembly by Deformation and Insertion

We consider the problem of joining two parts by deformation and insertion. After its decomposition into a sequence of assembly primitives (AP) that include deformation, folding, deformation relaxation and insertion, a single controller of hybrid force/position type is developed to guarantee the successful completion of each assembly primitive, while avoiding the appearance of irregularities and discontinuities in the application of the AP-sequence. Initially, the joining parts are assumed to be in contact. Further key properties of the designed controller are i) contact is preserved throughout assembly; ii) excessive contact forces that may lead to damaging the joining parts, are avoided. The applicability and effectiveness of the proposed methodology is tested via experiments using a KUKA LWR4+ robotic manipulator.
A Reliable and Fast ANN Based Behavioral Modeling Approach for GaN HEMT

The paper proposes an accurate, fast and advanced neural network approach to model the small signal behavior of GaN High Electron Mobility Transistor (HEMT). The presented approach makes use of the nonlinear autoregressive series-parallel and parallel architectures to model a 2×200μm device for a broad frequency range of 1GHz – 18GHz. A comparison is drawn between the two architectures based on the training algorithm, accuracy, convergence rate and number of epochs. An excellent agreement is found between the measured S-parameters and the proposed model for the complete broad frequency range. The proposed model can be embedded into computer aided design tool for an accurate and expedited design process of RF/microwave circuits and systems.