Miracle-dz/UniFAR-FTUs huggingface dataset:
Device Architectures for High-speed SiGe HBTs

This paper reviews recent developments in process technology of high-speed SiGe HBTs at IHP. Two device concepts, one with selective epitaxial growth and one with non-selective epitaxial growth of the base, are analyzed with respect to their impact on radio frequency performance. Both device architectures take advantage of a low-resistive base link formed by selective epitaxial growth of extrinsic base regions after emitter structuring. As an intermediate result of the European project TARANTO, HBTs with fT values of 470 GHz and fMAX values of 610 GHz are demonstrated in a 130 nm BiCMOS process.
A Review of Omega Based Portfolio Optimization

Portfolio optimization aims to pick risky assets to meet the goal of maximizing the return and minimizing the risk. One should model the best combination of assets by striving the optimal relationship between risk and return for an appropriate investor even when the constraints are present. This paper aims to study the risk measure Conditional Value At Risk with constraints, that are added in a portfolio and are analyzed in the optimization problem. It also focuses on how will the portfolio work when a threshold value $L(\alpha)$:- CVaR is fixed. The dataset was taken from YAHOO Finance consisting of weekly historical prices were implemented.
Total-Ionizing-Dose Effects and Low-Frequency Noise in 16-nm InGaAs FinFETs With HfO2/Al2O3 Dielectrics

Total-ionizing-dose mechanisms are investigated in 16-nm InGaAs FinFETs with an HfO2/Al2O3 gate-stack. Transistors are irradiated up to 500 krad(SiO2) and annealed at high temperatures. Irradiated devices show negative threshold-voltage Vth shifts, subthreshold stretch-out, and leakage current increases. These result from positive charge trapping in the gate oxide and shallow trench insulators, and increases in the interface and border-trap charge densities. Low-frequency noise measurements at different temperatures indicate a significant increase of noise magnitude in irradiated devices at an activation energy of ~0.4 eV. Density functional theory (DFT) calculations strongly suggest that transistor Vth shifts are due primarily to hole trapping at oxygen vacancies in HfO2, and the increased noise is due primarily to O vacancies in Al2O3. Additional contributions to the noise from defects in the GaAs buffer layer are also likely, primarily at low temperatures.
Piecewise Large Margin Learning for Partially Annotated Sequences

Supervised and semi-supervised sequence labeling methods require large amounts of fully annotated training sequences or exact annotations of structured outputs. The problem of learning from partially annotated sequences arises in many applications, for example, Natural Language Processing and Computational Biology. In this paper, we propose Piecewise Convex Learning from Partial Labels (PW-CLPL) which is an effective discriminative structured learning method for sequence labeling as global training is intractable for partially annotated sequences. A small number of constraints is reformulated for the optimization, which improve the efficiency of parameters learning. Experimental results on the reconstructed dataset CoNLL-2000 show the effectiveness of the proposed model in the setting of partial annotations.
A Control Parameter Design Method for Hybrid Multi-Terminal HVDC System

To achieve better control performance, a comprehensive control parameter design method that considers economics, stability and dynamic performance is essential for hybrid multi-terminal HVDC systems. In this paper, a hierarchical model for the control system of Hybrid-MTDC is constructed and the parameters are optimized on two layers. On the system layer, using the normalization processing method, a system-layer objective function that considers both the minimum network loss and the voltage offset is formed for Hybrid-MTDC systems and solved with the proposed penalty interior point method. On the converter layer, using the state-space matrix method, a generic small-signal stability range of each control parameter can be obtained by the traversal calculation of the eigenvalues. Then, to identify the stricter stable operating region for the DC voltage controller under severe conditions, an additional stability criterion based on the mixed-potential theory is deduced, and the design-oriented boundaries are generated and added to the feasible region. Finally, within the design-oriented boundaries, a general dynamic performance evaluation function is constructed to determine the optimal control parameters. Utilizing the proposed method, a typical Hybrid-MTDC system is investigated, and experimental verifications are provided to validate the effectiveness and accuracy.
Android Vulnerabilities: Taxonomy and nextGen Ecosystem

Popularity and openness of Android platform has attracted developers and attackers to find the loopholes in the Android based systems for exploitation. Listed Android vulnerabilities are the result of the sincere efforts made by Android users to make the platform robust. In this paper, we try to find the reasons, which causes this vulnerabilities to occur and become exploitable. We characterize the vulnerabilities based on their attributes and map them with specific issues. We have utilized the National Vulnerability Database (NVD) and crawled the CVEs (Common Vulnerability Exposures) specific to Android, from 2008 to 2018. This database helped in deducing and implicating the taxonomy of the Android vulnerabilities. In the end, we also propose a next generation Android ecosystem to protect and deter from the vulnerabilities.
Rubber Tapping Position and Harvesting Cup Detection Using Faster-RCNN with MobileNetV2

This paper presents the detection of rubber tree (Hevea brasiliensis) tapping position (tapping-path) and trunk-mounted harvesting cup on RGB-D images, which is the machine vision part of automatic rubber farming system. RGB-D images are collected in real tapping environment. Camera is placed about 1.00-meter distance to the trunk which is feasible for robot platform. Faster-RCNN (Region-based Convolutional Neural Network) object detector is proposed with ImageNet pretrained Mobilenet-v2 as the feature extraction layers to detect multiple object’s class – tapping path and harvesting cup. Our experiments show that cup detection achieve higher average precision on grayscale depth image detector than only RGB image detector. We also propose 3-channel combinations from 4 grayscales (RGB-D) then, put into specific detectors. Detector’s performances are computed using 0.5 and 0.75 intersection over union thresholds (IoU). The results show that Faster-RCNN with Mobilenet-v2 tapping position and cup detection can reach highest 0.95 mAP@0.5 IoU estimated with k-fold cross validation (k=5). Our main contribution is that detection of harvesting cup using preprocessed grayscale depth with color images are more well-localized than only color image, observed at 0.75 IoU but tapping position detection is highly depended on color images and this can be impacted to practical application since cup detection are not required lighting.
The effect of self-made Jiedu Liangxue decoction coloclysis in combination with Mesalazine for treatment of distal ulcerative colitis

Objective To explore the clinical efficacy of self-made Jiedu Liangxue decoction coloclysis in combination with Mesalazine against distal ulcerative colitis. Methods A total of 98 patients with distal ulcerative colitis were random Ly divided into control group and treatment group. Patients in control group were received treatment of Physiological saline coloclysis in combination with Mesalazine,and those in treatment group accepted self-made Jiedu Liangxue decoction coloclysis in combination with Mesalazine. Then the clinical efficacy of two groups were compared. Results The fiber colonoscopy integral,Endoscopic score and DAI score of treatment group were significantly lower than control group,the difference was statistically significant(P0.05);the total effective rate of treatment group were 98.0%, significantly higher than 79.6% of control group, the difference was statistically significant(P0.05);the platelet count and erythrocyte sedimentation rate of treatment group significantly lower than before treatment,the difference was statistically signif icant(P 0.05);the erythrocyte sedimentation rate of treatment group significantly lower than control group before treatment,the difference was statistically significant(P0.05);the TNF-α,IL-8 levels of treatment group significantly lower than control group before treatment,the difference was statistically significant(P0.05). Conclusion Self-made Jiedu Liangxue decoction coloclysis in combination with Mesalazine for treatment of distal ulcerative colitis improve the clinical symptoms and lighten inflammatory reaction. The clinical curative effect is remarkable. It can be applied in clinical practice.
A Fully Integrated K-Band Dual Down-Conversion Receiver for Radar Applications in 90 nm CMOS

A fully integrated K-band dual down-conversion receiver for phased array radar applications in 90 nm CMOS is presented. The receiver utilizes the dual down-conversion architecture to achieve superior performance. The integrated 1.15 GHz image-rejection filter (IRF) provides enough wideband (22 MHz) image rejection ratio at 140 MHz offset before the second down-conversion by utilizing the $Q$ -enhancing and frequency staggering techniques to compensate the component loss. The low noise amplifier realizes the single-to-differential-ended conversion at the input with a transformer and achieves good common-mode rejection. The 70 MHz intermediate frequency baseband consists of two cascaded 3rd-order band-pass active-RC filters (BPFs) and one automatic gain control (AGC) loop, with the integrator frequency compensation technique to lower down the requirements on the embedded Op-Amps. Two phase-locked loop (PLL) frequency synthesizers are integrated to provide the local oscillation (LO) signals for the down-conversions, where the matching of the charge-pump is improved by adding one extra current compensation branch. The measurements of the prototype show that the receiver converts the targeted mm-wave signal to 70 MHz intermediate frequency while achieving 8.3 dB noise figure (NF), 51–95 dB variable gain range and >45 dB image rejection ratio at 140 MHz offset with >22 MHz signal bandwidth. The receiver draws 74 mA current (excluding 2 PLLs) from the 1.2 V power supplies and occupies a core area of $4.58\times 0.53mm^{2}$ (excluding 2 PLLs).
Energy Management System for a Residential Microgrid in a Power Hardware-in-the-Loop Platform

In this paper, a microgrid scenario composed by a PV generator, a battery system and residential loads in a power Hardware-in-the-Loop (HIL) platform is developed and tested. The electrical structure of the system, the component software modelling and hardware implementation are described. This paper proposes an EMS for microgrid power balance and execution of Time of Use (TOU) and load shedding Demand Response (DR) functions. Experimental results of each component of the scenario are shown.
Continuous Terminal Sliding Mode Control for Active Power Filter

In this paper, a continuous terminal sliding mode control methods (CTSMC) are proposed for three-phase shunt active power filter as current controller. First, the dynamic model of active power filter with parameter perturbation and unknown external disturbance is introduced. Second, a continuous terminal sliding mode control applied to current control loop is designed to guarantee the fast and finite-time convergence. Moreover, compensation control term is designed to ensure the elimination of chattering phenomenon caused by the switching term. Compared to the conventional sliding mode control method, the proposed continuous terminal sliding control method obtains a faster convergence and better tracking performance. Simulation results demonstrate that the proposed control methods offer superior properties in both steady state and transient operation, reducing the THD of source current to less than 5% and improving the power quality in order to meet the recommendations of the IEEE 519 standard.
Reconstruction of Corrupted and Lost Segments from Photoplethysmographic Data Using Recurrent Neural Network

Finger pulse signal, commonly known as Photoplethysmogram (PPG) is an important physiological signal used in intensive care unit (ICU) for heart rate and blood oxygen saturation measurement. In ICU monitoring for long-term analysis, there may be occasional clinical data corruption or loss due to either patient’s hand movement or sensor detachment from the measurement site. In this paper, we describe an approach to predict the lost and highly corrupted data segments from short history (immediate proceeding four beats) of the time series PPG data based on recurrent neural network (RNN). For identification of corrupted data segments, a support vector machine (SVM) in conjunction with Kernel radial basis function was used. The reconstruction of the lost segments and the corrupted segments from PPG data were carried out on a beat-by-beat basis, by using a joint principal component analysis (PCA) based feature extraction and RNN based data prediction model with recursive feeding of outputs to the PCA unit. Using finger PPG records of 40 volunteers, PPG beat classification sensitivity and specificity were found as of 98.1% and 91.78% respectively, with maximum absolute error (MAE) for single, consecutive five, and consecutive ten lost beats segments were 0.38%, 2.24% and 5.98% respectively.
A Fast Iteration Algorithm for Coverage Optimization of Circular Sector Sensor Networks

In this paper, the coverage optimization of a circular sector sensor network is considered. To achieve the fast iteration and higher coverage rate tasks in industry, a novel fast iteration algorithm is proposed in this paper to replace the gradient ascent based algorithms in some exiting works. The key issue of the proposed algorithm is that the control gains are set to be time-varying and non-uniform for the mobile sensors. Moreover, some distributed conditions that only address the overlapped coverage rates of the sensors are proposed to trigger the control gains changing. The time-varying gains together with the distributed triggered conditions guarantee the less iteration steps and higher coverage rate of the sensor network. At last, three simulation examples are provided to illustrate the effectiveness of the proposed method via comparing the algorithm in this paper and some existing works.
Spatial Coordinates Correction Based on Multi-Sensor Low-Altitude Remote Sensing Image Registration for Monitoring Forest Dynamics

Tree species diversity plays a significant role in our ecosystem. In order to monitor forest dynamics, hyperspectral remote sensing equipped on a small unmanned aerial vehicle (UAV) is commonly applied, such as individual tree detection and classification. However, low resolution, positioning errors and the imaging perspective of small UAV affected by wind speed/direction, complex terrain, battery capacity, aircraft posture, flying height and other human factors result in relatively large positional errors (i.e., GPS errors) in such hyperspectral images, and the precise forest dynamics monitoring is limited, especially in spatial analysis. In order to reduce the positional errors of hyperspectral images captured from a small UAV and provide a precise forest dynamics monitoring, we present a novel spatial coordinates correction approach by registering low-altitude UAV visible light and hyperspectral images. The proposed method first employs visible light images and ground control points to stitch a geographic coordinate system as our groundtruth. Hyperspectral images (UHI) are then registered onto the stitched visible light image (UVI) via a novel image registration method. Finally, spatial coordinates of the registered hyperspectral images are updated by using the aforementioned groundtruth. Extensive experiments on image registration and spatial coordinates correction demonstrate the favorable performance of our method. Compared against four state-of-the-art registration methods, our method shows the best registration performance, and the positional errors of hyperspectral images are significantly reduced. Such accuracy is considered very high in this research.
Dynamic Energy Scheduling for Virtual Power Plant with Prosumer Resources Using Game Theory

The Smart Grid is characterized by a high penetration of distributed energy resources (DERs). For their effective integration into the Smart Grid, researchers have proposed the concept of a virtual power plant (VPP) to aggregate the DERs and present them to the grid as a single energy trader in the electricity market. However, to optimize the energy generation and consumption of the VPP participants, energy scheduling in the VPP must consider the pricing dynamics and generation uncertainties of the renewables. In this paper, a novel game theoretic approach to dynamic energy scheduling is proposed for VPPs. The targeted VPPs are those composed of buildings with producer-consumer (prosumer) resources in both residential and commercial environments, the latter of which the VPP operators have no direct control over their machineries or appliances. Digital simulation studies were conducted to show the effectiveness of the proposed algorithm where a better total payoff than current schemes is achieved, in particular with increasing number of prosumers in the VPP.
Whitelisting Cyber Attack Detection according to Estimated Operational States for CPS

These days, cyber attacks against cyber-physical systems (CPS) such as power plants are constantly growing, and therefore the countermeasures are essential. Whitelisting cyber attack detection, that detects anomalous activity as deviation from normal network communication, is especially attracting attention as one of the countermeasures because the traffic patterns are predictable in CPS. However, attackers can execute illegal operations within the normal network communication defined as the whitelisting detection rules because such operations tend to cause unstable control depending on operational states. In this work, we propose a new whitelisting cyber attack detection method that estimates the operational state of CPS and monitors the CPS network with the whitelisting detection rules according to the estimated state. The proposed method can define the more limited normal network communication to generate the whitelisting detection rules in each operational state. We experimented and evaluated the method on a plant simulator, and confirmed the effectiveness of the method: state estimation with support vector machine and attack detection with divided whitelisting detection rules.
Alternative Actuation Paths for Ship Applications in the Presence of Cyber-Attacks

Safety-critical Cyber-Physical Systems (CPS) always need to maintain operational normalcy, which may include reconfiguring the control system in response to cyber threats. This paper studies three different approaches to determine when redundancies in control and actuation exist and identify alternative actuation paths that keep the system operating safely in the event of an attack to the primary actuation path. The first two approaches use classical control tools in system identification to determine potential inputs based on the desired force traces, while the third employs a supervised learning algorithm. The three methods are then experimentally compared on a small-scale naval ship testbed. The testbed, although compact, allows the easy and fast evaluation of different control routines for CPS. Laboratory experimental results, when the rudder has been attacked, demonstrate the accuracy of all three approaches in determining correct alternative actuation paths and comparisons among the strategies are drawn.
Sensitivity Analysis of Generalized Gaussian Process Models for Variable Importance Measure

In machine learning, Gaussian process regression (GPR) has been gaining popularity due to its nonparametric Bayesian form. However, the traditional GPR model is designed for continuous real-valued outputs with a Gaussian assumption, which does not hold in some engineering application studies. For example, when the output variable is count data, it violates the assumptions of the GPR model. Generalized Gaussian process regression (GGPR) can overcome the drawbacks of the conventional GPR, and it allows the model outputs to be any member of the exponential family of distributions. Thus, GGPR is more flexible than GPR. However, since GGPR is a nonlinear kernel-based method, it is not readily accessible to understand the effect of each input variable on the model output. To tackle this issue, the sensitivity analysis of GGPR (SA-GGPR) is proposed in this work. SA-GGPR aims to identify factors that exert higher influence on the model output by utilizing the information from the partial derivative of the GGPR model output with respect to its inputs. The proposed method was applied to a nonlinear count data system. The application results demonstrated that the proposed SA-GGPR is superior to the PLS-Beta, PLS-VIP, and SA-GPR methods in identification accuracy.
Reconfiguration of Voltage Source Inverter for Field-Oriented Controlled Induction Motor Drive druing Open-Circuit and Short-Circuit Faults

In this paper, a strategy for reconfiguration of two-level voltage source inverter during open-circuit or short-circuit of power devices in any one of the inverter legs in a field-orient controlled (FOC) three-phase induction motor drive is proposed to obtain its performance during post-fault operation close to normal operation. Capacitor voltage balance issue in split capacitor at DC link of inverter is eliminated by a single capacitor and an additional capacitor is used to connect the motor terminal of the faulty leg of an inviter. The proposed reconfiguration of inverter operates the drive in full rated operating condition during post-fault operation. Simulation results of drive system before and after fault operation at various operating conditions are presented and are found to be satisfactory and also close to each other.
Region Dependent Mesh Refinement for Volumetric Video Workflows

This paper addresses high quality mesh optimization for volumetric video. Real persons are captured with multiple cameras and converted to 3D mesh sequences. These volumetric video assets can be used as dynamic 3D objects in arbitrary 3D rendering engines. In this way, 3D representations of real persons are achieved with a high level of detail and realism. Target use cases are augmented reality, virtual reality and mixed reality applications. However, the final rendering quality strongly depends on the hardware capabilities of the target rendering device. In this context, a novel region dependent mesh refinement approach is presented and evaluated with respect to existing workflows. The proposed approach is used in order to obtain a low overall polygon count while keeping details in semantically important regions such as human faces. It combines conventional 2D skin and face detection algorithms and transfers the results to the 3D domain. Further on, a dedicated camera region selection approach is presented which enhances the sharpness and quality of the resulting 3D texture mappings.