Miracle-dz/UniFAR-FTUs huggingface dataset:
Applying Hoeffding Adaptive Trees for Real-Time Cyber-Power Event and Intrusion Classification

Electricity transmission systems are networked cyber physical systems that are subject to many well-known control, weather, and equipment failure related contingencies which can disrupt power delivery. Cyber-attacks against electric transmission systems are another class of contingency which can disrupt power delivery. Wide area monitoring systems (WAMSs) enhanced with phasor measurement units provide high volume and high velocity power system sensor data which can be combined with traditional power system data sources and cyber data sources to enable real time detection of both types of contingencies. This paper describes research toward a cyber-power event and intrusion detection system (EIDS) which can be used for multiclass or binary-class classification of traditional power system contingencies and cyber-attacks. The continuous streams of high speed data from WAMS pose significant challenges in data storage, management, and handling. Data stream mining addresses the continuous data problem and can deal with very large data sizes. Hoeffding adaptive trees (HAT) augmented with the drift detection method (DDM) and adaptive windowing (ADWIN) can effectively be used to classify traditional and cyber contingencies in real time. Experiments performed for this paper demonstrate HAT + DDM + ADWIN provides classification accuracy of greater than 94% for multiclass and greater than 98% for binary class classification for a dataset with artifacts from 45 classes of cyber-power contingencies. Results also show that HAT + DDM + ADWIN has a small memory foot print and a fast evaluation time which enables real time EIDS.
Radar-Based High-Accuracy 3D Localization of UAVs for Landing in GNSS-Denied Environments

Global navigation satellite systems (GNSS) such as GPS are the de-facto standard for absolute and reliable unmanned aerial vehicles (UAV) localization due to their global availability and the maturity and wide spread of the underlying technology. However, as the spread and reliance upon autonomous UAV systems increases, the risk of jamming becomes a substantial safety problem. The area where jamming attacks are most likely to occur is the close vicinity of the UAV landing station. Hence, to ensure reliable operations, an alternative localization technology for the approach and landing phase is needed. This paper presents a localization system for 3D position estimation based on 24 GHz FMCW cooperative radar. It has very low infrastructure requirements thanks to the combination of ranging and direction-of-arrival (DOA) estimation techniques employed in the ground station. Jamming resistance is improved due to the large bandwidth of 250 MHz. A measurement campaign was conducted to prove the feasibility of the proposed concept.
Novel Mobile Tool Cart with Application into Factory Environment

This paper presents a novel application of a perception sensor Leap Motion to a mobile tool cart (MTC). Daily-used instructions are exercised as commands to control the MTC in the factory environment by using finger/hand position detection of the Leap Motion. A type of differential-driven mobile cart with a tool basket which is mounted on the top of the mobile cart comprise the mobile tool cart. The commercialized Leap Motion sensor is employed to detect hand/finger gestures of the worker. In the same WiFi environment, the worker uses Leap Motion sensor which is equipped on the operation platform to give different remote commands, for example, Go Forward, Draw Back, Turn Left, Turn Right, Start/Stop, Acceleration and Deceleration to the MTC. We define a set of trajectory determined by the hand/finger commend of the worker to do the reflexes and the experiment result shows the validity of proposed method.
Learning Robust Deep Features for Efficient Classification of UAV Imagery

This paper presents a deep learning approach for the classification of Unmanned Aerial Vehicle (UAV) images acquired by different sensors and different locations of the earth surface. In a first step, the labeled and unlabeled UAV images under analysis are fed to a pretrained convolutional neural network (CNN) for generating initial deep feature representations. Then in second step, we learn robust domain-invariant features using an additional network composed of two fully connected layers. This network aims to tackle the data-shift problem by reducing the discrepancy between the labeled and unlabeled data distributions. For such purpose, the first layer projects the labeled data to the space of the unlabeled data, while the second layer maintains the discrimination ability between the different land-cover classes. Experimental results obtained on two datasets acquired over the cities of Trento and Toronto with spatial resolutions of 2 cm and 15 cm, respectively, are reported and discussed.
Design and Analysis of Priority Encoder with Low Power MTCMOS Technique

In recent trends power optimization is becoming one of the most important factor in integrated circuits today. Low power MTCMOS technique has appeared as an encouraging technique to decrease power dissipation, leakage currents and leakage power. High performance priority encoder was designed using MTCMOS technique and compared with conventional CMOS technique. Comparative analysis showed that how these techniques were used to optimize the average power and shows significant improvement in terms of speed. After the comparative analysis, it has been concluded that as compared to the conventional design, MTCMOS technique reduced power by 37% at 90nm and 21% at 45nm. Leakage current is reduced to 54% at 90nm and 96% at 45nm. Moreover, energy consumption was also reduced to 32% at 90nm and 20% at 45nm. This paper addresses remarkable uses of low power techniques in high speed digital circuits, mobile phones and high operating frequencies.
Intelligent PC Games: Comparison of Neural Network Based AI against Pre-Scripted AI

Artificial Intelligence in PC games is a known concept and refers to designing the game such that even while playing games alone, there is a degree of intelligence in actions performed by the non-playable characters (NPCs) simulating human like behavior. It is often argued that video game AI is not truly intelligent and simply follows a set of instructions scripted by the developer. Hence, the objective is to study and compare traditional AI against a machine learned AI and make inferences based on the result. A game is developed in unity engine which is complex enough such that there is a balance of complexity in terms of difficulty to learn the game while also being simple enough to generate results as fast as possible.
2D RF Electronics: from devices to circuits - challenges and applications

We developed process technologies dedicated to high frequency graphene field-effect transistors (GFETs). We used graphene from different sources (graphene growth directly on silicon carbide (SiC) substrate by chemical vapor deposition (CVD) technique [1], CVD graphene growth on Cu foil and transferred on Si02/Si substrate [2]) to realize two GFET structures (top gate and back gate transistors). After fabrication, we have explored the high frequency performances of GFETs including noise performances. The performances of GFET achieved are comparable to the state of the art with the gate length. The noise parameters of the transistors are mandatory for circuit design (linear application such as amplifier). These parameters limit the performances of the device when small signals are considered. The effort was made here to measure the noise parameters of GFET $(\mathrm{NF}_{\min}, \mathrm{R}_{\mathrm{n}}, \Gamma_{\mathrm{opt}})$. It is well known that these parameters depend both on intrinsic properties of the graphene material (noise generated in the channel under the gate), as well as extrinsic parameters related to the technological process (mainly noise generated by access resistances, …). Based on the reliability of the process, RF circuits were designed and fabricated. These circuits demonstrated the potential to achieve high performances in the field of linear circuit such as amplifiers, and non-linear circuits such as mixers.
Multiphysics Design of a V-Shape IPM Motor

In this paper, a multiphysics model is proposed for a multi-V-shape interior permanent magnet motor with concentrated winding. This paper develops a nonlinear magnetic model that computes the flux density in the motor. The analytical model includes several novel aspects: it takes into account the local saturation near the iron bridges, it proposes a method for modeling the concentrated tooth winding, and it calculates the slot tangential leakage flux and includes it in the flux linkage calculation. The magnetic model is coupled with an electrical model that computes the power factor and the voltage at the motor terminals. A loss model is developed in order to calculate the copper and the iron losses. They are used as inputs to a nodal thermal model that introduces a thermal circuit for concentrated end-winding and computes the temperature of the motor. A mechanical model is developed. It evaluates the mechanical constraints encountered by the structure. The models are verified using finite element computations and numerical calculations performed with dedicated software. Besides, the coupled analytical model is experimentally validated using a prototype motor. Finally, two multiphysics bi-objective optimizations are carried out in order to design the motor for high-torque and low-speed application.
Performance Evaluation of UAV-Assisted mmWave Operation in Mobility-Enabled Urban Deployments

To meet the capacity demand of future cellular networks, which is expected to increase substantially by 2020, using mm Wave band has been suggested. Although this higher frequency band has larger amounts of spectrum and will lead to much higher capacity in 5G networks, it has some propagation constraints such as limited coverage and sensitivity to Line of Sight (LoS) blockage. A possible solution to these issues could be small cell densification combined with utilizing access points carried by Unmanned Aerial Vehicles (UAVs). This paper presents a performance evaluation of the UAV-assisted mmWave network in urban environments using ns-3 simulations.
Distributed Model Predictive Control of a HVAC System via a Projected Subgradient Method*

This paper presents a nonlinear model for the climate control of temperature, relative humidity and CO2 concentration in a multi-zone HVAC system. A constrained optimization problem has been formulated to meet the objectives of good thermal comfort and energy optimization. Soft constraints have been introduced to increase the feasible region and provide balance between these two objectives. A distributed model predictive control (MPC) algorithm based on projected subgradient method is proposed to design the controller for the HVAC system. The effectiveness of the proposed algorithm is verified by comparing with the existing works through numerical simulation.
Millimeter Wave Communication: A Comprehensive Survey

Millimeter wave (mmWave) communication has raised increasing attentions from both academia and industry due to its exceptional advantages. Compared with existing wireless communication techniques, such as WiFi and 4G, mmWave communications adopt much higher carrier frequencies and thus come with advantages including huge bandwidth, narrow beam, high transmission quality, and strong detection ability. These advantages can well address difficult situations caused by recent popular applications using wireless technologies. For example, mmWave communications can significantly alleviate the skyrocketing traffic demand of wireless communication from video streaming. Meanwhile, mmWave communications have several natural disadvantages, e.g., severe signal attenuation, easily blocked by obstacles, and small coverage, due to its short wavelengths. Hence, the major challenge is how to overcome its shortcomings while fully utilizing its advantages. In this paper, we present a taxonomy based on the layered model and give an extensive review on mmWave communications. Specially, we divide existing efforts into four categories that investigate: physical layer, medium access control (MAC) layer, network layer, and cross layer optimization, respectively. First, we present an overview of some technical details in physical layer. Second, we summarize available literature in MAC layer that pertains to protocols and scheduling schemes. Third, we make an in-depth survey of related research work in network layer, providing brain storming and methodology for enhancing the capacity and coverage of mmWave networks. Fourth, we analyze available research work related to cross layer allocation/optimization for mmWave communications. Fifth, we make a review of mmWave applications to illustrate how mmWave technology can be employed to satisfy other services. At the end of each section described above, we point out the inadequacy of existing work and identify the future work. Sixth, we present some available resources for mmWave communications, including related books about mmWave, commonly used mmWave frequencies, existing protocols based on mmWave, and experimental platforms. Finally, we have a simple summary and point out several promising future research directions.
Searching Toward Pareto-Optimal Device-Aware Neural Architectures

Recent breakthroughs in Neural Architectural Search (NAS) have achieved state-of-the-art performance in many tasks such as image classification and language understanding. However, most existing works only optimize for model accuracy and largely ignore other important factors imposed by the underlying hardware and devices, such as latency and energy, when making inference. In this paper, we first introduce the problem of NAS and provide a survey on recent works. Then we deep dive into two recent advancements on extending NAS into multiple-objective frameworks: MONAS [19] and DPP-Net [10]. Both MONAS and DPP-Net are capable of optimizing accuracy and other objectives imposed by devices, searching for neural architectures that can be best deployed on a wide spectrum of devices: from embedded systems and mobile devices to workstations. Experimental results are poised to show that architectures found by MONAS and DPP-Net achieves Pareto optimality w.r. t the given objectives for various devices.
The Relationship between Controlling Nutritional (CONUT) Score and Clinical Markers among Adults with Hepatitis C Virus Related Liver Cirrhosis

Aims: To identify the relationship between the Controlling Nutrition Status (CONUT) score and clinical parameters among adults with hepatitis C virus (HCV)-related liver cirrhosis (LC) (n = 264, 141 males and 123 females). Methods: The relationship between the CONUT score and clinical variables such as Child-Pugh classification were investigated. We also examined factors linked to poor nutritional state as determined by CONUT score. Results: According to the CONUT score, normal nutritional state was found in 57 patients, mild malnutrition state in 132, moderate malnutrition state in 68 and severe malnutrition state in 7. The CONUT score ranged from 0 to 9 (median = 2) in Child-Pugh A (n = 198), 0 to 10 (median = 6) in Child-Pugh B (n = 62) and 6 to 9 (median = 7.5) in Child-Pugh C (n = 4) (overall, p < 0.00001). Multivariate analysis revealed that FIB-4 index, branched-chain amino acid to tyrosine ratio and extracellular water to total body water ratio in bioimpedance analysis were significant for both CONUT score 2 or more, and 5 or more. FIB-4 index had the highest predictability for both CONUT score 2 or more and 5 or more among three parameters. Conclusion: The CONUT score well reflects liver functional reserve among adults with HCV-related LC. FIB-4 index can be useful for malnutrition.
Development of Automatic Virtual Guidance Visualization System for Teleoperation Using Object Detection

This paper proposes automatically generate a virtual guidance for teleoperation system using object detection. A virtual guidance that gives force feedback when performing a mission like a peg-in-hole using a slave robot with a haptic device is very helpful for the operator. The key point of our study is to automatically generate this virtual guidance using deep learning architecture. If the operator uses this information, there is no need to generate a virtual guidance one by one. In addition, our system can automatically and continuously generate virtual guidance in moving situations as well as stationary situations. The position of the target object is recognized using deep learning architecture and depth information. And visual information of the virtual guidance is visualized using the virtual environment visualization system. Our system helps the operator to recognize spatial information.
Decision-Level Fusion Method for Emotion Recognition using Multimodal Emotion Recognition Information

Human emotion recognition is an important factor for social robots. In previous research, emotion recognizers with many modalities have been studied, but there are several problems that make recognition rates lower when a recognizer is applied to a robot. This paper proposes a decision level fusion method that takes the outputs of each recognizer as an input and confirms which combination of features achieves the highest accuracy. We used EdNet, which was developed in KAIST based Convolutional Neural Networks (CNNs), as a facial expression recognizer and a speech analytics engine developed for speech emotion recognition. Finally, we confirmed a higher accuracy 43.40% using an artificial neural network (ANN) or the k-Nearest Neighbor (k-NN) algorithm for classification of combinations of features from EdN et and the speech analytics engine.
Fixed Head Short-term Hydrothermal Scheduling Using Whale Optimization Algorithm Considering the Uncertainty of Solar Power

In this article the authors have proposed the whale optimization algorithm to solve fixed-head short-term hydrothermal scheduling problem in the presence of solar powerconsidering multi-reservoir cascaded hydro plants. The generation of solar power mainly depends on the amount of solar irradiance, which is uncertain in nature. So, to deal with the uncertainty associated with solar energy, an efficient 2m point estimate method is used. To tackle the optimization part whale optimization algorithm is used. The hunting strategy of whales is very interesting and known as bubble-net feeding method. This strategy is very unique and mathematically modeled to perform an optimization process. To analyze the performance of the whale optimization algorithm, it has been tested on two test systems. The comparison of result shows that the proposed algorithm obtained better solution compare to other methods.
Out-of-Plane Motion Detection System Using Convolutional Neural Network for US-guided Radiofrequency Ablation Therapy

Radiofrequency ablation therapy support system aims to display the true tumor position by tracking the tumor at the time of treatment. As a major cause of the error, the tumor moves in the direction which is perpendicular to the scan plane of the ultrasound image. Therefore, in the proposed method, the six-axis movement amount is estimated from two ultrasound images by a convolution neural network. By this method, we estimated the amount of movement in the direction perpendicular to the ultrasound image plane and aimed at tracking more accurately. We conducted an experiment to estimate the probe movement amount with respect to the liver phantom and confirmed the certain effectiveness of this method.
Sentiment Analysis, Emotion Detection and Opinion Mining Algorithms, Applications and Challenges: An Exploratory Study

Sentiment Analysis (SA) and its relative branch Emotion detection are progressing field of research in data mining. SA is the computational treatment of conclusions, sentiments and subjectivity of substance. This survey paper handles an expansive design of the last stimulate in this field. Various beginning late proposed algorithms' progressions and differing SA applications are examined and indicated briefly in this survey. The related fields to SA (exchange learning, emotion detection, and building assets) that pulled in scientists beginning late are talked about. The key focus of this survey is to give about full picture of SA techniques and the related fields with brief reasons for interest. The real obligations of this paper incorporate the brain boggling solicitations of an expansive number as of late articles and the framework of the stream case of research in the sentiment analysis and its related regions.
Power Quality Affected by DC and AC LED Bulb Loading of a PV Generator Connected to a Microgrid

Ahstract-Light-Emitting Diode (LED) bulbs or lamps are popularly used nowadays because of their less power consumption and long lifespan. However, usages of LED bulbs including the DC and AC types affect power quality of the power system because the needs of DC-to-DC converters and/or inverters. This paper presents a comparison of power quality affected by the DC and AC LED bulb loads supplied by a PV generator (PVG) connected to a PV microgrid. Various loading conditions based on centralized and decentralized drivers are carried out. Also the effect switching frequency for DC-to-DC converter used inside LED drivers is explored. The power quality is measured by means of total distortion percentage in the power line currents on both DC and AC side. The study is based on simulations using the existing simulation models of a PV generator, a converter, an inverter, DC LED bulb, and AC LED bulbs. The results show that DC LED lamp loading has higher effects to the total distortion than AC LED has. Moreover, using the centralized driver has a small advantage over the decentralized case while the distortion increases when the switching frequency is reduced. Although the resulting distortions from the simulations may not be accurate, the comparison results may be used as a guideline for designing a PV microzrid.
Turbo-Fsk,a Physical Layer for LPWA: Synchronization and Channel Estimation

Turbo Frequency Shift Keying (Turbo-FSK) has been considered as a promising physical layer for low power wide area applications. Its constant envelope at the transmitter combined with performance close to the Shannon's limit enable to achieve a high energy efficiency. However, results published so far in the literature for this waveform have assumed perfect synchronization and channel estimation. This paper, presents a synchronization and channel estimation approach based on a specifically built preamble and adapted to the performance of the new modulation. Simulations have been performed for both time and frequency synchronization as well as channel estimation. Less than 1 dB degradation in comparison to perfect detection is achieved for the most severe types of channels.
Reduced-Order Model for Inter-Inverter Oscillations in Islanded Droop-Controlled Microgrids

Representing a major technology toward the proliferation of sustainable energy resources, droop controlled microgrids have attracted numerous modeling approaches with a view to improving their operation and applicability. Acknowledging the low inertia of distributed generators and the network characteristics at the distribution level, this paper proposes a reduced order model for inter-inverter oscillations in autonomous droop controlled microgrids. At first, a simplified equivalent network on the basis of Kron reduction is suggested as an effective topology for the study of inter-inverter oscillating currents. The focus shifts toward low-frequency modes following the detailed small signal model and its simplification according to the singular perturbation theory with multiple time scales. Assuming accurate and instantaneous tracking of the voltage by the inner controllers on their local reference frame, the quasi steady state study results in a simple closed-loop model. This model is successfully benchmarked against the detailed model and other germane models over the full range of operational damping ratios.