Miracle-dz/UniFAR-FTUs huggingface dataset:
A Fully-Integrated 60-GHz Voltage-Controlled Oscillator Synchronized by Optoelectronic Signal

This paper presents a low-phase-noise frequency generator at 60 GHz deploying injection locking of a voltage-controlled oscillator (VCO) to an optical signal. The VCO is a Colpitts push-push oscillator, which is fundamentally locked to an external optical reference laser source. The chip is fabricated using IHP SG25H4 EPIC SiGe technology [1]. Operated from a 3.3-V supply and consuming a DC current of 80 mA, the VCO has a tuning range from 58.5 GHz to 65.7 GHz. At 58.5 GHz, the free-running VCO has a phase noise of −85 dBc/Hz at 1-MHz offset and it was reduced to −111 dBc/Hz under injection locking. This indicated a significant improvement in the phase noise performance of the VCO with optical injection-locking technique.
Receive Spatial Modulation in Correlated Massive MIMO With Partial CSI

Spatial modulation is emerging as a promising signal transmission paradigm for future wireless communications relying on massive multiple-input multiple-output (MIMO) technology. In this paper, the receive spatial modulation (RSM) is applied to the downlink of multi-user MIMO systems with the base station equipped with a massive array of antennas. Due to the significant overhead associated with acquiring channel state information at the transmitter (CSIT) of frequency-division-duplex massive MIMO systems, herein we propose a preprocessing scheme for correlated channels that reduces the channel estimation overheads. The proposed preprocessing method relies on the partial CSIT and consists of two stages of beamforming and precoding. The beamforming is accomplished via block-diagonalization to combat multi-user interference while the precoding matrix is designed by formulating optimization problems aiming to eliminate inter-antenna interference and enhancing RSM transmission performance. The design criteria of the problems include bit-error-rate minimization and discrete-input continuous-output (DCMC) capacity maximization with a transmit power constraint. The arising non-convex problems are cast as semi-definite and second-order cone programs that can be solved efficiently. The proposed schemes are readily extended to generalized RSM as the most comprehensive case in RSM-MIMO scenarios. Simulation results demonstrate that the proposed schemes outperform the imperfect-CSIT based and two-stage zero-forcing procedures and offer similar or significant performance compared to conventional MIMO counterparts at low to mid signal-to-noise ratio regimes, depending on the configurations.
Real-Time Embedded System for Gesture Recognition

Recognition from body movement is a challenging domain of research that lies at an intersection of machine learning, biometric security and cognitive functions domain. It can be highly beneficial for expert systems, lie detectors, border control, medical emergencies, as well as search and rescue operations. This paper describes a first prototype of a real-time system capable of recognizing four gestures that correlate to human emotions based on the arm movements. Features extracted from the 3D skeleton using Kinect v2 sensor are classified using an SVM method. The system is tested in real-time on a Kinect database with the embedded system using an optimized algorithm for skeleton extraction in real-time.
Deep Learning Based Modeling for Cutting Energy Consumed in CNC Turning Process

This paper studies a predictive modeling for cutting energy consumption in CNC turning process by using deep learning methods. An analysis of energy consumption in cutting period is firstly presented, based on which the impact factors of energy are clarified. Then the data collection platform and data pre-processing are introduced, followed by a brief review of Convolutional Neural Network (CNN), Stacked Auto-Encoder (SAE) and Deep Belief Network (DBN). These modeling methods are tested by k-fold cross-validation. The obtained results show that SAE is the most suitable method to model the relationship between process parameters, machining configuration and cutting energy.
Discrete-time 3-D Attitude Synchronization Based on Passivity Shortage

This paper studies discrete-time attitude synchronization for a group of networked rigid bodies in three dimensions. The challenge is how to deal with 3-D attitude motion dynamics on the Special Orthogonal group: SO(3) in the discrete-time domain, and it is rigorously considered by employing exponential mapping. The rigid body network consisting of multiple bodies with discrete-time attitude dynamics, relative attitude measurements, and directed interconnection topology between the bodies is first defined. Attitude synchronization is next defined as the goal for the rigid body network. Then, as the main feature of this work, it is newly shown that each attitude dynamics has a passivity shortage property, and novel distributed attitude synchronization laws based on the property are proposed. Convergence analysis and simulation verification show the validity of the present approach.
Using PV string data to diagnose failure of solar panels in a solar power plant

Solar power plants have methods of diagnosing the failure of their solar panels. In this paper, we propose a remote automatic diagnose failure method that uses PV string data. Some string measurement devices are used for continuous remote monitoring of solar power panels. Solar panels generate low power due to panels breaking, shadows from structures, weeds, etc. If these failures can be classified by fault classification using remote string measurement data, a reduction in unnecessary repair actions and more efficient preparations are possible. We use machine learning to automatically classify causes of decreased solar power generation, such as broken panels, shadows, or weeds. We applied this diagnosis method to some large solar plants. The results of our experiment proved that the learning accuracy of this proposed method is 100%. When we tested this classification method in another solar plant, the classification accuracy was 99%. This result suggests that this diagnosis method is useful and can decrease maintenance work.
Periodic event-triggered supervisory control of nonlinear systems: dwell-time switching logic

We consider supervisory control of nonlinear systems which are implemented on digital networks. In particular, two candidate controllers are orchestrated by a supervisor to stabilize the origin of the plant by following a dwell time logic, i.e. evaluating a control-mode switching rule at instants which are at least spaced by some dwell time interval. The plant, the controllers and the supervisor communicate via a network and the transmissions are triggered by a mechanism at the discrete sampling instants, which leads to periodic event-triggered control. Thus, there are possibly two kinds of events generated at the sampling instants: the control-mode switching event to activate another control law and the transmission event to update the control input. We propose a systematic design procedure for periodic event-triggered supervisory control for nonlinear systems. We start from a supervisory control scheme which robustly stabilizes the system in the absence of the network. We then implement it over the network and design event-triggering rules to preserve its stability properties. In particular, for each candidate controller, we provide a lower bound for the control-mode dwell time, design criterion to generate transmission events and present an explicit bound on the maximum sampling period with which the triggering rules are evaluated, to ensure stability of the whole system. We show that there exist relationships among the control-mode dwell time, a parameter used to define the transmission event-triggering condition and the bound of the sampling period. An example is given to illustrate the results.
Prediction of Standardized Energy Consumption of Existing Buildings Based on Hybrid Systems Modeling and Control

With the emergence of smart grids and the development of legislation related to the energy consumption of buildings, the need for accurate and reliable energy prediction models has increased in order to support decision making processes. In this work, we present a novel automated methodology for predicting the energy consumption of existing buildings based on real measurements. The methodology relies mainly on the modeling of the indoor air temperature using a hybrid switching system approach with a Piece Wise Auto-Regressive eXogeneous inputs (PWARX) technique. This technique is afterwards coupled with a classification technique, namely, Support Vector Machine (SVM), and integrated in a closed loop with PID controllers designed for each one of the continuous sub-models. The estimation of the energy consumption using the proposed approach based on measurements collected from a test building located in Angers, France is close to the one computed by physics-based methods.
A Holistic Approach for Increasing the Electric Energy Efficiency of a Nearly Zero Energy Building

This paper investigates the control challenges and potential capabilities for increasing the efficiency of a nearly zero energy building (nZEB). In particular, a holistic approach for the nZEB microgrid by integrating several improved efficiency generating and consuming devices is presented and thus, the efficiency increase of the whole nZEB is attained. The scheme of the proposed improving interventions aims to the efficiency increase of domestic wind turbines, the higher exploitation of the energy storage system (ESS) and the reduction of the energy consumption of electric motor drives in residential applications, such as elevators, electric roll-up doors, air-conditions, etc. The above efficiency improvement interventions do not only provide reduction of the operating expenses (OPEX) in a nZEB, but they can contribute to the reduction of the required capital expenditure (CAPEX) for transforming a conventional building to nZEB. The latter is attained because, for the same level of energy autonomy of the building and without affecting the comfort of the residents, reduction of the power capacity of the renewable energy sources (RES) and the energy storage capability of the ESS can be accomplished. The methodology of each of the proposed technique is described in the paper and also, selective experimental results are presented that both verify the effectiveness of the theoretical considerations and validate the effectiveness and practicality of the proposed improving efficiency interventions in a nZEB.
A Supervised Machine Learning Based Approach for Automatically Extracting High-Level Threat Intelligence from Unstructured Sources

The last few years have seen a radical shift in the cyber defense paradigm from reactive to proactive, and this change is marked by the steadily increasing trend of Cyber Threat Intelligence (CTI) sharing. Currently, there are numerous Open Source Intelligence (OSINT) sources providing periodically updated threat feeds that are fed into various analytical solutions. At this point, there is an excessive amount of data being produced from such sources, both structured (STIX, OpenIOC, etc.) as well as unstructured (blacklists, etc.). However, more often than not, the level of detail required for making informed security decisions is missing from threat feeds, since most indicators are atomic in nature, like IPs and hashes, which are usually rather volatile. These feeds distinctly lack strategic threat information, like attack patterns and techniques that truly represent the behavior of an attacker or an exploit. Moreover, there is a lot of duplication in threat information and no single place where one could explore the entirety of a threat, hence requiring hundreds of man hours for sifting through numerous sources — trying to discern signal from noise — to find all the credible information on a threat. We have made use of natural language processing to extract threat feeds from unstructured cyber threat information sources with approximately 70\% precision, providing comprehensive threat reports in standards like STIX, which is a widely accepted industry standard that represents CTI. The automation of an otherwise tedious manual task would ensure the timely gathering and sharing of relevant CTI that would give organizations the edge to be able to proactively defend against known as well as unknown threats.
Development of a Demonstration-Guided Motion Planning for Multi-section Continuum Robots

Recently, a considerable trend is growing towards engaging continuum robots in the navigation of confined environments. Due to their increased Degrees of Freedom (DOF), having a fully autonomous motion planning could be challenging in continuum robots. In this paper, a Demonstration-Guided Motion Planning (DGMP) framework is developed to teach continuum robots how to achieve point-point spatial motions from given demonstrations. A flexible rod is used as an input interface to demonstrate motions for the robot via teleoperation. The Dynamic Movement Primitives (DMP) framework is adopted to learn, reproduce and generalize the given demonstrations while avoiding novel obstacles that could exist in the environment. Meanwhile, a Model Reference Adaptive Controller (MRAC) is developed to ensure the robustness towards executing the motions generated from the DGMP by the robot. The developed approach is assessed over a simulated kinematic model of a two-section continuum robot. The results show evidence that the proposed DGMP is effective in generating and tracking spatial motions for continuum robots, which encourages further investigation towards planning complex motions in future.
An Interleaved AC-DC ZCS-PWM Boost Rectifier

A novel AC-DC ZCS-PWM interleaved boost converter is proposed in this paper. The converter uses just one active auxiliary circuit to allow all main converter switches to operate with ZCS; moreover, the auxiliary circuit also operates with ZCS. This circuit does not increase the peak voltage or current stresses of the main switches and does not have any components in the main power path of current, which results in improved light-load efficiency. The operation of the converter is fully explained in this paper, the features of the converter are discussed, and guidelines for the design of key auxiliary circuit components are given. The feasibility of the proposed converter is confirmed with experimental results obtained from a prototype converter.
Progress in High and Ultrahigh Voltage Silicon Carbide Device Technology

The current developments in silicon carbide (SiC) device technology in various voltage ranges are introduced. These developments correspond to, in particular, next-generation high to ultrahigh-voltage devices, SiC super-junction metal oxide semiconductor field effect transistors, SiC insulated gate bipolar transistors, and the fundamental bipolar degradation suppression technology. We expect that these next generation devices will trigger a paradigm shift in power electronics.
The Impact of Carrier Frequency at 800 MHz and 3.5 GHz in Urban and Rural Environments Using Large Antenna Arrays

The widespread use of wireless mobile communications in past years has resulted in a mass use of the 2 GHz band. As a consequence, the 3.5 GHz and 800 MHz bands have emerged as an excellent alternative for mobile operators. In this paper, we investigate such alternatives using the 3GPP 3-dimensional (3D) channel model. We show the impact of both 800 MHz and 3.5 GHz carrier frequencies not only in an urban but also in a rural environment. We consider indoor and outdoor users, as well as line-of-sight (LOS) and non line-of-sight (NLOS) users. We evaluate and compare the performance in terms of Signal-to-Interference-plus-Noise Ratio (SINR), Reference Signal Received Power (RSRP), Received Signal Strength Indicator (RSSI) and Reference Signal Received Quality (RSRQ).
Switching Frequency Techniques for Universal Ambient Backscatter Networking

This work offers both analog and digital tag modulation schemes and respective receiver designs, for <italic>ultra</italic>-low power, <italic>high</italic> performance, ambient backscatter communications. All proposed techniques are based on simple, but careful, switching frequency control at the tag, allowing for the easy frequency-domain multiple access. First, a digital modulation scheme is offered, namely pseudo-frequency shift keying, assuming illumination from constant envelope-modulated signals and a fully coherent detector is derived along with closed-form probability of error. A second digital modulation scheme is also offered, based on a frequency-shifted form of binary phase shift keying (S-BPSK), relaxing the constant-envelope requirement for the illuminator and an illumination-agnostic detector is derived. Based on S-BPSK, short packet error correction coding is utilized for ambient backscatter communication, for the first time in the literature. It is shown that the proposed coded scheme under <italic>modulated</italic> ambient signal illumination & wireless channel variation, offers tremendous performance gains, i.e., modulation of the ambient signal is helpful. Finally, a third, purely analog, modulation scheme is analyzed, based on FM <italic>remodulation</italic> principles. A low-cost tag is implemented, demonstrating tag-to-receiver ranges up to 26 meters outdoors, power consumption of <inline-formula> <tex-math notation="LaTeX">${24} ~\mu $ </tex-math></inline-formula>Watts in <italic>continuous</italic> operation, able to be interrogated by <italic>any</italic> conventional frequency modulation (FM) receiver. The proposed techniques cover a large variety of omnipresent wireless industry systems, enabling <italic>universal</italic> ambient backscatter and relevant wireless information and power transfer (WIPT) applications.
A Smooth Construction of ISS Lyapunov Functions for Interconnected Systems

To establish input-to-state stability (ISS) of an interconnected nonlinear system, the small-gain framework makes use of nonlinear gain functions of components systems. Computing gain functions is usually hard without introducing Lyapunov functions to component systems. In the ISS formulation, Lyapunov functions of component systems naturally lead to a Lyapunov functions in the max-separable form for the interconnection. Since the maximization is not differentiable. technical modification or occurrence of artifical behavior is unavoidable in actual use of a max-separable Lyapunov function. This paper proposes a practical Lyapunov function by securing continuous differentiability and keeping it as simple and intuitive as the max-separable Lyapunov function.
Action Dependent Dual Heuristic Programming Solution for the Dynamic Graphical Games

The context of graphical games is employed to solve the cooperative control problem for multi-agent systems interacting on graphs. Together with the need to have faster solution mechanisms urged for new approaches that employ the Dual Heuristic and Action Dependent Dual Heuristic Programming. This class of gradient-based solutions undergoes two main challenges. First, they have to use complex update expressions for the solving gradient-based structures. Second, they may overlook the local neighborhood information, if simpler costate expressions are enforced. A novel approach based on Action Dependent Dual Heuristic Programming is developed to solve the dynamic graphical games and to handle the aforementioned concerns. This adaptive learning approach is implemented online using means of value iteration and neural networks. The approximation of the optimal policy does not have priori knowledge about the agents' dynamics, while the value function gradient approximation is shown to depend only on the drift dynamics of the agents. The convergence results of the adaptive learning approach are highlighted by simulation example.
Restoration of Images Affected by Welding Fume

Machine vision performs an important role in many applications, including robotics. Combined with classical instrumentation, welding robots can use a camera to perceive the scene and take a decision. A camera attached to the robot body and machine vision system work as the eyes of the robot during the welding process. However, image-based systems are susceptible to the interference of fumes, sparks, dust, and artifacts generated as a side effect of the welding process. Fume can adhere to the lenses and degrades the image, introducing a negative impact on the processing pipeline. This paper proposes a novel image fusion based algorithm that minimizes the effects caused by the fume adhered to the camera lens. Results show the proposed method is able to enhance the overall image quality, outperforming classical alternatives for similar problems.
Band Weighting Network for Hyperspectral Image Classification

Hyperspectral remote sensing images use hundreds of bands to describe the fine spectral information of the ground area. However, they inevitably contain a large amount of redundancy as well as noisy bands. Discovering the most informative bands and modeling the relationship among the bands are effective means to process the data and improve the performance of the subsequent classification task. Attention mechanism is used in computer vision and natural language processing to guide the algorithm towards the most relevant information in the data. In this paper, we propose a band weighting network by designing and integrating an attention module in the traditional convolutional neural network for hyperspectral image classification. Our proposed band weighting network has the capability to model the relationship among the bands and weight them according to their joint contribution to classification. One prominent feature of our proposed method is that it can assign different weights to different samples. The experimental results demonstrate the effectiveness and superiority of our approach.
On-Chip Self-Test Methodology With All Deterministic Compressed Test Patterns Recorded in Scan Chains

This paper presents a novel test architecture that combines the advantages of high-quality deterministic scan-based test and low-cost built-in self-test. The main idea is to record (store) all required compressed test data in a novel scan chain structure, and extract and decompress them during testing. This requires a very high compression ratio to obtain a low test data volume, that is, smaller than the number of scan cells in the circuit under test. To achieve such a high compression ratio, we propose a novel compression method that combines broadcast scan as well as a tailored single-input compression architecture. We also utilize the concept of scan chain partitioning and clock gating to reduce the test time and test power. An on-chip test controller is employed to automatically generate all required control signals for the whole test procedure. This significantly reduces the requirements on external automatic test equipment. Experimental results show that our method is well suitable for multicore designs. For example, experiments on the 8-core open-source OpenSPARC T2 processor with 5.7M gates show that all required test data for 100% testable stuck-at fault coverage can be stored in just 59.4% of the scan cells of the processor. Experimental results for transition faults are also presented, which show that more identical cores are needed in order to store all test data for transition faults. We also discuss how to extend this paper to address fault diagnosis and engineering change order problems.