Miracle-dz/UniFAR-FTUs huggingface dataset:
A Hybrid Control Framework for Fast Methods Under Invexity: Non-Zeno Trajectories with Exponential Rate

In this paper, we propose a framework to design a class of fast gradient-based methods in continuous-time that, in comparison with the existing literature including Nesterov's fast-gradient method, features a state-dependent, time-invariant damping term that acts as a feedback control input. The proposed design scheme allows for a user-defined, exponential rate of convergence for a class of nonconvex, unconstrained optimization problems in which the objective function satisfies the so-called Polyak-Łojasiewicz inequality. Formulating the optimization algorithm as a hybrid control system, a state-feedback input is synthesized such that a desired rate of convergence is guaranteed. Furthermore, we establish that the solution trajectories of the hybrid control system are Zeno-free.
A Finite-Time Protocol for Distributed Continuous-Time Optimization of Sum of Locally Coupled Strictly Convex Functions

In this paper we study a distributed optimization problem for continuous time multi-agent systems. In our setting, the global objective for the multi-agent system is to minimize the sum of locally coupled strictly convex cost functions. Notably, this class of optimization objectives can be used to encode several important problems such as distributed estimation. For this problem setting, we propose a distributed signed gradient descent algorithm, which relies on local observers to retrieve 2-hop state information that are required to compute the descent direction. Adaptive gains for the local observer are introduced to render the convergence independent from: i) the structure of the network topology and ii) the local gains of the per-agent signed gradient-descent update law. The finite-time convergence of the local observer and of the proposed signed gradient descent method is demonstrated. Numerical simulations involving a distributed weighted least-square (WLS) estimation problem, with the aim of identifying in the context of an advanced water management system for precision-farming the soil thermal properties in a large-scale hazelnut orchard, have been proposed to corroborate the theoretical findings.
Nonlinear Explicit Stochastic Attitude Filter on SO(3)

This work proposes a nonlinear stochastic filter evolved on the Special Orthogonal Group ${\mathbb{SO}}$ (3) as a solution to the attitude filtering problem. One of the most common potential functions for nonlinear deterministic attitude observers is studied and reformulated to address the noise attached to the attitude dynamics. The resultant estimator and correction factor demonstrate convergence properties and remarkable ability to attenuate the noise. The stochastic dynamics of the attitude problem are mapped from ${\mathbb{SO}}$ (3) to Rodriguez vector. The proposed stochastic filter evolved on ${\mathbb{SO}}$ (3) guarantees that errors in the Rodriguez vector and estimates steer very close to the neighborhood of the origin and that the errors are semi-globally uniformly ultimately bounded in mean square. Simulation results illustrate the robustness of the proposed filter in the presence of high uncertainties in measurements.
Device-Free Non-Privacy Invasive Indoor Human Posture Recognition Using Low-Resolution Infrared Sensor-Based Wireless Sensor Networks and DCNN

Human posture recognition is the foundation of the human activity monitoring. The activity monitoring system is in high demand for the elderly living alone to monitor their health status and accidental fall since the world elderly population will be doubled by 2050. Researchers have developed many camera or wearable device-based human recognition systems; however, they are considered to be privacy-invasive and/or not practical for the long-term monitoring. We propose a device-free unobtrusive indoor human posture recognition system leveraging a low-resolution infrared sensor-based wireless sensor network and deep convolutional neural network (DCNN). We integrated AMG8833 sensor module with 8×8 thermal sensors for sensing the human body temperature and WiFi module for a wireless sensor network. Three wireless sensor nodes are used to capture 3-axis human thermal image. Totally, 15063 samples are collected from four volunteers while they had performed eight human postures as the ground truth for the 10-fold cross-validation of DCNN models. Experimental results indicate that the highest average F1-score for the eight postures was 0.9981. Thus, the proposed system has the high potential for monitoring elderly daily activities, exercise, and fall in emergency cases. Moreover, we believe that our proposed system will be a milestone in the device-free unobtrusive sensing technology.
Semi-Parametric Kernel-Based Identification of Wiener Systems

We present a technique for kernel-based identification of Wiener systems. We model the impulse response of the linear block with a Gaussian process. The static nonlinearity is modeled with a combination of basis functions. The coefficients of the static nonlinearity are estimated, together with the hyperparameters of the covariance function of the Gaussian process model, using an iterative algorithm based on the expectation-maximization method combined with elliptical slice sampling to sample from the posterior distribution of the impulse response given the data. The same sampling method is then used to find the posterior-mean estimate of the impulse response. We test the proposed algorithm on a benchmark of randomly-generated Wiener systems.
Evaluation and Comparison of the Arc Erosion Characteristic of Ag/SnO2/In2O3 Contact Materials Used for an Automotive Relay

The arc erosion characteristic of contact materials is one of the key factors for the electric life of a relay. A new simulation testing device used for evaluating the arc erosion characteristic was built in order to meet the need of engineering application, and its action characteristic was almost the same as relays. The reliability and availability of the simulation testing device were confirmed by lots of designed parameter testing and analysis. The arc erosion characteristic of Ag/SnO2/In2O3 contact materials with different content of oxides and prepared methods was evaluated and compared by this apparatus. The research results indicated that the arc erosion of Ag/SnO2/In2O3 prepared by the same method became less with the increasing content of oxides, whatever the load type is lamp or resistance. Compared with the alloy internal oxidation (I.O.) method and power pre-oxidation (P.M.) method, the arc erosion of Ag/SnO2/In2O3 prepared by the former was less. Because of the existence of TeO2, the abilities of anti arc erosion and material transference of contact materials were improved. Moreover, this oxide may be beneficial for the characteristic of anti welding, and it was further identified by the electric life experiment of relays.
Annotating Animated AR Objects for Co-located Learning

Annotations in learning environments are used to extend information about objects and to enhance the teacher-student interactions. In augmented reality (AR) environments, learners annotate 3D virtual objects in order to draw attention to a particular area of concern. However, annotations become problematic when 3D objects move in space or their content is animated. Traditional annotation techniques (e.g., labels and sketches) have limitations when deployed with moving objects. In response, we develop an AR prototype system to determine the types of annotations that would enhance co-located learning with animated virtual objects. In our participant study, we observed physiotherapy teachers and students using the system to analyze and annotate human movement (prerecorded and live). We investigated human interaction with virtual objects and describe types of AR annotations. We find that classic annotations are not common with animated objects. Instead, participants construct imaginary cues (virtual lines and anchor points) that are then augmented with visual annotations.
Inferring the Climate in Classrooms from Audio and Video Recordings: A Machine Learning Approach

The classroom climate is shaped by a combination of teacher practices and peer relationships. The Classroom Assessment Scoring System (CLASS) has been designed to observe and code classroom interactions between students and teachers in order to provide formative feedback on teaching practices and improve teacher instruction. But the turnover time for training, observing and coding makes it hard to generate instant feedback. Since there are few automated assessment tools designed to measure the classroom climate, we propose a novel system for automatic assessment of classroom climate, based on speech, behavioral cues and video features by applying machine learning techniques. This paper elaborates on the design and validation of an audio-video analytics platform for predicting classroom climate. Employing machine learning classifiers instead of subjective measures can ease and expedite the coding. We presume our system can empower education systems to continuously review and improve teaching strategies thus promoting smart classroom in the future.
Driver drowsiness detection: a comparison between intrusive and non-intrusive signal acquisition methods

Driver drowsiness is a major cause of road accidents, many of which result in fatalities. A solution to this problem is the inclusion of a drowsiness detector in vehicles to alert the driver if sleepiness is detected. To detect drowsiness, physiologic, behavioral (visual) and vehicle-based methods can be used, however, only measures that can be acquired non-intrusively are viable in a real life application. This work uses data from a real-road experiment with sleep deprived drivers to compare the performance of driver drowsiness detection using intrusive acquisition methods, namely electrooculogram (EOG), with camera-based, non-intrusive, methods. A hybrid strategy, combining the described methods with electrocardiogram (ECG) measures, is also evaluated. Overall, the obtained results show that drowsiness detection performance is similar using non-intrusive camera-based measures or intrusive EOG measures. The detection performance increases when combining two methods (ECG + visual) or (ECG + EOG).
A Mixed Mahalanobis Kernel for Sparse Bayesian Classification

Relevance vector machine (RVM) is a typical sparse learning model. The most widely used kernel in RVM is Radial Basis Function (RBF), but it is based on the Euclidean distance and don't take the distribution information of training samples into consideration which sometimes leads to a poor performance. Therefore, in this paper, we propose a mixed Mahalanobis kernel for Sparse Bayesian classification which explores the distribution characteristics of data set by means of GMM, and built the kernel matrix based on the weighted Mahalanobis distances from all Gaussian components. The evaluation of the proposed kernel is conducted by means of kernel target alignment (KTA). Applying the proposed kernel to RVM, a new version of RVM, MM-RVM, is formed. The algorithm has been applied to several UCI datasets and the experimental results shows the superior performance of our proposed method and the effectiveness of the mixed Mahalanobis kernel.
TID Evaluation System With On-Chip Electron Source and Programmable Sensing Mechanisms on FPGA

Commercial-off-the-shelf system-on-chip field-programmable gate arrays (FPGAs) are gaining momentum in space applications and their radiation characterization becomes crucial. This paper targets a cost-effective test setup and procedures for total ionization dose testing of miniaturized complex devices. We combine hardware and software techniques to perform on-chip irradiation via a 90Sr/90Y electron source and assess in detail the degradation of complex SRAM FPGAs. Our methodology combines multiple sensing mechanisms with correlation analysis to facilitate modeling of effects and tuning of a custom ring-oscillator network for future autonomous in-flight use. Focusing on 28-nm Zynq7000 devices, our results show Mrad total ionizing dose tolerance with parameters’ degradation in the area of 5%, zero functional errors, and nonuniform spatial response.
Designing High-Performance Interconnection Networks with Host-Switch Graphs

This paper aims at establishing a method for designing high-performance network topologies to bridge a gap between theoretical and practical studies. To this end, we present a novel graph called a host-switch graph, which consists of host vertices and switch vertices with maximum degree 1 and <inline-formula><tex-math notation="LaTeX">$r$</tex-math><alternatives><inline-graphic xlink:href="yasudo-ieq1-2864286.gif"/></alternatives></inline-formula>, respectively. This graph represents a network topology of a practical parallel/distributed computer system with host computers connected by <inline-formula><tex-math notation="LaTeX">$r$</tex-math><alternatives><inline-graphic xlink:href="yasudo-ieq2-2864286.gif"/></alternatives></inline-formula>-port switches. We discuss important metrics for designing high-performance interconnection networks: the host-to-host average shortest path length (h-ASPL) and the bisection width (BiW). In particular, we explore a method for constructing host-switch graphs with low h-ASPL and high BiW that connect the fixed number of hosts via any number of <inline-formula><tex-math notation="LaTeX">$r$</tex-math><alternatives><inline-graphic xlink:href="yasudo-ieq3-2864286.gif"/></alternatives></inline-formula>-port switches. We demonstrate that the number of switches that provides the minimum h-ASPL can mathematically be approximated, and the minimum number of switches that provides a certain BiW can experimentally be approximated. On the basis of the approximations, we propose a randomized algorithm for searching host-switch graphs. We then apply the graphs to interconnection networks and compare them with typical network topologies. As compared with the torus, the dragonfly, and the fat-tree, our networks attain higher performance and smaller power and costs.
ZIGBEE Based Time and Energy Efficient Smart Parking System Using IOT

The need of this era is a fully automated vehicle parking system using IOT. The real time notification is made possible by integrating a Micro-controller (Arduino) with a wireless communication system (ZigBee) and is feed to the driver via a smart phone. This technique provides a time and energy efficient way to communicate information between sender and receiver. Additionally, this efficient system provides a better monitoring and control application for shopping malls, restaurants, offices and hospitals. In severe cases, this system will also update crowd capacity information to ambulance service, so that they could easily tackle the situation by adopting the less crowded area towards hospital. The comparison between experimental results of ZigBee with other popular wireless technologies such as Wi-Fi and Bluetooth show that it is more energy efficient. GSM module notifies the driver about the current location of empty parking spaces via SMS or software application. The IR sensors embedded in the parking lots provide the real time scenario to user with all possible details and allowing the easy access to parking lots available, ensuring minimal human interaction.
Waveguide Structure Compatible with TE, TM, and TEM Modes

We propose a novel waveguide structure capable of supporting three different transmission modes, namely, TE, TM, and TEM. It can thus be used both above and below a cutoff frequency. Therefore, it can be used for super-wide band applications. The divided structure of the waveguide realizes transmission under a cut-off frequency in TEM mode, as well as by making use of the waveguide functionality. In addition, it can transmit in TE and TM (waveguide) modes in the same way as a typical waveguide with low loss. Transmission both under and over the cut-off frequencies was confirmed both through simulation and by practical experiments with the proposed waveguide. We also addressed the connector structure necessary for the associated waveguide-coaxial conversion.
Scheme Evaluation of Automobile Comprehensive Performance Based on Grey Clustering Analysis

In view of the deficiency of traditional evaluation method of automobile comprehensive performance, the mathematical optimal solution usually cannot be filtered from several schemes in engineering. This paper introduces the theory of analytic hierarchy process (AHP) and grey clustering analysis. Then seven automobile overall schemes were selected to be evaluated, while six typical performance indicators were selected as evaluation indexes. AHP was used to calculate weight coefficient of each performance index, and grey clustering method was applied to obtain the weighted order of each scheme. The case study showed that the proposed method can evaluate the automobile overall schemes effectively. Seven schemes can be put in order according to the satisfaction degree with grey classification. Thus, the analysis result is practical.
Improving Noise Tolerance of Hardware Accelerated Artificial Neural Networks

The noise effect during training and inference for deep artificial neural network hardware acceleration is analyzed in this paper. The noise effect is extremely important when designing hardware for machine learning due to non-ideal devices and circuits. We found that both forward propagation noise and weight update noise can have detrimental effect on the inference, but may not necessarily harm the training result. Pipelining approach supporting redundant runs for each input image is proposed to address the forward propagation noise, and an additional approach of using parallel memory weight cells to represent one synaptic weight is proposed to address the weight update noise. By using these approaches, MNIST classification accuracy on a deep neural network can be improved close to the ideal accuracy when no noise is present.
A Parallel Code Generation Toolkit for Nonlinear Model Predictive Control

This paper presents a MATLAB software toolkit ParNMPC, which can automatically generate parallel C/C++ code and carry out closed-loop simulation for nonlinear model predictive control (NMPC). ParNMPC is built for shared-memory multi-core processors supporting the OpenMP programming interface. The underlying optimal control problem (OCP) in NMPC is solved based on a highly parallelizable Newton-type method in ParNMPC, which computes the optimal solution iteratively with at most $N$ cores, where $N$ is the number of discretization grids or the prediction horizon in the discrete-time case. ParNMPC provides an easy-to-use environment to formulate the OCP using symbolic representation and to generate customizable and highly optimized parallel C/C++ code automatically. The performance of ParNMPC was demonstrated by swinging up a double inverted pendulum and by controlling a quadrotor.
Design of a Compact Dual-Wideband Bandpass Filter with the Dual Stepped-Impedance Rectangular-Rings Resonator

In this paper, a compact dual-wideband bandpass filter is presented. Based on the dual stepped-impedance rectangular-rings resonator, the bandpass filter with dual passbands can be realized. In order to obtain wide dual passbands, the high-impedance coupled lines are adopted for input and output ports to enhance the coupling. Moreover, with the assistance of open stubs added to the central transmission line, the harmonics can be suppressed and a wide stopband can be generated. An exemplary bandpass filter, operated at 2.5 and 7.25 GHz as the central frequencies, is fabricated, and both the corresponding 3-dB absolute bandwidths are the same as 1.5 GHz. In addition, the measured stopband can be extended to 13.8 GHz with the rejection level of 32 dB.
Towards an Active Probing Extension for the ONOS SDN Controller

Network monitoring is a complex task and is always a trade-off between granularity of information and the performance impact of the monitoring itself on the network. SDN controllers, such as the ONOS SDN controller make this challenge easier as they can supply centralized information over the whole network. In our previous work, we analyzed the builtin detection mechanism of ONOS and revealed a lack of detection performance and the vulnerability of this process to jitter. These results have also been verified by measurements. In this paper, we present an active probing extension for the ONOS SDN controller that overcomes these shortcomings by emitting probing packets that are transmitted through the network. Packet loss is detected by calculating statistics of a list which contains data from previous packets. The evaluation by the means of measurements proves the benefits in terms of detection performance and controller load of this application. Furthermore, our extension is able to detect jitter in the data plane and to automatically adapt the probing process to these conditions.
A Robust and Less-Complex Phase-Locked Loop Algorithm Immune to Electricity Grid Disturbances for RES Applications

The increasing penetration of renewables requires advanced control algorithms to enable accurate control of the grid side converter (GSC) of renewable systems. The control of GSC mainly depends on the fast and accurate estimation of grid voltage phase angle and frequency under adverse grid conditions. Various grid disturbances occur on electricity grids, however, those that require special attention are harmonics, interharmonics and DC offset. Typically, a Phase-locked Loop (PLL) is used to obtain the grid voltage information. When designing a GSC control algorithm the accurate and proper design as well as the computational complexity, are important elements for the accurate real-time control. For real-time PV systems, embedded microcontrollers have limited computational resources and thus their effective utilization is critical. This paper proposes an advanced less-complex disturbance rejection PLL (LCDRPLL) that offers improved performance with reduced complexity. The proposed PLL is equipped with advanced features such as immunity to grid voltage unbalance, presence of harmonics, interharmonics and DC offset. More importantly, its computational complexity is very low. Experimental and simulation results are provided to justify the performance and complexity of proposed LCDRPLL.
Functional and Quality Requirements for Susceptibility Genetic Testing in Cancer Care: The Case of BRCA1/2 Testing in Jordan

Breast cancer is the most common cancer in Jordan. Mutations in BReast CAncer susceptibility gene 1 and BReast CAncer susceptibility gene 2 (BRCA1/2 genes) greatly increase the lifetime risk of developing breast and/or ovarian cancer in women. The aim of this paper is to identify functional and non-functional requirements for BRCA 1/2 testing, as a corner stone for establishing BRCA 1/2 testing services in the country and as a case example for other susceptibility genetic testing for cancer. During two sessions, the authors outlined the functional and non-functional requirements for BRCA1/2 testing, as types of categories of requirements, using the Sommerville approach. Six functional requirements were identified. Non-functional requirements were divided to: 1. Product related 2. Process related and 3. Externally related (Interoperability and Legal, Social, Ethical and Professional (LSEP) Implications). The authors will continue to build on their work in order to produce a full model for BRCA1/2 testing to provide the most comprehensive and advanced cancer care for patients and facilitate future development of innovate measures in the battle against cancer.