Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental Study of a Vibration Based Energy Harvesting System for an RFID Module

In recent years, an application of a piezoelectric transducer to harvest energy from ambient vibrations has been a subject of intense research. As the material technology advances, the amount of energy being able to produce by the transducer continues to increase. This paper presents an experimental study of a vibration based energy harvesting system for an RFID module. The system consists of a piezoelectric transducer, a power management circuit based on a buck converter, and an RFID module. The transducer converts mechanical movement produced by a finger press into a voltage to charge an input capacitor. The buck converter then converts the input capacitor voltage into a stable DC output voltage to power the RFID module. Experimental results are presented to validate the performance of the proposed energy harvesting system.
An Adaptive Approach to Sensor Bias Fault Diagnosis and Accommodation for a Class of Input-Output Nonlinear Systems

This paper presents an adaptive sensor fault diagnosis and accommodation scheme for multiple sensor bias faults for a class of input-output nonlinear systems subject to modeling uncertainty and measurement noise. The proposed scheme consists of a nonlinear estimation model that includes an adaptive component which is initiated upon the detection of a fault, in order to approximate the magnitude of the bias faults. A detectability condition characterizing the class of detectable sensor bias faults is derived and the robustness and stability properties of the adaptive scheme are presented. The estimation of the magnitude of the sensor bias faults allows the identification of the faulty sensors and it is also used for fault accommodation purposes. The effectiveness of the proposed scheme is demonstrated through a simulation example.
Evaluation of Automatic Collaborative Learning Process Coding Using Deep Learning Methods Based on Multi-Dimensional Coding Scheme

In computer-supported collaborative learning research, it is important to determine the guidelines for appropriate scaffolding by extracting indicators for distinguishing groups with poor progress in a collaborative process by analyzing the mechanism of interactive activation. Coding and statistical analysis are often adopted for analyzing such collaborative processes. In this study, using a multidimensional coding scheme with four dimensions designed to analyze the collaborative learning process more comprehensively and multilaterally, automatic coding is performed using deep learning methods and its accuracy is verified. In addition, this method is used to predict another dataset and determine its validity. The experiments of this study show that except for some typical misclassifications, the overall results were positive. However, in order to solve this misclassification problem, we also have to reconsider the redesign of the coding scheme and reexamine the classification technique by deep learning.
Heuristic Approximation of Early-Stage CNN Data Representation for Vision Intelligence Systems

Massive memory requirements and disruptive I/O traffic in Convolutional Neural Networks (CNNs) restrict hardware/software optimization for training and inference tasks using larger networks despite their success in improving the intelligence of systems into which they are integrated. Interestingly, it has been observed that the early CNN layers in vision tasks consistently produce edge-like feature representations that can be mimicked by traditional vision algorithms. In this work, we demonstrate how to exploit this to create common edge-like features for sharing among multiple CNNs, and how to heuristically approximate them in a reduced dimensionality. In our proposed approximation, the feature space of three representative CNNs decreases by 1.6×–5.1×, and the size of training dataset is halved. As a result, we enhance both inference throughput and training speed by 2×, while providing accuracies that are still close to the original versions. We anticipate that this approach will lead to new opportunities in distributed intelligence systems, and the technique for redesigning CNN models based on dimensional reduction of feature space is orthogonal to and compatible with many other existing hardware/software optimizations.
A Scalable Algorithm for Network Localization and Synchronization

The Internet of Things (IoT) will seamlessly integrate a large number of densely deployed heterogeneous devices and will enable new location-aware services. However, fine-grained localization of IoT devices is challenging as their computation and communication resources are typically limited and different devices may have different qualities of internal clocks and different mobility patterns. To address these challenges, we propose a cooperative, scalable, and time-recursive algorithm for network localization and synchronization (NLS). Our algorithm is based on time measurements and supports heterogeneous devices with limited computation and communication resources, time-varying clock and location parameters, arbitrary state-evolution models, and time-varying network connectivity. These attributes make the proposed algorithm attractive for IoT-related applications. The algorithm is furthermore able to incorporate measurements from additional sensors for positioning, navigation, and timing such as receivers for global navigation satellite systems. Based on a factor graph representation of the underlying spatiotemporal Bayesian sequential estimation problem, the algorithm uses belief propagation (BP) for an efficient marginalization of the joint posterior distribution. To account for the nonlinear measurement model and nonlinear state-evolution models while keeping the communication and computation requirements low, we develop an efficient second-order implementation of the BP rules by means of the recently introduced sigma point belief propagation technique. Simulation results demonstrate the high synchronization and localization accuracy as well as the low computational complexity of the proposed algorithm. In particular, in sufficiently dense networks, the proposed algorithm outperforms the state-of-the-art BP-based algorithm for NLS in terms of both estimation accuracy and computational complexity.
Future Trajectory Prediction via RNN and Maximum Margin Inverse Reinforcement Learning

In this paper, we propose a future trajectory prediction framework based on recurrent neural network (RNN) and maximum margin inverse reinforcement learning (IRL) for the task of predicting future trajectories of agents in dynamic scenes. Given the current position of a target agent and the corresponding static scene information, a RNN is trained to produce the next position, which is the closest to the true next position while maximizing the proposed reward function. The reward function is also trained at the same time to maximize the margin between the rewards from the true next position and its estimate. The reward function plays the role of a regularizer when training the parameters of the proposed network so the trained network is able to reason the next position of the agent much better. We evaluated our model on a public KITTI dataset. Experimental results show that the proposed method significantly improves the prediction accuracy compared to other baseline methods.
Emotion Recognition via Facial Expressions

For the last decade, a rising need for emotion recognition has been noticed in several domains, such as virtual reality, human-computer interaction, video games and health monitoring, etc. Effectively, emotion recognition via facial expressions attracts increasing attention. Based on geometrical facial features, this paper proposes a new facial emotion recognition method. We collected a novel dataset of 17 subjects facial performance of six emotional states (anger, fear, happiness, surprise, sadness, and neutral) using Kinect (vi) and Kinect (v2) and RGB HD camera. New positional features including a combination of angle and distance features are used to train the classifier. The K nearest neighbors (k-NN) is used as the main classification technique. To assess our proposed method performance, we use the leave-one-out subject cross-validation. A comparison between RGB and RGB-D data is provided. The obtained results show the superior performance of the RGB-D features provided by Kinect (v2). We observed in our experiment that the 2D images are not robust enough for facial emotion recognition due to the sensitivity of the RGB camera to the surrounding conditions.
A set-membership approach to find and track multiple targets using a fleet of UAVs

This paper presents a set-membership approach for the coordinated control of a fleet of UAVs aiming to search and track an a priori unknown number of targets spread over some delimited geographical area. The originality of the approach lies in the description of the perturbations and measurement uncertainties via bounded sets. A set-membership approach is used to address the localization and tracking problem. At each time step, sets guaranteed to contain the actual state of already localized targets are provided. A set containing the states of targets still to be discovered is also evaluated. These sets are then used to evaluate the control input to apply to the UAVs so as to minimize the estimation uncertainty at the next time step. Simulations considering several UAVs show that the proposed set-membership estimator and the associated control input optimization are able to provide good localization and tracking performance for multiple targets.
Real-Time 3-D Motion Gesture Recognition using Kinect2 as Basis for Traditional Dance Scripting

This preliminary study presents a system capable of recognizing human gesture in real-time. The gesture is acquired from a Kinect2 sensor which provides skeleton joints represented by three-dimensional coordinate points. The model set consists of eight motion gestures is provided for basis of gesture recognition using Dynamic Time Warping (DTW) algorithm. DTW algorithm is utilized to identify in real time manner by measuring the shortest combined distances in x, y, and z coordinates in order to determined the matched gesture. It can be shown that the system is able to recognize these 8 motions in real time with some limitations. The findings of the this study will provide solid foundation of further research in which the ultimate goal of the research is to create system to automatically recognize sequence of motions in Indonesian traditional dances and convert them into standardized Resource Description Framework (RDF) scripts for the purpose of preserving these dances.
Distributed Robust State Estimation for Sensor Networks: A Risk-Sensitive Approach

In this paper, we investigate a distributed robust state estimation problem for linear Gaussian systems measured by a sensor network, where the sensors can communicate only with their neighbors and each sensor runs a local filter to estimate the state of the process based on the measurements from its neighbors. We present a distributed risk-sensitive filtering algorithm, where the high-gain dynamic consensus filter is utilized to compute the fused measurement data and the fused covariance-inverse matrices, based on which, the local filter is updated in a Riccati-based linear recursive form. For linear time-invariant systems, the asymptotic stability of local estimators in the proposed distributed risk-sensitive filtering algorithm is guaranteed if the value of the risk-sensitive parameter is chosen such that the centralized risk-sensitive filter is asymptotically stable. The robustness of the proposed risk-sensitive filtering algorithm to system uncertainty is verified by simulation results.
Verilog-A model of ferroelectric memristors dedicated to neuromorphic design

Artificial neural networks (ANN) are well known for performing Recognition, Data mining and Synthesis (RMS) tasks. However, the most famous ANNs are software implemented on computers that never take into account the power consumption management. Chip designers are aiming at low-power consumption by developing the neuromorphic engineering field. The goal is to design and produce neural-inspired architectures allowing energy-efficient computation systems. One decade ago, neuromorphic engineering had a renewal of interest, in particular due to the unveiled memristive devices. Indeed, memristors own all the features necessary in order to play the role of plastic synapses in ANNs. Among all memristive technologies, ferroelectric devices present an important advantage for low power systems: their high resistance which implies low current. In this paper, we will present a Verilog-A model of ferroelectric memristors. This model is based on measurements and therefore takes into account the variability of devices in terms of RON, ROFF and switching characteristics. This realistic model will be helpful for designing neuromorphic systems based on these devices. Finally, we will present some Cadence simulations of learning in small neural networks composed of CMOS neurons and memristive synapses.
Risk-Based Testing of Self-Adaptive Systems Using Run-Time Predictions

Devising test strategies for specific test goals relies on predictions of the run-time behavior of the software system under test (SuT) based on specifications, models, or the code. For a system following a single strategy as run-time behavior, the test strategy can be fixed at design time. For an adaptive system, which may choose from several strategies due to environment changes, a combination of test strategies has to be found, which still can be achieved at design time provided that all system strategies and the switching policy are predictable. Self-adaptive systems, also adapting their system strategies and strategy switches according to the environmental dynamics, render such design-time predictions futile, but there also the test strategies have to be adapted at run time. We characterize the necessary interplay between system strategy adaptation of the SuT and test strategy adaptation of the tester as a Stochastic Game. We argue that the tester's part, formalized by means of a Markov Decision Process, can be automatically solved by the use of Reinforcement Learning methods where we discuss both model-based and model-free variants. Finally, we propose a particular framework inspired by Direct Future Prediction which, given a simulation of the SuT and its environment, autonomously finds good test strategies w.r. t. imposed quanti? able goals. While these goals, in general, can be initialized arbitrarily, our evaluation concentrates on risk-based goals rewarding the detection of hazardous failures.
Using Bluetooth Low Energy to trigger an ultra-low power FSK wake-up receiver

We present a novel approach that utilizes BLE packets sent from generic BLE capable radios to synthesize an FSK-(like) addressable wake-up packet. A wake-up receiver system was developed from off-the-shelf components to detect these packets. It makes use of two differential signal paths separated by passive band-pass filters. After the rectification of each channel a differential amplifier compares the signals and the resulting wake-up signal is evaluated by an AS3933 wake-up receiver IC. Overall, the combination of these techniques contributes to a BLE compatible wake-up system which is more robust than traditional OOK wake-up systems. Thus, increasing wake-up range, while still maintaining a low energy budget. The proof-of-concept setup achieved a sensitivity of −47.8 dBm at a power consumption of 18.5 uW during passive listening. The system has a latency of 31.8 ms with a symbol rate of 1437 Baud.
Design of a Biomimetic Control System for Tendon-driven Prosthetic Hand

Prosthetic hands are frequently rejected due to frustrations when encountering disturbances or manipulating deformable objects. Humans developed a unique ability of compliant control through a complex neuro-musculo-skeletal system. We present the design and implementation of a biomimetic control system, which aims for duplicating physiologically realistic behavior of human motor control in a tendon-driven prosthetic hand. The system receives surface EMG as the source of command, which activates a simple muscle model to compute for the expected tension on the tendon. A torque motor establishes the expected tension with closed-loop adjustment from a neural circuitry simulated in software. An able-bodied subject and two fore-arm amputees participated in the study. We validated their abilities of force loading with the index finger, and power grasping with the tendon-drive prosthetic hand. Results showed the participants could establish expected force using the biomimetic control system. They were also able to grasp/release sample objects with various stiffnesses, sizes, and weights. This is an important step toward a full-fledged biomimetic control for prosthetic hand with hardware-in-loop.
Identification of pedestrian attributes based on video sequence

Analysis of pedestrian attributes in surveillance scenes is a challenging task for computer vision due to the influence of illumination and occlusion. Most existing algorithms focus on using static images to estimate results. However, these detectors often fail to appliance in videos. In this paper, We explore the GoogLeNet network that join Short-Term Memory (LSTM) to identify pedestrian attributes in continuous video sequences. Instead of dealing with a single frame, the network is used to predict the order images in run-on time. Extensive experiments are performed on video data in different monitoring conditions such as subway entrance, market and intersection. Results show that the method has achieved competitive performance on attribute recognition.
Phase Noise Reduction in Low Cost 24/77 GHz FMCW Sensors

Frequency Modulation Continuous Wave (FMCW) is a widely used technique in radar applications such as Automotive Collision Avoidance systems, Perimeter Surveillance sensors &. … A key challenge in this type of sensors is phase noise, especially when long-range detections are required. Also, expensive low phase noise VCOs would limit the use of such sensors in growing quotidian applications (e.g. $\mathbf{IoT}, \ldots$). There are several kinds of software-based solutions to overcome this challenge. Here, two most robust of these solutions are described in detail; Their efficiencies are estimated and finally, estimations are verified by simulation. At simulation, a phase noise is introduced to a detection problem and, the above-mentioned methods of phase noise reduction are applied. At the end, these methods are compared.
Thin 3-D Bandpass Frequency-Selective Structure Based on Folded Substrate for Conformal Radome Applications

A new design method based on a folded substrate is proposed in this paper to reduce the thickness of 3-D bandpass frequency-selective structure (FSS). A 67% and 79% thickness reduction compared to the basic 3-D bandpass FSS is achieved by employing three-layer and five-layer folded substrates, respectively. Single- and dual-polarized designs are presented. By integrating one more substrate with a different dielectric constant, dual-band thin structure is achieved. One structure with a five-layer folded substrate is designed, fabricated, and measured using the parallel-plate waveguide measurement setup. It has a center frequency of 3.57 GHz with 26.9% transmission bandwidth. The structure thickness is only <inline-formula> <tex-math notation="LaTeX">$0.06\lambda _{0}$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$\lambda _{0}$ </tex-math></inline-formula> is the free-space wavelength at the center frequency of the passband. Stable frequency response is achieved under oblique incidence. A good agreement is accomplished between simulated and measured results. Moreover, a semicylindrical radome is constructed based on the thin 3-D FSS and integrated with a broadband horn antenna. The radiation characteristics of the entire antenna-radome system are finally investigated and its good filtering feature is demonstrated. A fabricated prototype of this radome is measured in the presence of a broadband horn antenna.
Efficient Visual Saliency Detection with Deep Learning

Visual saliency is an important component of attention. It helps animals survive and can also be used by computer vision applications to filter out irrelevant information from high volumes of data. In this work, we present a new convolutional neural network designed for detecting visual saliency, with architecture and data pre-processing methods specific for the task of visual saliency detection. Experiments carried out with the MIT300 benchmark presented state-of-the-art performance and a parameter reduction of 3/4 compared to similar models.
FIS-based Domestic Milling Machine PHM System Considering Multi-speed Frequency Variation

The purpose of this study is to use a real-time measurement system to collect the operation signals of the milling machine under multi-speed frequency variation, and to conduct prognostics and health management research. First, we installed a three-axis accelerometer sensor on a domestic milling machine with different health conditions. Then in the experimental project, we completed the data acquisition of the 4-step speed, and used the waterfall map to achieve the purpose of data visualization. In signal analysis, we perform data mining for the speed, frequency and amplitude of the frequency domain. Finally, the fuzzy inference system is used to construct the decision mechanism, and the representativeness of the input parameters is weighed by the weight of the rules. From the experimental results, the FIS-based PHM method can effectively identify the spindle motor state and predict the trend of health deterioration from the frequency characteristics.
Enabling Energy Efficient Data Ferrying

This paper investigates a combination of data ferrying and relaying to achieve energy efficient data ferrying between ground stations. The aim is to find the optimal path among ground stations for UxVs to pass data whilst minimising traveling distance. One mobile UxV is chosen as the data ferry and traverses a path defined by way-points. The rest of the UxVs are required to reach static data points associated with different ground stations where they act as realys, extending the ground stations' communication ranges to reach the way-points. The way-points and data points are searched for under an allocation scheme of UxVs to ground stations. Simulation examples are given to demonstrate the validity of the proposed algorithm.
A Fusion Method of Multi-spectral Image and Panchromatic Image Based on NSCT Transform and Adaptive Gamma Correction

The purpose of remote sensing images fusion is to produce a fused image that contains more clear, accurate and comprehensive information than any single image. an image fusion algorithm incorporating gamma-corrected is proposed based on non-subsampled Contourlet transform(NSCT). Firstly, the multispectral image is transformed to intensity-hue-saturation (IHS) system. Secondly, the panchromatic image and the component intensity of the multispectral image are decomposed by NSCT. Then the NSCT coe? cients of high and low frequency subbands are fused by di? erent rules, respectively. For the low frequency subbands, an adaptive gamma correction was used and mutual information as the threshold for the weighted coefficient fusion. The max-abs-based fusion rule is used to fuse the high frequency coefficients. Finally, the fusion image can be obtained by performing inverse NSCT and inverse IHS transform. Experiment results demonstrate that the fused image contains abundant detailed contents and preserves the saliency structure. Our proposed method achieves better visual quality than the current methods.