Miracle-dz/UniFAR-FTUs huggingface dataset:
Fingertip Force Learning with Enhanced Haptic Sensation using Stochastic Resonance

In many haptic training systems, the users need to wear a finger holder in order to interact with the system and learn fine motor skills. This procedural requirement of using a finger holder is known to decrease the user force-detection capability at the fingertip, which could lead to poor learning outcomes. In order to increase the fingertip force-detection capabilities of the user, one possible way is to use Stochastic Resonance (SR). However, the effects of SR on a fingertip placed inside a finger holder are hitherto unknown. In this paper, first we investigate the effects of SR on the user haptic performance when the fingertip is placed inside a finger holder. Then, we propose a new haptic training method that utilizes haptic feedback and SR to improves the user performance outcomes. The results show that the user fingertip sensitivity significantly increases even when the fingertip is enclosed by a finger holder. We performed a force learning task to compare the user performance under two conditions: (i) haptic feedback with SR (proposed method), and (ii) haptic feedback without SR. The results demonstrate that the user performance, measured through task accuracy, increases with the proposed method. These findings indicate the effectiveness of the proposed haptic training method and application of SR in motor-skill learning.
Machine Learning for Performance Prediction of Spark Cloud Applications

Big data applications and analytics are employed in many sectors for a variety of goals: improving customers satisfaction, predicting market behavior or improving processes in public health. These applications consist of complex software stacks that are often run on cloud systems. Predicting execution times is important for estimating the cost of cloud services and for effectively managing the underlying resources at runtime. Machine Learning (ML), providing black box solutions to model the relationship between application performance and system configuration without requiring in-detail knowledge of the system, has become a popular way of predicting the performance of big data applications. We investigate the cost-benefits of using supervised ML models for predicting the performance of applications on Spark, one of today's most widely used frameworks for big data analysis. We compare our approach with Ernest (an ML-based technique proposed in the literature by the Spark inventors) on a range of scenarios, application workloads, and cloud system configurations. Our experiments show that Ernest can accurately estimate the performance of very regular applications, but it fails when applications exhibit more irregular patterns and/or when extrapolating on bigger data set sizes. Results show that our models match or exceed Ernest's performance, sometimes enabling us to reduce the prediction error from 126-187% to only 5-19%.
Seamless Phantom Sensation Moving Across a Wide Range of Body

This paper reports experimental research aimed to provide seamlessly moving illusory tactile sensations across a large area of the body using a few vibration actuators. The human vibrotactile sensitivity differences among the body sites are calibrated using empirically-measured psychophysical magnitude functions. We present a new phantom sensation rendering method that uses the Gaussian function emphasizing the spatial continuity of perceived movement and the temporal consistency of perceived intensity. We demonstrate that our rendering method outperforms previous methods for phantom sensations via a perceptual experiment. Our method is tailored to eliciting the perception of illusory tactile sensation moving in a long distance, from the thigh to the upper back.
Privacy-Preserving Cloud-IoT Architecture (Abstract)

We propose the concept of Data Bank, which is a cloud-IoT architecture, as a guideline to service providers. The proposed framework aims to protect users' sensitive data by allowing users to control the data transmitted by their devices, and provides support for agreement visualisation and privacy-utility trade-off tools. It consists of several layers, from IoT objects in the lower layer to web and mobile applications in the top layer, with regulated communication mechanisms to transfer data from the lower level to data processing services in the top level.
A Switching Nonlinear MPC Approach for Ecodriving

In recent years many works focusing on improved vehicle fuel efficiency through advanced control have been carried out, reflecting the high interest in ecodriving of vehicles. Although many studies have shown the potential that optimal control based ecodriving can offer, these solution are often difficult to be translated into online control strategies, one of the reasons being the complexity of the optimal control problem and therefore the computational burden. To cope with this a novel online approach, based on switching Nonlinear Model Predictive Control (NMPC), is proposed. The NMPC strategy is developed for the case of conventional vehicles, where gear shifting and longitudinal dynamics are controlled. It is shown that our proposal can operate in real time, while recovering most of the performance achievable by an offline optimal solution. The development of the method is described in detail and its performance is analyzed. The results show that the proposed NMPC can successfully solve the ecodriving task and seems a good compromise between computational burden and performance suitable for field implementation.
A 0.12-mm2 1.2-to-2.4-mW 1.3-to-2.65-GHz Fractional-N Bang-Bang Digital PLL With 8- $\mu$ s Settling Time for Multi-ISM-Band ULP Radios

This paper describes a wideband ultra-fast-settling fractional-N bang-bang digital phase-locked loop (DPLL) for multi-ISM-band ultra-low-power (ULP) radios. We propose a mismatch-free digital-to-time-converter (DTC) gain calibration scheme to effectively shorten the calibration time, while the split coarse-fine PLL loops with different loop bandwidths accelerate the loop settling speed. The employed ring VCO (RVCO) aids to extend the frequency tuning range and generate multi-phase outputs. Prototyped in 65-nm CMOS, the DPLL consumes 1.2–2.4 mW over a wide frequency locking range of 68.3% (1.3–2.65 GHz) and occupies a die area of 0.12 mm<sup>2</sup>. The settling time measures <inline-formula> <tex-math notation="LaTeX">$8~\mu \text{s}$ </tex-math></inline-formula> at an 82-MHz initial frequency error.
An All Position Automatic Welding Machine of Large Diameter Penstock

An all position automatic welding machine was designed to deal with the problems of high labor intensity, low production efficiency and inconsistent weld quality in on-site welding of large diameter penstock. The welding machine adopts flexible track to adapt to different diameters. In order to meet the needs of different sizes and forms of welding grooves, welding torch swing device is applied. The deviation between weld seam and welding torch caused by inaccurate groove processing accuracy, improper site installation and deformation in welding process can be reduced and eliminated by the device. The welding control system uses ARM microcontroller for welding process control. Results show that the welding machine is suitable for all position welding of large diameter penstock which is static.
An Improved Loop Closure Detection for RatSLAM

RatSLAM is a brain-inspired simultaneous localization and mapping(SLAM) system used to construct experience maps for environments, requiring odometry data and visual scenes in the path traveled. But it does not perform well in complex indoor surroundings due to the rough visual processing approach. In this paper, we propose a modified RatSLAM, a feature-based monocular SLAM system that operates in real-time. The learned arrangements of three patch codes(LATCH) and multi-index hashing(MIH) algorithm are introduced to evaluate the similarity between the current scene and previously seen scenes. Then Bayesian filtering and sliding window are employed to filter out the most similar visual template and complete the loop closure detection(LCD). Experimental results show that the improved system achieves higher accuracy on place recognition and better experience map building performance in indoor environments.
Vision-based Waypoints Tracking Control for an Amphibious Spherical Robot

The localization plays an important role in the motion control and trajectory tracking of amphibious spherical robot. However, due to the complex environment in the water, some methods localization on land such as GPS cannot be applied to amphibious spherical robot. Therefore, a new method is needed to locate the amphibious spherical robot. This paper proposes an image recognition method using a global camera to locate the robot. In this method, the global camera is mounted on a platform with a known position, and the computer vision algorithm is used to identify the position of the amphibious spherical robot in the image, then calculate the coordinates and moving speed of the amphibious spherical robot. We applied this method to an amphibious spherical robot and verified it experimentally. In the experiment, the amphibious spherical robot can swim along the set rectangular trajectory by using this localization method, indicating that this localization method is effective.
A Novel Global Relocalization Method Based on Hierarchical Registration of 3D Point Cloud Map for Mobile Robot

Indoor service mobile robots need to relocate when they are kidnapped, powered off, or lost in long-term work, thus unable to perform daily tasks. Solving this problem is challenging, especially for 3D maps due to the computational complexity. In order to solve this issue, a novel relocalization algorithm based on hierarchical registration is proposed for a known 3D map in this paper. For 3D point cloud maps, the algorithm obtains multi-layer information in the vertical direction through hierarchical registration at the robot's current position. To obtain the best 3D pose for relocalization, we fuse the poses calculated by the multi-layered point cloud into one and use it as the initial pose of the iterative closest point algorithm. The hierarchical registration based algorithm solves the problem of unknown initial value for registration between two large point clouds, improves the recall rate, and ensures the accuracy of algorithm at the same time. The related relocalization experiments are carried out in the indoor environment and the results verify the effectiveness and robustness of the algorithm.
Phenological Prediction Algorithm Based on Deep Learning

The prediction of the future climate of the Earth is called phenological prediction, and accurate prediction of climate can be beneficial to avoid harm. The traditional phenological prediction method is not thorough enough to analyze the data, and it is time consuming and labor intensive. But the recent advancements in the field of artificial neural networks, especially in deep learning, have paved a new way for applying computer vision methods to phenological prediction. In this paper we proposes a deep learning algorithm combining FCN and Lstm to improve the accuracy and end-to-end generalization ability of the phenological prediction model. The algorithm use the dataset provided by DataCastle Big Data for training and verification. The data consists of vegetation density maps in a certain area in recent years. The algorithm aims to predict the vegetation density of the region in the future and then infer the future climate based on the future vegetation density. The experimental results show that the algorithm can well study the spatial information and time series information of the data, and accurately predict the vegetation density in different regions, so as to accurately predict the future climate.
Analysis of Variation in Steam Turbine Time Constant on Dynamic Frequency Regulation of a Deregulated Power System

At present, increase in renewable penetration has paved the way for the shutdown of existing thermal power plants. This forced system operator, to operate remaining thermal power plants mostly around 70 % to 90 % of its generation schedule. In this paper, effect of varying steam turbine time constant on a re-heater based two-area thermal power system under deregulated environment is analysed. The analysis has been carried out for different scheduled power output of the plant, such as 100 %, 90 %, 70 % and 50 %. The steam turbine time constants for each generation schedule is determined and simulations were carried out for three scenarios, such as unilateral, bilateral and a case of contract violation. In each case, four generation schedule mentioned earlier is taken into account. A comprehensive study was done by analysing all the aspects of a deregulated two area reheater based thermal power system, considering various generation schedules.
An Improved SSD Algorithm and Its Mobile Terminal Implementation

Aiming at the problem of low real-time detection and poor portability of SSD (Single Shot MultiBox detector) algorithm in moving end, a real-time target detection algorithm based on improved SSD is proposed. The improved algorithm uses MobileNet which merges BN (Batch Normalization) layer as the basic feature extract network to compress the network model size and improve speed of target detection. In the data preprocessing stage, a set of data augmentation method is designed to ensure the accuracy of target detection. For practical application, the algorithm transplantation is carried out on Android mobile phone, and the target detection system of mobile terminal is realized. After experiments, compared with SSD, the proposed algorithm improves the detection speed and reduces the memory footprint on the basis of ensuring the detection accuracy, and can basically achieve real-time target detection at the mobile end.
A simple structure iterative learning algorithm experimentally validated for mobile robot path-tracking

The general subject area of this paper is iterative learning control based on a linear model of the dynamics using the theory of discrete linear repetitive processes for analysis. Linear matrix inequality based computations are developed to compute the stabilizing feedback controller in the time domain and a feedforward controller that guarantees convergence in the trial-to-trial domain. Use of the generalized Kalman-Yakubovich-Popov lemma allows a direct treatment of finite frequency range performance specifications. Since weighting filters are not used, an unnecessary increase in the controller order is avoided. To illustrate the performance of the new design, the problem of path-tracking control for wheeled mobile robot is considered. Experimental verification results using a Lego EV3-based mobile robot are also given, including a comparison of trajectory tracking performance with an $\mathcal{H}_{\infty}$ based design.
Control and Optimization of Residential Photovoltaic Power Generation System With High Efficiency Isolated Bidirectional DC–DC Converter

Currently, residential photovoltaic power generation system is increasingly used worldwide. In this paper, an optimized structure of residential photovoltaic (PV) power generation system with 1500V DC bus is proposed. It includes PV panels, a three-level boost converter, a high efficiency isolated bidirectional DC-DC converter, battery and three-phase five-level DC-AC converter that can work under islanding mode or grid-connected mode. The higher DC bus voltage greatly reduces line loss and improves efficiency of the system. An energy management scheme used for the system is proposed in this paper to guarantee the stability of the system and to increase its economic benefits. Besides, the optimized method for the structure of the bidirectional dc-dc converter is proposed. This structure can achieve higher DC voltage gain and higher efficiency. Furthermore, for low voltage battery application in the residential system, LLC and CLLC under DC transformer (DCX) mode are evaluated and the LLC is selected as the isolated bidirectional DC-DC converter. The optimized designed method of bidirectional LLC is proposed. Finally, experiments are carried out to verify the performance of the optimized converters and the system.
An Aging-Aware SOC Estimation Method for Lithium-Ion Batteries using XGBoost Algorithm

An accurate state-of-charge (SOC) estimation for a lithium-ion battery is highly dependent on the knowledge of aging, which is usually costly or not available through online measurements. In this paper, novel aging-aware features which can simultaneously characterize battery aging and SOC are extracted from the discharging process. Then, the extreme gradient boosting (XGBoost) algorithm combined a stage division is applied to acquire the nonlinear relationship model between the proposed features and the battery SOC through the offline training. The proposed method does not require the initial SOC value, which implies that the SOC can be estimated by the trained model from any operating states of a battery. Moreover, a random sampling test to simulate the online real-time SOC estimation verifies that the proposed method is effective and potential to be applied in the battery management system.
A Cloud-Based AI Framework for Machine Learning Orchestration: A “Driving or Not-Driving” Case-Study for Self-Driving Cars

Self-driving cars rely on a plethora of algorithms in order to perform safe driving manoeuvres. Training those models is expensive (e.g. hardware cost, storage, energy) and requires continuous updates. This paper proposes a cloud-based framework for continuous training of self-driving AI models. In addition to training standalone models, the framework is capable of leveraging pre-trained models in expediting the training on environment changes (e.g. new driver or new car model). As use-case, this paper focuses on a driver's behaviour while the vehicle's control is being transferred between the driver and the self-driving AI. A human driver can hand over the control of a vehicle's driving tasks to an automated system, when that system's confidence level is high enough. Reciprocally, there are situations where that control has to be handed back to the human driver. This paper proposes a novel real-time system for Driving Not-Driving (DND) detection, which is able to capture the ability of the driver to re-take control of a vehicle when the automated driving system transitions from a higher to a lower level of automation (e.g. L3 to L2 vehicle automation). We are using a computer vision-based Driver Monitoring System (DMS) that captures in real-time head and eye movements. These are captured in the car and transferred to the cloud where a DND model is trained for a specific driver. The DND classification model is deployed in the vehicle and predicts if the driver is ready or not to resume control at a given time. The cloud-based framework proposed in this paper shows an end-to-end cycle of collecting, training and deploying self-driving AI technology, with the additional features of continuous and transfer learning.
Trajectory Forecasts with Uncertainties of Vulnerable Road Users by Means of Neural Networks

In this article, we present an approach to forecast trajectories of vulnerable road users (VRUs) including a numerical quantification of the uncertainty of the forecast. The uncertainty estimates are modeled as normal distributions by means of neural networks. Additionally, we present a method to evaluate the reliability of the forecasted uncertainty estimates, where we utilize quantile-quantile (Q-Q) plots, a graphical method to compare two distributions widely used in statistics. The positional accuracy is evaluated using Euclidean distances, in specific we use the average Euclidean error (AEE) and the average specific AEE (ASAEE). The model is trained and tested using a large dataset of 1311 cyclist trajectories, recorded at an urban intersection in real world traffic. Using this method, we achieve a similar positional accuracy compared to our previous work, where only positions are forecasted. The method is able to produce reliable uncertainty estimates for the motion types start, stop, turn left, and turn rightand produces underconfident uncertainty estimates for the motion types waitand move straight. Since uncertainties are not underestimated, the method can be used as a basis for trajectory planing in automated vehicles.
Unknown-input Observers for Metzlerian Linear Positive Systems

The objective of the paper are limitation in the unknown-input observer design task formulation for linear Metzlerian systems. Expanding the set of linear matrix inequalities warranting the strictly Metzler observer system matrix, there are defined sufficient conditions and formulated demarcations in establishing such an observer for nonnegative system state and output. It is shown that this problem can be solved through feasibility of given set of linear matrix inequalities. In contrast with the case of general linear systems, some related properties are deduced to characterize Metzlerian system output estimation.
Design of a Hierarchical Control System for Tetherless Snake Robot

In this paper, a hierarchical control architecture for the tetherless snake robot is presented, which is a modular, flexible, easily expanded and highly autonomous system. The control system is divided into three layers. The human-computer interaction layer is used to detect the environment and operate the robot. The head controller layer includes a compute stick, a camera and a communication unit, which communicates with PC via Wifi. The joint layer is used for motor-driven control and data acquisition. The multisensory system and hardware architecture provide the snake robot with the ability to sense the state of itself and the environment. Finally, the platform was build to carry out experiment and several gaits were adopted to evaluate the performance of the hierarchical control system.
An Integrated Vision-Based Approach for Efficient Human Fall Detection in a Home Environment

Falls are an important healthcare problem for vulnerable persons like seniors. Response to potential emergencies can be fastened timely detection and classification of falls. This paper addresses the detection of human falls using relevant pixel-based features reflecting variations in body shape. Specifically, the human body is divided into five partitions that correspond to five partial occupancy areas. For each frame, area ratios are calculated and used as input data for fall detection and classification. First, the detection of falls is addressed from a statistical point of view as an anomaly detection problem. Towards this end, an integrated approach merging a detection step with a classification step is proposed for enabling efficient human fall detection in a home environment. In this regard, an effective fall detection approach using generalized likelihood ratio (GLR) scheme is designed. However, a GLR scheme cannot discriminate between true falls and like-fall events, such as lying down. To mitigate this limitation, the support vector machine algorithm has been successfully applied on features of the detected fall to recognize the type of fall. Tests on two publicly available datasets show the effectiveness of the proposed approach to appropriately detecting and identifying falls. Compared with the neural network, $k$ -nearest neighbor, decision tree and naïve Bayes procedures, the two steps approach achieved better detection performance.