Miracle-dz/UniFAR-FTUs huggingface dataset:
An Anodic Current Pulse Modulation Active Charge Balancer for Implantable Electrical Stimulator

This paper presents an anodic phase modulation charge balancing method for implantable biphasic bipolar stimulator integrated circuit (IC). The proposed IC, designed using $0.18-\mu m$ CMOS process, consists of a current-mode electrical stimulator with active charge balancing circuit. The proposed active charge balancer uses a simple digital logic circuit consisting of subtractor, comparator, and D-latch and can perform accurate charge balancing by adjusting the magnitude of the anodic current pulse according to the measured net charge.
A novel experimental-based tool for the design of LoRa networks

The use of Long Range (LoRa) technology in Internet of Things (IoT) deployments is exponentially increasing, as it allows to form one-hop networks linking tiny nodes to one (or more) gateways and ensuring a low power consumption. In dense networks, predicting the number of supported nodes in relation to their position and the propagation environment is essential to ensure a reliable and stable communication and limit costs. In this paper, after comparing different path loss models based on a field measurement campaign of LoRa Received Signal Strength Indicator (RSSI) values within our University campus, we implement two main modifications to the LoRa Simulator tool, in order to improve its accuracy in the prediction of the number of sustainable nodes, according to the target Data Extraction Rate. By an improved path loss evaluation, and using three gateways, the number of nodes could increase theoretically from about 100 to about 6000. Future work includes the possibility to validate the accuracy of the tool, by designing a dense network operating in real conditions (i.e. large industrial plant, small/medium size city area) and testing its performances.
Automated Aortic Pressure Regulation in ex vivo Heart Perfusion

This paper presents the first system for automated ex vivo perfusion of an isolated heart and regulating the heart’s aortic pressure (AoP). An adaptive controller was developed for AoP regulation and maintained the heart’s physiological aerobic metabolism. A mathematical model of the perfusion system was established based on a nonlinear equivalent circuit fluid flow model. The model combined with a virtual controller forms a reference model to generate the ideal trajectory of AoP. An adaptation algorithm tunes the control parameters based on the reference model and the isolated heart. Experiments were conducted using large animal hearts (55±5 kg porcine, n=6) to validate the adaptive controller’s performance for stepwise and fast switching AoP references. The results confirmed that the the proposed controller is able to regulate the AoP of an isolated porcine heart in an accurate (mean error less than 2 mmHg) and fast (4$\sim$8s of settling time) manner.
An Efficient Biometric Identification in Cloud Computing With Enhanced Privacy Security

Biometric identification has developed rapidly in recent years because of its convenience and reliability. Due to the sensitivity of biometric data, many privacy-preserving biometric identification schemes have been put forward, exploiting either homomorphic encryption or matrix-transformation. However, existing schemes based on homomorphic encryption generally suffer from low computational efficiency, and existing matrix-transformation-based schemes are insufficiently secure. In this paper, we demonstrate that the matrix-transformation-based privacy-preserving biometric identification scheme recently proposed by Zhu et al. is vulnerable to a known-plaintext attack (KPA). To remedy this security flaw, we propose a new privacy-preserving biometric identification scheme, in which the property of the orthogonal matrix and additional randomness are utilized. Security analysis and comparisons indicate that our scheme can resist not only the KPA attack but also the more powerful chosen-plaintext attack (CPA), which is a reasonable attack in practice. Moreover, our scheme enjoys higher computational efficiency than other similar schemes, which implies our scheme can better support a huge database for practical biometric identification, and it also enhances privacy security of sensitive biometric data.
Improving the Robustness of Visual-Inertial Extended Kalman Filtering

Visual-inertial navigation methods have been shown to be an effective, low-cost way to operate autonomously without GPS or other global measurements, however most filtering approaches to VI suffer from observability and consistency problems. To increase robustness of the state-of-the-art methods, we propose a three-fold improvement. First, we propose the addition of a linear drag term in the velocity dynamics which improves estimation accuracy. Second, we propose the use of a partial-update formulation which limits the effect of linearization errors in partially-observable states, such as sensor biases. Finally, we propose the use of a keyframe reset step to enforce observability and consistency of the normally unobservable position and heading states. While all of these concepts have been used independently in the past, our experiments demonstrate additional strength when they are used simultaneously in a visual-inertial state estimation problem.In this paper, we derive the proposed filter and use a Monte Carlo simulation experiment to analyze the response of visual-inertial Kalman filters with the above described additions. The results of this study show that the combination of all of these features significantly improves estimation accuracy and consistency.
A Ka-Band Shaped-Beam Circularly Polarized Reflectarray Antenna

A 0.6 m circularly polarized shaped-beam reflectarray antenna has been designed, manufactured, and tested. Contour of the Russian Federation from a point of 90° eastern longitude on geostationary orbit was used to execute iterative phase synthesis method. For reflectarray realization a single-layer element consisting of a ring with a gap was selected because of their simplicity and the independence of reflected phase from the manufacturing tolerance. Measurements of the reflectarray prototype were carried out in an anechoic chamber by the method of near-field scanning. The minimum measured gain and cross-polarization isolation in the coverage area were 25.6 dBi and 16 dB relatively. Minimum gain of 26.5 dBi and 20 dB cross-polarization isolation are obtained over more than 88% of the coverage area over a frequency range from 20 to 21 GHz.
GraspFusion: Realizing Complex Motion by Learning and Fusing Grasp Modalities with Instance Segmentation

Recent progress of deep learning improved the capability of a robot to find a proper grasp of a novel object for different grasp modalities (e.g., pinch and suction). While these previous studies consider multiple modalities separately, several studies develop multi-modal grippers that can achieve simultaneous pinch and suction grasp (multi-modal grasp fusion) for more capable and stable object manipulation. However, the previous studies with these grippers restrict the situations: simple object geometry and uncluttered environments. To overcome these difficulties, we propose a system that consists of: 1) object-class-agnostic grasp modality detection; 2) object-class-agnostic instance segmentation; and 3) grasp template matching for different modalities. The key idea of our work is the introduction of instance segmentation to fuse multiple modalities regarding each instance eluding a grasp of multiple objects at once. In the experiments, we evaluated the proposed system on the real-world picking task in clutter. The experimental results show that the effectiveness of modality detection, instance segmentation, and the integrated system as a whole.
Force-controllable Quadruped Robot System with Capacitive-type Joint Torque Sensor

This paper introduces a force-controllable quadruped robot system consists of twelve Actuator Modules embedded a novel Capacitive-type Joint Torque Sensor (CJTS) which is accurate (0.05 Nm), robust to impact, and easy to manufacture at low cost. The Actuator Module with CJTS shows accurate joint torque controllability in range of ±70 Nm (90 % settling time 0.04 s). The leg made by the three Actuator Modules is capable of generating forces in the z-axis up to 350 N and shows force control performances with zero-force control and lifting weights in three-dimensional space. To reduce the reflected limb inertia, all the Actuator Modules are located on the body frame and light-weight limbs made of the carbon pipe (3.6 % of total body weight). The introduced robot performed the motion on various terrains with walking/trot gaits.
Development of a strain gauge based disturbance estimation and compensation technique for a wheeled inverted pendulum robot

The ongoing development of the Inverted PENdulum Type Assistant Robot (I-PENTAR) is being undertaken by the authors. The I-PENTAR has many tasks like lifting objects with unknown mass, pushing and pulling a carts and many more. During execution of these tasks unknown disturbances enter the system and cause dynamic responses and errors. Most notably, the wheels of the robot move with excessively transients. To alleviate this problem, a strain gauge based sensor is attached to the existing structure of the robot to gain information regarding the disturbances. This allows minimal change to the system design while improving robustness to disturbances. In this paper the development of the sensor, the corresponding model and a method of updating the existing purely estimation based control is presented together with tests.
Contact-Event-Triggered Mode Estimation for Dynamic Rigid Body Impedance-Controlled Capture

This paper presents a contact-event-triggered filter using only a force-torque sensor with impedance control for non-cooperative, rotating, heavy object capture. Contact events are modeled for prediction, and detected to trigger the particle filter’s updating process. By combining these features, a computationally efficient, contact-event-triggered filter is proposed. For our purpose of capture using impedance control, expected contact events, collisions and sliding are defined for prediction and detection. This novel method is implemented in an air bearing robotic system, and has demonstrated its superiority with the highest success rate (100%) for sliding contact mode cases, whereas the previous method could only yield a success rate of 87.9%. The computation resource is demonstrated to be limited, with a computation time of 4.2 milliseconds on average and 8.3 milliseconds at worst.
External Wrench Estimation for Multilink Aerial Robot by Center of Mass Estimator Based on Distributed IMU System

External wrench estimation is very helpful for aerial exploration and manipulation tasks. During the exploration, there might be unseen obstacles to cause dangerous collisions. The estimation of the external force and torque is also beneficial in aerial manipulation tasks. In this paper, we present a framework of estimating the external wrench for the aerial multilink robot based on the onboard inertial measurement unit (IMU) sensors, joints state and robot dynamic models. Compared to the conventional multirotor robot, the center of mass (CoM) is always changing when the robot transforms. The sensor could not be attached to CoM to observe the acceleration data. Consequently, we present a novel method by applying a distributed IMU system to estimate the CoM linear and angular accelerations for the external wrench estimation. With the help of the robot model, the position of the contact point could be estimated, which is useful in exploring tasks to safely interact with the physical world. We design the contact-aided navigation strategy and computationally efficient motion primitives library to help our robot react to the unexpected collision. We experimentally validate our framework with a two-dimensional multilink aerial robot to show the results of external wrench estimator and its further applications2.2Experiment video: https://youtu.be/R-WDReLnWWI
Feasibility Study to Preserve the Health of an Industry 4.0 Worker: a Radar System for Monitoring the Sitting-Time

In this paper, a feasibility study on a novel sensor to monitor the worker’s sitting time for an Industry 4.0 environments is presented.A sedentary job considerably affects the worker’s health but at the same time, the dramatic lack of valid aids is well assessed. The proposed sensor is capable of recognizing the presence of an adequate worker’s activity, highlighting the movements from the workstation.The sensor is based on a frequency-modulated continuous-wave radar system operating within the 24 GHz industrial, scientific and medical bandwidth. It is able of analyzing the worker’s activity in a specific working space by computing its distance and by detecting its motion. The use of radar technology allows to fulfill the expected performance by preserving the individual privacy, in a safe and cost-effective way. This is of particular relevance in the scenario of Industry 4.0 where the human movement is reduced to a minimum due to a more efficient space management and the need of minimizing risks.
Improving collective decision accuracy via time-varying cross-inhibition

We investigate decentralised decision-making, in which a robot swarm is tasked with selecting the best-quality option among a set of alternatives. Individual robots are simplistic as they only perform diffusive search, make local noisy estimates of the options’ quality, and exchange information with near neighbours. We propose a decentralised algorithm, inspired by house-hunting honeybees, to efficiently aggregate noisy estimations. Individual robots, by varying over time a single decentralised parameter that modulates the interaction strength, balance exploration and agreement. In this way, the swarm first identifies the options under consideration, then rapidly converges on the best available option, even when outnumbered by lower quality options. We present stochastic analyses and swarm robotics simulations to compare the novel strategy with previous methods and to quantify the performance improvement. The proposed strategy limits the spreading of errors within the population and allows swarms of simple noisy units with minimal communication capabilities to make highly accurate collective decisions in predictable time.
A Tensor-Based Method for Completion of Missing Electromyography Data

This paper discusses the recovery of missing data in surface electromyography (sEMG) signals that arise during the acquisition process. Missing values in the EMG signals occur due to either the disconnection of electrodes, artifacts and muscle fatigue or the incapability of instruments to collect very low-amplitude signals. In many real-world EMG-related applications, algorithms need complete data to make accurate and correct predictions, or otherwise, the performance of prediction reduces sharply. We employ tensor factorization methods to recover unstructured and structured missing data from the EMG signals. In this paper, we use the first-order weighted optimization (WOPT) of the parallel factor analysis (PARAFAC) decomposition model to recover missing data. We tested our proposed framework against non-negative matrix factorization (NMF) and PARAFAC on simulated as well as on off-line EMG signals having unstructured missing values (randomly missing data ranging from 60% to 95%) and structured missing values. In the case of structured missing data having different channels, the percentage of missing data of a channel goes up to 50% for different movements. It has been observed empirically that our proposed framework recovers the missing data with relatively much-improved accuracy in terms of relative mean error (up to 50% and 30% for unstructured and structured missing data, respectively) compared with the matrix factorization methods even when the portion of unstructured and structured missing data reaches up to 95% and 50%, respectively.
Automated Seedling Height Assessment for Tree Nurseries Using Point Cloud Processing

This paper presents a prototype of an automated seedling height assessment system for tree nurseries. The proposed system can acquire and store real-time 3D point-cloud data of seedlings; and perform offline identification, measurement, and report generation of seedling heights with an overall system accuracy that meets a 5mm accuracy specification. Periodic growth information of seedlings allows quantifying effects of different factors on the overall seedling development process for research and production optimization purposes. However, current manual sampling approaches used at these facilities produce quite limited data samples, and the process is rather time-consuming and labor intensive for industrial scale operations. In contrast, the proposed system is capable of significantly increasing the measurement sample size, measurement resolution, and frequency of measurement by automating the seedling measurement process using a scanning laser profilometer and an application specific point-cloud processing algorithm. The performance of the proposed profilometry solution for point-cloud generation is compared with several other point-cloud generation methods such as a 3D structured light sensing, light intensity detection and ranging (LiDAR), stereovision, and photogrammetry. This comparison results demonstrate a superior performance of the laser-profilometer over other sensing solutions available for seedling height measurement. The proposed system is experimentally validated for its measurement accuracy and repeatability. The field-test of the measurement system was conducted at Centre for Agriculture and Forestry Development, Wooddale, Newfoundland and Labrador (NL), Canada, and the results demonstrate the practical applicability and technological readiness of the proposed system for field deployment.
Application of Convolutional Neural Networks to Regenerate Deterministic Test Patterns for BIST

This study displays preliminary results on a simple technique to improve fault coverages in the BIST technology. The technique assumes that a circuit which implements an ANN can be used with the target circuit, and effective test patterns are given beforehand. Such ANN circuit is utilized as a degraded memory which approximately regenerates given test patterns. In computational illustrations, fault coverages of test patterns by the technique are calculated on c7552 of the ISCAS'85 benchmark.
A Linear-Complexity EKF for Visual-Inertial Navigation with Loop Closures

Enabling real-time visual-inertial navigation in unknown environments while achieving bounded-error performance holds great potentials in robotic applications. To this end, in this paper, we propose a novel linear-complexity EKF for visual-inertial localization, which can efficiently utilize loop closure constraints, thus allowing for long-term persistent navigation. The key idea is to adapt the Schmidt-Kalman formulation within the multi-state constraint Kalman filter (MSCKF) framework, in which we selectively include keyframes as nuisance parameters in the state vector for loop closures but do not update their estimates and covariance in order to save computations while still tracking their cross-correlations with the current navigation states. As a result, the proposed Schmidt-MSCKF has only O(n) computational complexity while still incorporating loop closures into the system. The proposed approach is validated extensively on large-scale real-world experiments, showing significant performance improvements when compared to the standard MSCKF, while only incurring marginal computational overhead.
A Study on a Lane Keeping System using CNN for Online Learning of Steering Control from Real Time Images

Using CNN for lane keeping in autonomous driving control system has been proposed. Learning of the CNN is performed using road images and steering operations as teaching data. When driving, steering operations are generated from camera images by CNN. Even on roads where white lines are missing or unpaved roads without white lines, the learned CNN can perform autonomous driving control. However, learning of the existing CNN method is “batch learning” which is batch processing, and the prediction phase for driving uses the CNN fixed beforehand. It is difficult to control the driving operations on a road which has different features from the roads used for learning. In this paper, to realize “learning while driving,” we propose using “online learning” in the driving control system, which means learning is performed while receiving input data in real-time. It makes possible to learn additionally for unlearned roads when driving. For evaluation experiments, we implement an online learning system of automatic driving control using CNN on a small robot car to investigate the feasibility. We introduce a divided buffer which classifies and inputs the input data to reduce the influence of the bias of the training data which is the weak point of online learning. From the result of the experiments, there is a possibility that “learning while driving” can be realized by using online learning to a CNN autonomous driving control system.
A Predictive Reward Function for Human-Like Driving Based on a Transition Model of Surrounding Environment

Driving is a complex task that requires the perception of the surrounding environment, decision making and control of the vehicle. Human drivers predict how surrounding objects move and decide an appropriate driving behavior. As with human drivers, autonomous driving vehicles should consider the condition of the surrounding environment and behave naturally so as not to disturb the traffic flow. We propose a reward function for learning how natural the driving is based on the hypothesis that the movement of surrounding vehicles becomes unpredictable when the ego vehicle takes an unnatural driving behavior. The reward function is based on the prediction error of a deep predictive network that models the transition of the surrounding environment. Occupancy grid image is used to perceive the surrounding environment and the predictions up to two seconds are used to calculate the reward function. We evaluated the reward function using both simulated and the real world data. We trained the prediction network using real driving data and trained a reinforcement learning agent based on the reward function. Then we compared the speed planned by the agent and a human driver, which showed a correlation of 0.52. We also confirmed the benefit of taking prediction into account by observing the behavior of the agent in a specific traffic scenario.
Characterizing Visual Localization and Mapping Datasets

Benchmarking mapping and motion estimation algorithms is established practice in robotics and computer vision. As the diversity of datasets increases, in terms of the trajectories, models, and scenes, it becomes a challenge to select datasets for a given benchmarking purpose. Inspired by the Wasserstein distance, this paper addresses this concern by developing novel metrics to evaluate trajectories and the environments without relying on any SLAM or motion estimation algorithm. The metrics, which so far have been missing in the research community, can be applied to the plethora of datasets that exist. Additionally, to improve the robotics SLAM benchmarking, the paper presents a new dataset for visual localization and mapping algorithms. A broad range of real-world trajectories is used in very high-quality scenes and a rendering framework to create a set of synthetic datasets with ground-truth trajectory and dense map which are representative of key SLAM applications such as virtual reality (VR), micro aerial vehicle (MAV) flight, and ground robotics.