Miracle-dz/UniFAR-FTUs huggingface dataset:
Thermal sensation presentation to the forehead using electrical stimulation: comparison with other tactile modalities*

Presentation of warm and cold sensations is effective in improving the immersion of virtual reality contents, and there were several proposals to embed temperature presentation device in head mounted displays (HMD). However, many thermal presentation methods with HMD have issues such as heat accumulation and inability to present quick change of temperature. Here we propose to use electrical stimulation to present the temperature sensation on the forehead. We used an electro-tactile display with 192 electrodes to investigate distribution of cold, warm, pressure, vibration and pain sensations elicited by electrical stimulation. We also compared between anodic and cathodic stimuli. As a result, it was confirmed that cold sensation was easier to present by electrical stimulation than warm sensation, but it was not as robust as pressure and vibration sensations.
Compact Double Shorted Loop Sub-6-GHz Dual-Band MIMO Quad-Antenna System

This paper presents the design of a compact double-loop sub-6-GHz dual-band MIMO quad-antenna system for 5G laptops. To meet the market demand for laptops with a high screen-to-body ratio, the sub-6-GHz dual-band MIMO quad-antenna system is realized to have a low-profile height of only 3 mm. To make the MIMO quad-antenna system suitable for laptops with a narrow bezel, the four antenna elements are printed on the same FR4 substrate, which is mounted on the upper edge of and coplanar with the display ground plane of a laptop. The four antenna elements have the same double shorted loop structure and the same size of $15\times $ 3 mm2. In the loop structure, a meandered path is embedded in one of the two shorted-end sections, and a series chip capacitor is embedded in the other. The low-frequency resonance mode can be adjusted by varying the meandered path, and the high-frequency resonance mode can be tuned using the meandered path and the series chip capacitor. The four antenna elements are aligned in the same direction with an end-to-end gap of 5 mm so that satisfactory isolation can be achieved without any additional isolation elements. The MIMO quad-antenna system, although measuring only $75\times $ 3 mm2 with the three gaps included, can cover 3400–3800 and 4800–5000 MHz for 5G dual-band operations. The measured isolations between the four antenna elements are larger than 10 dB, the envelope correlation coefficient (ECC) values calculated from the measured radiation patterns are all below 0.27, and the measured radiation efficiencies of all four antenna elements are over 42%.
Robust Design for UAV-Enabled Multiuser Relaying System With SWIPT

Combination of wireless power transfer and unmanned aerial vehicle (UAV) is considered as an effective solution to achieving sustainable, flexible and energy-efficient communications. In this paper, we study a UAV-enabled multiuser relaying system with simultaneous wireless information and power transfer, where the users are capable of power splitting. Our aim is to jointly optimize the beamforming matrixes of the base station and UAV relay and the power splitting factors of users to maximize the sum harvested energy of users while satisfying the quality of service and the transmit power constraints. We propose an alternating optimization scheme to tackle the intractable no-convex problem. To reduce the computational cost, the low-complexity scheme based on singular value decomposition-zero forcing (SVD-ZF) technique is designed. Moreover, we discuss the convergence and computational complexity of the two proposed schemes. Simulation results verify the effectiveness of the proposed schemes.
Real-Time Obstacle Avoidance Using Dual-Type Proximity Sensor for Safe Human-Robot Interaction

This letter introduces a dual-type proximity sensor and a control strategy for a robot manipulator to realize safe human-robot interactions (HRI) by using the sensor. Safety is an essential condition for HRI in practical scenarios. To achieve this condition, information about the relationship between an external objects and the robot is required. To obtain this information, we employ a dual-type proximity sensor, which consists of capacitive and inductive transducers and can detect the distance between a robot and external objects. Further, we propose a real-time trajectory planning method to deal with obstacles by using admittance control and distance measurements. To update the motion of the manipulator according to our control strategy, a Weight-Prioritized solution based on a QP (quadratic programming) formalism was applied. Further, the problem of self-sensing is solved via machine learning using a training dataset consisting of data corresponding to random joint positions. The proposed method was implemented on a collaborate robot (UR10). Experiments were conducted considering realistic human-robot interactions, and safety improvement was validated.
Seat-Occupancy Detection System and Breathing Rate Monitoring Based on a Low-Cost mm-Wave Radar at 60 GHz

This work presents a seat-occupancy detection system based on a PCR (Pulsed Coherent Radar) at the unlicensed 60 GHz ISM band. The radar can measure distances with sub-millimeter resolution. Therefore, the system can detect the presence of people occupying the seats by measuring small movements of the body, such as those produced by breathing. In consequence, the system not only measures seat-occupancy but also breathing rate, which is estimated from the amplitude peaks after filtering the amplitude measured by the radar to remove the noise. The effect of the car vibrations and random body movement is experimentally studied. Measurements performed with adults, as well as with a baby emulator sitting in a child seat, are presented. Comparison of the system with another that measures the breath based on airflow shows good agreement and permits its validation.
A parallel learning particle swarm optimizer for inverse kinematics of robotic manipulator

In this study, a novel parallel learning particle swarm optimizer (PLPSO) is proposed. The evolutionary strategy of the algorithm is quite different from that of the existing PSO algorithms. To enhance the global search capability of the particle swarm, the original particle swarm is divided into two parallel evolving independent particle subpopulations to explore the search space simultaneously. In addition, according to the evolutionary factor in each iteration, the poorly performing particles in the two subpopulations are spurred and learned to improve the search speed of the particle swarm. In the process of particle learning, delay information is added to the velocity update item to reduce the occurrence of local capture. Twenty‐eight benchmark functions of CEC2013 are used to evaluate the performance of the proposed PLPSO algorithm. Numerous comparisons demonstrate that the performance of the PLPSO algorithm is better than that of other nine PSO algorithms. Afterwards, the PLPSO algorithm is applied to the inverse kinematics (IK) solution of a robotic manipulator. By comparing with other three intelligent algorithms, the PLPSO algorithm shows an excellent performance in solving the IK problem. Finally, tests are carried out on an UR5 manipulator to confirm the practicability of the proposed PLPSO algorithm further. Thus, the feasibility of using the PLPSO algorithm for solving the IK problem of a robotic manipulator is verified.
BS-HTIS: Buffer Sizing for Heterogeneous Traffic and Integrated System

Buffer sizing for switching and routing devices is of significance for guaranteeing the Quality of Service (QoS) of critical services on the Internet of Things (IoT), continuously evolving scheduling mechanisms and complex traffic characteristics pose new challenges for the traditional method of static buffer sizing based on rule-of-thumb. In this paper, the scope of buffer sizing is extended from a basic scheduling system under homogeneous arrival traffic input to an integrated scheduling system under heterogeneous arrival traffic input which is more ubiquitous. In this context, Voices, videos and other heterogeneous data in the IoT are categorized into short-range-dependent (SRD) and long-range-dependent (LRD) traffic, and the integrated scheduling system is decomposed into single-server-single-queue (SSSQ) systems by not only decoupling the complex dependencies among heterogeneous traffic inputs but also taking the impact of SRD and LRD traffic burstiness on the buffer sizing into account. On this basis, expressions for the relation between the minimum buffer size and the maximum overflow probability are presented. The numerical analysis results and simulation analysis results reveal that the average arrival rate, traffic burst level and scheduling priority are positively correlated with the required buffer size, and once the overflow probability is set, the minimum buffer size can be determined correspondingly. The achievements of this paper will provide theoretical guidance for IoT manufacturers and technicians to set buffers more reasonably and use resources more efficiently.
Simplified SVM Equalization Algorithm Based on Single Hyperplane Training Enabled 50Gb/s PAM-4/8 With 10-G Optics in NG-PON System

In this paper, we firstly present a novel equalizer only with a single support vector machine (SVM) training hyperplane to compensate the distortion of multi-class PAM-M signals in low-cost limited-bandwidth next generation passive optical network (NG-PON) system. For this scheme, only single SVM-based hyperplane is trained, and then it is extracted and used to realize the multi-classification task of PAM-M by adjusting the corresponding offset, which would greatly reduce the complexity of equalization algorithm and enhance its application in future high-speed PON system. Moreover, the 50-Gb/s PAM-4/8 per wavelength transmission experiment with 10-G optics in 20km standard single mode fiber (SSMF) PON system are set up and utilized to manifest the feasibility and effectiveness of our scheme. The results show that, compared with the common SVM scheme with multi-classification hyperplane training, our method can reduce M-1 times computational complexity caused by hyperplane training for multiple-level pulse amplitude modulation (PAM-M) signals. Except this, the propose scheme can also maintain almost the same bit error ratio (BER) performance as the common SVM classifier with multi-hyperplane case.
Determination of Vitamin D3 in Fortified Iranian Flat Bread (Taftoon) Using High-Performance Liquid Chromatography

Background and Objectives: : Vitamin D3 measurement in food and fortified food matrixes is usually interrupted by several variables. This study aimed to elaborate a specific vitamin D3 quantification method for fortified Taftoon bread considering different influential variables in each step of measurement. Materials and Methods: Dough was fortified with (4μg/100g) and the standard protocol No.13579 was followed with some modification in the extraction and determination steps. Results: Addition of ethanol before adding potassium hydroxide and heating up the samples in tight containers in saponification step ceased clumping the extracts and helps it’s homogeneity. In extraction, distilled water addition following extraction with diethyl ether and Petroleum ether, adding ethanol at each step of washing and using SPE column result in an appropriate experimental extract. In the chromatography step, mobile phase substitution from methanol to 5:95 water, methanol mixture provided the method with more differentiation power. Using the above condition, commercial vitamin D3 was recovered from fortified Taftoon dough with 82-100% recovery and recovery of spiked vitamin D2 from dough and bread were ranged as 94 and 86.5%, respectively, Limit Of Detection(LOD) and Limit Of Quantification(LOQ) of the method were 0.04 and 0.1mg L respectively, which equaled 5μg/Kg and 10μg/Kg. True retention for the added commercial vitamin D3 in Taftoon bread was obtained as 83.4% which increased to 92% after six months storage in freezer. Conclusions: Results of this study can be used to specify a protocol for vitamin D3 measurement in fortified bread samples. Modified experiment which is projected in this study would specify the general protocol of vitamin D3 measurement that was previously proposed in the national standard of Iran for developing a new and practical national standard document.
Tactile Transfer of Finger Information through Suction Tactile Sensation in HMDs

Most head-mounted display (HMD)-based virtual reality (VR) experiences are visual and auditory. These experiences can be enriched by adding tactile modality. Haptic devices have been attached to hands or installed on desks, which require additional setup time. We developed a suction-type tactile display device embedded in an HMD that realizes a hands-free tactile experience by transferring the finger sensation in VR to the face. In this study, we optimized the suction device to provide pressure sensation rather than suction and experimented to verify that the device can present simple tactile cues along with the physical properties of softness and hardness for objects in virtual contact.
Data-Driven Heuristic Assisted Memetic Algorithm for Efficient Inter-Satellite Link Scheduling in the BeiDou Navigation Satellite System

Inter-satellite link (ISL) scheduling is required by the BeiDou Navigation Satellite System (BDS) to guarantee the system ranging and communication performance. In the BDS, a great number of ISL scheduling instances must be addressed every day, which will certainly spend a lot of time via normal metaheuristics and hardly meet the quick-response requirements that often occur in real-world applications. To address the dual requirements of normal and quick-response ISL schedulings, a data-driven heuristic assisted memetic algorithm (DHMA) is proposed in this paper, which includes a high-performance memetic algorithm (MA) and a data-driven heuristic. In normal situations, the high-performance MA that hybridizes parallelism, competition, and evolution strategies is performed for high-quality ISL scheduling solutions over time. When in quick-response situations, the data-driven heuristic is performed to quickly schedule high-probability ISLs according to a prediction model, which is trained from the high-quality MA solutions. The main idea of the DHMA is to address normal and quick-response schedulings separately, while high-quality normal scheduling data are trained for quick-response use. In addition, this paper also presents an easy-to-understand ISL scheduling model and its NP-completeness. A seven-day experimental study with 10 080 one-minute ISL scheduling instances shows the efficient performance of the DHMA in addressing the ISL scheduling in normal (in 84 hours) and quick-response (in 0.62 hour) situations, which can well meet the dual scheduling requirements in real-world BDS applications.
Hasti: Haptic and Audio Synthesis for Texture Interactions

Multi-sensory stimuli can greatly enhance immersion in interactive virtual environments. Advances in graphics algorithms and technologies like VR displays have pushed the appearance of interactive virtual worlds to unprecedented fidelity, but rendering sound and, especially, touch feedback of comparable quality remains a challenge. We describe a method for real-time synthesis of vibrotactile haptic and audio stimuli for interactions with textured surfaces in 3-D virtual environments. Standard descriptions of object geometry and material properties, including displacement and roughness texture maps typically used for physically-based visual rendering, are employed to generate realistic sound and touch feedback consistent with appearance. Our method reconstructs meso-and microscopic surface features on the fly along a contact trajectory, and runs a micro-contact dynamics simulation whose outputs drive vibrotactile haptic actuators and modal sound synthesis. An exploratory, absolute identification user study was conducted as an initial evaluation of our synthesis methods.
Evidential Fermatean fuzzy multicriteria decision‐making based on Fermatean fuzzy entropy

Fermatean fuzzy set (FFS) is an effective tool to depict expert reasoning information in the decision‐making process. In this study, we first propose a novel Fermatean fuzzy entropy measure to describe the fuzziness degree of FFSs. The new Fermatean fuzzy entropy takes into account the uncertainty information and the indeterminacy degree of FFSs. Subsequently, we prove that Fermatean fuzzy entropy satisfies the axiom requirement of fuzzy entropy measure. Thereafter, a novel Fermatean fuzzy multicriteria decision‐making approach is developed based on Dempster–Shafer theory with the help of the Fermatean fuzzy entropy. The proposed method modeled each Fermatean fuzzy number as a piece of evidence, and the weights of criteria are determined by the entropy measure of FFSs. Then, the weighted average evidence for the alternatives under all criteria is computed from the weights of criteria. Later, Dempster's combination rule is leveraged to combine the weighted average evidence of the alternatives to obtain the final evaluation information about each alternative. The proposed approach can effectively deal with the uncertain information in decision‐making problems and help reduce the information loss in the decision‐making process. Ultimately, the feasibility and validity of the proposed approach are demonstrated through two practical instances.
Global Place Recognition using An Improved Scan Context for LIDAR-based Localization System

Global place recognition is a crucial ability for unmanned ground vehicles since it is the premise of localization in a prior map. However, global place recognition using only 3D LIDAR is still a challenging problem. In this paper, a LIDAR-based place recognition method within a 3D Point cloud map is proposed. The global descriptor Scan Context (SC) is adopted and improved by building a local reference frame so that it’s more robust to viewpoint changes. Accordingly, a three-stage matching algorithm is proposed to efficiently perform place recognition. The proposed method is verified on the KITTI dataset. The experimental results show that the proposed method can perform precise place recognition in about 200ms and the success rate is around 98%, which is 16% higher than SC.
Customized Instruction on RISC-V for Winograd-Based Convolution Acceleration

Convolution operation accounts for the major work-load in convolutional neural networks (CNN). However, standard instruction set for RISC-V processor cannot efficiently perform the matrix convolution between kernel and input matrices. In this paper, we construct a custom instruction under the RISC-V ISA that can perform the F(2×2,3×3) convolution within one single execution. Particularly, optimized by the Winograd algorithm, the operation only needs 16 multiplications instead of 36 multiplications as needed by standard ISA. Benefit from this cycles, as compared to 140 cycles using standard instructions. Thenew instruction, F(2×2,3×3) can be calculated within 19 clock power consumed during convolution operation is also reduced significantly.
Teleoperation Method by Illusion of Human Intention and Time

Shared control, in which teleoperation and autonomous control are combined to move the robot, is expected to improve the efficiency of the user teleoperation problem. However, a problem exists whereby the user acceptance decreases owing to the conflict of intention between the teleoperation and autonomous control. In this study, we address this problem by providing an illusion to humans. We propose a teleoperation method named the “Illusory Control” that can achieve both mobility efficiency and user acceptance by implementing a cyber-physical system that controls a robot in real space through robot operations in virtual space. Illusory Control has two functions: the “Illusion of Intention,” which provides the illusion that the robot is operating according to human intention, and “Illusion of Time,” which provides the illusion of time to fill the gap by changing human behavior when the robot positions in the virtual space and real space diverge. Preliminary teleoperation experiments with subjects demonstrated that the system improves the operational efficiency and acceptance of the system compared to conventional teleoperation methods, namely direct teleoperation and shared control.
Facial Thermal Image Analysis with Deep Convolutional Neural Network Architectures for Subject Dependent Drunkenness Diagnosis

This paper presents a new approach to subject dependent drunkenness diagnosis using Deep Convolutional Neural Networks (DCNNs) to process facial thermal images. Three different DCNN architectures are proposed for this research; the first module has 15 layers, the second one is composed of 7 layers and the last one is GoogLeNet, composed of 22 layers and trained using transfer learning. The proposed DCNNs are evaluated using a large dataset that contains facial thermal infrared images of 41 subjects. The dataset contains 100 thermal images for each subject. Half of them corresponds to the sober condition of the subject, obtained before alcohol consumption, and the other half for the drunkenness condition, obtained 1 hour after each subject drank 4 glasses of wine. The experimental results clearly show that DCNNs produce good results when used to process facial thermal images. The detection scores obtained had their lowest value 93.17% and the highest value 98.54%.
Human-Aware RRT-Connect: Motion Planning for Safe Human-Robot Collaboration

This paper proposes a human-aware motion planner building on RRT-Connect, dubbed Human-Aware RRT-Connect. The planner considers a composite cost function that includes four criteria: human separation distance, human-robot center of mass distance, robot inertia and visibility. This choice of criteria ensures the robot maintains a safe distance and low inertia during motion while being as visible as possible to the human. A simulation study is conducted to demonstrate the planner performance. For the simulation study, the proposed offline Human-Aware RRT-Connect planner is compared to other offline planners through a set of scenarios that vary in environment and task complexity. Several human-robot configurations are tested in a shared workspace involving a simulated Franka Emika Panda arm and a human model. The paths generated by the Human-Aware RRT-Connect planner maintain larger separation distances from the human, are of lower inertia, and are more visible.
The immunological link between neonatal lung and eye disease

Bronchopulmonary dysplasia (BPD) and retinopathy of prematurity (ROP) are two neonatal diseases of major clinical importance, arising in large part as a consequence of supplemental oxygen therapy used to promote the survival of preterm infants. The presence of coincident inflammation in the lungs and eyes of neonates receiving oxygen therapy indicates that a dysregulated immune response serves as a potential common pathogenic factor for both diseases. This review examines the current state of knowledge of immunological dysregulation in BPD and ROP, identifying similarities in the cellular subsets and inflammatory cytokines that are found in the alveoli and retina during the active phase of these diseases, indicating possible mechanistic overlap. In addition, we highlight gaps in the understanding of whether these responses emerge independently in the lung and retina as a consequence of oxygen exposure or arise because of inflammatory spill‐over from the lung. As BPD and ROP are anatomically distinct, they are often considered discreet disease entities and are therefore treated separately. We propose that an improved understanding of the relationship between BPD and ROP is key to the identification of novel therapeutic targets to treat or prevent both conditions simultaneously.