Miracle-dz/UniFAR-FTUs huggingface dataset:
The Method of Data Collection Based on Multiple Mobile Nodes for Wireless Sensor Network

In traditional wireless sensor networks (WSN), data is transmitted to base stations through multiple-hop. However, multi-hop transmission will cause problems such as energy holes, uneven energy consumption, and unreliable data transmission. Aiming at the defects of traditional networks, this paper proposes a scheme for collaborative data collection using multiple mobile nodes (MN) as sink nodes. In this scenario, an important issue is how to reasonably plan the data collection path of each mobile node under a series of constraints such as energy. This paper studies routing strategy and path planning. Firstly, a dynamic clustering algorithm is used to cluster the randomly arranged sensor nodes, and then a sensor node with higher energy is manually arranged at the virtual cluster center generated by the clustering algorithm as a cluster head node to establish a data collection cluster. Then the monitoring area is divided into several parts according to the number of MN, so that the MN traverses the cluster head nodes in the respective monitoring areas for data collection. At the same time, in order to enable the MN to complete data collection within the energy limit and improve the path balance, a path-based path equalization algorithm (PEABR) is proposed to adjust the path of the MN, further reduce and equalize the path length of the MN to satisfy the constraint conditions, and optimize the path planning scheme. Finally, simulation was carried out by using Matlab simulator, and the simulation experiments which were performed in a laboratory environment verified the feasibility and effectiveness of the algorithm.
A Divide-and-Conquer Framework for Attention-based Combination of Multiple Investment Strategies

In order to maximize the profit, investors usually examine diverse investment strategies based on various information when constructing their portfolios. However, they can hardly always construct a profitable portfolio due to the dynamic performance of the strategies and the dynamics of the market state along with the time. To address this challenge, we propose a 2D-attention framework to capture the dynamics of the above two factors in this paper. To capture the dynamic of the first factor, we design a strategy-wise attention model to dynamically combine multiple strategies according to their respective effectiveness. To deal with the second factor, we design a divide-and-conquer framework to learn multiple strategy-wise attention models, which categorizes the whole market periods into a few stable states and jointly learn respective models for each state and then build a state-wise attention model to combine them dynamically for the final task. Extensive experiments on real-world data demonstrate that our 2D-attention framework can significantly outperform several widely-used baselines.
Rapid and Robust Monocular Visual-Inertial Initialization with Gravity Estimation via Vertical Edges

Monocular visual-inertial tracking without good initialization easily fails due to its non-linear nature. Rapid and accurate metric initialization is crucial. In this paper, we propose a novel monocular visual-inertial initialization method which can initialize the IMU states, camera poses, and scale in a rapid and robust way. To avoid mixing gravity and accelerometer bias, we propose to use the detected vertical edges to estimate a better gravity. This improves the observability to the underlying problem even without sufficient movement, so we can solve all the states crucial for a good initialization. We evaluate our approach on EuRoC dataset and compare with existing state-of-the-art methods. The experimental results demonstrate the effectiveness of the proposed method.
Industrial Loads Used as Virtual Resources for a Cost-Effective Optimized Power Distribution

Industrial Customers are dispersed at various levels of the electrical network and fed together with other customers’ categories in a distributed environment. Optimizing industrial processes in the presence of other customers’ categories supplied by the same infrastructure is a challenging issue. Existing studies have analyzed the effect of different Industrial Demand Response Programs on the distribution network, which also supplies other customers’ categories. They show the need for improving the distribution performance although multiple demand response programs have been suggested for this purpose. In this paper, a new approach is presented considering an optimal synchronized process among all consumers’ categories. It shows that the balance between generation and demand is maintained, the customer satisfaction is guaranteed, the profit is maximized and the cost is minimized for all customers. Various time constraints set by different industry productions are considered in the optimization process. Fairness problems, multiple pricing schemes and formulation for the same are elaborated. The method is validated through a simulation on Matlab using K-Means Clustering and multi-objective particle swarm optimization (MOPSO) along with data prediction.
Robot Finger with Remote Center of Motion Mechanism for Covering Joints with Thick Skin

An end-effector such as a gripper or multi-fingered hand is essential to enable robots to grasp and manipulate objects of various size and shape. Soft skin increases the grasp stability and can provide space for tactile sensors. However, covering the joints with skin is challenging, typically causing a considerable surface area of multi-segment robot fingers not to be covered by skin. This also creates the risk that objects get pinched in the joints when flexing the fingers. The current paper suggests using a remote center motion (RCM) mechanism to move the center of joint rotation to the surface of a thick skin layer. In particular, a 6-bar mechanism is used. Thereby, a thick soft skin layer with a continuous surface can be realized. Furthermore, adaptive joint coupling with linkages is implemented. In the current paper a 2-fingered gripper is realized, and objects of various size and shape are grasped (from thin paper to objects of 135 mm diameter). The current gripper was manufactured with 3D-printed material to enable rapid prototyping, therefore the payload was limited to only 1 kg for this version. Overall, this paper shows the feasibility of an RCM for a robot finger and discusses the benefits and limitations of such a mechanism.
Improved Mechanical Design and Simplified Motion Planning of Hybrid Active and Passive Cable-Driven Segmented Manipulator with Coupled Motion

Cable-driven segmented manipulators (CDSMs) featured by superior dexterity, light and slender body are excellent candidates for operations in confined environments. However, the stiffness and load capacity of such manipulators have been a challenge due to their structural elasticity. In this paper, we propose an improved mechanism design based on the preliminary work to enhance the linkage accuracy and arm continuity without sacrificing the dexterity, high stiffness and load capacity of CDSM. The manipulator is composed of 4 improved hybrid active-passive linkage segments. Its short and long linkage cables with pretension mechanism are designed to keep equal angles of adjacent joints. An improved separable small driving control box is also designed with both quick release and load mechanism and stroke amplification mechanism. Then the size of control box can still remain small, even the number of segments and the joint limit angles increase. Considering the improved in-segment linkage characteristic, traditional kinematic equations and Jacobian matrix are greatly simplified with Denavit-Hartenberg (D-H) method. Further trajectory tracking planning based on the simplified kinematics solved the Cartesian space planning for task design. Finally, a prototype system is developed to perform the linkage accuracy and comprehensive obstacle avoidance experiments. Experimental results show that the developed hybrid active and passive CDSM has relatively high accuracy and super dexterity.
Analysis and Performance Evaluation of Deep Learning on Big Data

Deep Learning (DL) and Big Data (BD) have converged to a hybrid computing paradigm that merges the dynamic processing in DL models with the computational power of the distributed processing of the BD frameworks. In this context, this work aims to conduct an analysis and performance evaluation of DL applications in BD. The experiments evaluate how the application training completion time can be related to the model’s precision loss and the impacts of distributed computing in DL models. The experiments were performed in Microsoft Azure using BigDL framework, which allows using both Spark and TensorFlow on top of a Yarn cluster. The outcomes revealed a speedup of up to 8x and accuracy higher than 95%.
Analysis on Emotion Detection and Recognition Methods using Facial Microexpressions. A Review

Facial recognition is already an integral part of many commercial applications and, of course, the recognition of emotions based on facial expressions may become as usual soon. It is currently used in applications in the field of psychotherapy, security systems, forensics, etc. The face is the primary means of communicating emotions, but it is also the one that hides them most easily. Micro-expressions are a very accurate indicator of our subtle and involuntary thoughts, showing what we really feel. Unfortunately, their duration and intensity are very short, which makes their detection very difficult, even trained people can detect below 50%. For this reason, automatic detection becomes more interesting and challenging, and research in this direction is in full development. The article contributes to this area by presenting the general process of automatic detection and synthesizing the latest techniques in classification and recognition of facial micro-expressions, which, although diverse, have an accuracy of recognition around 80%.
Real-time monitoring of human task advancement

In collaborative robotics applications, human behaviour is a major source of uncertainty. Predicting the evolution of the current human activity might be beneficial to the effectiveness of task planning, as it enables a higher level of coordination of robot and human activities. This paper addresses the problem of monitoring the advancement of human tasks in real-time giving an estimate of their expected duration. The proposed method relies on dynamic time warping to align the current activity with a reference template. No training phase is required, as the prototypical execution is learnt online from previous instances of the same activity. The applicability and performance of the method within an industrial context have been verified on a realistic assembly task.
Radiowave Propagation over Black Sea – Measurements Survey

Propagation measurements survey over Black Sea was performed between Crimea peninsula, Turkey and Bulgaria. Twenty one very high frequency range radio broadcasting transmitters were used, transmitting both, analog and digital signals from five different points. Eight propagation paths with path lengths from 265 to 484 km and different antenna heights were monitored almost simultaneously. Detailed data and results are provided and might be used for propagation model verification, or detailed evaluation of interference between stations, working in the same or adjacent frequency channels, to avoid unacceptable interference due to long-distance tropospheric propagation.
Minimizing Multi-Controller Deployment Cost in Software-Defined Networking

Aiming at various issues, such as performance, load-balance and robustness, of Software-Defined Networking (SDN) controller cluster, several deployment strategies have been proposed. As a supplement, our work delivers a cost-effective multicontroller deployment strategy in terms of economic benefits. In this paper, we formalize a Cost-optimized Multi-Controller Deployment (CO-MCD) problem by concerning the controller’s deployment cost and the network service’s delay cost. Meanwhile, we characterize the communication between the controller and the switch using a queuing system, and design an algorithm to determine the optimal deployment in the specified environment. Simulation results validate that the proposed algorithm can always obtain a deployment solution which minimizes the economic cost of the entire SDN network. This work aims at being a useful primer to provide insights regarding the economic impact of multiple controllers in the realistic SDN deployment, so that network owners plan their investments more rationally.
Seeking the Analytical Approximation of the Stance Dynamics of the 3D Spring-Loaded Inverted Pendulum Model By Using Perturbation Approach

The Spring-Loaded Inverted Pendulum (SLIP) has been widely exploited in both biomechanical and robotics research due to its simple form in mathematics and high accuracy in fitting experimental biology data. However the intrinsic nonlinearity of the SLIP dynamics makes accurate analytical representation unavailable. Traditional methods take advantage of numerical integration to handle this issue while several existing analytical approximations focusing on 2D-SLIP model. The 3D-SLIP suitable to physical reality is rarely investigated. This paper presents a novel perturbation-based approach to obtain the closed-form analytical approximations of the 3D-SLIP model in stance phase. In contrast to existing work ignoring the gravitational forces, the proposed approach just relies on assumptions of small leg compression and small leg swept angle. The performance of the derived approximations has been evaluated via comprehensive numerical analysis. The quality of accurate apex prediction promises the approximation as an advantageous and reliable tool for locomotion control of legged robots.
Robotic Tracking Control with Kernel Trick-based Reinforcement Learning

In recent years, reinforcement learning has been developed dramatically and is widely used to solve control problems, e.g., playing games. However, there are still some problems for reinforcement learning to perform robotic control tasks. Fortunately, the kernel trick-based methods provide a chance to deal with those challenges. This work aims at developing a kernel trick-based learning control method to carry out robotic tracking control tasks. A reward system, in this work, is presented in order to speed up the learning processes. And then, a kernel trick-based reinforcement learning tracking controller is presented to perform tracking control tasks on a robotic manipulator system. To evaluate the policy and assist the reward system to accelerate the speed of finding the optimal control policy, a critic system is introduced. Finally, from the comparison with the benchmark, the simulation results illustrate that our algorithm has faster convergence rate and can execute tracking control tasks effectively, the reward function and the critic system proposed in this work is efficient.
Wideband Wearable Antenna for Biomedical Telemetry Applications

This paper presents a wideband, low-profile and semi-flexible antenna for wearable biomedical telemetry applications. The antenna is designed on a semi-flexible material of RT/duroid 5880 (<inline-formula> <tex-math notation="LaTeX">$\epsilon _{r} = 2.2$ </tex-math></inline-formula>, tan<inline-formula> <tex-math notation="LaTeX">$\delta = 0.0004$ </tex-math></inline-formula>) with an overall dimensions of 17 mm <inline-formula> <tex-math notation="LaTeX">$\times25$ </tex-math></inline-formula> mm <inline-formula> <tex-math notation="LaTeX">$\times0.787$ </tex-math></inline-formula> mm (<inline-formula> <tex-math notation="LaTeX">$0.2\lambda _{\circ }\times 0.29\lambda _{\circ }\times 0.009\lambda _{\circ }$ </tex-math></inline-formula>). A conventional rectangular patch is modified by adding rectangular slots to lower the resonant frequency, and the partial ground plane is modified to enhance the operational bandwidth. The final antenna model operates at 2.4 GHz with a 10-dB bandwidth (fractional bandwidth) of 1380 MHz (59.7 % at the centre frequency of 2.4 GHz). The proposed antenna maintains high gain (2.50 dBi at 2.4 GHz) and efficiency (93 % at 2.4 GHz). It is proved from the simulations and experimental results that the antenna has negligible effects in terms of reflection coefficient, bandwidth, gain, and efficiency when it is bent. Moreover, the antenna is simulated and experimentally tested in proximity of the human body, which shows good performance. The proposed wideband antenna is a promising candidate for compact wearable biomedical devices.
Application of Digging Control based on the Center-of-Mass Velocity of the Attachment of a Hydraulic Excavator

Superior operation skills for hydraulic excavators are learned over time. Therefore, it is difficult to improve the skills of non-expert operators quickly, and their work needs to be supported. This paper presents a control system, which realizes smooth digging (similar to that performed by expert operators), based on the Center of Mass velocity of the attachment as an index. In addition, corresponding to the system switching caused by the digging reaction force, a controller gain tuning method that uses the Fictitious Exogenous Signal is proposed. The proposed method is applied to a hydraulic excavator, and its effect is verified.
One-Shot Object Localization Using Learnt Visual Cues via Siamese Networks

A robot that can operate in novel and unstructured environments must be capable of recognizing new, previously unseen, objects. In this work, a visual cue is used to specify a novel object of interest which must be localized in new environments. An end-to-end neural network equipped with a Siamese network is used to learn the cue, infer the object of interest, and then to localize it in new environments. We show that a simulated robot can pick-and-place novel objects pointed to by a laser pointer. We also evaluate the performance of the proposed approach on a dataset derived from the Omniglot handwritten character dataset and on a small dataset of toys.
The spread spectrum GFDM schemes for integrated satellite-terrestrial communication system

Recently, integrated Satellite-Terrestrial (S-T) communication system, especially the integration of satellite communication with 5G/6G, is regarded as a research hotpot. Future integrated S-T communication systems are demanding a more compatible and robust physical layer waveform. Considering physical layer access waveform design, this paper proposed a novel Spread Spectrum Generalized Frequency Division Multiplexing (SS-GFDM) scheme for integrated S-T communication system. Traditional GFDM has many advantages such as excellent adaptability and low out-of-band (OOB) radiation. However, because of intrinsic inter carrier interference (ICI) and low signal-to-noise ratio (SNR), the multiple access performance is degraded. In this paper, we introduced CDMA technology into GFDM. Two different spread spectrum modes, Cyclic Code Shift Keying (CCSK) soft spread spectrum and Direct Sequence Spread Spectrum (DSSS), are considered and compared in this paper to illustrate the benefits of GFDM-CDMA in low SNR scenario. Moreover, this scheme integrates the slot-ALOHA protocol with GFDM-CDMA, which extends access freedom in frequency, time and code domain. The simulation and analysis results show that the proposed GFDM-CDMA scheme reduces the performance degradation caused by interference. It is effective in typical satellite channel with low complexity. Meanwhile, the peak-average-power-ratio (PAPR) and access performance has been enhanced significantly.
Effect of Vibration on Twisted String Actuation Through Conduit at High Bending Angles

This paper studies an effect of vibration on twisted string actuation through conduit at high bending angles. In our previous work [1], we have demonstrated that twisted string actuators can be used to transmit power even at significant deflection angles of the conduit. However, several undesirable effects, namely pull-back, hysteresis, and chattering, were present during actuation due to friction between strings and the internal sheath of the conduit. This paper reports the results of experimental study on effects of vibration on twisted string actuation inside deflected conduits. We have demonstrated that applying vibration generated near natural frequency of the system during the later stages of twisting and untwisting cycles helped reduce pull-back and hysteresis and increase string contraction. In case when sheath was deflected by 180° under a constant load of 3 kg, we were able to achieve over 70% decrease in pull-back and 30% decrease in hysteresis, compared with no vibration case.
Towards Chronic Liver Dysfunction Self-monitoring: a Proof-of-Concept Study

The liver is our very own chemical processing plant as it plays a vital role in maintaining the body’s metabolic balance. Liver’s health is assessed by a group of clinical tests (such as blood tests, ultrasonographic imaging, liver biopsy) most of which are invasive and burdensome for the patients. In the setting of severely scarred liver, toxic substances, such as ammonia, have fewer opportunities to be detoxified. Accumulation of ammonia in the systemic circulation and in the brain may result in Hepatic Encephalopathy (HE), a spectrum of neuropsychiatric abnormalities which entails changes in consciousness, intellectual functions, behavior. Minimal HE has attracted increasing attention, as it does not cause detectable changes in personality or behaviour, but the complex and sustained attention is impaired. Hence, it can be detected only by specific but biased, time-consuming and burdensome examinations, such as blood ammonia levels assessment and neuro-psychological tests. The obstrusivity of the majority of the liver function clinical tests, and, in case of minimal HE, the lack of reliable examinations, are encouraging the scientific community to look for alternative diagnostic methods. For this purpose, the exploitation of a non-invasive technique such as breath analysis, to identify chronic liver disease, discriminate among its degree of severity and detect the onset of HE, could be a step forward for clinical diagnosis. In this paper, we report a proof-of-concept study that aimed at detecting ammonia in the breath of patients suffering from chronic liver disease by means of a low-cost, easy-to-use, gas-sensors based device. Not only, we also aimed at investigating the possibility of discriminating the several severity degree of liver impairment on the basis of the detected ammonia.