Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Heuristic A*: Efficient Graph Search using Neural Network

In this paper, we consider the path planning problem on a graph. To reduce computation load by efficiently exploring the graph, we model the heuristic function as a neural network, which is trained by a training set derived from optimal paths to estimate the optimal cost between a pair of vertices on the graph. As such heuristic function cannot be proved to be an admissible heuristic to guarantee the global optimality of the path, we adapt an admissible heuristic function for the terminating criteria. Thus, proposed Learning Heuristic A* (LHA*) guarantees the bounded suboptimality of the path. The performance of LHA* was demonstrated by simulations in a maze-like map and compared with the performance of weighted A* with the same suboptimality bound.
Implementing a Deep Learning Model for Intrusion Detection on Apache Spark Platform

Internet evolution produced a connected world with a massive amount of data. This connectivity advantage came with the price of more complex and advanced attacks. Intrusion Detection System (IDS) is an essential component for security in modern networks. The IDS methodology is either signature-based detection or anomaly behavior detection. Recently, researchers adopted Deep Learning (DL) because it has a better performance than traditional machine learning algorithms. The use of DL to produce a model for the IDS may take a long time because of computation complexity and a large number of hyperparameters. Different DL models for IDS on Apache Spark have been implemented in this article. This article uses the famous Network Security Lab - Knowledge Discovery and Data Mining (NSL-KDD) dataset and presents a computation delay comparison between Apache Spark and regular implementation. Moreover, an enhanced model is used to improve attack detection accuracy.
Compliant Electromagnetic Actuator Architecture for Soft Robotics

Soft materials and compliant actuation concepts have generated new design and control approaches in areas from robotics to wearable devices. Despite the potential of soft robotic systems, most designs currently use hard pumps, valves, and electromagnetic actuators. In this work, we take a step towards fully soft robots by developing a new compliant electromagnetic actuator architecture using gallium-indium liquid metal conductors, as well as compliant permanent magnetic and compliant iron composites. Properties of the new materials are first characterized and then co-fabricated to create an exemplary biologically-inspired soft actuator with pulsing or grasping motions, similar to Xenia soft corals. As current is applied to the liquid metal coil, the compliant permanent magnetic tips on passive silicone arms are attracted or repelled. The dynamics of the robotic actuator are characterized using stochastic system identification techniques and then operated at the resonant frequency of 7 Hz to generate high-stroke (>6 mm) motions.
Posture Control for a Low-Cost Commercially-Available Hexapod Robot*

Posture control for legged robots has been widely developed on custom-designed robotic platforms, with little work being done on commercially-available robots despite their potential as low-cost research platforms. This paper presents the implementation of a Walking Posture Control system on a commercially-available hexapod robot which utilizes low-cost joint actuators without torque control capabilities. The hierarchical control system employs Virtual Model Control with simple foot force distribution and a novel, position-based Foot Force Controller that enables direct force control during the leg’s stance phase and active compliance control during the swing phase. Ground truth measurements in experimental tests, obtained with a Vicon motion capture system, demonstrate the improvement to posture made by the control system on uneven terrain, with the results comparing favorably to those obtained in similar tests on more sophisticated, custom-designed platforms.
A Bio-Signal Enhanced Adaptive Impedance Controller for Lower Limb Exoskeleton

The problem of human-exoskeleton interaction with uncertain dynamical parameters remains an open-ended research area. It requires an elaborate control strategy design of the exoskeleton to accommodate complex and unpredictable human body movements. In this paper, we proposed a novel control approach for the lower limb exoskeleton to realize its task of assisting the human operator walking. The main challenge of this study was to determine the human lower extremity dynamics, such as the joint torque. For this purpose, we developed a neural network-based torque estimation method. It can predict the joint torques of humans with surface electromyogram signals (sEMG). Then an radial basis function neural network (RBF NN) enhanced adaptive impedance controller is employed to ensure exoskeleton track desired motion trajectory of a human operator. Algorithm performance is evaluated with two healthy subjects and the rehabilitation lower-limb exoskeleton developed by Shenzhen Institutes of Advanced Technology (SIAT).
Robust and Efficient Estimation of Absolute Camera Pose for Monocular Visual Odometry

Given a set of 3D-to-2D point correspondences corrupted by outliers, we aim to robustly estimate the absolute camera pose. Existing methods robust to outliers either fail to guarantee high robustness and efficiency simultaneously, or require an appropriate initial pose and thus lack generality. In contrast, we propose a novel approach based on the robust "L2-minimizing estimate" (L2E) loss. We first define a novel cost function by integrating the projection constraint into the L2E loss. Then to efficiently obtain the global minimum of this function, we propose a hybrid strategy of a local optimizer and branch-and-bound. For branch-and-bound, we derive effective function bounds. Our approach can handle high outlier ratios, leading to high robustness. It can run reliably regardless of whether the initial pose is appropriate, providing high generality. Moreover, given a decent initial pose, it is suitable for real-time applications. Experiments on synthetic and real-world datasets showed that our approach outperforms state-of-the-art methods in terms of robustness and/or efficiency.
An Outsole-Embedded Optoelectronic Sensor to Measure Shear Ground Reaction Forces During Locomotion

Online estimation of 3D ground reaction forces (GRFs) is becoming increasingly important for closed-loop control of lower-extremity robotic exoskeletons. Through in-verse dynamics and optimization models, 3D GRFs can be used to estimate net joint torques and approximate muscle forces. Although instrumented footwear to measure vertical GRFs in out-of-the-lab environments is available, accurately measuring shear GRFs with foot-mounted sensors still remains a challenging task.In this paper, a new outsole-embedded optoelectronic sensor configuration that is able to measure biaxial shear GRFs is proposed. Compared with traditional strain-gauge based solutions, optoelectronic sensors allow for a more affordable design. To mitigate the risk of altering the wearer’s natural gait, the proposed solution does not involve external modifications to the footwear structure. A preliminary validation of the outsole-embedded sensor was conducted against validated laboratory equipment. The test involved two sessions of treadmill walking at different speeds. Experimental results suggest that the proposed design may be a promising solution for measuring shear GRFs in unconstrained environments.
Design, Modeling and Study of Inverter Integrated Reactive Power Compensation for Isolated Rural Microgrid

Diesel/Biogas generators are generally accompanied with renewable based isolated system due to the sporadic nature of renewable resources. One of the main difficulties associated with small sized isolated rural system is voltage fluctuation due to small disturbances. The rural load mainly comprises of household lighting, agriculture pumps & equipment and small commercial/industrial blocks. The voltage fluctuation affects the performance as well as the life of the electrical equipment. In this paper an isolated rural microgrid is considered with renewable energy sources being supported by diesel generator. The renewable energy resources are hydro with induction generator and Photo Voltaic System. The system is modeled such that inverter associated with Photo Voltaic System provides reactive power compensation apart from the diesel genset. Accordingly, DC Link capacitor and Inductor associated with PV Inverter are designed. The objective of the system study is to deliver uninterrupted and reliable power supply by maintaining regulation of the voltage. The results are verified with step change in load and step change in input solar power to study the dynamic performance of the system. The modeling has been carried out in Simulink.
A 84.48-Gb/s 64-QAM CMOS D-Band Channel-Bonding Tx Front-End With Integrated Multi-LO Frequency Generation

In this letter, an ultra-broadband D-band wireless front-end transmitter (TX) in 45-nm CMOS SOI is proposed. Based on channel bonding and 64-QAM modulation, it achieves 84.8-Gb/s data rate by combining $8\times 2.16$ GHz baseband channels. The eight baseband channels are aggregated at intermediate frequency (IF) in two sets of four channels and then upconverted to two adjacent sub-bands at D-band. The required millimeter-wave local oscillators (LOs) are generated on-chip. The IF band is 57.96 to 66.66 GHz, and the considered transmission band is from 140.895 to 158.265 GHz. The presented TX integrates two upconverter mixers, two power amplifiers, and an on-chip power combiner in addition to the two LO genertors with a total dc power consumption of 420 mW from a 1-V voltage supply, and occupies a compact active area of 2.98 mm2.
A Critical Insight of Contemporary Tools and Techniques in View of Social Synchrony

The evolvement of social networking generates the term social synchrony and its investigation for knowledge processing tasks as a challenge. This paper provides an attempt to contemplate the contemporary social networking analysis tools and techniques with regard to social synchrony has been performed. This paper summarizes some of the potential techniques and approaches, which play an important role in analyses of social synchrony in different OSN and discusses numerous challenges faced by each technique that hinders their usage and seeks attention of research community.
Recent Advances and Tendency in Fiber Bragg Grating-Based Vibration Sensor: A Review

Vibration sensing is critical to monitor and ultimately preserve the health state of engineering systems. These systems with a large structure are typically working in some harsh environments including strong magnetic fields. However, traditional electrical sensors are difficult to accurately measure the vibration under harsh environments. Besides these instinct advantages of normal fiber optic sensors (FOS) sensors such as compact size, passive sensing, resistance to electromagnetic interference, etc., fiber Bragg grating (FBG) sensors have a capability of distributed sensing based on wavelength demodulation and resistance to light intensity fluctuation and unwanted fiber bending losses. Such merits lead them to be a hot topic in FOS field and excellent candidates for vibration sensing. Three types of FBG-based vibration sensors have been classified based on the difference of vibration-strain coupling way to FBG in this survey, which are pasted FBG-based, axial property of FBG-based and transverse property of FBG-based, respectively. FBG-based vibration sensors’ principles and designs have been introduced and discussed. Recent advances in the applications of FBG-based vibration sensors have been investigated. The limitations and prospects of the FBG-based vibration sensing technologies have been analyzed and discussed.
Reducción de la biohidrogenación del ácido linoleico y alfa linolénico por la adición de diferentes proporciones de ácido eicosapentaenoico y docosahexaenoico

Se evaluo el efecto de la adicion de acido docosahexaenoicoe (DHA) y eicosapentaenocio (EPA) a una mezcla de acido linoleico (ALi) y alfa-linolenico (ALn), sobre su biohidrogenacion, en fluido ruminal in vitro. A 500 mg de kikuyo, se agregaron 18 μL de una mezcla de ALi:ALn (75:25) (Control) o una mezcla de 14,9 μL ALi:ALn (75:25) con 3,1 μL de una mezcla que contenia cuatro relaciones de DHA:EPA (100:0, 75:25, 25:75, 0:100). El kikuyo sin adicion de acidos grasos (AG) (Control negativo) o con adicion de estos, fue incubado con fluido ruminal por triplicado, durante 0, 5 o 16 horas, en tubos de 50 mL. Los lipidos del material incubado fueron extraidos, metilados y analizados para AG por CG-FID. Las concentraciones relativas (g/100 g AG) de los AG incubados y los principales productos de la isomerizacion y biohidrogenacion se compararon mediante un modelo completamente al azar, usando el PROC GLM de SAS. La concentracion relativa de ALi y ALn disminuyo con el tiempo de incubacion, pero no fue diferente entre tratamientos con EPA y DHA a las 16 horas. La adicion de DHA y EPA puros o en mezclas disminuyo la concentracion relativa de C18:0 (P < 0,01) e incremento la de C18:1 trans-11 (ATV) (P < 0,01) y C18:2 trans-11, cis-15 (P < 0,01) (ALC), mientras que los cambios en la concentracion relativa del C18:2 cis-9, trans-11 no fueron claros. Los resultados sugieren que la accion inhibitoria del EPA y DHA ocurre en los pasos finales de la biohidrogenacion del ALi y del ALn.
A Path Planning Method Based on Improved Single Player-Monte Carlo Tree Search

With the thorough application and development of Intelligent Logistics System, the Path Planning Problem, which is one of the key problems of logistics scheduling, is accelerating to the direction of dynamics, diversification and complexity. In order to better adapt to the new changes, this paper proposes a path planning method based on improved SP-MCTS (Single-Player Monte Carlo Tree Search) algorithm. Unlike the previous heuristic algorithms, SP-MCTS does not completely depend on parameters, and can more effectively solve variable combinatorial optimization problems. Aiming at the problem of path planning, this paper enhances the simulation process of SP-MCTS by clonal selection algorithm, in order to improve the algorithm’s accuracy of locating high-potential branches, and further improve the accuracy of the decisions made by SP-MCTS. Experiments show that the method proposed in this paper is less affected by the parameters and can better solve the changing path planning problem. Compared with the existing path planning methods and original SP-MCTS algorithm, it can solve the changeable path planning problem more stably, faster and more accurately.
Design and Analysis of Power Efficient IoT Based Capacitive Sensor System to Measure Soil Moisture

With the advancement of new technologies now a days sensors systems have become more intelligent and compact in size as compared to other traditional sensor systems. In this research work, an IoT (Internet of Things) based low cost and power efficient hardware system is presented to monitor soil moisture. A capacitive soil moisture sensor is designed and calibrated in this work. The output data are compared with a standard SEN0193 capacitive soil moisture sensor to check the reliability of the sensor. To make the sensor system a 32 bit ESP32 controller has been used. The controller was programmed according to an algorithm named "Low Threshold Power Optimization (LTPO)" to consume low power. The ESP32 controller has an inbuilt IoT chip that provides more flexibility to the system. A small OLED display is also interfaced with the controller to monitor sensor value directly in the field. In this paper an open source server ThingSpeak.com is used to visualize and store moisture data.
Broadband Dielectric Measurement Of LCP Substrate Materials By Differential Phase Length Method

Liquid crystal polymer (LCP) is a material that has gained attention as a potential microwave and millimeter-wave substrate and package materials. The differential phase length method has been proposed to determine the dielectric constant of LCP substrate ranging 10MHz-40GHz. In this paper, the results shown that the dielectric constant keeping stable in a bandwidth frequency is about 3.2, and the measurement can be strongly affected by transition of probe-to-GCPW in the test circuits. Meanwhile, the improvement result for the circuits with the difference length of 20 mm is obtained, whose dielectric constant with a little changing in a wide band range.
Grasp Control for Enhancing Dexterity of Parallel Grippers

A robust grasp controller for both slipping avoidance and controlled sliding is proposed based on force/tactile feedback only. The model-based algorithm exploits a modified LuGre friction model to consider rotational frictional sliding motions. The modification relies on the Limit Surface concept where a novel computationally efficient method is introduced to compute in real-time the minimum grasping force to balance tangential and torsional loads. The two control modalities are considered by the robot motion planning algorithm that automatically generates robot motions and gripper commands to solve complex manipulation tasks in a material handling application.
A Metallic 3D Printed Miniaturized Quasi Log Periodic Koch-Dipole Antenna Using T-Shaped Top Loading

A quasi log periodic Koch-dipole array (LPKDA) antenna is miniaturized by decreasing the length of the antenna using top loading structure. The antenna is manufactured by three-dimensional (3-D) printing technology. The simulated -10-dB impedance bandwidth is 117.4% (0.91GHz~3.54GHz). The simulated realized gain is 6.6 dBi. Compared to a λ/2-element LPDA. , the proposed antenna features 40% length reduction.
A Novel Minimum Variance Beamformer and Its Circuit Design for Ultrasound Beamforming

This paper describes new approaches to minimum variance distortionless response (MVDR) beamforming for coherent plane-wave compounding (CPWC) in ultrasound imaging. Imaging results based on actual received and simulated waveforms show improvements over the traditional Delay and Sum (DAS) method as well as other MVDR-based methods, in terms of spatial and contrast resolutions. We designed detail circuits that can be configured to implement several of the aforementioned ultrasound beamforming methods effectively. The simulated operating frequency for the circuits implemented in FPGA is up to 62. 5MHz.
Feature Extraction to Heterogeneous Cross Project Defect Prediction

Cross Project Defect Prediction (CPDP) predicts faults in a target project (which has deficient faulty data) by defect prediction models learned from another project’s fault data. Nonetheless, these studies have a prevalent problem that needs uniform metrics, i.e. to describe themselves; distinct projects must have similar features. This article emphasizes heterogeneous CPDP (HCPDP) modeling that does not require the same set of metrics between two applications, and also builds model of defect prediction based on matched heterogeneous metrics that show comparable distribution in their values for a given pair of datasets. HCPDP modeling consists of three main phase feature ranking and feature selection, metric matching, and finally, the binary classification of unlabeled instances in the target application is performed as clean or buggy instances using an appropriate classification. This paper empirically and theoretically evaluates the effect of an additional modeling phase i.e. extraction of features on performance of the HCPDP model. Selection of features is to weed out obsolete or redundant features from your dataset. The key difference between selection and extraction of features is that selection of features preserves a subset of the original features while extraction of features also produces new ones. This paper compares the performance of the proposed HCPDP model on 13 benchmarked datasets of three source project groups AEEEM, ReLink& SOFTLAB with and without applying feature extraction phase using three machine learning classifiers. We have used Chi Square Test (CST) to pick features, and Principle Components Analysis (PCA) method to extract brand features of the dataset. Results show that for prediction pairs (JDT, ar1) & (Safe, ar3), prediction accuracies were significantly improved when we employed feature extraction phase in model. The comparative analysis among all three classifier demonstrates that GBM performs best among all for both prediction pairs.
Novel Transformerless High Voltage Gain DC-DC Converter for Solar PV applications

Due to resistance of power switches, rectifier diodes, equivalent series resistance (ESR) of capacitors and inductors, providing high step-up gains is difficult in Conventional boost converters. In this paper, a transformer-less high gain dc-dc converter is proposed to obtain high step-up voltage gain with nominal or reduced duty ratios. The proposed converter has two inductors of same value which are charged in parallel in one period and when all switches are OFF they will discharge in series with the source to the load. The voltage gain equation of the proposed converter in continuous conduction mode (CCM) is extracted. Then, the critical capacitance, critical inductance, and voltage stresses are calculated. Different topologies are discussed and compared with the proposed converter. The average modelling and the small signal analysis of the converter is discussed in detail. A type-III controller/compensator is designed for making the system stable and for making output voltage constant for an input range of 10-30V. The closed loop simulation of the converter is done by using MATLAB and obtained results are shown.
Tri-band Metasurface for Multi-mode Vector Beam Conversion

Metasurfaces have been widely utilized to control arbitrary wavefronts based on phase modulation. Here, a tri-band metasurface for cylindrical vector beam conversion is proposed. Full 2π phase modulations at three frequencies can be achieved independently by changing the orientations of the three different resonators constructing meta-atom based on Pancharatnam-Berry phase principal. As proofs of concept, a tri-band metasurface made up of the proposed meta-atoms is numerically investigated. It generates azimuthally polarized vector beam at 8.12 GHz, radially polarized vector beam at 13.78 GHz and hybrid polarized vector beam at 19.20 GHz.
Robot Plan Model Generation and Execution with Natural Language Interface*

Verbal interaction between a human and a robot may play a key role in conveying suitable directions for a robot to achieve the goal of a user’s request. However, a robot may need to correct task plans or make new decisions with human help, which would make the interaction inconvenient and also increase the interaction time. In this paper, we propose a new verbal interaction-based method that can generate plan models and execute proper actions without human involvement in the middle of performing a task by a robot. To understand the verbal behaviors of humans when giving instructions to a robot, we first conducted a brief user study and found that a human user does not explicitly express the required task. To handle such unclear instructions by a human, we propose two different algorithms that can generate a component of new plan models based on intents and entities parsed from natural language and can resolve the unclear entities existed in human instructions. An experimental scenario with a robot, Cozmo, was tried in the lab environment to test whether or not the proposed method could generate an appropriate plan model. As a result, we found that the robot could successfully accomplish the task following human instructions and also found that the number of interactions and components in the plan model could be reduced as opposed to the general reactive plan model. In the future, we are going to improve the automated process of generating plan models and apply various scenarios under different service environments and robots.