Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Walking on Compliant and Uneven Terrain using DCM and Passivity-based Whole-body Control

This paper presents a complete trajectory generation and control approach for achieving a robust dynamic walking gait for humanoid robots over compliant and uneven terrain. The work uses the concept of Divergent Component of Motion (DCM) for generating the center of mass (CoM) trajectory, and Cartesian polynomial trajectories for the feet. These reference trajectories are tracked by a passivity-based whole-body controller, which computes the joint torques for commanding our torque-controlled humanoid robot TORO. We provide the implementation details regarding the trajectory generation and control that help preventing discontinuities in the commanded joint torques, which facilitates precise trajectory tracking and robust locomotion. We present extensive experimental results of TORO walking over rough terrain, grass, and, to the best of our knowledge, the first report of a humanoid robot walking over a soft gym mattress.
Research on the Community Number Evolution Model of Public Opinion Based on Stochastic Competitive Learning

Human activities are usually collective, so clustering has become an important feature of human behavior. This paper studied the evolution of the community in the process of public opinion propagation so as to put forward a public opinion evolution model for the network community number. This study proposed the community number evolution model of public opinion based on stochastic competitive learning, and the proposed model consists of an increase in the number of communities and a decrease in the number of communities. The highlight of this model is that on the one hand, it realizes the research on the evolution of public opinions on the dynamic network; on the other hand, unlike other public opinion evolution models, this model pays attention to the community number increase and decrease rules in the evolution of public opinions. Then, as an extension of the community number evolution model of public opinion, the community number prediction model had been proposed. Based on Twitter data from the 2017 London Bridge attack, the proposed models were validated by experiments. In the verification section of this paper, two methods had been introduced as a comparison. The experimental results show that the community number evolution model of public opinion is correct.
Novel Nonlinear Hypothesis for the Delta Parallel Robot Modeling

In previous investigations, the nonlinear hypothesis use the linear bounded maps. Nonlinear hypothesis are described as the combination of the first order terms, and after of the mentioned combination, one bounded map is applied to alter the result. This document proposes two nonlinear hypothesis which use different structures instead of using the linear bounded maps. They are termed as novel nonlinear hypothesis and second order nonlinear hypothesis and their goal is to improve the second order processes modeling. The proposed nonlinear hypothesis are described as the combination of the first order and second order terms. Since the delta parallel robot is a second order process, it is an excellent platform to prove the effectiveness of the two proposed hypothesis.
Modeling and Analysis of a PV-based Shunt Active Filter with BESS for Power Quality Enhancement Under Dynamic Load Conditions

In recent times, there is an increase in power quality issues due to rise in various non-linear loads and load unbalances in three-phase systems due to single-phase loads. In this paper, a PV-based shunt active filter is modeled for the mitigation of various power quality issues in a system consisting of various three-phase and single-phase active and reactive loads as well as non-linear loads. The shunt active filter is controlled using conservative power theory which provides selective compensation through the active filter. The various disturbances in the grid currents due to the loads are alleviated using this current decomposition technique. A Battery Energy Storage System (BESS) is connected across the solar PV array to store energy during surplus production of power and to provide energy when the PV array output is nil or very low. The modeled system is then analyzed for different operating conditions such as dynamic load change using MATLAB Simulink and the results obtained are presented.
On the power level dependence of PA and DPD Volterra models

This work presents a novel power amplifier behavioral model, referred to as power dependent Volterra (PDV) model, which is devoted to keep track on the average power of the input signal. The behavior of the parameters under varying power conditions is theoretically investigated using circuit-level knowledge including electrical and thermal subcircuits, and the dependence on average power is demonstrated. Experimental results employing a GaN HEMT power amplifier show that the model performance at $\mathrm{P}_{\mathrm{o}}=+33.8\mathrm{dBm}$ is maintained over a range of about 15 dB.
ACENTRIST: An Augmented Census Transform Histogram for Aerial Image Classification

Aerial image classification has become one of the most important topics to the computer vision researchers because of its numerous real world application. A great number of census transform based descriptors have been introduced in recent years to classify the aerial images. But the major drawback of these census transform based techniques is, most of these techniques works only with the center pixel information of an image with respect to their neighboring pixels. Hence, no information about the relationship among the neighboring pixels is obtained. To mitigate this problem, we introduce an Augmented Census Transform Histogram (ACENTRIST) for aerial image classification which encodes both the center pixel information and neighboring pixel information. The proposed technique augments two local binary pattern based descriptor which encodes the center pixel information with respect to the neighboring pixels and information of the angular difference of the neighboring pixels. We have conducted thorough experiments in two of the well-known aerial image dataset, UC Merced Land Use (Land Use 21) and In-House (Banja Luka), and the experimental result shows that the proposed methodology gains considerable higher accuracy over the state of the art methods.
Spatio-temporal Mapping of Green House Gas Emission in Urban Settings using a Vehicle Mounted IoT Enabled Pollution Sensing Modules

This paper represents the spatial variability in the pollution by virtue of industrial and residential areas located in almost all the urban settings located at different places. Hence the pollutant gases data of a few locations cannot give an accurate overall estimate of the pollution index of the city. Hence a Spatio-temporal mapping is required to get a fair idea about the pollution in different areas at different times and would also help in identifying any high pollution zones so that remedial actions can be taken to reduce the effluent emissions. This work focuses on the use of IoT enabled gas sensing nodes which are proposed to be mounted on any public transport vehicle or administration vehicle of the focused city to get a continuous indication of the concentration of various greenhouse gases and to use an open-source database management system for warehousing and retrieval of Spatio-temporal pollution data through web-interface
Placing Objects with prior In-Hand Manipulation using Dexterous Manipulation Graphs

We address the problem of planning the placement of a grasped object with a robot manipulator. More specifically, the robot is tasked to place the grasped object such that a placement preference function is maximized. For this, we present an approach that uses in-hand manipulation to adjust the robot's initial grasp to extend the set of reachable placements. Given an initial grasp, the algorithm computes a set of grasps that can be reached by pushing and rotating the object in-hand. With this set of reachable grasps, it then searches for a stable placement that maximizes the preference function. If successful it returns a sequence of in-hand pushes to adjust the initial grasp to a more advantageous grasp together with a transport motion that carries the object to the placement. We evaluate our algorithm's performance on various placing scenarios, and observe its effectiveness also in challenging scenes containing many obstacles. Our experiments demonstrate that re-grasping with in-hand manipulation increases the quality of placements the robot can reach. In particular, it enables the algorithm to find solutions in situations where safe placing with the initial grasp wouldn't be possible.
Improving the Performance of Stress Detection Systems by Minimizing Person Specific Variations

In this paper, we explore several approaches for improving the performance of a stress detection system (SDS) with electrocardiogram (ECG) signal as its input. Time and frequency domain features extracted from the heart rate variability (HRV), is input to a support vector machine (SVM) backend classifier, for the baseline SVM-SDS. We used ECG data from normative population and patients with severe stress disorders for validating the effectiveness of SDS. We observed that there are variations in the HRV parameters among the normative population, and the patients with stress disorder, usually referred to as person specific variations. To minimize the person specific variations, we explored three methods: 1. Locality-constrained linear coding (LLC) - input feature vectors are expressed in terms of person independent basis vectors. 2. Fisher vector encoding (FVE) -input features are expressed in a person independent feature space using Gaussian mixture models (GMM) trained patient independently. 3. Covariance normalization (CVN) - minimizes the person specific variations in the input vector through feature normalization. For the CVN-SDS, we obtained a sensitivity of 87.50% absolute, specificity of 100.00% absolute and accuracy of 91.66% absolute, which is 31.43%, 31.43% and 33.33% absolute improvement over the SVM-SDS.
Multi Modal Medical Image Fusion using Convolution Neural Network

Medical image fusion have very important rolefor disease diagnosis and medical image analysis.An application to get complementary information from multiple images of different modalities. It is extensively used to combineinfor-mation from multiple images into single image with good accuracy. In our paper multimodal medical image fusion based on convolutional nueral network(CNN) is proposed. In this method a CNN model is created which will contain the pixel activity information of the input images. Image is decomposed into highly matching and low matching and separatefusion method is applied to both type of images. Beside this main important factor is to reduce noise because noise will affect the pixel intensities.so we will implement a new method to reduce noise in this manner. This method is to combine affected pixels of different images we are going to fuse. Different affected images will undergo an test for checking whether it is having noise or not. Then effected image will undergo a filtering algorithmtogetnoiselessimageforprovidingmoreclarity.
Copy Sensitive Graphical Code Estimation: Physical vs Numerical Resolution

Recent papers point out the vulnerability of Copy Sensitive Graphical Codes (CSGC) while an opponent uses a neural network approach to estimate a pattern then prints it as an original one: such a fake can successfully pass the authentication test. Here, we show that a GAN-like network can be even more powerful. A SRGAN-based architecture including superresolution can tolerate a lower scanner resolution and decode efficiently. Besides, the use of such a decoding technique to perform the authentication test can improve the resistance of CSGC to estimation attacks.
Closed-chain Pose Estimation from Wearable Sensors

Inertial measurement unit sensors are commonly used for human pose estimation. However, a systematic and robust method to incorporate position and orientation constraints in the kinematic structure during environmental contact is lacking. In this paper, we estimate the pose using the extended Kalman filter, linearize the closed loop constraints about the predicted Kalman filter state, then project the unconstrained state estimate onto the constrained space. Multiple constraints that are representative of real world scenarios are derived. The proposed technique is tested on two human movement datasets and demonstrated to outperform unconstrained Kalman filter.
Design and Development of Opto-Resistive Type Quasi-digital Sensor and Instrument to In-situ Monitoring of the Quality of Lubricant Oil

In large rotating machinery, lubricating oils are employed to reduce the wear, minimize the friction between gears and bearings, remove heat, takeaway wear particles and to protect against corrosion. Lubricant oil slowly degrades over a period of time due to oxidation and contamination. Consequently looses its defensive properties and effects the machinery performance. This may leads to catastrophic failure, unplanned shutdown and capital loss. Replacing the in-service oil with fresh oil before degradation can prevent the above consequences but changing the lubricant too early is uneconomical. Therefore, it is better to predict the remaining effective life of the oil under operating conditions. Monitoring the healthiness, predicting the remaining useful life of the lubricant oil and warning the operator with an alarm can prevent catastrophic failure. It is observed that the oil color becomes darker with contamination, oxidation, and aging. Thus, the color of the lubricant is considered as an essential parameter for describing the quality of the oil. This paper describes the design and development of a lowcost, low power instrument and Opto-resistive quasi-digital sensor. It explains the capability of the instrument about in-situ monitoring of quality, predicting the effective life of lubricant by measuring the transmittance of the light through lubricant oil and alerting the operator by annunciation cum display.
Transients detection using Artificial Neural Network based HS-transform

This paper proposes HS-transform technique and Artificial Neural Network (ANN) algorithm for the detection and classification of voltage transients. Transients due to energization of capacitor banks has been considered for the analysis. In the proposed methodology, the HS-transform is employed to generate the instantaneous frequency vectors for S-matrix of the simulated transient signals. Using the statistical features of S-matrix vectors, the occurrence of transient is easily distinguished from the normal condition and from the other disturbance conditions by employing ANN algorithm. The advantage of the proposed algorithm is its accuracy in identifying the presence of transients and its classification accuracy. The performance of the proposed algorithm is manifested by the simulation of oscillatory transient event using MATLAB/SIMULINK software. The test results proved that the proposed algorithm is simple, fast and more accurate in identifying the event.
Mining to Discover Association of Psychological Factors with Student Academic Performance

Predicting the student behavior is important as it leads to the scope of improvement in their academic achievement. Association refers to a process of identifying the frequent item sets or patterns from the given dataset in order to provide recommendations about the instances of specific item based on the identified patterns. This paper focuses on association rule mining technique to elicit pragmatic information from the primary data collected. The work carried here explores the relationship between student's intellectual performance and their psychological factors with the use of Apriori algorithm. It aims at predicting and improving academic performance of students by reshaping their psychological parameters.
Evaluating the Effect of Transmission Range (TX) on the Performance of DSDV, AODV and DSR in Vehicular Ad hoc Network (VANET) Traffic Scenarios

Vehicular Ad-hoc Networks (VANETs) is an emerging technology that derives from the well-established Mobile Adhoc Network (MANET). VANETs could face a great challenge arising from the dynamic nature of the nodes which occurs due to the frequent change in network topology. In a wireless network, Transmission Range (TX) is an essential part of successful packet delivery. In addition, TX affects network performance by changing the antenna height. If the transmission range is minimized or maximized, that consequently has effects on the network performance such as throughput, packet delivery ratio, the end to end delay and routing load. In this work, various scenarios have constructed to evaluate the effect of transmission range on DSDV, AODV and DSR routing protocol in a VANET highway design using Network Simulator (NS-2).
Statistical Analysis of an Outdoor mmWave Channel Model at 73 GHz for 5G Networks

In recent years, an increasing interest in the millimeter wave (mmWave) communications plays a vital solution for the growing demands of the data and speed. As a vast unused spectrum, mmWave is expected to handle a key role in the access network of the fifth generation (5G) mobile technologies. Despite its ability to provide higher transmission rate and spread spectrum capability, mmWave suffers from several major drawbacks such as high attenuation and sensitivity due to its weather dependency which causes the modeling of an mmWave channel to be very challenging. In this research, we investigate a statistical analysis of an outdoor channel in 73 GHz for access network of an urban micro cell. A well known mmWave channel modeling simulator NYUSIM is used for both LOS and NLOS scenarios. Simulation scenarios consider some crucial network parameters such as angle of arrival, angle of departure, power delay profiles, and path loss to characterize an mmWave channel in the 73 GHz E band. These investigations may be utilized to demonstrate the potential of 73 GHz mmWave band and its suitability for the 5G outdoor networks.
Mobile Robot Localization via Unscented Kalman Filter

Mobile robot localization concerns estimating the position and heading of the robot relative to its environment. Basically, the mobile robot moves around without initial knowledge of the environment. Therefore, a scheme to handle it is necessary, such as the Kalman Filters. Rather than the Extended Kalman Filter, we choose to employ the sigma points approach. In this paper, we take into consideration the method proposed by Van Der Merwe to determine the sigma points in Unscented Kalman Filter. The simulation and results verify that the Unscented Kalman Filter works pretty well for locating the mobile robot.
Detailed analytical and experimental investigations on voltage source inverter fed Induction Heating prototype

In this paper a design procedure for a voltage source inverter fed multi-turn multi-layer induction heating coil is given. An induction heating system consists of a coil and a controlled power electronic converter. The fabricated induction heating coil is applicable to a different type of work-piece of any length. This work is a continuation of an earlier research work in which an induction heating coil was fed from a current source inverter. The nominal ratings and the coil overall dimensions have been kept the same in the two cases. Different experimental results are presented to show the working of the coil without and with work-piece that is to be heated at 10 kHz frequency with a VSI.
Study by the FDTD Method of Multiband Microstrip Patch Antenna Loaded with L-Shaped Slot

This paper presents an effective design of multiband microstrip-patch antenna. In this, the patch antenna can be designed for dual band, triple band and multiband application by cutting L-slot on the patch. Return loss is studied with the aid of the finite difference time domain (FDTD) numerical analysis. The new antenna can be used for several applications, especially in the GSM domain, and for Wi-Fi, Bluetooth, and several other applications.
Group-Wise Listen-Before-Talk Protocol for Dynamic Spectrum Sharing: A New Framework for Full Frequency Reuse

In existing dynamic spectrum sharing (DSS) systems, each node arbitrates channel access independently based on carrier-sensing mechanisms such as the listen-before-talk (LBT) protocol. Owing to the uncoordinated channel access between all nodes, the channel is occupied in a random pattern. This makes it difficult to reduce the mismatch in channel quality indicator (CQI) while increasing the spatial reuse gain between the different nodes; therefore, the areal capacity gain obtained by adding nodes is much lower in existing DSS systems than in the cellular system. In this paper, we propose a different means of improving the areal capacity for downlink DSS systems. It is a group-wise DSS approach that ensures full frequency reuse in each group of base stations (BSs) by performing LBT only between the representative BSs, each of them selected by each group. Once a channel is secured by each group, all its member BSs share a channel simultaneously. This approach makes closed-loop feedback-based link adaptation practical while boosting spatial reuse gain even in the DSS environment. To implement the group-wise DSS, we propose a single unified framework that employs elementary algorithms for BS grouping and carrier-sensing threshold adjustment. Our system-level simulation results demonstrate that the proposed framework boosts the areal capacity gain by approximately 4.42 times as much as the conventional approach.
Multi-Layer 3D MIMO Precoding Algorithm Based on Partitioned Regions

3D MIMO is an effective communication technology to improve system capacity. Through of vertical dimension and horizontal, a 3D MIMO precoding algorithm with higher efficiency spectrum and lower complexity can be designed. Traditional MIMO precoding depends on instantaneous channel characteristics and has high dimensional matrix computational complexity, which reduces the system spectrum efficiency. This paper proposes a multi-layer 3D MIMO precoding algorithm based on region partition horizontal and vertical, the first layer designs to eliminate the interference between different regions based on the statistics of channel information. On the horizontal dimension, the first layer of precoding will be designed based on and rate maximization guidelines. The second layer of precoding for each group of users between the interference to zero processing. Simulation results show that, compared with traditional multi-layer precoding and traditional ZF precoding, this algorithm has low complexity while maintaining good spectral efficiency.
Review of techniques used to detect the attack in WSN

Social media now days become heart and soul of modern world. Peoples who are far away from each other interact with each other using this medium. Social network forms the complex network as its size grows beyond bar and is exposed to attacks. Most common attack in social media is clone attack. This paper provide in depth analysis of clone attack along with techniques used to tackle this type of attack. Comparative analysis also suggests the best possible mechanism that can be selected for future enhancements.