Miracle-dz/UniFAR-FTUs huggingface dataset:
Robot localization performance using different SLAM approaches in a homogeneous indoor environment

Robot localization has always been a significant concern in the robotic field. This paper presents robot localization performance for a ROS-based differential platform using four distinct measuring tools. The majority of mobile robots deploy the odometry data for localization, which in turn is highly influenced by the accuracy of encoders and IMUs as well as the quality of tracking surface. The main objective of this research is to eliminate the odometry and to estimate the robot's position and orientation (Pose) merely through an image or a laser scan. Several SLAM methods have been proposed to extract the robot's Pose out of visual data. Despite the emergence of visual sensors in recent years, no pertinent study could be found on the influence of camera types on the Pose accuracy. In this work, Visual SLAM is implemented using Xbox 360 Kinect, Xbox One Kinect, Realsense D435, and Laser SLAM is implemented utilizing Hokuyo UTM30-LX. At last, the benefits and drawbacks of using each are discussed by referring to their performance.
Process Variation Analysis of 10T SRAM Cell for Low Power, High Speed Cache Memory for IoT Applications

SRAM (static random access memory) based cache memories are widely used due to their high speed. On-chip SRAMs are considered as one central part of the SOCs (system on chips) circuits as they determine the power dissipation of SoCs and the speed of its operation. Hence, having a low-power SRAMs is very important. Due to the downsizing of CMOS (complementary metal-oxide-semiconductor), low power design has become the major challenge of modern chip designs to make the devices portable and compact. The significant zone of worry in today's technology regards to speed, power consumption, size, and reliability to accomplish better performance. In conventional 6T SRAM, power consumption is very high. Thus, to overcome the problems, researchers have reported many SRAM cells such as 8T, 9T, 10T, NC, etc. The study and comparison of a different SRAM cell based on various performance metrics is done. This paper explores the design and analysis of a new 10T SRAM cell which majorly focuses on optimizing power consumption, delay, and stability.
Silent speech classification based upon various feature extraction methods

Envisioned speech captured by EEG signals is a fascinating area of research as this is useful in bio-medical applications for the patients suffering from motor neuron diseases and also in those areas where silent speech is desirable. This work examines the possibility of better feature extraction techniques from which a robust model with the help of a classifier can be built. Results showed that Common Spatial Patterns (CSP) filter coefficients in a combination of statistical features with Random Forest as a classifier turn out to be a suitable choice. Three imagined tasks /a/, /u/ and /rest/ were discriminated with highest accuracy reaching up to 89%.
Human Body Modeling for Ground Contact Force Estimation of RoboWalk

RoboWalk is a lower limb rehabilitation robot for Human body weight assistance. To achieve a proper design of RoboWalk, there is a need to understand the dynamics of human and foot contact forces with the ground that is the focus of this paper. In fact, an important component of human-Robo Walk forward dynamics simulation is foot-ground contact modelling. Different models have been proposed to simulate the contact of the foot with the ground. In this study, the dynamic equations of human body is developed with 19 degrees of freedom, while three points of collision with the ground are considered for each foot. Next, the kinematics information obtained from the experimental data are introduced to the dynamical equations, and the joint torques are estimated by solving the inverse dynamics of the human model. The results are compared to the experimental data for model validation. However, it is assumed that the numerical value of the foot contact model parameters are not available, so the pelvis is constrained in such a way that the human model foot does not hit the ground. Therefore, these uncertain parameters does not affect the validation results. Next, a model is selected from the existing models to express the foot contact behavior with the ground, while such models should provide stable walking with selecting the appropriate parameters. To determine the shear and normal contact force, numerical values of the parameters are required while these values depend on the shoe and the ground material and surface. Then, the path traveled by the pelvis in the human dynamic model was compared with the path of the actual pelvic motion and their difference was used to form a cost function to determine the parameters. In the next step, by choosing appropriate values for the contact parameters of the foot and the springs and the damping of the toe joint, the path of the pelvis becomes closely identical to the actual pelvis motion. Finally, after the human gait stabilized, the developed dynamics model is compared with the experimental data.
Distributed Day-Ahead Peer-to-Peer Trading for Multi-Microgrid Systems in Active Distribution Networks

Developing a reasonable, efficient distributed market transaction mechanism is an important issue in distribution systems. The gaming relation between distributed transaction market entities has yet to be fully elucidated in various trading links, and the impact of distributed transactions on distribution network operations has yet to be comprehensively analyzed. This paper proposes a novel distributed Peer-to-Peer (P2P) day-ahead trading method under multi-microgrid congestion management in active distribution networks. First, a flexible load model for price-based demand response load and an autonomous microgrid economic scheduling model are constructed. Second, under normal operation of the distribution network, a non-cooperative game model and Stackelberg game model are employed to separately and comprehensively analyze gaming relationship among sellers, and between sellers and buyers. Thereafter, a congestion management method based on market capacity is established from the perspective of distribution network control centers. Finally, the impact of end energy consumption characteristics on microgrid economic scheduling and P2P trading is analyzed through a modified IEEE 33-node power distribution system. The economic and technical benefits such as congestion mitigation and network loss reduction that produced by P2P trading to the operation of microgrid systems are analysed with specific indicators.
Meandered Signal Coplanar Sensor (MSCS) for Glucose Detection

In this work, a microwave sensing mechanism based Meandered Signal Coplanar Sensor (MSCS) for real-time and label-free glucose detection has been demonstrated. In this design, a meandered structure has been embedded in the signal conductor of the CPW transmission line where central part of the meandered structure has been utilized as the sensing area using PDMS (Polydimethylsiloxane) for Sample Under Test (SUT). RF measurements of MSCS with SUT which is aqueous solution of glucose with concentration varying from 0.0 g/ml to 0.6 g/ml, has been closely observed. The experimentally measured RF parameters showed a significant shift in magnitude and frequency which is 3.53 dB with 100 MHz for S11 and 2.65 dB with 15 MHz for S21 respectively. The regression analysis showed the sensitivity 5.76 dB/g/ml and 4.50 dB/g/ml with regression coefficients 0.997 and 0.989 whereas sensitivity is 153.57 MHz/g/ml and 10.71 MHz/g/ml with regression coefficients 0.970 and −0.021 for S11 & S21 parameters respectively.
Application Development for Wearable Internet of Things using Hexiwear

Wearables are highly reliable and portable Internet of Things devices that can be worn, implanted or used in any other similar situation where the device can be put-on or taken-off conveniently. Various wearables are available as commercial products which include Kapture, Fitbit, Muse, and Sensoria. They have sensors, activators, microcontrollers, and communication radios within a small form-factor device (they are also available as a single chip CPUs) which may display various vital parameters within the device, permit taking actions, process data and communicate wirelessly with a neighboring gateway which in turn puts it on to the Internet. This work discusses wearable Internet of Things technology including application development for wearables (device, gateway and cloud applications). It also presents various available hardware and software platforms that can be used to produce wearable Internet of Things devices. Further, Hexiwear –a modular wearable device having capabilities of expanding its application domain using clicker boards is explored and discussed in terms of its hardware features and application development. Furthermore, the paper demonstrated wearable application development for Hexiwear and its expandability by developing and demonstrating imaging, air-quality and motion detection applications.
RTS: A Robust and Trusted Scheme for IoT-Based Mobile Wireless Mesh Networks

Wireless mesh networks consist of various mesh clients that are organized in an unfixed infrastructure and packets are forwarded using a multi-hop model. Routing protocols have a significant impact on mesh networks because their performance has a crucial effect on nodes connectivity and throughput. Recently, the integration of mesh clients with the Internet of Things (IoT) has gained significant importance to connect billions of machines and achieve fast coverage with minimum network cost. However, if mesh clients are mobile, then data routing via intermediate nodes gives a noteworthy effect on the network performance and latency. Furthermore, over the Internet, a malicious node may be a part of the mesh network and as a result, the sending information can be manipulated and compromised. Therefore, this research article aims to propose a robust and trusted scheme (RTS) for IoT-based mobile mesh networks to provide reliable routing, data confidentiality, and integrity. Firstly, the proposed scheme presents a robust data routing among mobile mesh clients, routers and gateway devices based on the network parameters and measurement of wireless channels. Moreover, the wireless channels between mesh devices are formulated based on the efficacy of link costs for data dissemination. Besides, the location of mobile mesh clients is determined by computing the distance vector at a regular time interval. Secondly, a secure and authentic data protection technique is proposed using public-private key cryptography, which aims to increase the protection of mesh clients with minimal overhead. The competence of the proposed scheme is significantly improved with respect to network throughput by an average of 14%, packet loss rate by an average of 37%, latency rate by an average of 12%, computational overhead by an average of 34%, and energy efficiency by an average of 20% as compared to other works.
Diabetic Retinopathy Classification using Downscaling Algorithms and Deep Learning

Diabetic Retinopathy (DR) is an art and science of recording and classifying the retinal images of a diabetic patient. DR classification deals with classifying retinal fundus image into five stages on the basis of severity of diabetes. One of the major issue faced while dealing with DR classification problem is the large and varying size of images. In this paper we propose and explore the use of several downscaling algorithms before feeding the image data to a Deep Learning Network for classification. For improving training and testing; we amalgamate two datasets : Kaggle and Indian Diabetic Retinopathy Image Dataset. Our experiments have been performed on a novel Multi Channel Inception V3 architecture with a unique self crafted preprocessing phase. We report results of proposed approach using accuracy, specificity and sensitivity, which outperform the previous state of the art methods.
Poster-Based Multiple Movie Genre Classification Using Inter-Channel Features

As the scale of the film industry grows, the demand for well-established movie databases is also growing. The genre of a movie supplies information on its overall content and has multiple values. Therefore, it should be well classified utilizing the characteristics of movies, without omissions in the database. In this study, we extract the optimal information and characteristics from movie posters to aid the classification of movies into genres and propose the use of a Gram layer in a convolutional neural network (CNN). The Gram layer first extracts style features by applying the Gram matrix to produce a feature map of a poster image. Using this as a style weight, the existing feature map is merged with style information to perform the genre classification task. The proposed Gram layer performed multi-genre classification tasks with higher efficiency than a residual neural network (ResNet), which is the current CNN model used for such tasks. We compared the activation map with the Squeeze-and-Excitation network, which gives weight to the image, and we confirmed that the introduction of the Gram layer actually focuses on the style of the movie poster. To classify the movie genres, we reconstructed the poster dataset into 12 multi-genres that emphasized the characteristics of each poster.
Robustifying Visual Place Recognition with Semantic Scene Categorization

This research proposes an accurate loop closure detection method for the real-time robot navigation in changing light, viewpoint and weather conditions. It focuses on enhancing the accuracy performance of visual place recognition by integrating the semantics of image scenes with handcrafted features. This method reduces the computational cost of the feature matching process by segmentation of the dataset. The results presented depicts that scene recognition can improve the place recognition even in the large viewpoint, weather and light changes
RSImageNet: A Universal Deep Semantic Segmentation Lifecycle for Remote Sensing Images

In real applications, there is a lack of labeled data to train a proper deep neural network (DNN) model for map generation of remote sensing images. The aim of newly acquired data in spaceborne or airborne platforms is often to consistently observe the Earth for new tasks in the applications such as disaster monitoring, climate change, disease control. To fulfill the tasks, the corresponding classification maps should be obtained traditionally based on the assumption that a classification model should be learnt by the labeled data for the same task from the same scene or at least from the historical labeled remote sensing image pixels provided by domain experts in the same areas by the same sensor, which is denoted as labeled target data. In the paper, a universal deep semantic segmentation lifecycle is proposed against the assumption aforementioned, i.e., there is no need to have the labeled data for the same/similar task from the same locations and sensors to define a proper DNN model. In particular, a general labeled dataset is generated through a feature binding strategy in terms of real-world existed remote sensing images, which is named RSImageNet. In addition, a special training strategy is proposed by using the RSImageNet dataset to train a universal deep semantic segmentation model with a balanced constraint for the loss function. Without the labeled target data from the area observed, we gain an average overall accuracy of 77.32% in the range of 67.28–94.63% on 6 real world datasets by taking advantage of the proposed universal deep semantic segmentation lifecycle and the generated RSImageNet dataset.
Evaluation of endothelial dysfunction and its improvement after cardiac rehabilitation

Endothelial function is considered an independent predictor for risk of future cardiovascular events in cardiovascular and noncardiovascular patients. Given the prevalence of cardiovascular disease (CVD), preclinical detection may help modify atherosclerosis and prevent events. Although traditional cardiovascular risk factors relate to future CVD, the predictive value of these risk factors can be relatively limited. Contemporary scientific attention has focused on alternative direct measures of arterial function . Studies have provided unique insights into the pathophysiology of disease processes, the response to treatment and prognostic information that may be helpful in risk prediction. Peripheral measures of endothelial function are now established surrogate markers of vascular risk and have become important markers for clinical research. Cardiac rehabilitation (CR) is of critical importance in the secondary prevention of cardiovascular disease. It has been reported that CR was negatively related to reinfarction, cardiac morbidity and all-cause mortality. The beneficial influence of cardiac rehabilitation on endothelial dysfunction has been demonstrated in many studies, but the the mechanisms by which exercise improves endothelial function are not fully clarified.
Building Collaborative e-Learning Teams in a Smart Education Environment

Building Collaborative e-Learning Teams in a Smart Education Environment Abstract: Collaborative learning is a teaching method requiring to group students according to various ways and types, it is the most used approaches to arrange students for an effective learning in the classroom. However, with the large number of students in e-learning context, and their different settings, the task to establish cooperation and collaboration among them becomes harder and complex. Hence, several researchers proposed different approaches to overcome this diversity and find the best solution for building a good collaborative learning environment. In this context, this work discuss the Ant Colony optimisation (ACO) bio-inspired approaches and their added values in Data Analysis which leads us to assume the collaborative e-learning teams (CeLT) formation as a data clustering problem. Therefore, we analyse and discuss some ant clustering algorithms, and adapt the best one so it can be applied on an educational data sets. The results show a good starting point for future works on a real smart education Environment.
Enhancement of FRT Capability of DFIG Based Wind Farm by a Hybrid Superconducting Fault Current Limiter With Bias Magnetic Field

Doubly fed induction generators (DFIGs) have some enthralling features which made them one of the most popular choices in power system. However, any kind of system abnormalities will directly affect the DFIGs as their stator windings are directly linked to the grid. To comply with the grid code, every DFIG based wind farms are required to ride through the fault keeping the healthy part of the system undisturbed. So, fault ride through (FRT) capability improvement is a mandatory requirement for every DFIG based wind farms. Superconducting fault current limiters (SFCLs) have been popular for decades in this regard. In this paper, a new hybrid superconducting fault current limiter (HSFCL) with bias magnetic field is put forth. The HSFCL is designed by combining a double split reactor with a high temperature superconducting (HTS) magnet which is non-inductive. For the validation, its performance has been compared with that of bridge-type fault current limiter (BFCL) and series dynamic braking resistor (SDBR). MATLAB/Simulink environment is used to perform the simulations. It has been found that, the proposed HSFCL outperforms both BFCL and SDBR.
Active Power Flow Control of Three-Port Converter for Virtual Power Plant Applications

This paper proposes a compact power conversion system (PCS) for virtual power plants (VPPs) formed by an IoT aggregation of many residential solar and battery systems. The falling costs of IoT technology, batteries and solar systems have exposed power conversion costs as a growing part of the total system cost. The proposed PCS is based on an isolated three port converter (TPC) and an inverter stage to facilitate grid connection. The TPC solution seeks to reduce costs by combining several power conversion devices into a single package. Any single-phase conversion system must deal with double line frequency (DLF) power fluctuations. This TPC conversion allows the DLF power components to be selectively directed to any port. The DLF components can be restrained to a filter at the grid connection port or directed to a battery system. It is generally desirable to direct DLF currents away from the PV port. A 3-level square wave modulation method is adopted to control DLF flows in the PCS and to the converters soft switching capability. The simulation model with the proposed control methods for the PCS is developed in Matlab/Simulink. The simulation and experimental results show that the DLF voltage ripple on the dc-link is suppressed. The results also show that the active power of the PV port is free of DLF oscillations.
A Comparative Study of Recent Real Time Semantic Segmentation Algorithms for Visual Semantic SLAM

Visual Simultaneous Localization and Mapping (vSLAM) has gained much attention for localization and mapping of autonomous vehicle and many impressive and robust vSLAM systems have been developed and achieved considerable performance in recent years. However, some problem have still not been solved because of limited information from geometrical features. In this paper we provide a comparative analysis of computationally effective pixel-wise semantic segmentation algorithms that can be used in visual semantic SLAM.
Efficient Calibration Method of Multiple LiDARs on Autonomous Vehicle Platform

Autonomous vehicles need to use various multiple sensors in order to perform their missions perfectly on outdoor roads. Therefore, it is very important to calibrate the extrinsic parameters to describe the relations between sensor frames with respect to the reference frame. In this paper, we propose an efficient calibration method of multiple LiDARs using a simple rectangular board. For the efficient calibration using a simple rectangular calibration board, we propose an accurate corner point detection method of the calibration board using the iterative Hough transform.
Joint Scan Rate and Emitter Location Estimation Algorithm in SBR System Based on AOA and DTOST Measurements

A joint scan rate and emitter location estimation algorithm in scan-based radar (SBR) system based on the angle-of-arrival (AOA) and the difference time of scan time (DTOST) measurements is proposed in this paper. SBR localization is a kind of passive geolocation technique for stationary pulsed radars. To date, a huge number of passive localization techniques using AOA, DTOST or other measurements for SBR have been developed assuming prior knowledge of the scan rate. However, it is usually hard to obtain the accurate scan velocity in most of practical civilian and military applications. In this paper, by using the linear correlation between AOA and DTOST, the maximum likelihood (ML) estimation algorithm based on linear constraints is given to obtain the scan rate of SBR. And the new AOA information after fusion is acquired on this basis. Then the weighted least square (WLS) method is used for SBR localization and the Cramer-Rao lower bound (CRLB) is derived for SBR location estimation. A detailed analysis of scan rate and emitter location estimation process is given and the effectiveness of the proposed localization algorithm is illustrated with computer simulations.