Miracle-dz/UniFAR-FTUs huggingface dataset:
Histogram Feature-Based Approach for Walking Distance Estimation Using a Waist-Mounted IMU

This paper presents a new method of walking distance estimation using an inertial measurement unit (IMU) placed on the user’s waist. When the sensor is mounted on the waist, the walking steps can be easily detected. However, step length estimation is a challenging task. In this paper, a walking distance estimation method based on histogram features is proposed. The histogram features, including the uniform and logarithmic quantizations, are derived from each step segment of the acceleration norm data. Then, machine learning algorithms such as support vector regression (SVR), gaussian process regression (GPR), and linear regression (LR) are applied based on the histogram features to estimate the walking distance. Two experiments are conducted to evaluate the walking distance estimation accuracy, where test walking paths consist of a straight line corridor (80 m) and a rectangular path (about 1282 m). Experimental results show that the average absolute errors using the SVR model are 0.76% for straight line corridors and 1.14% for the rectangular paths. The proposed method is shown to outperform existing methods from comparison study.
A Simple Terahertz Orthomode Transducer Based on Equivalent Circuits Analysis

This letter presents a terahertz (THz) orthomode transducer (OMT) based on a simple structure and the equivalent circuits of these two modes are given. As one equivalent inverter is introduced, the bandwidth of one mode is broadened. The measured results of a back-to-back setup shows a relative good performance in the frequency of 210 GHz to 220 GHz. For both polarization, the reflection coefficients are all below -10 dB, and the cross polarization transmission coefficients are below -30 dB. At the center frequency of 216 GHz, the insertion loss is less than -3 dB and the isolation is below -55 dB.
Internet of Things and its Applications in Healthcare-A Survey

In this paper, the Internet of Things(IoT) and its applications in the healthcare industry have been discussed. The amalgamation of IoT with healthcare has instigated an increase in the improvement of devices for personalized healthcare management like smart watches, wearable sensors, etc. which have eliminated the need of expensive medical tools, painful procedures and reduced the dependency of patients on medical personnel. It has paved the way for better health opportunities and self-care. The paper also presents a comparison between various sensors used in the field of healthcare and their types, the IoT architecture, tools and technologies used to develop IoT systems, and m-Health apps. The objective of the paper is to clarify the concept of IoT to the reader and to make the reader aware of the present trends used in IoT healthcare.
Design and Analysis of MEMS Racetrack Resonators for Force Sensing Applications

We describe, a integrated optical MEMS racetrack resonator based force sensor. This sensor works on the change in the refractive index of the optical sensing element. The resonant wavelength of the sensing element is changed under the applied force of the sensor, which can be read out at the drop-port of the IO racetrack resonator. This force sensor provides a wavelength shift obtained is 120 pm per 1µN, Q-factor of 8153.
Dynamic Actor-Advisor Programming for Scalable Safe Reinforcement Learning

Real-world robots have complex strict constraints. Therefore, safe reinforcement learning algorithms that can simultaneously minimize the total cost and the risk of constraint violation are crucial. However, almost no algorithms exist that can scale to high-dimensional systems to the best of our knowledge. In this paper, we propose Dynamic Actor-Advisor Programming (DAAP), as an algorithm for sample-efficient and scalable safe reinforcement learning. DAAP employs two control policies, actor and advisor. They are updated to minimize total cost and risk of constraint violation intertwiningly and smoothly towards each other’s direction by using the other as the baseline policy in the Kullback-Leibler divergence of Dynamic Policy Programming framework. We demonstrate the scalability and sample efficiency of DAAP through its application on simulated robot arm control tasks with performance comparisons to baselines.
Enhanced Logistics Security Techniques Using IoT and 5G

Internet of Things is an evolving technology which apposes a large number of devices on a single communicating platform, through a connected heterogeneous network. Ubiquitous connectivity, pervasive presence and seamless communication with zero latency were the target points which were stumbling blocks for 4G networks. Accession of 5G networks on top of 4G networks for Internet of Things surmounts these drawbacks. This paper uses Internet of Things communicating over 5G networks and Cloud computing technology to propose an algorithm to improve security of Logistics and Transportation.
Some mathematical aspects to detect fake news: a short review

The aim of this work is to illustrate some mathematical aspects to detect fake news. The fake news phenomenon is currently a significant and constantly evolving problem. In today’s society, fake news is spreading significantly broader and faster. The detection mechanism should predict the information very quickly in order to stop the spread of fake news. This work is a review of two methods to detect fake news recently appeared in the literature [16], [25]. The first based on statistical approach and the second based on artificial neural network. Furthermore, some results are shown.
Cookies in a Cross-site scripting: Type, Utilization, Detection, Protection and Remediation

In accordance to the annual report by the Cisco 2018, web applications are exposed to several security vulnerabilities that are exploited by hackers in various ways. It is becoming more and more frequent, specific and sophisticated. Of all the vulnerabilities, more than 40% of attempts are performed via cross-site scripting (XSS). A number of methods have been postulated to examine such vulnerabilities. Therefore, this paper attempted to address an overview of one such vulnerability: the cookies in the XSS. The objective is to present an overview of the cookies, it’s type, vulnerability, policies, discovering, protecting and their mitigation via different tools/methods and via cryptography, artificial intelligence techniques etc. While some future issues, directions, challenges and future research challenges were also being discussed.
Recent Developments in the Vehicle Steer-by-Wire System

The next generation of steering systems in the auto manufacturing industry would be steer-by-wire (SBW) in which the mechanical connection between vehicle steering wheel and tires is removed. Numerous advantages are envisaged for this modification, but there are still some under-research technical and safety-related challenges to be addressed. This article presents a thorough review on the history of vehicle steering systems first. The structure and components of the cutting edge technology SBW system are subsequently described with the advantages and disadvantages discussed in detail. The main challenges of this system, including the required motor drives equipped with advanced controllers and reliability concerns, are studied and proposed solutions presented in the literate are reviewed. Finally, a projection of the future of steering systems considering driverless vehicles with integrated steering and traction systems is briefly presented.
Unmanned Quadrotor Transportation Systems with Payload Hoisting/Lowering: Dynamics Modeling and Controller Design

With high maneuverability and flexibility, aerial transportation by suspending the payload beneath the quadrotor is an ideal choice. To adapt more complex traffic conditions and extend the application scope of aerial transportation systems, the ability of payload hoisting/lowering is studied in this paper. The dynamic model is established by Lagrange's modeling method, and main characteristics are then analyzed based on the derived model. Subsequently, through an energy-based analysis method, we design a nonlinear controller for the corresponding system. By using Lyapunov techniques and LaSalle's invariance principle, the equilibrium point of the overall closed-loop system is proven to be asymptotically stable. Two groups of simulation are given to demonstrate the proposed control law's performance. Specifically, we compare the performance of the proposed system with the one with invariant rope length.
Observation-augmented EKF Based Local 3-D Localization Method for Moving Target

High accuracy and frequency local localization is the key technology of unmanned aerial vehicles (UAVs) in the confined areas and complicated conditions such as indoor flight, landing on ship deck and flight in GPS-denied environments and so on. In this paper, based on ultra-wideband (UWB) technique an observation-augmented EKF based local 3-D localization method is proposed for improving the accuracy when a moving target is tracked. In the first, a weighted least square (WLS) localization algorithm is proposed based on range measurements for improving the accuracy of 3-D localization. In the second, the position estimated by WLS is taken as partial measurements in an Extended Kalman Filter (EKF) to achieve the localization of a moving target so that the observation of the system is augmented and the accuracy is thus improved. Besides that, the ranging error model is analyzed with P440, a commercial UWB range sensor. Based on the result, the simulations are conducted to validate of the proposed method and the results show that the localization accuracy of the proposed method is greatly improved without remarkable computational complexity.
Grasp for Stacking via Deep Reinforcement Learning

Integrated robotic arm system should contain both grasp and place actions. However, most grasping methods focus more on how to grasp objects, while ignoring the placement of the grasped objects, which limits their applications in various industrial environments. In this research, we propose a model-free deep Q-learning method to learn the grasping-stacking strategy end-to-end from scratch. Our method maps the images to the actions of the robotic arm through two deep networks: the grasping network (GNet) using the observation of the desk and the pile to infer the gripper’s position and orientation for grasping, and the stacking network (SNet) using the observation of the platform to infer the optimal location when placing the grasped object. To make a long-range planning, the two observations are integrated in the grasping for stacking network (GSN). We evaluate the proposed GSN on a grasping-stacking task in both simulated and real-world scenarios.
Design and Compensation Control of a Flexible Instrument for Endoscopic Surgery

Snake-like robots for endoscopic surgery make it possible to reach deep-seated lesions. With the use of small flexible tendon-driven instruments, it is possible to perform bimanual micro-surgical tasks that are challenging for standard endoscopic surgeries. Existing devices, however, lack articulated wrists and rolling motion of the end-effector. This paper presents a new instrument design with a distal-roll gripper for snake-like robots. The developed 5 DoFs miniaturized instruments with a diameter of 3 mm enable the deployment into narrow endoluminal channels. Issues related to actuation coupling, tendon slack, and backlash are addressed. Experimental results show that the distal-roll gripper can rotate 106°, and the actuated joints can achieve good repeatability and accuracy with the proposed compensation control scheme.
Negative Comments Multi-Label Classification

It is a known fact that on the daily basis significant amount of information is produced due to large number of people being connected to social networking sites. Online interaction is now included in our lifestyle whether it is through a tweet, a message or through commenting on each other posts on different platforms. Online interaction contributes a significant part to our society but also contains several dangerous results. This paper mainly focuses on the cons of social interaction and a way through which it can be decreased. Aggression and related activities such as trolling peoples, harassing online involves hate comments in various forms. There are numerous such cases coming in present time and sites respond by closing down their remark areas. After the introduction of Machine Learning and having data in massive amount now its quite logical to build a tool which can tackle this situation. While there are algorithmic solution for these probelm but they are slow and expensive which make us curious about new approaches and frameworks. Four models are used for the purpose of classifying the comments, however the model which performed better than other three is Bi-LSTM + Bi-GRU model with accuracy of 97.89.
Artificial Neural Network based Dual Layered Predictive Model for Rare Attack Detection

Computer network is expanding day by day and number of users are increasing on the network. There are many attacks on the network with makes user on risk, leading to security of their data. Attackers are using novel method for getting information of users. Intrusion detection system (IDS) is used to detect such attack done on the network by monitoring the network traffic. In general attacks are of four types on the network in which Denial-of-Service (DoS) and Prob attacks are of majority categories (generally found or common), User-to-Root (U2R) and Remote-to-Login (R2L) are of minority categories (rarely found or rare). The rare attacks are very harmful for a host. The present day systems are not able to effectively detect such attacks. In this approach, a model is proposed of two layers to improve the detection rate of minority attacks. Feature selection for minor attacks is done using statistical analysis and a multi classifier artificial neural network is trained with a separate data set to improve the rate of detection of the minority attacks. The accuracy of 99.34 % is recorded for minor attacks detection
A Millimeter-Wave Frequency Synthesizer for 60 GHz Wireless Interconnect

A millimeter-wave (mmW) frequency synthesizer for 60GHz wireless transceiver is presented. To achieve wide range and low noise operation, a new sampling PD based PLL (S-PLL) is proposed. In contrast to conventional sub-sampling PD based PLLs, it provides a wider capture range without suffering from harmonic lock problems. Also, compared to conventional CP-based PLLs, the inband noise is improved by 13 dB while the reference spur is improved by more than 16 dB. Implemented in TSMC 28nm CMOS technology, the core circuit occupies a chip area of 0.175mm2. The measured phase noise from a 48 GHz carrier is -95.7dBc/Hz at 1MHz offset. The power consumption is 54mW.
Dual-Band RF-to-DC Rectifier with High Efficiency for RF Energy Harvesting Applications

Radio Frequency (RF) energy harvesting is emerging as a potentially reliable method to replace the costly and, difficult to maintain, source of low-power wireless sensors networks. In this paper, a dual-band RF-to-DC rectifier, that operates at LoRaWAN and GSM-900 cellular communication frequency bands, is presented. The system is composed of an impedance matching circuit, an RF-to-DC rectifier, that converts the ambient RF energy into DC voltage able to feed low power devices, and an output load. The proposed system resonates at two different frequencies of 866 MHz and 937 MHz. Numerical results demonstrate that the overall system exhibits an efficiency of 80% with 4.32 V of output voltage at 0 dBm of input power.
Evaluation of the Pedagogical Quality of Mobile Math Games in App Marketplaces

The number of available educational games has enormously grown and it is difficult for users to identify which games are pedagogically effective among the multitude of options in app marketplaces. Recent studies on math games have highlighted the importance of (1) linking learning attributes and gameplay and (2) game design that supports students' deliberate practise. Using these as a measure for pedagogical quality, we investigated 109 math games found on Google Play Store (n=61) and iOS App Store (n=48). Furthermore, monetization solutions, data use permissions, target age group and type of mathematical content were retrieved from the apps. The analysis showed that only 11,0% of the games integrated learning attributes with gameplay and 12,0% of the games contained tasks which support learners' deliberate practice. The most commonly featured math subject was arithmetic and the games were targeted mostly to early childhood (6–12 years). Finally, games following curricula content and recommended design principles were difficult to locate using the search tools of the app marketplaces.
Magnetic Docking Mechanism for Free-flying Space Robots with Spherical Surfaces

The autonomous operation of robots in the International Space Station (ISS) is required to maximize the use of limited resources and enable astronauts to concentrate more on valuable tasks. To achieve these goals, we are developing a station where the robot approaches, docks, charges, and then undocks. In this paper, the authors propose a magnetic docking mechanism for free-flying robots with spherical surfaces that makes it possible for a robot to dock securely without requiring highly precise guidance, navigation and control capability. By making use of a slider guide and repelling pairs of magnets, this mechanism can achieve tolerance for larger robot position error as compared with the conventional fixed guide mechanism. The experimental results show that the proposed mechanism can effectively enlarge the acceptable error range of poses, and also reduce acceleration at the moment of impact. We also introduce a model to predict the success or failure of docking from the contact condition of the robot and the guide by using a machine learning technique - Gaussian Process Regression (GPR). The prediction results shows that the learnt model can express the contact condition of successful docking.
Flocking for Multiple Subgroups of Multi-Agents With Different Social Distancing

In this paper, considering the difference in social distancing among individuals, according to the extent of social distancing, a group composed of $N$ mobile agents is divided into multiple different subgroups. Especially, from the perspective of differential game theory, the flocking problem of different subgroups can be regarded as collision avoidance between neighboring agents, or obstacle avoidance between agents and virtual static/dynamic obstacles. To explore the internal mechanism of this interesting problem, a novel flocking algorithm with multiple virtual leaders is designed. The proposed algorithm is a modified version of the traditional flocking and semi-flocking algorithms. Based on the Lyapunov stability theorem and LaSalle’s invariance principle, the stability analysis of the proposed algorithm is then proven. Furthermore, considering the complex environment that swarm robots or unmanned aerial vehicles (UAVs) may face when performing military missions such as surveillance, reconnaissance, and rescue, etc., we also investigate the flocking problem of multi-agents in both virtual static and dynamic obstacles environment. Finally, three kinds of simulation results are provided to demonstrate the effectiveness of the proposed results.
Floating-Gate MOSFET based Class-AB Flipped Voltage Follower Cell

The paper presents a novel low-voltage/low-power class-AB flipped voltage follower cell. The proposed circuit utilizes the advantages of floating-gate and bulk-driven techniques. The floating-gate MOSFET allows low-voltage operation and improves the current sinking capability whereas bulk-driven transistor improves the current sourcing capability without increasing complexity of the circuit. The proposed circuit has approximately unity voltage gain, low output resistance, large bandwidth, high current driving capability and improved linearity. The proposed circuit has been simulated using 180 nm CMOS technology of BSIM3v3.
Modelling and Mining Brain Network Data for Diagnosis of Neurodegenerative Diseases

Connectomes are brain networks represented as a graph with the vertices being the regions of the brain and weighted edges representing strength of connections between the regions inferred from brain imaging techniques such a Functional MRI (fMRI). An intense research activity is to use the connectomes to identify markers for brain disorders, especially neuro-degenerative diseases such as Autism Spectrum Disorder (ASD) by studying the differences in the connectomes of healthy subjects and patients. This paper presents a novel data model for the connectome data and analyzes its efficacy in the classification of ASD and Typically Developing (TD) (healthy) connectomes. The proposed data modelling begins by clustering the vertices (brain regions) using the Graph Spectral Clustering into fixed number of clusters, the number of clusters chosen as four based on the empirical evidence. The resulting clustering is used to map the vertices into a binary matrix which is then converted into a binary row vector to form a vector space model of the connectome data that is employed to classify the connectome data. The developed model is first validated using Human Connectome Protocol (HCP) FMRI derived connectome data of 812 healthy patents. Binary data models of the UCLA Autism dataset with fMRI and DTI scans of 42 ASD and 37 TD subjects are generated and employed for their classification. Different classification algorithms are trained, tested and their performance evaluated using the resulting dataset. Cross Validation (CV) estimates identified the best performance (83% recall and 83% precision) for DTI data and (73% recall and 89% precision) for the fMRI data achieved using Logistic Regression.
Digital Fitness - Goal for the Chief Digital Officer

Digital Transformation (DT) causes a technology-driven change in organizations and society. But a precise state, an organization should strive for by this change, is not discussed adequately. Anyway, organizations implement a Chief Digital Officer (CDO) as a strategic management role, to control the journey of DT. This new role should be supported by assessment and control guidance. In this contribution we aim to understand and conceptualize the target state of DT. Based on a discussion of literature and expert interviews, we found out, practitioners themselves are not aware of the target state, although they must decide what is to reach on the DT journey. As a starting point for the development of guidance for the CDO, we secondly propose a model of digital fitness that conceptualizes the target state of DT comprising of three determinants: Digital Adoption, Digital Expertise and the Adoption Rate of Digital Innovations.