Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of an Agile Omnidirectional Mobile Robot With GRF Compensated Wheel-leg Mechanisms for Human Environments

Wheeled mobile robots have been receiving continuous attention in various fields because of their stability, efficiency, and simple mechanisms. However, most of them require significantly larger occupied space than humans and have limited maneuverability in the environment for humans, such as crowded corridors, floor thresholds, ramps, and elevators. This letter presents a new wheel-leg mobile robot that combines the legs for changeable support polygon and mecanum wheels for holonomic motion. The robot has a human-like slim shape with 4 wheel-leg mechanisms, each of which has one 2-DOF leg frame and one mecanum wheel. To allow 2-DOF leg motion while maintaining the orientation of the mecanum wheels, a unique parallel link mechanism was devised. In order to achieve agile motion and impact and vibration absorption capability, a back-drivable high payload compound planetary gear was developed. The ground reaction force (GRF) compensation mechanism in each leg reduces the required payload of the actuators. The letter describes the detailed mechanical design of the robot and the actuators. The effectiveness of the proposed robot was verified in various experiments, fast omnidirectional motion test, a climbing test of a single step, and a motion test in the elevator with people.
Statistical Analysis of the Low-Bit Quantizer’s Output in Presence of Multipath Fading Effects

The proposed research performs a closed-form statistical analysis of the input and output signal of the low-resolution ADC functioning in presence of the multipath propagation and fading. The ADC device is represented by the quantization unit with antisymmetric characteristic, which resolves the possible overload situation with either the saturation or the reset strategies. To account for the distortions of the output signal caused by the envelope fluctuations at the input of the nonlinear quantizer, an energy suppression coefficient is introduced and evaluated in closed-form for the case of the Nakagami-m envelope distribution. The derived results are applied to estimate the energy suppression level depending on the system/signal parameter: bit-depth resolution, input process root-mean-square level, fading severity and overload strategy. The possible ranges of analyzed parameters values yielding minimum suppression are calculated, and the worst-case scenarios are determined.
Iterative Modulation Classification Algorithm for Two-Path Successive Relaying Systems

Two-path successive relaying (TPSR) systems have been recently proposed as powerful candidates for improving the spectral efficiency of cooperative communications. The previously reported investigations for such systems are limited to information-theoretic analysis, parameters estimation, interference cancellation, and data detection. In this contribution, we discuss the problem of modulation classification (MC) for TPSR systems for the first time in the literature. We exploit the specific nature of the received signal to derive a maximum-likelihood (ML) estimate of the modulation format of a received signal. Since the exact ML solution is shown to be too challenging for real applications, we resort to the expectation-maximization (EM) method as a low complexity iterative procedure. Channel estimation is also introduced as an auxiliary task for the proposed algorithm. The computational complexity analysis and simulation results confirm the effectiveness of the proposed iterative algorithm where no existing alternatives are available.
Identification of Plant Diseases Using Image Processing and Image Recognition

In the field of agriculture, image processing is a constantly evolving field of research and progress. Currently, several plant disease identification studies are underway. Identifying plant diseases can not only help farmers increase yields, but also promote a variety of agricultural practices. This paper proposes an algorithmic program for the diseases detection and categorization with the assistance of machine learning mechanisms and image recognition tools. First detect and record the contaminated area and then perform image pre-processing. Then collect the fragments, identify the infected area, and perform feature extraction on it. This article discusses the methods of using leaf photography to detect plant diseases. In addition, this article also introduces some feature segmentation and extraction algorithms for plant disease detection.
Shannon Capacity under Relative Motion

The classical form of Shannon capacity describes the quasi-static capacity, i.e., the movement of receivers or transmitters is negligible compared with the signal propagation speed (SPS). When the speed of relative motion (SRM) between transmitters and receivers is comparable with SPS, the capacity may deviate from the classical form. This study takes a different perspective on the Shannon capacity and extends the classical form of capacity with constraints of SRM and SPS. The special theory of relativity is used to analyze the capacity when SRM approaches the speed of light. Results imply the threshold below in which we can neglect the capacity deviating from the classical form for wireless radios. While for low SPS which is far less than light speed, modifying factors should be particularly added to the classical capacity form.
Highly Available Service Access Through Proactive Events Execution in LTE NFV

The explosion of mobile applications and phenomenal adoption of mobile connectivity by end users make all-IP based 4G LTE as an ideal choice for providing Internet access on the go. LTE core network which handles device control-plane and data-plane traffic becomes susceptible to network resource constraints. To ease these constraints, Network Function Virtualization (NFV) provides high scalability and flexibility by enabling dynamic allocation of LTE core network resources. NFV achieves this by decomposing LTE Network Functions (NF) into multiple instances. However, LTE core network architecture which is designed considering fewer NF boxes does not fit well where decomposed NF instances add delays in network event execution. Certain control-plane events being time critical hurt data-plane traffic requirements defined by LTE standard. This paper proposes Fat-proxy which acts as a stand-alone execution engine of these critical network events. Through space uncoupling, we execute several signalling messages in parallel while skipping unnecessary messages to reduce event execution time and signalling overhead while ensuring highly available service access. We build our system prototype of open source LTE core network over virtualized platform. Our results show that we can reduce event execution time and signalling overhead upto 50% and 40%, respectively.
Two-Sheet Type Rotary-Driven Thin Bending Mechanism Realizing High Stiffness

Thin construction is an advantage in the design of mechanisms. Among them, the soft-bending thin sheet actuator can fit into the shape of an object and grasp it after inserting a finger into a narrow space. However, to facilitate bending, these mechanisms are thin or made of soft materials, which leads to low stiffness. In this study, we proposed a thin metal sheet actuator that deforms its cross-section in accordance with the bending motion to achieve both thinness and rigidity. It was composed of two metal sheets connected with rotational joints and links. We focused on the anisotropic bending stiffness of the metal sheet and realized a three-dimensional deformation structure; it can be driven only with rotary-driven input using both torsional and bending deformations of the sheet. The experimental results indicate that the stiffness is up to 8.7 times higher compared to the undeformed sheet structure. In addition, we used this mechanism to realize a thin finger mechanism with a lifting motion after inserting a narrow gap between the object and the floor. Additionally, we realized a compliant contacting motion by focusing on different elasticities depending on the direction of bending.
Data Sanitisation and Redaction for Cyber Threat Intelligence Sharing Platforms

The recent technological advances and changes in the daily human activities increased the production and sharing of data. In the ecosystem of interconnected systems, data can be circulated among systems for various reasons. This could lead to exchange of private or sensitive information between entities. Data Sanitisation involves processes and practices that remove sensitive and private information from documents before sharing them with entities that should not have access to this information. This paper presents the design and development of a data sanitisation and redaction solution for a Cyber Threat Intelligence sharing platform. The Data Sanitisation and Redaction Plugin has been designed with the purpose of operating as a plugin for the ECHO Project’s Early Warning System platform and enhancing its operative capabilities during information sharing. This plugin aims to provide automated security and privacy-based controls to the concept of CTI sharing over a ticketing system. The plugin has been successfully tested and the results are presented in this paper.
Information Cascade Mechanism and Measurement of Indonesian Fake News

The high number of social media actors has the potential to produce fake news. Fake news could be motivated by various agendas, such as politics, government, and health. Therefore, we need to know how the mechanism and measurement of the spread of fake news. One approach to studying the spread of it is the information cascade. This paper will model the information cascade mechanism using Social Network Analysis (SNA) and Susceptible-Infected (SI) model. SNA is adopted to investigate the spreading mechanism and determine the proportion of actors exposed to fake news. SI is applied to measure the speed of transmission of fake and true news. Using several topic samples, the results allow us to understand the mapping of cascade information from fake news by a level that differentiates the node level from the source news to the rest of the nodes in the network. Our finding fake news can reach 0,6414 more fractions and spread 4,6 times faster than true news.
A Polarization Splitter Using Dual Core Photonic Crystal Fiber

This work presents a dual core hexagonal structure photonic crystal fiber as a compact polarization splitter. The simulation is performed by full vector finite element based tool. The use of gold metal ensures the value of CLR=2 and aids in achieving a small length of 481.73 μm with a high extinction ratio of -98 dB at 1.55 μm. This confirms a wider range of bandwidth of 200 nm from 1450 nm to 1650 nm, covering nearly all the bands of communication (S, C, L and U) with extinction ratio larger than -20dB making it suitable to be used in communication system.
FDispatcher: Using Flow Director to Accelerate NFV Packet Dispatching

Packet dispatching forwards arrival traffic from the network interface card (NIC) to the virtual network functions (VNFs) in each server in network function vir-tualization (NFV) platforms and has been recognized as a fundamental problem. Traditionally, the dispatching has been CPU-intensive, while existing offloading approaches rely on expensive software-defined network (SDN) switches or Smart-NICs. Our paper proposes FDispatcher, an NFV framework that leverages the Flow Director (FDir) feature on the inexpensive off-the-shelf NICs, to bind flows with destination VNFs for CPU-bypassed packet dispatching. However, the number of bindings that FDir supports is far less than the flow concurrency that an NFV middlebox handles. We divide packet dispatching into FDir-assisted fast path and software-based slow path, and then enable fast-path flows monitoring, reduce FDir reconfiguration overhead, and eliminate potential heavy I/O during scaling. FDispatcher improves packet dispatching throughput by 1.6x~2.8x with a 38x driver efficiency and costs no more than 2 ms for load balancing during scaling.
Investigation of Personal Rapid Transit Station Capacities using Microscopic Traffic Simulation

Personal Rapid Transit (PRT) is a promising on-demand transport system, where small-sized autonomous vehicles bring passengers directly from A to B on their dedicated guideways with a relatively high operational speed at low costs. However, served capacities of PRT systems are often less than attractive alternative modes such as mass rail transit and capacity plays a crucial role in the acceptance of PRT. Capacity of a PRT is affected mostly by the main guideway capacity, number of vehicles and station capacity. This paper focuses on the latter factor and aims at investigating the station capacities by modelling various station layouts in a microscopic traffic simulation environment. Five station layouts were modelled in the microsimulation software Vissim for different numbers of berths and different boarding/alighting behaviors by using the autonomous vehicle car following models. Results show that the loosened design of a serial station with an additional station guideway offers the highest capacity among other layouts. The station layouts where vehicles need to reverse or require time for using the 90-degree parking module lead to a considerable loss in capacity. It has also been found out that increasing number of berths increase the capacity, yet the relation is not directly proportional since the capacity increase slows down per additional berth. Benefits and drawbacks of the methodology as well as ideas for further research in the field are also discussed at the end of the paper.
Performance analysis of Grid Integrated Hybrid Renewable Energy System configuration application to Residential Buildings

Recent technological advancements and cost reduction drives increasing the penetration of renewable energy sources with grid. Utilisation factor of renewable energy sources is low due to dependency on the environmental conditions. Basically, hybrid energy systems provide constant, high-quality power for the remote communities. The sizing of the hybrid system is significant to meet the load demand of residential buildings. In this paper, A simple optimal sizing algorithm is used to model the Wind turbine and PV system to meet the residential load. Quantitave feedback theory (QFT) based robust controller is implemented for the proposed sub-module integrated PV converter and the PMSG based wind energy conversion system to extract maximum power using simple genetic algorithm. Performance of the QFT controller is analysed through MATLAB simulations under varying irradiance and wind conditions.
Correlation between Nitrogen Pollution and WWTPs Discharge in Lake Taihu

Too much nitrogen (N) and phosphorus (P) in the water can cause severe eutrophication, which can make the water undrinkable. In order to find the trends and causes of N pollution in Lake Taihu over the past two decades, we collected and analyzed water quality data of Lake Taihu and surrounding wastewater treatment plants (WWTPs) discharge data. We found that the average total nitrogen (TN) concentration in the northern part of Lake Taihu reached its peak in 2006 and began to decline after that. Spatially, the concentration of TN is getting higher from the center of the lake to the north bank. Moreover, we found that the decrease in TN concentration in Lake Taihu has a moderately strong correlation with the discharge of ammonia nitrogen (NH4-N) in WWTPs. Finally, we used two machine learning models to predict the monthly average TN in Lake Taihu and discussed features of significant importance.
Design and Implementation of a Low-cost Real-Time Vein Imaging for Developing Countries

Real-Time Vein Imaging is a device with which we aim to locate the veins in our arms easily for any medical purpose for intravenous (IV) access. Most electronic vein viewers are very expensive or complex in design. Our work presents an affordable, simple designed, easy to use portable device which can overcome the difficulties faced by phlebotomists or doctors during vein puncture. This device can be used irrespective of age, skin color, thickness or collapsed veins. The design of our prototype has been developed to capture the vein images using multispectral near infrared (NIR) source. The veins can be seen and traced on the skin on exposure of NIR making it easy to view and select the right vein and not cause patients unnecessary pain due to multiple punctures. Utilization of this device could help the jobs of the doctors and phlebotomists of developing countries such as Bangladesh.
Inter-satellite token ring Ad hoc network technology for micro-nano satellite cluster collaboration

With the rapid increase of satellite formation network demand, inter-satellite link technology has become a research hot-spot issue of space-based Ad hoc network. Token ring protocol is a mature networking protocol for LAN, which has been widely used in Ad hoc networks. As a non-compete protocol, it can effectively solve the problem of channel access control and avoid the problems of hidden and exposed nodes in wireless communication. Based on token ring protocol, we proposed an inter-satellite token ring Ad hoc protocol, and its state transfer mechanism, network access process, leaving network process, and frame type are discussed in detail. We determined the hardware which is composed of SoC platform of Zyqn7000 series and AD9361, and conducted relevant performance analysis in combination with the specific micro-nano satellite cluster geometry. By using the inter-satellite wireless self-organizing token ring protocol, the networking time of the 15 satellites in this cluster is less than 10s, and the end-to-end delay of the satellite network is controlled in seconds. In addition, the protocol supports new nodes to enter the network and fault nodes to leave the network, which greatly improves the network flexibility.
Health Care Devices for Diabetic Patient Monitoring Using IOT

Diabetes case management is a disciplined method that can provide us with accurate data about diabetic patients. The monitoring framework for diabetics plays an important role in monitoring patient welfare, especially when using Internet- based devices (IoT). The framework is basically able to evaluate diabetic patients and store information about blood sugar, internal temperature and surface area. Part of the framework is not limited to examining patients, it can also organize information using artificial intelligence technology. Patients are critical because they can help diabetics, their families, healthcare professionals, and clinical analysts develop treatment plans for diabetics based on a wealth of information. Calculate exposure and accuracy ap. This is about recalculation, which is the opposite of choosing the best with certain restrictions.
Implementation of Simulated Annealing-Support Vector Machine on QSAR Study of Indenopyrazole Derivative as Anti-Cancer Agent

Cancer is a disease that occurs due to the uncontrolled growth of abnormal cells causing body tissue damage. This disease is considered as a deadly disease. In 2019, 1700 deaths occur every day due to cancer. Some effective anti-cancer agents are known to cause temporary to chronic toxic effects. There are several compounds that have the potential to become anticancer drugs, one of them is indenopyrazole. Recently, not many QSAR studies have been conducted to exploit the potential of indenopyrazole as anti-cancer agent. The goal of this research is to implement simulated annealing and support vector machine method in the QSAR study to predict the activity of indenopyrazole derivatives as anti-cancer drugs. Simulated annealing is used for feature selection and support vector machine is used for model development. In this research, we used three kernel models for SVM, namely SVM with RBF kernel, SVM with linear kernel, and SVM with the polynomial kernel. From three models that were regressed, SVM with RBF kernel has parameter C=10, gamma=scale and epsilon=0.1 produce R2 score train and test 0.79 and 0.60, respectively. SVM with linear kernel has parameter C=1000, degree=1 and epsilon=0.1 produce R2 score train and test 0.61 and 0.63, respectively. SVM with polynomial kernel has parameter C=1000, degree=2 and epsilon=0.1 produce R2 score train and test 0.72 and 0.50, respectively. Based on the validation results, only model with RBF kernel which parameters satisfy all the criteria. From the result we can conclude that the model with RBF kernel is the best model and acceptable.
How Wide Is Enough? Effects of Screen Height, Task Type, and Hand Length on Rollable Display Requirements

Although rollable displays must be unrolled for on-screen interaction, it is unknown whether screen height, task type, and hand length affect rollable display requirements. This study examined the effects of screen height, task type, and hand length on the rollable display requirements. A total of 30 young individuals (22.9 ± 2.3 years; 10 in each hand-length group) performed three tasks (web searching, video viewing, and e-mail composition) using three prototypes with different screen heights (H) of 50, 120, and 190 mm. Dependent variables were preferred screen width, preferred screen width-to-height aspect ratio, user satisfaction, gripping comfort, device portability, design attractiveness, and gripping method. As screen height increased, the preferred screen width increased, but the preferred screen aspect ratio decreased. The 95th-percentile screen width (aspect ratio) of 100 mm (2:1) was required for 50H versus 204 mm (1.7:1) for 120H and 304 mm (1.6:1) for 190H. The highest 95th-percentile screen aspect ratio of 1.9:1 was required for video viewing. The long-hand-length group preferred significantly wider screens for 190H only. Bilateral grasping was predominantly used for 50H and 120H, whereas non-grasping was for 190H due to limited thumb reach and insufficient screen reaction force. Considering user satisfaction, device portability, and design attractiveness, 120H was recommended, and a screen aspect ratio of 2:1 appeared sufficient for the performance of three mobile tasks on a 120H rollable screen.
STRIDE-AI: An Approach to Identifying Vulnerabilities of Machine Learning Assets

We propose a security methodology for Machine Learning (ML) pipelines, supporting the definition of key security properties of ML assets, the identification of threats to them as well as the selection, test and verification of security controls. Our proposal is based on STRIDE, a widely used approach to threat modeling originally developed by Microsoft. We adapt STRIDE to the Artificial Intelligence domain by taking a security property-driven approach that also provides guidance in selecting the security controls needed to alleviate the identified threats. Our proposal is illustrated via an industrial case study.
A Fingerprint Based Smart Attendance and Security System Using IoT and Ultrasonic Sensor

This project tends to develop a system for tracking the attendance of students and teachers as well as enhancing the security of an educational institution using a fingerprint module, sonar sensor, and IoT. This system includes hardware and a web server. The hardware system is able to collect attendance data and monitor the security of the classroom. The fingerprint module checks the authorization of a person and sends a command to the door locking module. The ultrasonic sensor ensures whether the person actually enters the room or not. After taking the attendance the wi-fi module sends the Id or roll to the web server. The web server stores data in the database and processes it for further action. A touch screen is used to interface with the system.