Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling and Analysis of Data Harvesting Architecture Based on Unmanned Aerial Vehicles

This paper explores an emerging wireless Internet-of-things (IoT) architecture based on unmanned aerial vehicles (UAVs). We consider a network where a fleet of UAVs at a fixed altitude flies on planned trajectories and IoT devices on the ground are scheduled to transmit their data to the UAVs when the latter are nearby. In such a system, the UAVs’ motion triggers the uplink transmissions of the IoT devices. As a result, network performance is determined by the geometric and dynamic characteristics of the system. We propose a joint stationary model for UAVs and IoT devices and then evaluate the interference, the coverage probability, and the data rate of the typical UAV. To assess the harvesting capability of the proposed architecture, we derive a formula for the amount of data uploaded from each IoT device to a UAV. We also establish a linear relationship between the UAV coverage and the harvesting capability of the network, which provides insights into the design of the proposed harvesting scheme. In addition, we use our analytical results to numerically show that there exists a trade-off between the uploaded data and the size of the IoT scheduling window. Specifically, for a given UAV and IoT geometry, there exists an optimal scheduling window that maximizes the harvesting capability of the proposed network.
Portable colorimetric melamine detection device based on nanoparticle interference synthesis

Melamine is a toxic adulterant known to be mixed with milk to defy protein content tests. Monitoring this adulteration is infeasible in developing countries due to the high cost and limited availability of the established methods such as liquid chromatography, mass spectrometry etc. This work presents a portable melamine measurement device that utilizes a Single Board Computer (SBC) for image acquisition and processing to quantify color changes due to interference synthesis of gold nanoparticles. Melamine interferes with the nanoparticle (NP) synthesis by binding to the reducing agent (ascorbic acid) used in the instant synthesis of NP, leading to altered NP size distribution and aggregation. The resulting color change is assessed using on-board image processing to arrive at a test result within 10 seconds. The system can quantify as low as 0.1 ppm of melamine, while providing a linear response till 2 ppm – the permissible melamine limit in food. This unprecedented level of portability, instantaneous quantification and low cost qualifies the presented device as the most accessible melamine detection system yet. This would be very beneficial to milk producers and consumers alike.
Creating a Soft Tactile Skin Employing Fluorescence Based Optical Sensing

Currently, optical tactile sensors propose solutions to measure contact forces at the tip of flexible medical instruments. However, the sensing capability of normal pressures applied to the surface along the tool body is still an open challenge. To deal with this challenge, this letter proposes a sensor design employing an angled tip optical fiber to measure the intensity modulation of a fluorescence signal proportional to the applied force. The fiber is used as both emitter of the excitation light and receiver of the fluorescence signal. This configuration allows to (i) halve the number of optical fibers and (ii) improve the signal to noise ratio thanks to the wavelength shift between excitation and fluorescence emission. The proposed design makes use of soft and flexible materials only, avoiding the size constraints given by rigid optical components and facilitating further miniaturization. The employed materials are bio-compatible and guarantee chemical inertness and non-toxicity for medical uses. In this work, the sensing principle is validated using a single optical fiber. Then, a soft stretchable skin pad, containing four tactile sensing elements, is presented to demonstrate the feasibility of this new force sensor design.
An Intermittent Frequency Synthesizer With Accurate Frequency Detection for Fast Duty-Cycled Receivers

An intermittent frequency synthesizer for fast duty-cycled receivers is presented in this paper. Different from state-of-art techniques which try to eliminate the initial phase error that degrades the intermittent frequency detection, a new frequency detector is proposed to maintain an accurate frequency detection regardless of the initial phase error. Moreover, an averaged fraction division scheme (AFDS) is integrated in the synthesizer to improve the frequency resolution. The frequency synthesizer is implemented and verified by using discrete devices and an FPGA. The measurement results show that the frequency detector provides a frequency detection accuracy of 0.88%, and the synthesizer achieves a frequency resolution of 0.625 MHz when controlled by a duty-cycled signal with a 6-<inline-formula> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula> period and a 2-<inline-formula> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula> time duration. The frequency synthesizer can perform an intermittent frequency hopping from 160MHz to 140MHz while requiring a period of <inline-formula> <tex-math notation="LaTeX">$430~\mu \text{s}$ </tex-math></inline-formula> without disturbing the VCO’s oscillation.
A Hamiltonian Decomposition-based Splitting Method for Interior Point Solvers in Model Predictive Control

Model Predictive Control (MPC) is an advanced control technique that is widely used in industry. At the core of the MPC algorithm lies an optimization problem that is solved by a numerical method at every time step. Increased demand for more self-contained modular processes has seen MPC embedded in small-scale platforms, such as Programmable Logic Controllers (PLCs). This has prompted a need for custom-made and highly efficient numerical optimization algorithms. In this paper, we propose a novel approach for factorizing the Newton system of the interior point method. This factorization is based on the eigenvalue decomposition of the Hamiltonian system associated with the MPC optimization problem. Once the augmented system is in the Hamiltonian form, the matrix can be decomposed into the sum of a constant matrix and a variable one. We show that most of the factorization of the constant matrix can be computed offline, whereas the remaining part can be computed using a splitting method. Numerical experiments demonstrate that the proposed approach is feasible and efficient compared to other state-of-the-art methods.
A Management Scheme of Multi-Level Retention-Time Queues for Improving the Endurance of Flash-Memory Storage Devices

As flash memory technology has been scaled down to 1x nm and more bits can be stored in a cell, the storage density of flash memory has been significantly improved. However, these technical trends also severely hurt the programming speed and endurance of flash memory. The internal data retention time is the duration for which a flash cell can correctly hold data. By relaxing internal data retention time, both the page programming speed and the block endurance could be improved. However, the retention time of flash memory typically requires to last for several years according to the industrial standard. Thus a refreshment scheme is required to deal with the decreasing of retention time. In this article, we propose multi-level retention-time queues with a management scheme to meet the retention-time requirement for a reliable storage system. Observing that many data are overwritten in hours or days in real workloads, multiple retention-time queues could effectively separate data with different update frequencies. There are three challenge issues for a proper design: (1) Since access pattern might change from time to time, a technical issue is how to promote/demote data so that data could be maintained in the proper retention-time queue to minimize the refreshment overhead. (2) Another technical issue is how to refresh each retention-time queue in time to guarantee data integrity. (3) Since blocks resided in different retention-time queue would suffer from different level of wearing, the third technical issue is how to estimate wearing status of flash-memory blocks in an effective and efficient manner to achieve wear leveling. In our scheme, data allocator, multi-level refresh module, garbage collector, and wear leveler are introduced to deal with these technical issues. Based on our experimental results, not only endurance and performance but also energy consumption of the flash-memory storage system could be significantly improved by our scheme.
Toward the Standardization of Grant-Free Operation and the Associated NOMA Strategies in 3GPP

The dramatic increase in Internet traffic to and from wireless devices poses significant challenges for network operators. While the current growth of traffic is mostly due to consumers communicating more frequently and larger amounts of data over the wireless infrastructure, much of the future growth is predicted to originate from non-human-operated devices or so-called IoT communication. The 3GPP standardization activities toward 5G cellular systems envisages two IoT-centric scenarios: mMTC and URLLC. In this article, we quantify the advantages of grant-free operation for 5G mMTC using latency, signaling overhead, and power consumption aspects. We propose the high-level design of a new grant-free state of operation and explain its interaction with the legacy LTE operating states. We also describe promising grantfree NOMA solutions with respect to synchronization and HARQ, and resource collision handling procedures. Our discussion and proposal relate closely to the current 5G standardization activities in 3GPP and highlight solutions that can easily be integrated into the current LTE framework.
Teaching Introductory Cryptography using a 3D Escape-the-Room Game

This Innovative Practice Full Paper reports on a pilot study of our 3D game that was designed to teach students introductory concepts in security and cryptography. Game based learning and serious games are getting attention for their potential effect on increasing learner enjoyment and understanding of educational concepts and skills. The game we discuss here is a version of the “Escape the Room” puzzles where the player needs to find and solve a sequence of problems to escape from a room. Students must guide their avatar around a room containing a computer, a trash can, a locked door, and other objects. When they approach an object they are allowed to interact with it in various ways which provides clues and challenges that can only be solved with an understanding of certain cryptographic concepts. This game was tested with a group of 16 students, the students were asked to fill a pre-test and post-test survey about their experience with games, their demographics, and their understanding of certain cryptography concepts. A paired t-test shows a significant increase in understanding of cryptographic concepts among the students after playing the game, this increase in learning was independent of their gender and almost all of the students enjoyed the experience of learning in a game-based way.
Position Sensorless Permanent Magnet Synchronous Machine Drives—A Review

Owing to the competitive advantages of cost reduction, system downsizing, and reliability enhancement, position sensorless control methods for permanent magnet synchronous machine drives have drawn increasing attention from academia to industrial applications. In this article, a survey of the major sensorless control techniques for a wide speed range from low to high speeds is presented. The different high frequency signal injection schemes, fundamental pulsewidth modulation excitation methods, and model-based sensorless control are displayed and compared, which is able to facilitate the sensorless control implementation.
Control System for Assessing Reliability Functioning of the Complex Radio Electronic Equipment Using Machine Learning Methods

We propose a novel application of machine learning methods to predict the state of complex structured radio electronic systems. The model for collecting data about the functioning of a radio electronic system based on an integrated monitoring system of its components states is proposed and justified. The proposed model made possible to adapt one of the most widespread methods of machine learning, gradient boosting, on a sample of historical data, from the General Designer stand and embedded control system, to solve the problem of forecasting the state of the whole system. Operating experience showed that component failures are extremely rare. Therefore, the distribution of statistics of failures of the past break-in elements was considered according to Poisson’s law, and the elements introduced for the first time according to Weibull’s law with different coefficients, the prediction of such coefficients based on historic data is just the result of the machine learning algorithm
Provable Reactive Navigation of Mobile Robots to a Moving Target in Unpredictable Dynamic Scenes*

A speed-limited robot travels in a dynamic environment cluttered with arbitrarily shaped moving obstacles. There also is an unpredictably moving target in the scene. The robot measures the heading angle to the target and the distance to the nearest obstacle along any ray emitted from the robot. A simple reactive navigation strategy is presented that autonomously drives the robot to the target in a finite time provided that the algorithm is properly tuned. This is shown via a mathematically rigorous global convergence result under minor and partly unavoidable technical assumptions. Feasible closed-form recommendations on controller tuning are offered. Theoretical results are confirmed by computer simulation tests.
Design and Implementation of Network-Aware VNF Migration Mechanism

By separating network functionalities from the dedicated hardware and implementing them in the form of Virtual Network Function (VNF), Network Function Virtualization (NFV) provides a new service paradigm called Service Function Chain (SFC). It is unrealistic to replace the running VNFs due to the stateful information accumulated, which makes the current VNF re-deployment or replacement approaches no longer effective enough. Such a situation is even worse when VNFs are shared. Instead of replacing VNFs, this paper proposes a VNF migration approach to maintain the network balance and performance by migrating VNFs from poor-state nodes to good-state nodes. In particular, two situations are considered, which are node-aware and link-aware VNF migrations respectively. Via migrating the VNFs that are related to the nodes or links causing Quality of Service (QoS) issues, the network and service performance can be optimized. In addition, the migration impact is also minimized by perceiving the global network states. The experimental results indicate that the service performance is improved and the load balance is achieved after the VNF migration.
A Review of Targeted Therapies in the Management of Advanced-stage Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) ranks third as a leading cause of cancer death worldwide and has steadily increased in incidence. Due to the asymptomatic nature of early HCC, most patients unfortunately present with advanced or unresectable tumour. While traditional cytotoxic agents have failed to demonstrate a clear survival benefit, the development of newer, molecular targeted therapies has shown promising results in the management of HCC. Notably, sorafenib, an oral multikinase inhibitor, has proven its efficacy in two randomised phase III studies and is currently the only approved systemic therapy in HCC. While the overall survival benefit for patients on sorafenib is modest, it has highlighted the unmet medical need of patients with advanced HCC. Currently, there are several agents in development as both first-line and second-line therapies. Critical to their successful development will be the study designs and the selection of patients most likely to benefit taking into account the complex interplay between underlying liver disease/cirrhosis, cancer and outcomes. The recent failure of sunitinib, a promising oral multikinase inhibitor, because of a greater incidence of adverse events and failure to extend overall survival emphasises these points. This review highlights and summarises the evidence and ongoing challenges in the development of new molecular targeted therapy in HCC.
An Ultraminiaturized Dual-Stopband Frequency Selective Surface for Ultra High Frequency

In this paper, an ultraminiaturized dual-stopband frequency selective surface (FSS) for ultra high frequency (UHF) is investigated. The proposed FSS uses one dielectric layer with two different metal layers printed on its surfaces and the metal layers are composed of meandering lines and metal vias. It has an ultraminiaturized unit cell size of <inline-formula> <tex-math notation="LaTeX">$0.021\lambda _{0} \times 0.021\lambda _{0}$ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">$\lambda _{0}$ </tex-math></inline-formula> refers to the wavelength at first resonance frequency) and produces two stopbands centered at 0.75 GHz and 1.8 GHz, respectively. The simulated results exhibit great stability when the incident wave angle changes from 0° to 85° for both TE and TM polarization. Furthermore, it has a good controllability for both resonance frequencies. The miniaturization and stability of the proposed FSS is better than the other designs in the recent research, which may have potential applications for UHF. The accuracy equivalent circuit is derived for better explaining mechanism of the proposed FSS. Finally, a dual-stopband FSS is fabricated and the measured results are much the same with simulated ones.
IoT-KEEPER: Detecting Malicious IoT Network Activity Using Online Traffic Analysis at the Edge

IoT devices are notoriously vulnerable even to trivial attacks and can be easily compromised. In addition, resource constraints and heterogeneity of IoT devices make it impractical to secure IoT installations using traditional endpoint and network security solutions. To address this problem, we present IoT-Keeper, a lightweight system which secures the communication of IoT. IoT-Keeper uses our proposed anomaly detection technique to perform traffic analysis at edge gateways. It uses a combination of fuzzy C-means clustering and fuzzy interpolation scheme to analyze network traffic and detect malicious network activity. Once malicious activity is detected, IoT-Keeper automatically enforces network access restrictions against IoT device generating this activity, and prevents it from attacking other devices or services. We have evaluated IoT-Keeper using a comprehensive dataset, collected from a real-world testbed, containing popular IoT devices. Using this dataset, our proposed technique achieved high accuracy (≈0.98) and low false positive rate (≈0.02) for detecting malicious network activity. Our evaluation also shows that IoT-Keeper has low resource footprint, and it can detect and mitigate various network attacks—without requiring explicit attack signatures or sophisticated hardware.
Optically Transparent Protective Coating for ITO-Coated PET-Based Microwave Metamaterial Absorbers

In spite of indium tin oxide (ITO)-coated polyethylene terephthalate (PET) sheets being a thriving candidate for flexible and transparent microwave metamaterial absorbers (MMAs), these materials are not robust under severe environmental and handling conditions. The ITO coatings are brittle and have poor scratch resistance when coated over flexible PET sheets; hence, their electrical performance degrades in time. Here, thin films of polyvinyl butyral (PVB) and also its composite with 3-aminopropyltriehoxysilane (APTES) processed at room temperature have been investigated as protective coatings over ITO-coated PET sheets, particularly for MMA application. Prior to coating, the effectiveness of various surface treatments to increase the ITO’s wettability by exposure to UV light, chemicals, and air plasma was demonstrated by contact angle measurements. Furthermore, the consequences of surface treatments on the adhesion of the protective coatings to the ITO surface were studied using microindentation, pencil scratch test, ultrasonication test, water soak test, and shower test. Measurements of microwave absorption of the coated and uncoated substrates (PET and ITO-PET) over the $X$ -band (8.2–12.4 GHz) showed a similar electromagnetic (EM) response approximately. Finally, free-space measurements of the MMA before and after PVB coating at its top and bottom were compared from 4 to 16 GHz, and the maximum EM absorption was found to have increased from 95.94% to 98.56%. Optical transparency of coated ITO-PET was quantified by measuring the optical transmittance at 632.8-nm wavelength using red laser and found to be equal to 68.9 ± 6.9. The mechanical strength of the developed coatings is analyzed by nanoindentation technique by obtaining values of hardness and elastic modulus.
Intelligent Recruitment System

this paper reviews studies and developments in recruitment techniques interact with computer science. And novel approach proposed as Intelligent Recruitment System (IRS) which consist of resume classification and ranking with deep learning alongside with NLP techniques, Automatic question generation(AQG) system which measure technical proficiency of an applicant using the knowledge base of merged ontology designed using the web and local sources and soft skills measuring with question answering about different skill sets and matching the similarity of the predefined answers against the applicant given answer by using both the syntactical similarity measurement and semantic similarity measurements. The final output comes from a combination of the above input parameters. The purpose of this model is to propose a new framework based on the fuzzy inference system (FIS) and Mamdani's method. The decision-making mechanism of the IRS is based on the final total score of each parameter and maximum quota. If two or more candidates receive the same final total score, it is difficult to make a definitive decision. Therefore, a FIS approach is proposed to help decision makers choose the most qualified applicants and avoid unnecessary problems.
A Machine Learning Model for Air Quality Prediction for Smart Cities

Air quality of a certain region can be used as one of the major factor determining pollution index also how well the city’s industries and population is managed. Urban air quality monitoring has been a constant challenge with the advent of industrialization. Air pollution has remained a major challenge for the public and the government all over the world. Air pollution causes noticeable damage to the environment as well as to human health resulting into acid rain, global warming, heart diseases and skin cancer to the people. This paper addresses the challenge of predicting the Air Quality Index (AQI), with the aim to minimize the pollution before it gets adverse, using two Machine Learning Algorithms: Neural Networks and Support Vector Machines. The air pollution databases were extracted from the Central Pollution Control Board (CPCB), Ministry of Environment, Forest and Climate change, Government of India. The proposed Machine Learning (ML) model is promising in prediction context for the Delhi AQI. The results show improvement of the prediction accuracy and suggest that the model can be used in other smart cities as well.