Miracle-dz/UniFAR-FTUs huggingface dataset:
An Element Shift Quorum based Medium Access Protocol for Internet of Things

Billions of mobile sensing devices in Internet of Things (IoT) collect data from surrounding environment in which the Medium Access protocol is employed. Quorum-based protocols are most important medium access control protocol (MAC) for wake-up based devices. However, existing quorum-based protocols cannot achieve both low latency and long lifetime at the same time. In this paper, a novel Element Shift Quorum (ESQ) based MAC is proposed in the sensor based systems. Data transmission is usually launched from the periphery of the network through multi hop routing to the data collector (DC), thus the data operation time of devices are different based on the distance from the DC. In the ESQ protocol, the Quorum time slot (QTS) of sensing devices is scheduled in the time when the devices just have data to process, then the latency and energy consumption can be reduced. In this way, sufficient QTS are allocated for nodes when data operation is needed, and nodes can stay at sleeping state when no data forwarding is required, thus enhancing the energy efficiency and lowering the network delay. Theoretical analysis and experimental results show that, the lifetime is increased by 16% using the proposed method. Meanwhile, the latency is reduced by a factor between 9.08%-29.32%.
Multi-Path Routing in Green Multi-Stage Upgrade for Bundled-Links SDN/OSPF-ECMP Networks

This paper considers the novel problem of upgrading a legacy network into a Software Defined Network (SDN) over multiple stages and saving energy in the upgraded network, or hybrid SDN. That is, in each stage, the problem at hand is to select and replace legacy switches with SDN switches and reroute traffic to power off as many unused cables as possible to save energy. Also, the operator must consider: (i) the available budget at each stage, (ii) maximum path delays, (iii) maximum link utilization, (iv) per-stage increase (decrease) in traffic size (upgrade cost), and (v) the Open Shortest Path First - Equal Cost Multi-Path protocol. This paper addresses two multi-path routing scenarios: 1) non-link-disjoint and 2) link-disjoint. It outlines a Mixed Integer Program and a heuristic algorithm for each scenario. The experimental results show that: (i) both solutions produce only up to 0.63% higher energy saving in scenario-1 than in scenario-2, (ii) the mixed integer program (heuristic algorithm) for both scenarios give an energy saving up to 71.93% (71.64%), (iii) using a larger budget and/or number of stages can increase the energy saving, and (iv) the saving achieved by the heuristic solution for each scenario is within 4% from the optimal saving.
Scooping Manipulation Via Motion Control With a Two-Fingered Gripper and Its Application to Bin Picking

This letter introduces a new method for scooping manipulation in constrained environments. The presented technique lends itself to relatively thin objects, with a large width-to-thickness ratio, which are commonly seen in many domestic and industrial tasks yet can cause considerable difficulty in handling. We present a novel approach to scooping based on a mobility analysis that leads to a new way to perform the operation via motion control with a two-fingered gripper. Our method thus expands the ways in which the manipulation of scooping can be performed, without necessitating underactuated control or a compliant mechanism. Our variable-length digit is suggested as a key facilitator for realizing the motion-controlled scooping maneuver. We present an extensive set of experiments showing the effectiveness of our approach in various scenarios featuring a constrained workspace, including bin picking from clutter.
Extracting Key Factors of Cyber Hygiene Behaviour Among Software Engineers: A Systematic Literature Review

The advent of new technologies and the rapid growth of internet users have given birth to the menace of cyber-crime. Unfortunately, it is increasing at an alarming pace. This situation calls for good cyber hygiene behavior to secure digital lives. Cyber hygiene behaviour holds a significant role in terms of cybersecurity across the globe. There is a dire need to understand better the user variations associated with good or bad cyber hygiene behaviour and an improved view of what users do to encourage good cyber hygiene. Cybersecurity attacks are rising due to recent advancements in ICT and the Industrial Revolution 4.0 (IR 4.0). Software development organizations are among the crucial sectors suffering from cybersecurity issues. These organizations are more vulnerable to cyber-attacks because they lack proper cybersecurity culture. Although many initiatives have been taken by academia and industry to address this rising issue, the problem still exists for Software development organizations because good cyber hygiene behaviour is not observed, which is a prerequisite to reduce cyber threats. This study performed a Systematic Literature Review (SLR) of research papers published during 2010 – 2020. The key factors influencing software engineers’ cyber hygiene behaviour intention are extracted from the published literature. The study examined 35 research papers out of 5,270 found from IEEE Xplore, Emerald Insight, SpringerLink, and ScienceDirect databases. The study reviewed number of factors such as the role of personal, social, socio-cognitive, environmental, & technological factors that may individually or collectively influence software engineers’ cyber hygiene behaviour. The positive and negative factors associated with the cyber hygiene behaviour of software engineers are also categorized. This study enriches the understanding of the potential factors related to software engineers’ cyber hygiene behaviours. It provides valuable insights to researchers, software development organizations, governments, and individuals associated with the field of Software Engineering. This research will assist in changing the software engineers’ behaviour towards cyber hygiene, which will ultimately lead to mitigate the issues of Cybersecurity.
Forecasting real estate prices in Portugal : A data science approach

The real estate market is vital from an economic and social perspective. It is why it is essential to understand its behaviour. In this context, our work aims to analyze the prices' evolution and understand what the main components are impacting the price. To understand the evolution and predict prices, we perform a time series analysis, including ARIMA. To understand the most relevant components, we perform a regression analysis. In this analysis, we used several approaches. We have collected our data from INE and PORDATA, the Databases of Contemporary Portugal, to construct the models and forecast house prices in Portugal.
Novel Miniaturized All-Metal UWB Magneto-Electric Monopole (MEM) Antenna for Multistandard Applications

We propose a novel miniaturized all-metal ultrawideband (UWB) magneto-electric monopole (MEM) antenna in this communication. The proposed antenna is composed of a half-ellipse electric monopole and a quarter magnetic loop. The half-ellipse electric monopole consists of a half ellipse with a rectangular slot, a loaded patch, and an L-shape ground fed by a coaxial connector. Enhanced robustness to severe surrounding environments and lower loss can be achieved by employing an all-metal structure instead of the structure including dielectric materials. A prototype was fabricated and measured. Results show that the proposed MEM antenna can operate in the frequency band of 2.5–13 GHz even higher frequency with the miniaturized dimensions of $0.25\lambda \times 0.24\lambda \times 0.17\lambda $ that is much smaller than a magneto-electric dipole. A unidirectional wide beam radiation pattern with a 10 dB front to back ratio can be obtained. Moreover, the group delay is analyzed which reveals the antenna with good time domain characteristics. The proposed MEM antenna can be employed as a multifunctional antenna operating for multistandard communication networks in a limited space.
Learning Age From Gait: A Survey

Age is an important human attribute that needs to be determined for various purposes, including security, health, human identification, and law enforcement. Hence, there is an increasing research interest in automatic age estimation using biometric traits such as face and gait. In recent years, gait analysis has received growing attention due to the pervasive nature of video surveillance. Gait signals that measure the manner of walking can be obtained using vision and sensor-based techniques. Individual gait patterns obtainable from videos, images, or sensors are shown unconsciously and are not easily obscured. Additionally, gait signals can be obtained unobtrusively with cameras placed at a long distance because gait does not require high-resolution images. However, the extraction of age-associated gait features is a challenging task due to various gait covariates. These covariates include clothing and view changes for vision-based gait; walking slope and footwear for sensor-based gait. This paper provides a survey of scientific literature on age estimation using gait features. We focus on the approaches to extracting age-associated gait features, namely, vision-based and sensor-based approaches, how they may be affected by the different covariates, and domain-specific applications. To make this work useful for as wide of an audience as possible, we also include discussions on key topics such as existing datasets, evaluation strategies, and open challenges that should be addressed in the future.
Optimal Transportation Methods in Nonlinear Filtering

How data became one of the most powerful tools to fight an epidemic is a question that a recent (10 June 2020) The New York Times article poses in its title. Indeed, the spread of COVID-19 involves dynamically evolving hidden data (for example, the number of infected people, the number of asymptomatic people) that must be deduced from noisy and partially observed data (for example, the number of daily deaths, the number of daily hospitalizations, and the number of daily positive tests). The underlying mathematics for posing and solving this and several other partially observed dynamic problems is familiar to control theorists
Wideband Generalized Beamspace Modulation (wGBM) for mmWave Massive MIMO Over Doubly-Selective Channels

Generalized beam modulation (GBM) is a recently proposed spectrum efficiency (SE)-enhanced index modulation (IM) scheme for narrowband millimeter wave (mmWave) massive multiple-input multiple-output (mMIMO) systems with hybrid transceivers. Unlike most existing fully-digital IM techniques whose application to wideband channels is straightforward after adopting the orthogonal frequency division multiplexing (OFDM) technique, extending GBM from the narrowband to wideband channels is quite challenging. The reason is that the per-subcarrier process in hybrid OFDM systems is no longer independent across individuals as all subcarriers share a common analog beamformer. Hence, the designed wideband GBM (wGBM) has to be compatible with hybrid OFDM systems while maintaining the SE-enhanced merit of GBM. Towards this objective, a symbol-based instead of the prevalent subcarrier-based paradigm is applied to guide wGBM design. To avoid the high detection complexity in large-scale OFDM systems, we devise a low-complexity detector by transforming the complicated symbol (block) detection into the manageable subcarrier (sub-block) detection. Finally, a simple first-order Doppler compensator is carefully designed to enhance the robustness of wGBM against Doppler. Both theoretical analyses and numerical simulations have been carried out to demonstrate the superiority of wGBM over existing alternatives in terms of error performance and energy efficiency.
Online Equivalent Degradation Indicator Calculation for Remaining Charging-Discharging Cycle Determination of Lithium-Ion Batteries

Online remaining charging-discharging cycle (RCDC) prognosis is of great significance for lithium-ion batteries. The conventional method is usually based on whether the state-of-health (SOH) of capacity reaches the end-of-life (EoL) threshold. However, the most available prediction methods have two problems that need to be solved. First, the SOH degradation curve of the lithium-ion battery is nonlinear and non-Gaussian, and the battery capacity regeneration phenomena (CRP) has a direct impact on RCDC estimation efficiency. These factors challenge the precise forecast of RCDC and increase the risk of prediction failure. Second, existing methods have insufficient early-stage prediction ability for capacity degradation because too little data are available to facilitate establishing and optimizing the prediction models. To overcome the above-mentioned drawbacks, this study introduces the Mann-Kendall trend analysis to generate an equivalent degradation indicator (EDI), and to replace the capacity-based SOH. The proposed EDI has good linearity and monotonicity, and is conducive to adopt a simple structured prediction model to determine the RCDC. Besides, this study is based on the “SOH-EDI” synchronization mapping relationship and applies an one-degree polynomial regression model to estimate the EoL threshold on the EDI curve. From the perspective of computational complexity, the proposed framework uses two polynomial prediction models with simple structures, which realizes a low computational burden and online RCDC prediction. To verify the efficiency of the proposed method, this paper introduces three methods for comparison. Experimental results show that the proposed framework has satisfied early-stage prediction ability of RCDC and has a superior prognosis efficiency.
Effect of polyethylene glycol on the antibacterial properties of polyurethane/carbon nanotube electrospun nanofibers

The discovery of antibacterial functions for carbon nanotubes (CNT) has triggered great interest because of the excellent antibacterial properties of CNT. However, there are two obstacles, i.e., cell toxicity and CNT aggregation in a polymer matrix, which greatly limit the antibacterial application of CNT in medical devices. In this study, a facile, cost-effective, time-saving and environmentally friendly approach was proposed to impart the antibacterial property to thermoplastic polyurethane (TPU) electrospun nanofibers with CNT. The ultrasonication technique was explored to in situ anchor the CNT onto the TPU electrospun nanofibers to achieve the bactericidal property. This effectively circumvented the aggregation of CNT when the TPU/CNT electrospun nanofibers were prepared. In addition, the anchor preparation was efficient taking only 10 min to complete and it was also non-toxic because the green solvent ethanol was used as the dispersion solvent. PEG was chemically grafted onto the TPU (TPU-g-PEG) electrospun nanofibers through UV photo-graft polymerization. Although CNT exhibit a good bactericidal property, they could be toxic to human cells. Incorporation of PEG could not only effectively reduce the toxicity of CNT to the human cells but also decrease bacterial attachment. The TPU-g-PEG/CNT nanofibers exhibited excellent hemocompatibility, including suppression of red blood cell adhesion, and lower hemolysis ratios. Importantly, the as-prepared nanofibers had better antibacterial properties due to the bacterial resistance of the grafted PEG and the bactericidal effect of CNT. Our facile approach has significant potential for the infection-resistant wound dressing.
Efficient Dual-Band Rectifier Using Stepped Impedance Stub Matching Network for Wireless Energy Harvesting

This letter presents an efficient dual-band rectifier using stepped impedance stub matching circuit. Theoretical analysis of the dual-band impedance matching circuit comprising a stepped impedance stub is carried out, which plays a key role in designing the resultant dual-band rectifier. The proposed dual-band matching circuit can achieve wide frequency ratio which is analyzed and predicted by simulation. For demonstration, a dual-band rectifier working at 0.915 and 2.45 GHz is fabricated with dimensions of 21.47 mm <inline-formula> <tex-math notation="LaTeX">$\times18.93$ </tex-math></inline-formula> mm. The measured results show that with a <inline-formula> <tex-math notation="LaTeX">$1500~\Omega $ </tex-math></inline-formula> load, the maximum efficiencies of the rectifier reach 74% and 73% at 0.915 and 2.45 GHz, respectively. Due to the simple but efficient structure of the dual-band matching network, the dual-band rectifier in this work exhibits merits of compact size and high efficiency.
Naproxen Cocrystals with Pyridinecarboxamide Isomers

A screening of naproxen cocrystals with coformers picolinamide, nicotinamide, isonicotinamide, and pyrazinamide is performed by the Kofler contact method and mechanochemistry. The solids obtained by mechanochemistry are characterized by differential scanning calorimetry, DSC, polarized light thermomicroscopy, PLTM, infrared spectroscopy, FTIR, and X-ray powder diffraction, XRPD. No cocrystal could be prepared under the experimental conditions investigated between naproxen and pyrazinamide, which bears two aromatic nitrogen atoms, ortho and meta to the amide group. For the o-, m-, and p-pyridinecarboxamide isomers, regardless of the aromatic nitrogen position, the coformer interacts with naproxen to give rise to new cocrystals: naproxen:picolinamide, naproxen2:nicotinamide, and naproxen:isonicotinamide. A supramolecular acid:aromatic nitrogen heterosynthon is found in all these cocrystals. The structure of the new naproxen:isonicotinamide compound was solved by single-crystal X-ray diffraction, SXD. As nic.
Research on EEG Feature Decoding Based on Stimulus Image

Rapid serial visual presentation (RSVP) is a typical brain-computer interface system (BCI) paradigm in the target detection field. Electroencephalography (EEG) measured non-invasively BCI while associate with sparse target clips forms Event-related potentials (ERPs). Although ERP results from our brain’s physiological neural response to the target, there is no direct evidence to prove that it is the brain’s direct result to the visual target. The brain’s complete processing information to the visual received image is still in the overall EEG information. This paper proposes a deep-learning method through a triple-RSVP paradigm to establish a correlation between EEG and stimulus images. Using LSTM to find the feature connection between the EEG and the stimulus image. The EEG feature associated with the image encoding feature is sent to the image decoder to obtain a decoded image similar to the stimulus image. The research in this article hopes to provide some new ideas for researchers in the field of brain science.
Predicting the prevalence of mental disorders among different age groups: A Review

Mental health is an integral part of the overall health of an individual. It is not just the nonexistence of any mental disorder but also includes one’s emotional, psychological, and social well-being. It affects our lives to a great extent by having an impact on the way we feel, act, and perceive. It is vital to pay attention to mental health as is the case with maintaining physical health to work constructively and to realize one’s potential. Apart from depression, stress, and anxiety, there are several other types of mental disorders prevailing like Personality disorders, Psychotic disorders, eating disorders, and many more. Anyone can become a victim of a mental issue irrespective of gender, age, profession, and standard of living. Such issues must be predicted in advance to accelerate the process of diagnosing the problem so as to enhance the quality of life of the patient. Many such predictive models have been implemented with the help of algorithms associated with modern domains such as machine learning, deep learning, image processing, voice detection, and many more. This study aims at providing a comparative study of the works that have already been done in this domain and to find the best algorithm to predict mental illness thereby taking appropriate actions to combat such issues.
Waveform Design for Beampattern Shaping in 4D-imaging MIMO Radar Systems

In this paper we design transmit beampattern for 4D-imaging automotive radar systems. To this end, we consider spatial- Integrated Sidelobe Level (ISLR) as the design metric and optimize it under continuous and discrete phase constraints. The problem formulation leads to a quadratic fractional programming, where we propose an effective Coordinate Descent (CD) -based method to solve the problem, which minimizes the objective function monotonically. In every iterations of the proposed method, we find global optimum solution for a single variable problem under continuous phase constraint. Under discrete phase constraint, we use Fast Fourier Transform (FFT) to solve problem which is efficient in terms of implementation. Through numerical simulations, we illustrate the performance of our proposed method and compare it with state-of-the art.
Control Aware of Limitations of Manipulators With Claw for Aerial Robots Imitating Bird's Skeleton

Winged animals such as birds, flying mammals or insects have lightweight limbs which allow them to perform different tasks. Although in robotics there are some examples of winged robots (called ornithopters), it has not been yet studied how to add them some manipulation-like capabilities, similarly to the anatomy of animals limbs. Adding those capabilities to ornithopters will outperform multirotor platforms giving the possibility to perch in unaccessible places, grasp objects and perform some kind of manipulation while being in proximity to humans. The special manipulator imitates the anatomy of the birds, having a kinematic chain with actuated joints except the first passive one that resembles the claw of a bird with a grasping force. This work analyzes in depth these ornithopter-like manipulators and proposes a nonlinear controller aware of the limitation in the grasping force of the claw, modeled as static friction. The solution is based on a methodology to control constrained-nonlinear systems via diffeomorphisms providing an explicit controller with low torques demand to meet aerial requirements. It is verified on a realistic simulator with 5DOF links—claw, low/upp-er leg, body, neck and beak–, and experimentally validated in a simpler 3DOF prototype.
Statistical Channel Selection Method for Detecting Drowsiness Through Single-Channel EEG-Based BCI System

Electroencephalogram (EEG) is an essential tool used to analyze the activities effectively and different states of the brain. Drowsiness is a short period state of the brain that is also called an inattentiveness state. Drowsiness can be observed during the transition from being awake state to a sleepy state. Drowsiness can reduce a person’s alertness that increases accidental risks when involved in their personal and professional activities like vehicle driving, operating a crane, mine blasts, and so on. Drowsiness detection (DD) has a significant role in preventing accidents. Neuroscience with artificial intelligence algorithms used to detect drowsiness is also popularly known as brain–computer interface (BCI) systems. Single-channel EEG BCIs are highly preferred for convenient use in real-time applications, even though there are many challenges in the actual experimental process. They are choosing the best single-channel and classifier. In this article, a novel channel selection approach is proposed for a single-channel EEG-BCI system by integrating the statistical characteristics of the available channel’s EEG signal. In addition to this, a deep neural network (DNN) classifier is developed using the stack ensemble process for better classification accuracy. Simulated-virtual-driving driver and physionet sleep analysis EEG datasets (PSAEDs) are used to test the proposed model. Subject-wise, cross-subject-wise, and combined subject-wise validations are also employed to improve the generalization capability of the proposed techniques in this article.