Miracle-dz/UniFAR-FTUs huggingface dataset:
A Measurement Platform for Label-Free Detection of Biomolecules Based on a Novel Optical BioMEMS Sensor

In this article, a novel optical Bio-microelectromechanical system (MEMS) sensing platform is proposed based on a tunable laser and its lasing wavelength to detect the biomolecules and measure their quantities. The present biosensor consists of a BioMEMS cantilever and a proposed external cavity tunable laser. While the target samples (i.e., DNA, mRNA, or protein) are exposed to the cantilever surface, target-analyte bindings are happened. This can induce a surface stress on the MEMS cantilever and results in its bending due to the surface stress difference in each side of the cantilever. Thus, the gap size between the laser cavity and the gain medium is changed which can be measured by the wavelength variations of the proposed tunable laser source. Consequently, by analyzing the output response, one can detect the amount of target biomolecules in the sample and assign a level of contamination, infection, or bioparticles, caused by the specific disease. Various parameters of the proposed device are designed by numerical and analytical approaches. Furthermore, functional characteristics of the present BioMEMS sensor are obtained as follows: mechanical sensitivity of $1.8~\mu \text{m}$ /Nm−1, optical sensitivity of 422 nm/RIU, Q-factor of 610, and resonant frequency of 6.43 kHz. The obtained functional characteristics of the proposed device show that the present optical BioMEMS sensor can be appealing for highly sensitive diagnoses of various types of diseases and their progress level.
A Rehabilitation System to Assist Diplegia by Repetitive Exercise using LabVIEW

Diplegia is a disorder caused by brain and spinal cord injuries which is typically enduring and it affects the same area on both sides, like both arms and both legs. It may not be fully healed but can be improved by a proper phase of rehabilitation. Moreover, recurrent and lavish physiotherapy treatments are difficult for low-income community people to afford. Many rehabilitation devices are available but most of the existing devices having its own drawbacks such as size, cost, weight and necessitating assistant to wear and train the physically challenged and paresis person. To overcome this problem, an automated rehabilitation device can be designed by interfacing hardware with LabVIEW software. LabVIEW offers a graphical programming approach that helps us to envisage every feature of our relevance together with hardware configuration, measurement data, and debugging. DC motor with worm pinion gear system helps to drive the upper and lower extremities tied to the exoskeleton of automated rehabilitation system, which in turn provides regular therapeutic exercise to the users without an assistant. Therefore, the goal of this project to reduce human support for providing physiotherapy and to restore the functionality of affected limbs.
Formation of Well‐Ordered Heterojunctions in Polymer:PCBM Photovoltaic Devices

The nanoscale morphology in polymer:PCBM based photovoltaic devices is a major contributor to overall device performance. The disordered nature of the phase‐separated structure, in combination with the small length scales involved and the inherent difficulty of reproducing the exact morphologies when spin‐coating and annealing thin blend films, have greatly hampered the development of a detailed understanding of how morphology impacts photovoltaic device functioning. In this paper we demonstrate a double nanoimprinting process that allows the formation of nanostructured polymer:PCBM heterojunctions of composition and morphology that can be selected independently. We fabricated photovoltaic (PV) devices with extremely high densities (1014 mm−2) of interpenetrating nanoscale columnar features (as small as 25 nm; at or below the exciton diffusion length) in the active layer. By comparing device results of different feature sizes and two different polymer:PCBM combinations, we demonstrate how double imprinting can be a powerful tool to systematically study different parameters in polymer photovoltaic devices.
Aspects to Foster Competences for Engineering Graduates: Education 4.0 Paradigm

With the amalgamation of information technology capabilities, the fourth industrial revolution transforms the industrial processes to become more connected and predictable. The response to the changing requirements of the fourth industrial revolution (IR4.0) is Education 4.0, which will enable to harness the potential of these digital technologies. In order to strengthen the student centric learning process in Engineering Education 4.0, it is imperative to provide abundant skill development opportunities and avenues. This paper presents the relevance of professional skill development activities. It highlights the competencies developed through the proposed framework.
Design of a Hexa-Directional FSO Communication for UAVs

The Unmanned Aerial Vehicles (UAVs) are increasingly used for their agile and eclectic applications. UAVs have experienced a widespread acceptance in sectors ranging from military operations to delivery packages. With increasing density of UAVs in the sky, the need for effective communication methods between two UAVs has become a necessity. This paper proposes a communication using Free Space Optical (FSO) communication to ensure all-around transmission through multiple transmitters and receivers around a UAV to transmit and receive high data rate. The development presented in this paper is designed to mitigate the major con of line-of-sight in UAVs using FSO communication. We study the architecture, model the communication channel, present the results of Quality Factor and BER analysis for proposed system.
Synthesis of Low Cost Hydrogel for Artificial Cartilage

Articular cartilage is an avascular, aneural and alymphatic smooth white tissue that wraps the ends of a bone where it comes together to form joints. Cartilage is neither hard nor rigid but it possesses stiffness and less flexibility. It can be wrecked by an injury or normal wear and tear which leads to Osteoarthritis. Articular cartilage has sparse retention for self-repair and untreated damages. Mess of this situation can be overwhelmed by placing artificial cartilage instead of articular cartilage. Artificial cartilage is brewed by using hydrogel and it has the potential to endure against the abrasion and deformation. Hydrogel can be prepared by gelatine, PEG (Poly-Ethylene Glycol), castor oil, EDTA (Ethylene Di-Amine Tetra Acidic acid) and it can be used to replace the wrecked cartilage for the patients who suffer from osteoarthritis. This current technology projected in such a way to create possible advancement of artificial cartilage to serve the osteoarthritis.
Intelligent Modeling and Design of a Novel Temperature Control System for a Cantilever-Based Gas-Sensitive Material Analyzer

Devices used to set and control the environmental temperature are critical to the performance of gas-sensitive material analyzers, which use silicon microcantilevers to characterize the gas-sensitive materials. This paper describes a novel microtemperature-control device that uses a double Peltier structure to replace the traditional refrigerant temperature control system. A proportional-integral-derivative (PID) algorithm is used to achieve accurate and fast temperature control, with a long short-term memory (LSTM) network trained to identify the nonlinear dynamics of the Peltier system. A neighbor hybrid mean center opposition-based learning particle swarm optimization (NHCOPSO) algorithm is proposed to optimize the PID controller. The LSTM network identification is obviously better than that of previous Peltier system identification methods, and the NHCOPSO algorithm is found to be superior to other improved PSO and evolutionary algorithms on benchmark functions and in PID parameter optimization. Experimental results show that the proposed temperature control device greatly improves the accuracy and efficiency of gas-sensitive material analysis with a temperature control range of −40 to 180°C, a temperature control tolerance within ±0.05°C, a maximum heating rate of 20°C/min, and a maximum cooling rate of −10°C/min.
Probabilistic Binary Offloading for Wireless Powered Mobile Edge Computing System

In recent years, with the advancement of the Internet-of-Things (IoT), some problems are listed. First, the computation resource is limited because of miniaturization and decreasing costs for IoT devices and sensors. Mobile edge computing (MEC) that can compute alternatively heavy tasks of wireless devices (WDs) has been proposed for solving this problem. Second, the WDs’ battery management is troublesome because of the increasing number of IoT devices and sensors. Wireless power transfer (WPT) has been proposed for solving this problem. In WPT, an access point (AP) wirelessly charges batteries of WDs. Wireless powered-mobile edge computing (WP-MEC) system combining WPT and MEC is expected to solve these problems. This paper proposes a probabilistic binary offloading (PBO) system that selects one of two modes for each WD; offloading and local computing. The mode selection is determined by probabilistic control instead of the centralized control by the AP. Moreover, we propose a mode switching method that switches from offloading to local computing when the WD fails to offload tasks. We aim to reduce energy consumption of WDs and task processing delay and improving communication quality.
Channel Modeling for Forest Backhaul Networks using Millimeter Wave in 5G Communications

Tremendous increase of use of smart phones have risen mobile data volume to many folds resulting in spectrum hungry issue in addition to the need of coverage and huge capacity bandwidth in cellular networks. Hence millimeter wave communication would reduce this hunger despite of its drawbacks in both access network and backhaul network. Foliage loss, atmosphere and distance affects the mmWave communication immensely. In this paper a solution to this issue is presented by the statistical channel modeling of a forest backhaul networks. It describes the Angle of Arrival (AoAs), Angle of Departure (AoD), power delay profiles and scatter plots for co-polarized and cross-polarized antennas at 60 GHz and 73 GHz.
Design and optimization of non-volatile memory based on Memristor System

Memristor is a type of memory resistor that has analog behaviour. That is, the resistor is programmable between the two modes with the minimum resistance and the maximum resistance, and its values are retained until subsequent voltage implementations. Non-volatility, memory friendliness, analog and high switching performance, minuteness and its capability to integrate with integrated circuits are prominent advantages of this element. In this paper, we perform a physical and behavioural modelling using a structural model designed to evaluate memristor behaviour. Initially, a 4-by-4 cross-over structure is designed. The memory array consists of four vertical nano-wires and four horizontal nano-wires that cross over each other and a memristor is located at the intersection of each nano-wire. In the Memristor structure positive terminal is connected to the vertical nano-wire and its negative terminal to the horizontal nano-wire. The data sequence [1010] in the first column of the array is written in (4, 1), (3, 1), (2, 1), (1,1) cells respectively and then is read from them. Finally, by examining the details of the element behaviour, changing its parameters such as frequency, input voltage and varying Roff and Ron range and employment of a model, designed for simulation, the output results are obtained. Keywords— Memristor, Physical Model, Thin Layer, Logical Operational Implementation
Plasmonic Polarizer on Photonic Crystal Fiber for Two Communication Windows

A photonic crystal fiber based polarizer, capable to work at two communication windows of around 1310 nm and 1550 nm concurrently in which plasmonics plays an important role to achieve filtering characteristics, is reported. Based on the numerical outcomes the loss magnitude for y- and x-polarized modes are about 1014 dB/cm and 0.05 dB/cm, respectively, at 1310 nm meaning that y-polarized light is 20812 times lossy than that of x-polarized case. In addition, at the wavelength of 1550 nm, the loss of x-polarized light is 6439 times lower than the y-polarized light which makes the structure as polarization filter at second window as well. The promising filtering characteristics of the proposed structure may find applications in two communication windows at the same time.
Improving the Capability of Real-Time Face Masked Recognition using Cosine Distance

During the pandemic, people around the world are expected to wear masks. So far, the ability to recognize the identity of a person who wears a mask is still a challenge. Face recognition is widely used in schools, hospitals, and companies as an attendance system, even as a criminal watchlist examination system. Thus, face recognition implementation is difficult to obtain the identity of people who are wearing masks, and moreover, computer systems might fail to detect the faces. This study used Haar-cascade Face detection and MobileNet while proposing the addition of the cosine distance method. This method compares the middle position of face detection results within the previous frame and the current. The proposed system can generate a person’s name and identification number while wearing a mask. The system is designed to utilize multi-threading by comparing the transfer learning methods of VGG16 and Triplet Loss FaceNet for face mask recognition with an accuracy rate of 100% and 82.20%. Real-time implementation speed resulted in 4 FPS and 22 FPS and successfully added cosine distance to generate a person’s ID number.
Estimation of Mixed Noise Parameters in Hyperspectral Images Based on Super-pixel Segmentation and Multiple Linear Regression

Noise widely exists in hyperspectral image, which reduces the quality of hyperspectral image and affects the application of hyperspectral image. In many hyperspectral image processing, it is necessary to estimate the noise level in the image. This paper presents an algorithm to estimate the standard deviation of signal independent noise (SI noise) and signal dependent noise (SD noise) in hyperspectral images. Firstly, the image is divided into several homogeneous image patchs by super-pixel segmentation, multiple linear regression is used to separate the signal and noise of image patchs, and the noise extraction of hyperspectral image is realized by combining the noise of each image patch. Then the maximum likelihood function of noise standard deviation is established and the standard deviation of SI noise and SD noise is solved by conjugate gradient method. The accuracy and robustness of the algorithm are verified by simulation data experiments.
Enhancement of AC Breakdown Strength of Epoxy TiO2 Nanocomposite by Removing Agglomerates

Epoxy nanocomposites (NCs) are known to have advantages when used in the electrical insulation of power equipment. Agglomerates of nanoparticles, which inevitably form during the fabrication process, can affect the insulating properties of the resultant nanocomposites. Our previous research has clarified that the impulse breakdown strength of the solid part, rather than the surface, of epoxy–TiO2 NCs with agglomerates removed was higher than that of unfilled epoxy resin. Here, we investigated the dependence of the TiO2 filler volume fraction on the AC breakdown strength of NCs whose agglomerate size was controlled. The volume fraction and the maximum agglomerate size were controlled using a simple dilution procedure and a centrifugation technique. The AC breakdown strength of the NCs was found to increase with decreasing volume fraction of TiO2 nanoparticles when the maximum agglomerate size was controlled at the same level. The variation of the AC breakdown strength of the NCs decreased with decreasing maximum agglomerate size, suggesting that the existence of agglomerates larger than a critical size could affect the breakdown.
Effect of Transformer Oil Reconditioning on Oil Properties and Dissolved Gases

Health assessment and life-management of transformer is one of the most discussed topic in industry. In-time action, based on oil analysis, can save costlier transformer failure or its oil replacement. Action requirement can be decided depending on the oil parameter trend and the limits given in IEEE and IEC standards. Oil reconditioning is the cheapest and easiest life management action to improve oil properties. It can improve vital parameters like breakdown voltage, water content etc. though it cannot overcome oil ageing and fault inside transformer. Oil reconditioning of transformer helps in preventing further deterioration of oil and paper insulation. In this study, it has been shown how oil reconditioning not only improves breakdown voltage and water content, but also specific resistivity, dielectric dissipation factor and acidity upto some extent. It is also shown that the dissolved gases can be reduced to very low value by using effective oil reconditioning.
Controlled Excitation of Waveguide High-Order Modes for a Simple and Accurate Monopulse Tracking System Test Bench

Monopulse tracking systems require a calibration procedure, which is typically performed using a calibration tower or a known satellite, leading to high costs, antenna downtime, and potential inaccuracies. This work proposes a simple circular waveguide component, based on a step junction, able to excite high-order modes (HOMs) in a controlled way, so as to mimic the modes generated by the tracking antenna. This allows testing in laboratory the tracking system, avoiding the drawbacks of the traditional techniques. The theoretical working principle of the step-waveguide junction is discussed and an analytical model for the quick calculation of the offset between the two waveguides to achieve a desired HOM excitation coefficient is derived. Then, a Ku-band mode exciter is designed and its effectiveness is proved in conjunction with a designed TE21 tracking coupler. Finally, the fabrication and experimental validation of a prototype is reported, demonstrating the claimed performance.
Separation and Structural Characterization of Lignin from Hybrid Poplar Based on Complete Dissolution in DMSO/LiCl

In this paper, the physicochemical properties and structural features of six lignin preparations separated with a total dissolution of ball-milled wood in dimethyl sulfoxide and lithium chloride (DMSO/LiCl) followed by extraction with ethanol/water were investigated. These isolated lignin fractions were characterized using wet chemical analysis, FT-IR, and 1H and13C-NMR techniques. Experimental results showed that separated lignin preparations were relatively free of associated polysaccharides. These lignin fractions were classified as guaiacyl-syringyl lignin type: mainly composed of guaiacyl units with noticeable amounts of syringyl units and fewer p-hydroxyphenyl units. The molar ratio of non-condensed guaiacyl units to syringyl units (G/S) decreased as the ratio of LiCl to poplar weight increased. The results also showed that these lignin preparations consisted mainly of β-O-4 ether bonds combined with small quantities of β-β′ and β-5 carbon-carbon linkages. Furthermore, considerable amounts of esterified p-hydroxybenzoic acids and minor amounts of esterified p-coumaric acid were also detected in these lignin fractions.
Exploring the Topological Properties of the Tor Dark Web

The web graph can be used to get insight into the internal structure and connectivity of the Tor dark web. This paper analyzes the internal structure of the Tor dark web graph and examines the presence of bow-tie structure as found in the World Wide Web. The web graph is generated from the data collected by the Python crawler customized to scrape data from the Tor dark web. Each of the nodes in the graph represents an individual Tor hidden service, and an edge denotes the hyperlink from one hidden service to the other. Various graph metrics are then computed and analyzed for both directed and undirected graphs using the Python NetworkX package. It was found that most of the nodes of the graph have in-degree and out-degree less than ten. The presence of power-law in degree distribution could neither be confirmed nor denied. The Tor web graph is sparse with a few connected pairs of nodes. Like the surface web, the dark web can also be decomposed into a bow-tie structure though with small component sizes. Several important and well-known websites on the surface web have incoming links from the dark web. Moreover, the Tor network also shows the characteristics of small-world and scale-free networks.
Critical agglomerate sizes to improve the insulation characteristics of epoxy/TiO2 nanocomposites

Agglomerates of nanoparticles which inevitably occur in the fabrication process of epoxy nanocomposite can affect the insulation properties of epoxy nanocomposite. This paper presents an attempt to clarify the effect of agglomerate size on two important insulation properties of epoxy/TiO2 nanocomposite (NC). One is intrinsic AC breakdown strength and the other is long-term partial discharge lifetime. We used a centrifugation technique to control the maximum size of the agglomerates in the range from 5 μm down to 0.05 μm in each sample. As the results, AC intrinsic breakdown strength was increased with decreasing the maximum agglomerate size and became even higher than the neat epoxy resin when the maximum size is less than around 0.05um. On the other hand, partial discharge lifetime decreased with decreasing the maximum agglomerate size. It should be noted that the PD lifetime distinctively decreased when the agglomerate size is less than around 0.2 um indicating the existence of a critical agglomerate size which contribute to the suppression of partial discharge development resulting in the extension of the partial discharge lifetime.
Speaker Age Estimation Using Age-Dependent Insensitive Loss

This paper proposes a new speaker age estimation method that uses an age-dependent insensitive loss. Most conventional speaker age estimation frameworks ignore the ambiguity of a perceptual speaker age. These “over-sensitive” frameworks can cause critical errors far from the actual age. We propose an age-dependent insensitive loss for speaker age estimation. The key idea of the proposed method is that the age estimator should allow some ambiguity of actual age and this ambiguity should depend on age. The age-dependent insensitivity is learned by 5-MAE (mean absolute error) loss and soft target cross entropy loss in regression and classification problems. Experimental results showed that the proposed method improves the mean absolute error and the ratio of critical error by 5.2% and 5.7% for the regression problem and 9.6% and 31.5% for the classification problem.