This paper presents the design and development of a planar Aligned-Gap and Centered-Gap Rectangular Multiple Split Ring Resonator (SRR) for microwave sensors that operates at a resonance frequency around 5 GHz. The sensor consists of a microstrip transmission line loaded with two elements of rectangular SRR on both sides. The proposed metamaterial sensors were designed and fabricated on Rogers RT5880 substrate having dielectric constant of 2.2 and thickness of 0.787 mm. The final dimension of the proposed sensor was measured at 35 × 14 mm2. Measured results show good agreement with simulated ones as well as exhibiting high Q-factor for use in sensing application. A remarkably shift of resonance frequency is observed upon introduction of several sample with different dielectric value.
In the present study, the construction of arrays on silicon for naked-eye detection of DNA dengue was demonstrated. The array was created by exposing a polyethylene glycol (PEG) silane monolayer to 254 nm ultraviolet (UV) light through a photomask. Formation of the PEG silane monolayer and photomodifed surface properties was thoroughly characterized by using atomic force microscopy (AFM), X-ray photoelectron spectroscopy (XPS), and contact angle measurements. The results of XPS confirmed that irradiation of ultraviolet (UV) light generates an aldehyde functional group that offers conjugation sites of amino DNA probe for detection of a specific dengue virus target DNA. Employing a gold enhancement process after inducing the electrostatic interaction between positively charged gold nanoparticles and the negatively charged target DNA hybridized to the DNA capture probe allowed to visualize the array with naked eye. The developed arrays demonstrated excellent performance in diagnosis of dengue with a detection limit as low as 10 pM. The selectivity of DNA arrays was also examined using a single base mismatch and noncomplementary target DNA.
Microfluidics-based lab-on-chip (LOC) systems are an active research area that is revolutionising high-throughput sequencing for the fast, sensitive and accurate detection of a variety of pathogens. LOCs also serve as portable diagnostic tools. The devices provide optimum control of nanolitre volumes of fluids and integrate various bioassay operations that allow the devices to rapidly sense pathogenic threat agents for environmental monitoring. LOC systems, such as microfluidic biochips, offer advantages compared to conventional identification procedures that are tedious, expensive and time consuming. This paper aims to provide a broad overview of the need for devices that are easy to operate, sensitive, fast, portable and sufficiently reliable to be used as complementary tools for the control of pathogenic agents that damage the environment.
A new photonics biosensor configuration comprising a Double-side Ring Add-drop Filter microring resonator (DR-ADF) made from SiO2-TiO2 material is proposed for the detection of Salmonella bacteria (SB) in blood. The scattering matrix method using inductive calculation is used to determine the output signal's intensities in the blood with and without presence of Salmonella. The change in refractive index due to the reaction of Salmonella bacteria with its applied antibody on the flagellin layer loaded on the sensing and detecting microresonator causes the increase in through and dropper port's intensities of the output signal which leads to the detection of SB in blood. A shift in the output signal wavelength is observed with resolution of 0.01 nm. The change in intensity and shift in wavelength is analyzed with respect to the change in the refractive index which contributes toward achieving an ultra-high sensitivity of 95,500 nm/RIU which is almost two orders higher than that of reported from single ring sensors and the limit of detection is in the order of 1 × 10(-8) RIU. In applications, such a system can be employed for a high sensitive and fast detection of bacteria.
Gait analysis is an essential tool for detecting biomechanical irregularities, designing personalized rehabilitation plans, and enhancing athletic performance. Currently, gait assessment depends on either visual observation, which lacks consistency between raters and requires clinical expertise, or instrumented evaluation, which is costly, invasive, time-consuming, and requires specialized equipment and trained personnel. Markerless gait analysis using 2D pose estimation techniques has emerged as a potential solution, but it still requires significant computational resources and human involvement, making it challenging to use. This research proposes an automated method for temporal gait analysis that employs the MediaPipe Pose, a low-computational-resource pose estimation model. The study validated this approach against the Vicon motion capture system to evaluate its reliability. The findings reveal that this approach demonstrates good (ICC(2,1) > 0.75) to excellent (ICC(2,1) > 0.90) agreement in all temporal gait parameters except for double support time (right leg switched to left leg) and swing time (right), which only exhibit a moderate (ICC(2,1) > 0.50) agreement. Additionally, this approach produces temporal gait parameters with low mean absolute error. It will be useful in monitoring changes in gait and evaluating the effectiveness of interventions such as rehabilitation or training programs in the community.
A sensitive and selective optical DNA biosensor was developed for dengue virus detection based on novel square-planar piperidine side chain-functionalized N,N'-bis-4-(hydroxysalicylidene)-phenylenediamine-nickel(II), which was able to intercalate via nucleobase stacking within DNA and be functionalized as an optical DNA hybridization marker. 3-Aminopropyltriethoxysilane (APTS)-modified porous silica nanospheres (PSiNs), was synthesized with a facile mini-emulsion method to act as a high capacity DNA carrier matrix. The Schiff base salphen complexes-labelled probe to target nucleic acid on the PSiNs renders a colour change of the DNA biosensor to a yellow background colour, which could be quantified via a reflectance transduction method. The reflectometric DNA biosensor demonstrated a wide linear response range to target DNA over the concentration range of 1.0 × 10-16-1.0 × 10-10 M (R² = 0.9879) with an ultralow limit of detection (LOD) at 0.2 aM. The optical DNA biosensor response was stable and maintainable at 92.8% of its initial response for up to seven days of storage duration with a response time of 90 min. The reflectance DNA biosensor obtained promising recovery values of close to 100% for the detection of spiked synthetic dengue virus serotypes 2 (DENV-2) DNA concentration in non-invasive human samples, indicating the high accuracy of the proposed DNA analytical method for early diagnosis of all potential infectious diseases or pathological genotypes.
This paper aims to provide an overview of the experimental and simulation works focused on the detection, localisation and assessment of various defects in pipes by applying fast-screening guided ultrasonic wave techniques that have been used in the oil and gas industries over the past 20 years. Major emphasis is placed on limitations, capabilities, defect detection in coated buried pipes under pressure and corrosion monitoring using different commercial guided wave (GW) systems, approaches to simulation techniques such as the finite element method (FEM), wave mode selection, excitation and collection, GW attenuation, signal processing and different types of GW transducers. The effects of defect parameters on reflection coefficients are also discussed in terms of different simulation studies and experimental verifications.
This study aimed to evaluate the motion accuracy of novice and senior students in Baduanjin (a traditional Chinese sport) using an inertial sensor measurement system (IMU). Study participants were nine novice students, 11 senior students, and a teacher. The motion data of all participants were measured three times with the IMU. Using the motions of the teacher as the standard motions, we used dynamic time warping to calculate the distances between the motion data of the students and the teacher to evaluate the motion accuracy of the students. The distances between the motion data of the novice students and the teacher were higher than that between senior students and the teacher (p < 0.05 or p < 0.01). These initial results showed that the IMU and the corresponding mathematical methods could effectively distinguish the differences in motion accuracy between novice and senior students of Baduanjin.
Solar cells are pivotal in harnessing renewable energy for a greener and more sustainable energy landscape. Nonetheless, eco-friendly materials for solar cells have not been as extensive as conventional counterparts, highlighting a significant area for further investigation in advancing sustainable energy technologies. This study investigated natural dyes from cost-effective and environmentally friendly blueberries and mulberries. These dyes were utilized as alternative sensitizers for dye-sensitized solar cells (DSSCs). Alongside the natural dyes, a green approach was adopted for the DSSC design, encompassing TiO2 photoanodes, eco-friendly electrolytes, and green counter-electrodes created from graphite pencils and candle soot. Consequently, the best-optimized dye sensitizer was mulberry, with an output power of 13.79 µW and 0.122 µW for outdoor and indoor environments, respectively. This study underscored the feasibility of integrating DSSCs with sensitizers derived from readily available food ingredients, potentially expanding their applications in educational kits and technology development initiatives.
The protection of concrete structures against corrosion in marine environments has always been a challenge due to the presence of a saline solution-A natural corrosive agent to the concrete paste and steel reinforcements. The concentration of salt is a key parameter influencing the rate of corrosion. In this paper, we propose an optical fiber-based salinity sensor based on bundled multimode plastic optical fiber (POF) as a sensor probe and a concave mirror as a reflector in conjunction with an intensity modulation technique. A refractive index (RI) sensing approach is analytically investigated and the findings are in agreement with the experimental results. A maximum sensitivity of 14,847.486/RIU can be achieved at RI = 1.3525. The proposed technique is suitable for in situ measurement and monitoring of salinity in liquid.
Dielectrophoresis (DEP) is a label-free, accurate, fast, low-cost diagnostic technique that uses the principles of polarization and the motion of bioparticles in applied electric fields. This technique has been proven to be beneficial in various fields, including environmental research, polymer research, biosensors, microfluidics, medicine and diagnostics. Biomedical science research is one of the major research areas that could potentially benefit from DEP technology for diverse applications. Nevertheless, many medical science research investigations have yet to benefit from the possibilities offered by DEP. This paper critically reviews the fundamentals, recent progress, current challenges, future directions and potential applications of research investigations in the medical sciences utilizing DEP technique. This review will also act as a guide and reference for medical researchers and scientists to explore and utilize the DEP technique in their research fields.
We present ARTYCUL (ARTifact popularitY for CULtural heritage), a machine learning(ML)-based framework that graphically represents the footfall around an artifact on display at a museum or a heritage site. The driving factor of this framework was the fact that the presence of security cameras has become universal, including at sites of cultural heritage. ARTYCUL used the video streams of closed-circuit televisions (CCTV) cameras installed in such premises to detect human figures, and their coordinates with respect to the camera frames were used to visualize the density of visitors around the specific display items. Such a framework that can display the popularity of artifacts would aid the curators towards a more optimal organization. Moreover, it could also help to gauge if a certain display item were neglected due to incorrect placement. While items of similar interest can be placed in vicinity of each other, an online recommendation system may also use the reputation of an artifact to catch the eye of the visitors. Artificial intelligence-based solutions are well suited for analysis of internet of things (IoT) traffic due to the inherent veracity and volatile nature of the transmissions. The work done for the development of ARTYCUL provided a deeper insight into the avenues for applications of IoT technology to the cultural heritage domain, and suitability of ML to process real-time data at a fast pace. While we also observed common issues that hinder the utilization of IoT in the cultural domain, the proposed framework was designed keeping in mind the same obstacles and a preference for backward compatibility.
Nowadays, social media has become the main source of news around the world. The spread of fake news on social networks has become a serious global issue, damaging many aspects, such as political, economic, and social aspects, and negatively affecting the lives of citizens. Fake news often carries negative sentiments, and the public's response to it carries the emotions of surprise, fear, and disgust. In this article, we extracted features based on sentiment analysis of news articles and emotion analysis of users' comments regarding this news. These features were fed, along with the content feature of the news, to the proposed bidirectional long short-term memory model to detect fake news. We used the standard Fakeddit dataset that contains news titles and comments posted regarding them to train and test the proposed model. The suggested model, using extracted features, provided a high detection accuracy of 96.77% of the Area under the ROC Curve measure, which is higher than what other state-of-the-art studies offer. The results prove that the features extracted based on sentiment analysis of news, which represents the publisher's stance, and emotion analysis of comments, which represent the crowd's stance, contribute to raising the efficiency of the detection model.
The need for environmental protection and water pollution control has led to the development of different sensors for determining many kinds of pollutants in water. Ammonia nitrogen presence is an important indicator of water quality in environmental monitoring applications. In this paper, a high sensitivity sensor for monitoring ammonia nitrogen concentration in water using a tapered microfiber interferometer (MFI) as a sensor platform and a broad supercontinuum laser as the light source is realized. The MFI is fabricated to the waist diameter of 8 µm producing a strong interference pattern due to the coupling of the fundamental mode with the cladding mode. The MFI sensor is investigated for a low concentration of ammonia nitrogen in water in the wide wavelength range from 1500-1800 nm with a high-power signal provided by the supercontinuum source. The broad source allows optical sensing characteristics of the MFI to be evaluated at four different wavelengths (1505, 1605, 1705, and 1785 nm) upon exposure towards various ammonia nitrogen concentrations. The highest sensitivity of 0.099 nm/ppm that indicates the wavelength shift is observed at 1785 nm operating wavelength. The response is linear in the ammonia nitrogen range of 5-30 ppm with the best measurement resolution calculated to be 0.5 ppm. The low concentration ammonia nitrogen detected by the MFI in the unique infrared region reveals the potential application of this optical fiber-based sensor for rivers and drinking water monitoring.
Ethanol is a highly combustible chemical universally designed for biomedical applications. In this paper, optical sensing performance of tapered multimode fiber tip coated with carbon nanotube (CNT) thin film towards aqueous ethanol with different concentrations is investigated. The tapered optical multimode fiber tip is coated with CNT using drop-casting technique and is annealed at 70 °C to enhance the binding of the nanomaterial to the silica fiber tip. The optical fiber tip and the CNT sensing layer are micro-characterized using FESEM and Raman spectroscopy techniques. When the developed sensor was exposed to different concentrations of ethanol (5% to 80%), the sensor reflectance reduced proportionally. The developed sensors showed high sensitivity, repeatability and fast responses (<55 s) towards ethanol.
Wake-up radio is a promising approach to mitigate the problem of idle listening, which incurs additional power consumption for the Internet of Things (IoT) wireless transmission. Radio frequency (RF) energy harvesting technique allows the wake-up radio to remain in a deep sleep and only become active after receiving an external RF signal to 'wake-up' the radio, thus eliminating necessary hardware and signal processing to perform idle listening, resulting in higher energy efficiency. This review paper focuses on cross-layer; physical and media access control (PHY and MAC) approaches on passive wake-up radio based on the previous works from the literature. First, an explanation of the circuit design and system architecture of the passive wake-up radios is presented. Afterward, the previous works on RF energy harvesting techniques and the existing passive wake-up radio hardware architectures available in the literature are surveyed and classified. An evaluation of the various MAC protocols utilized for the novel passive wake-up radio technologies is presented. Finally, the paper highlights the potential research opportunities and practical challenges related to the practical implementation of wake-up technology for future IoT applications.
As a fundamental but difficult topic in computer vision, 3D object segmentation has various applications in medical image analysis, autonomous vehicles, robotics, virtual reality, lithium battery image analysis, etc. In the past, 3D segmentation was performed using hand-made features and design techniques, but these techniques could not generalize to vast amounts of data or reach acceptable accuracy. Deep learning techniques have lately emerged as the preferred method for 3D segmentation jobs as a result of their extraordinary performance in 2D computer vision. Our proposed method used a CNN-based architecture called 3D UNET, which is inspired by the famous 2D UNET that has been used to segment volumetric image data. To see the internal changes of composite materials, for instance, in a lithium battery image, it is necessary to see the flow of different materials and follow the directions analyzing the inside properties. In this paper, a combination of 3D UNET and VGG19 has been used to conduct a multiclass segmentation of publicly available sandstone datasets to analyze their microstructures using image data based on four different objects in the samples of volumetric data. In our image sample, there are a total of 448 2D images, which are then aggregated as one 3D volume to examine the 3D volumetric data. The solution involves the segmentation of each object in the volume data and further analysis of each object to find its average size, area percentage, total area, etc. The open-source image processing package IMAGEJ is used for further analysis of individual particles. In this study, it was demonstrated that convolutional neural networks can be trained to recognize sandstone microstructure traits with an accuracy of 96.78% and an IOU of 91.12%. According to our knowledge, many prior works have applied 3D UNET for segmentation, but very few papers extend it further to show the details of particles in the sample. The proposed solution offers a computational insight for real-time implementation and is discovered to be superior to the current state-of-the-art methods. The result has importance for the creation of an approximately similar model for the microstructural analysis of volumetric data.
This paper demonstrates the use of Universal Software Radio Peripheral (USRP), together with Raspberry Pi3 B+ (RP3) as the brain (or the decision making engine), to develop a distributed wireless network in which nodes can communicate with other nodes independently and make decision autonomously. In other words, each USRP node (i.e., sensor) is embedded with separate processing units (i.e., RP3), which has not been investigated in the literature, so that each node can make independent decisions in a distributed manner. The proposed testbed in this paper is compared with the traditional distributed testbed, which has been widely used in the literature. In the traditional distributed testbed, there is a single processing unit (i.e., a personal computer) that makes decisions in a centralized manner, and each node (i.e., USRP) is connected to the processing unit via a switch. The single processing unit exchanges control messages with nodes via the switch, while the nodes exchange data packets among themselves using a wireless medium in a distributed manner. The main disadvantage of the traditional testbed is that, despite the network being distributed in nature, decisions are made in a centralized manner. Hence, the response delay of the control message exchange is always neglected. The use of such testbed is mainly due to the limited hardware and monetary cost to acquire a separate processing unit for each node. The experiment in our testbed has shown the increase of end-to-end delay and decrease of packet delivery ratio due to software and hardware delays. The observed multihop transmission is performed using device-to-device (D2D) communication, which has been enabled in 5G. Therefore, nodes can either communicate with other nodes via: (a) a direct communication with the base station at the macrocell, which helps to improve network performance; or (b) D2D that improve spectrum efficiency, whereby traffic is offloaded from macrocell to small cells. Our testbed is the first of its kind in this scale, and it uses RP3 as the distributed decision-making engine incorporated into the USRP/GNU radio platform. This work provides an insight to the development of a 5G network.
Nowadays, several neurological disorders and neurocrine tumours are associated with dopamine (DA) concentrations in various biological fluids. Highly accurate and ultrasensitive detection of DA levels in different biological samples in real-time can change and improve the quality of a patient's life in addition to reducing the treatment cost. Therefore, the design and development of diagnostic tool for in vivo and in vitro monitoring of DA is of considerable clinical and pharmacological importance. In recent decades, a large number of techniques have been established for DA detection, including chromatography coupled to mass spectrometry, spectroscopic approaches, and electrochemical (EC) methods. These methods are effective, but most of them still have some drawbacks such as consuming time, effort, and money. Added to that, sometimes they need complex procedures to obtain good sensitivity and suffer from low selectivity due to interference from other biological species such as uric acid (UA) and ascorbic acid (AA). Advanced materials can offer remarkable opportunities to overcome drawbacks in conventional DA sensors. This review aims to explain challenges related to DA detection using different techniques, and to summarize and highlight recent advancements in materials used and approaches applied for several sensor surface modification for the monitoring of DA. Also, it focuses on the analytical features of the EC and optical-based sensing techniques available.
Hard-shell thoracolumbar sacral orthoses (TLSOs) are used for treating idiopathic scoliosis, a deformation of the spine with a sideways curvature. The pressure required inside the TLSO for ideal corrective results remains unclear. Retrofitting TLSOs with commercially available pressure measurement systems is expensive and can only be performed in a laboratory. The aim of this study was to develop a cost-effective but accurate pressure sensor system for TLSOs. The sensor was built from a piezoresistive polymer, placed between two closed-cell foam liners, and evaluated with a material testing machine. Because foams are energy absorbers, the pressure-conductance curve was affected by hysteresis. The sensor was calibrated on a force plate with the transitions from loading to unloading used to establish the calibration curve. The root mean square error was 12% on average within the required pressure range of 0.01-0.13 MPa. The sensor reacted to the changing pressure during breathing and different activities when tested underneath a chest belt at different tensions. The peak pressure reached 0.135 MPa. The sensor was further tested inside the scoliosis brace during different activities. The measured pressure was 0.014-0.124 MPa. The results from this study enable cheaper and mobile systems to be used for clinical studies on the comfort and pressure of braces during daily activities.