Displaying publications 1 - 20 of 706 in total

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  1. Zainul R, Abd Azis N, Md Isa I, Hashim N, Ahmad MS, Saidin MI, et al.
    Sensors (Basel), 2019 Feb 22;19(4).
    PMID: 30813385 DOI: 10.3390/s19040941
    This paper presents the application of zinc/aluminium-layered double hydroxide-quinclorac (Zn/Al-LDH-QC) as a modifier of multiwalled carbon nanotubes (MWCNT) paste electrode for the determination of bisphenol A (BPA). The Zn/Al-LDH-QC/MWCNT morphology was examined by a transmission electron microscope and a scanning electron microscope. Electrochemical impedance spectroscopy was utilized to investigate the electrode interfacial properties. The electrochemical responses of the modified electrode towards BPA were thoroughly evaluated by using square-wave voltammetry technique. The electrode demonstrated three linear plots of BPA concentrations from 3.0 × 10-8⁻7.0 × 10-7 M (R² = 0.9876), 1.0 × 10-6⁻1.0 × 10-5 M (R² = 0.9836) and 3.0 × 10-5⁻3.0 × 10-4 M (R² = 0.9827) with a limit of detection of 4.4 × 10-9 M. The electrode also demonstrated good reproducibility and stability up to one month. The presence of several metal ions and organic did not affect the electrochemical response of BPA. The electrode is also applicable for BPA determination in baby bottle and mineral water samples with a range of recovery between 98.22% and 101.02%.
  2. Abu Hasan R, Sulaiman S, Ashykin NN, Abdullah MN, Hafeez Y, Ali SSA
    Sensors (Basel), 2021 Jul 18;21(14).
    PMID: 34300624 DOI: 10.3390/s21144885
    Adults are constantly exposed to stressful conditions at their workplace, and this can lead to decreased job performance followed by detrimental clinical health problems. Advancement of sensor technologies has allowed the electroencephalography (EEG) devices to be portable and used in real-time to monitor mental health. However, real-time monitoring is not often practical in workplace environments with complex operations such as kindergarten, firefighting and offshore facilities. Integrating the EEG with virtual reality (VR) that emulates workplace conditions can be a tool to assess and monitor mental health of adults within their working environment. This paper evaluates the mental states induced when performing a stressful task in a VR-based offshore environment. The theta, alpha and beta frequency bands are analysed to assess changes in mental states due to physical discomfort, stress and concentration. During the VR trials, mental states of discomfort and disorientation are observed with the drop of theta activity, whilst the stress induced from the conditional tasks is reflected in the changes of low-alpha and high-beta activities. The deflection of frontal alpha asymmetry from negative to positive direction reflects the learning effects from emotion-focus to problem-solving strategies adopted to accomplish the VR task. This study highlights the need for an integrated VR-EEG system in workplace settings as a tool to monitor and assess mental health of working adults.
  3. Ali MS, AbuZaiter A, Schlosser C, Bycraft B, Takahata K
    Sensors (Basel), 2014 Jul 10;14(7):12399-409.
    PMID: 25014100 DOI: 10.3390/s140712399
    This paper reports a method that enables real-time displacement monitoring and control of micromachined resonant-type actuators using wireless radiofrequency (RF). The method is applied to an out-of-plane, spiral-coil microactuator based on shape-memory-alloy (SMA). The SMA spiral coil forms an inductor-capacitor resonant circuit that is excited using external RF magnetic fields to thermally actuate the coil. The actuation causes a shift in the circuit's resonance as the coil is displaced vertically, which is wirelessly monitored through an external antenna to track the displacements. Controlled actuation and displacement monitoring using the developed method is demonstrated with the microfabricated device. The device exhibits a frequency sensitivity to displacement of 10 kHz/µm or more for a full out-of-plane travel range of 466 µm and an average actuation velocity of up to 155 µm/s. The method described permits the actuator to have a self-sensing function that is passively operated, thereby eliminating the need for separate sensors and batteries on the device, thus realizing precise control while attaining a high level of miniaturization in the device.
  4. Liu Y, Gan Y, Song Y, Liu J
    Sensors (Basel), 2021 Mar 13;21(6).
    PMID: 33805702 DOI: 10.3390/s21062037
    Contemporarily, almost all the global IT giants have aimed at the smart home industry and made an active strategic business layout. As the early-stage and entry-level product of the voice-enabled smart home industry, the smart speakers have been going through rapid development and rising fierce market competition globally in recent years. China, one of the most populous and largest markets in the world, has tremendous business potential in the smart home industry. The market sales of smart speakers in China have gone through rapid growth in the past three years. However, the market penetration rate of related smart home devices and equipment still stays extremely low and far from mass adoption. Moreover, the market sales of smart speakers have also entered a significant slowdown and adjustment period since 2020. Chinese consumers have moved from early impulsive consumption to a rational consumption phase about this early-stage smart home product. Trust in the marketing field is considered an indispensable component of all business transactions, which plays a crucial role in adopting new technologies. This study explores the influencing factors of Chinese users' perceived trust in the voice-enabled smart home systems, uses structural equation modeling (SEM) to analyze the interaction mechanism between different variables, and establishes a perceived trust model through 475 valid samples. The model includes six variables: system quality, familiarity, subjective norm, technology optimism, perceived enjoyment, and perceived trust. The result shows that system quality is the essential influence factor that impacts all other variables and could significantly affect the perceived trust. Perceived enjoyment is the most direct influence variable affected by system quality, subjective norm, and technology optimism, and it positively affects the perceived trust in the end. The subjective norm is one of the most distinguishing variables for Chinese users, since China has a collectivist consumption culture. People always expect their behavior to meet social expectations and standards to avoid criticism and acquire social integration. Therefore, policy guidance, authoritative opinions, and people with important reference roles will significantly affect consumers' perceived trust and purchase intention. Familiarity and technology optimism are important influential factors that will have an indirect impact on the perceived trust. The related results of this study can help designers, practitioners, and researchers of the smart home industry produce products and services with higher perceived trust to improve consumers' adoption and acceptance so that the market penetration rate of related products and enterprises could be increased, and the maturity and development of the voice-enabled smart home industry could be promoted.
  5. Manogaran G, Shakeel PM, Fouad H, Nam Y, Baskar S, Chilamkurti N, et al.
    Sensors (Basel), 2019 Jul 09;19(13).
    PMID: 31324070 DOI: 10.3390/s19133030
    According to the survey on various health centres, smart log-based multi access physical monitoring system determines the health conditions of humans and their associated problems present in their lifestyle. At present, deficiency in significant nutrients leads to deterioration of organs, which creates various health problems, particularly for infants, children, and adults. Due to the importance of a multi access physical monitoring system, children and adolescents' physical activities should be continuously monitored for eliminating difficulties in their life using a smart environment system. Nowadays, in real-time necessity on multi access physical monitoring systems, information requirements and the effective diagnosis of health condition is the challenging task in practice. In this research, wearable smart-log patch with Internet of Things (IoT) sensors has been designed and developed with multimedia technology. Further, the data computation in that smart-log patch has been analysed using edge computing on Bayesian deep learning network (EC-BDLN), which helps to infer and identify various physical data collected from the humans in an accurate manner to monitor their physical activities. Then, the efficiency of this wearable IoT system with multimedia technology is evaluated using experimental results and discussed in terms of accuracy, efficiency, mean residual error, delay, and less energy consumption. This state-of-the-art smart-log patch is considered as one of evolutionary research in health checking of multi access physical monitoring systems with multimedia technology.
  6. Yang C, Simon G, See J, Berger MO, Wang W
    Sensors (Basel), 2020 May 27;20(11).
    PMID: 32471231 DOI: 10.3390/s20113045
    Collecting correlated scene images and camera poses is an essential step towards learning absolute camera pose regression models. While the acquisition of such data in living environments is relatively easy by following regular roads and paths, it is still a challenging task in constricted industrial environments. This is because industrial objects have varied sizes and inspections are usually carried out with non-constant motions. As a result, regression models are more sensitive to scene images with respect to viewpoints and distances. Motivated by this, we present a simple but efficient camera pose data collection method, WatchPose, to improve the generalization and robustness of camera pose regression models. Specifically, WatchPose tracks nested markers and visualizes viewpoints in an Augmented Reality- (AR) based manner to properly guide users to collect training data from broader camera-object distances and more diverse views around the objects. Experiments show that WatchPose can effectively improve the accuracy of existing camera pose regression models compared to the traditional data acquisition method. We also introduce a new dataset, Industrial10, to encourage the community to adapt camera pose regression methods for more complex environments.
  7. Wali SB, Abdullah MA, Hannan MA, Hussain A, Samad SA, Ker PJ, et al.
    Sensors (Basel), 2019 May 06;19(9).
    PMID: 31064098 DOI: 10.3390/s19092093
    The automatic traffic sign detection and recognition (TSDR) system is very important research in the development of advanced driver assistance systems (ADAS). Investigations on vision-based TSDR have received substantial interest in the research community, which is mainly motivated by three factors, which are detection, tracking and classification. During the last decade, a substantial number of techniques have been reported for TSDR. This paper provides a comprehensive survey on traffic sign detection, tracking and classification. The details of algorithms, methods and their specifications on detection, tracking and classification are investigated and summarized in the tables along with the corresponding key references. A comparative study on each section has been provided to evaluate the TSDR data, performance metrics and their availability. Current issues and challenges of the existing technologies are illustrated with brief suggestions and a discussion on the progress of driver assistance system research in the future. This review will hopefully lead to increasing efforts towards the development of future vision-based TSDR system.
  8. Ismail A, Idris MYI, Ayub MN, Por LY
    Sensors (Basel), 2018 Dec 10;18(12).
    PMID: 30544660 DOI: 10.3390/s18124353
    Smart manufacturing enables an efficient manufacturing process by optimizing production and product transaction. The optimization is performed through data analytics that requires reliable and informative data as input. Therefore, in this paper, an accurate data capture approach based on a vision sensor is proposed. Three image recognition methods are studied to determine the best vision-based classification technique, namely Bag of Words (BOW), Spatial Pyramid Matching (SPM) and Convolutional Neural Network (CNN). The vision-based classifiers categorize the apple as defective and non-defective that can be used for automatic inspection, sorting and further analytics. A total of 550 apple images are collected to test the classifiers. The images consist of 275 non-defective and 275 defective apples. The defective category includes various types of defect and severity. The vision-based classifiers are trained and evaluated according to the K-fold cross-validation. The performances of the classifiers from 2-fold, 3-fold, 4-fold, 5-fold and 10-fold are compared. From the evaluation, SPM with SVM classifier attained 98.15% classification accuracy for 10-fold and outperformed the others. In terms of computational time, CNN with SVM classifier is the fastest. However, minimal time difference is observed between the computational time of CNN and SPM, which were separated by only 0.05 s.
  9. Shokravi H, Shokravi H, Bakhary N, Heidarrezaei M, Rahimian Koloor SS, Petrů M
    Sensors (Basel), 2020 Jun 19;20(12).
    PMID: 32575359 DOI: 10.3390/s20123460
    Bridges are designed to withstand different types of loads, including dead, live, environmental, and occasional loads during their service period. Moving vehicles are the main source of the applied live load on bridges. The applied load to highway bridges depends on several traffic parameters such as weight of vehicles, axle load, configuration of axles, position of vehicles on the bridge, number of vehicles, direction, and vehicle's speed. The estimation of traffic loadings on bridges are generally notional and, consequently, can be excessively conservative. Hence, accurate prediction of the in-service performance of a bridge structure is very desirable and great savings can be achieved through the accurate assessment of the applied traffic load in existing bridges. In this paper, a review is conducted on conventional vehicle-based health monitoring methods used for bridges. Vision-based, weigh in motion (WIM), bridge weigh in motion (BWIM), drive-by and vehicle bridge interaction (VBI)-based models are the methods that are generally used in the structural health monitoring (SHM) of bridges. The performance of vehicle-assisted methods is studied and suggestions for future work in this area are addressed, including alleviating the downsides of each approach to disentangle the complexities, and adopting intelligent and autonomous vehicle-assisted methods for health monitoring of bridges.
  10. Saad MA, Jaafar R, Chellappan K
    Sensors (Basel), 2023 Jun 12;23(12).
    PMID: 37420692 DOI: 10.3390/s23125526
    Data gathering in wireless sensor networks (WSNs) is vital for deploying and enabling WSNs with the Internet of Things (IoTs). In various applications, the network is deployed in a large-scale area, which affects the efficiency of the data collection, and the network is subject to multiple attacks that impact the reliability of the collected data. Hence, data collection should consider trust in sources and routing nodes. This makes trust an additional optimization objective of the data gathering in addition to energy consumption, traveling time, and cost. Joint optimization of the goals requires conducting multiobjective optimization. This article proposes a modified social class multiobjective particle swarm optimization (SC-MOPSO) method. The modified SC-MOPSO method is featured by application-dependent operators named interclass operators. In addition, it includes solution generation, adding and deleting rendezvous points, and moving to the upper and lower class. Considering that SC-MOPSO provides a set of nondominated solutions as a Pareto front, we employed one of the multicriteria decision-making (MCDM) methods, i.e., simple additive sum (SAW), for selecting one of the solutions from the Pareto front. The results show that both SC-MOPSO and SAW are superior in terms of domination. The set coverage of SC-MOPSO is 0.06 dominant over NSGA-II compared with only a mastery of 0.04 of NSGA-II over SC-MOPSO. At the same time, it showed competitive performance with NSGA-III.
  11. Sikandar T, Rabbi MF, Ghazali KH, Altwijri O, Alqahtani M, Almijalli M, et al.
    Sensors (Basel), 2021 Apr 17;21(8).
    PMID: 33920617 DOI: 10.3390/s21082836
    Human body measurement data related to walking can characterize functional movement and thereby become an important tool for health assessment. Single-camera-captured two-dimensional (2D) image sequences of marker-less walking individuals might be a simple approach for estimating human body measurement data which could be used in walking speed-related health assessment. Conventional body measurement data of 2D images are dependent on body-worn garments (used as segmental markers) and are susceptible to changes in the distance between the participant and camera in indoor and outdoor settings. In this study, we propose five ratio-based body measurement data that can be extracted from 2D images and can be used to classify three walking speeds (i.e., slow, normal, and fast) using a deep learning-based bidirectional long short-term memory classification model. The results showed that average classification accuracies of 88.08% and 79.18% could be achieved in indoor and outdoor environments, respectively. Additionally, the proposed ratio-based body measurement data are independent of body-worn garments and not susceptible to changes in the distance between the walking individual and camera. As a simple but efficient technique, the proposed walking speed classification has great potential to be employed in clinics and aged care homes.
  12. Loh KS, Lee YH, Musa A, Salmah AA, Zamri I
    Sensors (Basel), 2008 Sep 18;8(9):5775-5791.
    PMID: 27873839
    Magnetic nanoparticles of Fe₃O₄ were synthesized and characterized using transmission electron microscopy and X-ray diffraction. The Fe₃O₄ nanoparticles were found to have an average diameter of 5.48 ±1.37 nm. An electrochemical biosensor based on immobilized alkaline phosphatase (ALP) and Fe₃O₄ nanoparticles was studied. The amperometric biosensor was based on the reaction of ALP with the substrate ascorbic acid 2-phosphate (AA2P). The incorporation of the Fe₃O₄ nanoparticles together with ALP into a sol gel/chitosan biosensor membrane has led to the enhancement of the biosensor response, with an improved linear response range to the substrate AA2P (5-120 μM) and increased sensitivity. Using the inhibition property of the ALP, the biosensor was applied to the determination of the herbicide 2,4-dichlorophenoxyacetic acid (2,4-D). The use of Fe₃O₄ nanoparticles gives a two-fold improvement in the sensitivity towards 2,4-D, with a linear response range of 0.5-30 μgL-1. Exposure of the biosensor to other toxicants such as heavy metals demonstrated only slight interference from metals such as Hg2+, Cu2+, Ag2+ and Pb2+. The biosensor was shown to be useful for the determination of the herbicide 2, 4-D because good recovery of 95-100 percent was obtained, even though the analysis was performed in water samples with a complex matrix. Furthermore, the results from the analysis of 2,4-D in water samples using the biosensor correlated well with a HPLC method.
  13. Abdollahi A, Pradhan B
    Sensors (Basel), 2021 Jul 11;21(14).
    PMID: 34300478 DOI: 10.3390/s21144738
    Urban vegetation mapping is critical in many applications, i.e., preserving biodiversity, maintaining ecological balance, and minimizing the urban heat island effect. It is still challenging to extract accurate vegetation covers from aerial imagery using traditional classification approaches, because urban vegetation categories have complex spatial structures and similar spectral properties. Deep neural networks (DNNs) have shown a significant improvement in remote sensing image classification outcomes during the last few years. These methods are promising in this domain, yet unreliable for various reasons, such as the use of irrelevant descriptor features in the building of the models and lack of quality in the labeled image. Explainable AI (XAI) can help us gain insight into these limits and, as a result, adjust the training dataset and model as needed. Thus, in this work, we explain how an explanation model called Shapley additive explanations (SHAP) can be utilized for interpreting the output of the DNN model that is designed for classifying vegetation covers. We want to not only produce high-quality vegetation maps, but also rank the input parameters and select appropriate features for classification. Therefore, we test our method on vegetation mapping from aerial imagery based on spectral and textural features. Texture features can help overcome the limitations of poor spectral resolution in aerial imagery for vegetation mapping. The model was capable of obtaining an overall accuracy (OA) of 94.44% for vegetation cover mapping. The conclusions derived from SHAP plots demonstrate the high contribution of features, such as Hue, Brightness, GLCM_Dissimilarity, GLCM_Homogeneity, and GLCM_Mean to the output of the proposed model for vegetation mapping. Therefore, the study indicates that existing vegetation mapping strategies based only on spectral characteristics are insufficient to appropriately classify vegetation covers.
  14. Goh SY, Tan WS, Khan SA, Chew HP, Abu Kasim NH, Yin WF, et al.
    Sensors (Basel), 2014;14(5):8940-9.
    PMID: 24854358 DOI: 10.3390/s140508940
    Bacteria realize the ability to communicate by production of quorum sensing (QS) molecules called autoinducers, which regulate the physiological activities in their ecological niches. The oral cavity could be a potential area for the presence of QS bacteria. In this study, we report the isolation of a QS bacterial isolate C10B from dentine caries. Preliminary screening using Chromobacterium violaceum CV026 biosensor showed that isolate C10B was able to produce N-acylhomoserine lactones (AHLs). This bacterium was further identified as a member of Burkholderia, an opportunistic pathogen. The isolated Burkholderia sp. was confirmed to produce N-hexanoyl-L-homoserine lactone (C6-HSL), N-octanoyl-L-homoserine lactone (C8-HSL), N-decanoyl-L-homoserine lactone (C10-HSL) and N-dodecanoyl-L-homoserine lactone (C12-HSL).
  15. Tripathy A, Pramanik S, Manna A, Shasmin HN, Radzi Z, Abu Osman NA
    Sensors (Basel), 2016 Nov 30;16(12).
    PMID: 27916913
    Since humidity sensors have been widely used in many sectors, a suitable humidity sensing material with improved sensitivity, faster response and recovery times, better stability and low hysteresis is necessary to be developed. Here, we fabricate a uniformly porous humidity sensor using Ca, Ti substituted Mg ferrites with chemical formula of CaMgFe1.33Ti₃O12 as humidity sensing materials by solid-sate step-sintering technique. This synthesis technique is useful to control the grain size with increased porosity to enhance the hydrophilic characteristics of the CaMgFe1.33Ti₃O12 nanoceramic based sintered electro-ceramic nanocomposites. The highest porosity, lowest density and excellent surface-hydrophilicity properties were obtained at 1050 °C sintered ceramic. The performance of this impedance type humidity sensor was evaluated by electrical characterizations using alternating current (AC) in the 33%-95% relative humidity (RH) range at 25 °C. Compared with existing conventional resistive humidity sensors, the present sintered electro-ceramic nanocomposite based humidity sensor showed faster response time (20 s) and recovery time (40 s). This newly developed sensor showed extremely high sensitivity (%S) and small hysteresis of <3.4%. Long-term stability of the sensor had been determined by testing for 30 consecutive days. Therefore, the high performance sensing behavior of the present electro-ceramic nanocomposites would be suitable for a potential use in advanced humidity sensors.
  16. Sathish K, Cv R, Ab Wahab MN, Anbazhagan R, Pau G, Akbar MF
    Sensors (Basel), 2023 May 17;23(10).
    PMID: 37430763 DOI: 10.3390/s23104844
    Underwater Wireless Sensor Networks (UWSNs) have recently established themselves as an extremely interesting area of research thanks to the mysterious qualities of the ocean. The UWSN consists of sensor nodes and vehicles working to collect data and complete tasks. The battery capacity of sensor nodes is quite limited, which means that the UWSN network needs to be as efficient as it can possibly be. It is difficult to connect with or update a communication that is taking place underwater due to the high latency in propagation, the dynamic nature of the network, and the likelihood of introducing errors. This makes it difficult to communicate with or update a communication. Cluster-based underwater wireless sensor networks (CB-UWSNs) are proposed in this article. These networks would be deployed via Superframe and Telnet applications. In addition, routing protocols, such as Ad hoc On-demand Distance Vector (AODV), Fisheye State Routing (FSR), Location-Aided Routing 1 (LAR1), Optimized Link State Routing Protocol (OLSR), and Source Tree Adaptive Routing-Least Overhead Routing Approach (STAR-LORA), were evaluated based on the criteria of their energy consumption in a range of various modes of operation with QualNet Simulator using Telnet and Superframe applications. STAR-LORA surpasses the AODV, LAR1, OLSR, and FSR routing protocols in the evaluation report's simulations, with a Receive Energy of 0.1 mWh in a Telnet deployment and 0.021 mWh in a Superframe deployment. The Telnet and Superframe deployments consume 0.05 mWh transmit power, but the Superframe deployment only needs 0.009 mWh. As a result, the simulation results show that the STAR-LORA routing protocol outperforms the alternatives.
  17. Bi Y, Xu X, Chua SY, Chow EMT, Wang X
    Sensors (Basel), 2018 Mar 07;18(3).
    PMID: 29518889 DOI: 10.3390/s18030798
    Laser sensing has been applied in various underwater applications, ranging from underwater detection to laser underwater communications. However, there are several great challenges when profiling underwater turbulence effects. Underwater detection is greatly affected by the turbulence effect, where the acquired image suffers excessive noise, blurring, and deformation. In this paper, we propose a novel underwater turbulence detection method based on a gated wavefront sensing technique. First, we elaborate on the operating principle of gated wavefront sensing and wavefront reconstruction. We then setup an experimental system in order to validate the feasibility of our proposed method. The effect of underwater turbulence on detection is examined at different distances, and under different turbulence levels. The experimental results obtained from our gated wavefront sensing system indicate that underwater turbulence can be detected and analyzed. The proposed gated wavefront sensing system has the advantage of a simple structure and high detection efficiency for underwater environments.
  18. Li M, Mathai A, Lau SLH, Yam JW, Xu X, Wang X
    Sensors (Basel), 2021 Jan 05;21(1).
    PMID: 33466530 DOI: 10.3390/s21010313
    Due to medium scattering, absorption, and complex light interactions, capturing objects from the underwater environment has always been a difficult task. Single-pixel imaging (SPI) is an efficient imaging approach that can obtain spatial object information under low-light conditions. In this paper, we propose a single-pixel object inspection system for the underwater environment based on compressive sensing super-resolution convolutional neural network (CS-SRCNN). With the CS-SRCNN algorithm, image reconstruction can be achieved with 30% of the total pixels in the image. We also investigate the impact of compression ratios on underwater object SPI reconstruction performance. In addition, we analyzed the effect of peak signal to noise ratio (PSNR) and structural similarity index (SSIM) to determine the image quality of the reconstructed image. Our work is compared to the SPI system and SRCNN method to demonstrate its efficiency in capturing object results from an underwater environment. The PSNR and SSIM of the proposed method have increased to 35.44% and 73.07%, respectively. This work provides new insight into SPI applications and creates a better alternative for underwater optical object imaging to achieve good quality.
  19. Selvarajan RS, Rahim RA, Majlis BY, Gopinath SCB, Hamzah AA
    Sensors (Basel), 2020 May 06;20(9).
    PMID: 32384631 DOI: 10.3390/s20092642
    Nephrogenic diabetes insipidus (NDI), which can be congenital or acquired, results from the failure of the kidney to respond to the anti-diuretic hormone (ADH). This will lead to excessive water loss from the body in the form of urine. The kidney, therefore, has a crucial role in maintaining water balance and it is vital to restore this function in an artificial kidney. Herein, an ultrasensitive and highly selective aptameric graphene-based field-effect transistor (GFET) sensor for ADH detection was developed by directly immobilizing ADH-specific aptamer on a surface-modified suspended graphene channel. This direct immobilization of aptamer on the graphene surface is an attempt to mimic the functionality of collecting tube V 2 receptors in the ADH biosensor. This aptamer was then used as a probe to capture ADH peptide at the sensing area which leads to changes in the concentration of charge carriers in the graphene channel. The biosensor shows a significant increment in the relative change of current ratio from 5.76 to 22.60 with the increase of ADH concentration ranging from 10 ag/mL to 1 pg/mL. The ADH biosensor thus exhibits a sensitivity of 50.00 µA· ( g / mL ) - 1 with a limit of detection as low as 3.55 ag/mL. In specificity analysis, the ADH biosensor demonstrated a higher current value which is 338.64 µA for ADH-spiked in phosphate-buffered saline (PBS) and 557.89 µA for ADH-spiked in human serum in comparison with other biomolecules tested. This experimental evidence shows that the ADH biosensor is ultrasensitive and highly selective towards ADH in PBS buffer and ADH-spiked in human serum.
  20. Ng KJ, Islam MT, Alevy AM, Mansor MF
    Sensors (Basel), 2020 Apr 26;20(9).
    PMID: 32357426 DOI: 10.3390/s20092456
    This paper presents an ultralow profile, low passive intermodulation (PIM), and super-wideband in-building ceiling mount antenna that covers both the cellular and public safety ultra high frequency (UHF) band for distributed antenna system (DAS) applications. The proposed antenna design utilizes a modified 2-D planar discone design concept that is miniaturized to fit into a small disc-shaped radome. The 2-D planar discone has an elliptical-shaped disc monopole and a bell-shaped ground plane, a stub at the shorting path, with asymmetrical structure and an additional proximity coupling patch to maximize the available electrical path to support the 350 MHz band range. The proposed design maximizes the radome area with a reduction of about 62% compared to similar concept type antennas. Besides, the proposed design exhibits an improved radiation pattern with null reduction compared to a typical dipole/monopole when lies at the horizontal plane. A prototype was manufactured to demonstrate the antenna performance. The VSWR and radiation pattern results agreed with the simulated results. The proposed antenna achieves a band ratio of 28.57:1 while covering a frequency range of 350-10,000 MHz. The measured passive intermodulation levels are better than -150 dBc (2 × 20 Watts) for 350, 700 and 1920 MHz bands.
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