A new cellulose nanocrystal-reduced graphene oxide (CNC-rGO) nanocomposite was successfully used for mediatorless electrochemical sensing of methyl paraben (MP). Fourier-transform infrared spectroscopy (FTIR) and field-emission scanning electron microscopy (FESEM) studies confirmed the formation of the CNC-rGO nanocomposite. Cyclic voltammetry (CV) studies of the nanocomposite showed quasi-reversible redox behavior. Differential pulse voltammetry (DPV) was employed for the sensor optimization. Under optimized conditions, the sensor demonstrated a linear calibration curve in the range of 2 × 10-4-9 × 10-4 M with a limit of detection (LOD) of 1 × 10-4 M. The MP sensor showed good reproducibility with a relative standard deviation (RSD) of about 8.20%. The sensor also exhibited good stability and repeatability toward MP determinations. Analysis of MP in cream samples showed recovery percentages between 83% and 106%. Advantages of this sensor are the possibility for the determination of higher concentrations of MP when compared with most other reported sensors for MP. The CNC-rGO nanocomposite-based sensor also depicted good reproducibility and reusability compared to the rGO-based sensor. Furthermore, the CNC-rGO nanocomposite sensor showed good selectivity toward MP with little interference from easily oxidizable species such as ascorbic acid.
In this paper, a new control-centric approach is introduced to model the characteristics of flex sensors on a goniometric glove, which is designed to capture the user hand gesture that can be used to wirelessly control a bionic hand. The main technique employs the inverse dynamic model strategy along with a black-box identification for the compensator design, which is aimed to provide an approximate linear mapping between the raw sensor output and the dynamic finger goniometry. To smoothly recover the goniometry on the bionic hand's side during the wireless transmission, the compensator is restructured into a Hammerstein-Wiener model, which consists of a linear dynamic system and two static nonlinearities. A series of real-time experiments involving several hand gestures have been conducted to analyze the performance of the proposed method. The associated temporal and spatial gesture data from both the glove and the bionic hand are recorded, and the performance is evaluated in terms of the integral of absolute error between the glove's and the bionic hand's dynamic goniometry. The proposed method is also compared with the raw sensor data, which has been preliminarily calibrated with the finger goniometry, and the Wiener model, which is based on the initial inverse dynamic design strategy. Experimental results with several trials for each gesture show that a great improvement is obtained via the Hammerstein-Wiener compensator approach where the resulting average errors are significantly smaller than the other two methods. This concludes that the proposed strategy can remarkably improve the dynamic goniometry of the glove, and thus provides a smooth human-robot collaboration with the bionic hand.
A new biosensor for the analysis of nitrite in food was developed based on hemoglobin (Hb) covalently immobilized on the succinimide functionalized poly(n-butyl acrylate)-graphene [poly(nBA)-rGO] composite film deposited on a carbon-paste screen-printed electrode (SPE). The immobilized Hb on the poly(nBA)-rGO conducting matrix exhibited electrocatalytic ability for the reduction of nitrite with significant enhancement in the reduction peak at −0.6 V versus Ag/AgCl reference electrode. Thus, direct determination of nitrite can be achieved by monitoring the cathodic peak current signal of the proposed polyacrylic-graphene hybrid film-based voltammetric nitrite biosensor. The nitrite biosensor exhibited a reproducible dynamic linear response range from 0.05⁻5 mg L−1 nitrite and a detection limit of 0.03 mg L−1. No significant interference was observed by potential interfering ions such as Ca2+, Na⁺, K⁺, NH₄⁺, Mg2+, and NO₃− ions. Analysis of nitrite in both raw and processed edible bird’s nest (EBN) samples demonstrated recovery of close to 100%. The covalent immobilization of Hb on poly(nBA)-rGO composite film has improved the performance of the electrochemical nitrite biosensor in terms of broader detection range, lower detection limit, and prolonged biosensor stability.
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.
"Internet of Things (IoT)" has emerged as a novel concept in the world of technology and communication. In modern network technologies, the capability of transmitting data through data communication networks (such as Internet or intranet) is provided for each organism (e.g. human beings, animals, things, and so forth). Due to the limited hardware and operational communication capability as well as small dimensions, IoT undergoes several challenges. Such inherent challenges not only cause fundamental restrictions in the efficiency of aggregation, transmission, and communication between nodes; but they also degrade routing performance. To cope with the reduced availability time and unstable communications among nodes, data aggregation, and transmission approaches in such networks are designed more intelligently. In this paper, a distributed method is proposed to set child balance among nodes. In this method, the height of the network graph increased through restricting the degree; and network congestion reduced as a result. In addition, a dynamic data aggregation approach based on Learning Automata was proposed for Routing Protocol for Low-Power and Lossy Networks (LA-RPL). More specifically, each node was equipped with learning automata in order to perform data aggregation and transmissions. Simulation and experimental results indicate that the LA-RPL has better efficiency than the basic methods used in terms of energy consumption, network control overhead, end-to-end delay, loss packet and aggregation rates.
The increase in drone misuse by civilian apart from military applications is alarming and need to be addressed. This drone is characterized as a low altitude, slow speed, and small radar cross-section (RCS) (LSS) target and is considered difficult to be detected and classified among other biological targets, such as insects and birds existing in the same surveillance volume. Although several attempts reported the successful drone detection on radio frequency-based (RF), thermal, acoustic, video imaging, and other non-technical methods, however, there are also many limitations. Thus, this paper investigated a micro-Doppler analysis from drone rotating blades for detection in a special Forward Scattering Radar (FSR) geometry. The paper leveraged the identified benefits of FSR mode over conventional radars, such as improved radar cross-section (RCS) value irrespective of radar absorbing material (RAM), direct signal perturbation, and high resolutions. To prove the concept, a received signal model for micro-Doppler analysis, a simulation work, and experimental validation are elaborated and explained in the paper. Two rotating blades aspect angle scenarios were considered, which are (i) when drone makes a turn, the blade cross-sectional area faces the receiver and (ii) when drone maneuvers normally, the cross-sectional blade faces up. The FSR system successfully detected a commercial drone and extracted the micro features of a rotating blade. It further verified the feasibility of using a parabolic dish antenna as a receiver in FSR geometry; this marked an appreciable achievement towards the FSR system performance, which in future could be implemented as either active or passive FSR system.
Biometric verification methods have gained significant popularity in recent times, which has brought about their extensive usage. In light of theoretical evidence surrounding the development of biometric verification, we proposed an experimental multi-biometric system for laboratory testing. First, the proposed system was designed such that it was able to identify and verify a user through the hand contour, and blood flow (blood stream) at the upper part of the hand. Next, we detailed the hard and software solutions for the system. A total of 40 subjects agreed to be a part of data generation team, which produced 280 hand images. The core of this paper lies in evaluating individual metrics, which are functions of frequency comparison of the double type faults with the EER (Equal Error Rate) values. The lowest value was measured for the case of the modified Hausdorff distance metric - Maximally Helicity Violating (MHV). Furthermore, for the verified biometric characteristics (Hamming distance and MHV), appropriate and suitable metrics have been proposed and experimented to optimize system precision. Thus, the EER value for the designed multi-biometric system in the context of this work was found to be 5%, which proves that metrics consolidation increases the precision of the multi-biometric system. Algorithms used for the proposed multi-biometric device shows that the individual metrics exhibit significant accuracy but perform better on consolidation, with a few shortcomings.
Many receiver-based Preamble Sampling Medium Access Control (PS-MAC) protocols have been proposed to provide better performance for variable traffic in a wireless sensor network (WSN). However, most of these protocols cannot prevent the occurrence of incorrect traffic convergence that causes the receiver node to wake-up more frequently than the transmitter node. In this research, a new protocol is proposed to prevent the problem mentioned above. The proposed mechanism has four components, and they are Initial control frame message, traffic estimation function, control frame message, and adaptive function. The initial control frame message is used to initiate the message transmission by the receiver node. The traffic estimation function is proposed to reduce the wake-up frequency of the receiver node by using the proposed traffic status register (TSR), idle listening times (ILTn, ILTk), and "number of wake-up without receiving beacon message" (NWwbm). The control frame message aims to supply the essential information to the receiver node to get the next wake-up-interval (WUI) time for the transmitter node using the proposed adaptive function. The proposed adaptive function is used by the receiver node to calculate the next WUI time of each of the transmitter nodes. Several simulations are conducted based on the benchmark protocols. The outcome of the simulation indicates that the proposed mechanism can prevent the incorrect traffic convergence problem that causes frequent wake-up of the receiver node compared to the transmitter node. Moreover, the simulation results also indicate that the proposed mechanism could reduce energy consumption, produce minor latency, improve the throughput, and produce higher packet delivery ratio compared to other related works.
Evaluating water level changes at intertidal zones is complicated because of dynamic tidal inundation. However, water level changes during different tidal phases could be evaluated using a digital surface model (DSM) captured by unmanned aerial vehicle (UAV) with higher vertical accuracy provided by a Global Navigation Satellite System (GNSS). Image acquisition using a multirotor UAV and vertical data collection from GNSS survey were conducted at Kilim River, Langkawi Island, Kedah, Malaysia during two different tidal phases, at high and low tides. Using the Structure from Motion (SFM) algorithm, a DSM and orthomosaics were produced as the main sources of data analysis. GNSS provided horizontal and vertical geo-referencing for both the DSM and orthomosaics during post-processing after field observation at the study area. The DSM vertical accuracy against the tidal data from a tide gauge was about 12.6 cm (0.126 m) for high tide and 34.5 cm (0.345 m) for low tide. Hence, the vertical accuracy of the DSM height is still within a tolerance of ±0.5 m (with GNSS positioning data). These results open new opportunities to explore more validation methods for water level changes using various aerial platforms besides Light Detection and Ranging (LiDAR) and tidal data in the future.
Conventional air quality monitoring systems, such as gas analysers, are commonly used in many developed and developing countries to monitor air quality. However, these techniques have high costs associated with both installation and maintenance. One possible solution to complement these techniques is the application of low-cost air quality sensors (LAQSs), which have the potential to give higher spatial and temporal data of gas pollutants with high precision and accuracy. In this paper, we present DiracSense, a custom-made LAQS that monitors the gas pollutants ozone (O₃), nitrogen dioxide (NO₂), and carbon monoxide (CO). The aim of this study is to investigate its performance based on laboratory calibration and field experiments. Several model calibrations were developed to improve the accuracy and performance of the LAQS. Laboratory calibrations were carried out to determine the zero offset and sensitivities of each sensor. The results showed that the sensor performed with a highly linear correlation with the reference instrument with a response-time range from 0.5 to 1.7 min. The performance of several calibration models including a calibrated simple equation and supervised learning algorithms (adaptive neuro-fuzzy inference system or ANFIS and the multilayer feed-forward perceptron or MLP) were compared. The field calibration focused on O₃ measurements due to the lack of a reference instrument for CO and NO₂. Combinations of inputs were evaluated during the development of the supervised learning algorithm. The validation results demonstrated that the ANFIS model with four inputs (WE OX, AE OX, T, and NO₂) had the lowest error in terms of statistical performance and the highest correlation coefficients with respect to the reference instrument (0.8 < r < 0.95). These results suggest that the ANFIS model is promising as a calibration tool since it has the capability to improve the accuracy and performance of the low-cost electrochemical sensor.
A simple, compact sickle-shaped printed antenna with a slotted ground plane is designed and developed for broadband circularly polarized (CP) radiation. The sickle-shaped radiator with a tapered feed line and circular slotted square ground plane are utilized to realize the wideband CP radiation feature. With optimized dimensions of 0.29λ × 0.29λ × 0.012λ at 2.22 GHz frequency for the realized antenna parameters, the measured results display that the antenna has a 10 dB impedance bandwidth of 7.70 GHz (126.85%; 2.22⁻9.92 GHz) and a 3 dB axial ratio (AR) bandwidth of 2.64 GHz (73.33%; 2.28⁻4.92 GHz). The measurement agrees well with simulation, which proves an excellent circularly polarized property. For verification, the mechanism of band improvement and circular polarization are presented, and the parametric study is carried out. Since, the proposed antenna is a simple design structure with broad impedance and AR bandwidth, which is a desirable feature as a candidate for various wireless communication systems. Because of the easy printed structure and scaling the dimension with broadband CP characteristics, the realized antenna does incorporate in a number of CP wireless communication applications.
Channel rendezvous is an initial and important process for establishing communications between secondary users (SUs) in distributed cognitive radio networks. Due to the drawbacks of the common control channel (CCC) based rendezvous approach, channel hopping (CH) has attracted a lot of research interests for achieving blind rendezvous. To ensure rendezvous within a finite time, most of the existing CH-based rendezvous schemes generate their CH sequences based on the whole global channel set in the network. However, due to the spatial and temporal variations in channel availabilities as well as the limitation of SUs sensing capabilities, the local available channel set (ACS) for each SU is usually a small subset of the global set. Therefore, following these global-based generated CH sequences can result in extensively long time-to-rendezvous (TTR) especially when the number of unavailable channels is large. In this paper, we propose two matrix-based CH rendezvous schemes in which the CH sequences are generated based on the ACSs only. We prove the guaranteed and full diversity rendezvous of the proposed schemes by deriving the theoretical upper bounds of their maximum TTRs. Furthermore, extensive simulation comparisons with other existing works are conducted which illustrate the superior performance of our schemes in terms of the TTR metrics.
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.
This paper investigated the throughput performance of a secondary user (SU) for a random primary user (PU) activity in a realistic experimental model. This paper proposed a sensing and frame duration of the SU to maximize the SU throughput under the collision probability constraint. The throughput of the SU and the probability of collisions depend on the pattern of PU activities. The pattern of PU activity was obtained and modelled from the experimental data that measure the wireless local area network (WLAN) environment. The WLAN signal has detected the transmission opportunity length (TOL) which was analyzed and clustered into large and small durations in the CTOL model. The performance of the SU is then analyzed and compared with static and dynamic PU models. The results showed that the SU throughput in the CTOL model was higher than the static and dynamic models by almost 45% and 12.2% respectively. Furthermore, the probability of collisions in the network and the SU throughput were influenced by the value of the minimum contention window and the maximum back-off stage. The simulation results revealed that the higher contention window had worsened the SU throughput even though the channel has a higher number of TOLs.
An experimental system for early screening of a breast tumor is presented in this article. The proposed microwave imaging (MI) system consists of a moveable array of nine improved negative-index metamaterial (MTM)-loaded ultrawideband (UWB) antenna sensor with incorporation of a corresponding SRR (split-ring resonator) and CLS (capacitively loaded strip) structure, in a circular array, the stepper motor-based array-mounting stand, the adjustable phantom hanging platform, an RF switching system to control the receivers, and a personal computer-based signal processing and image reconstruction unit using MATLAB. The improved antenna comprises of four-unit cells along one axis, where an individual unit cell integrates a balancing SRR and CLS pair, which makes the antenna radiation omnidirectional over the operating frequencies. The electrical dimensions of this proposed antenna are 0.28λ × 0.20λ × 0.016λ, measured at the lowest operating frequency of 2.97 GHz as the operating bandwidth of this is in between 2.97⁻15 GHz (134.82% bandwidth), with stable directional radiation pattern. SP8T 8 port switch is used to enable the eight receiver antennas to sequentially send a 3⁻8.0 GHz microwave signal to capture the backscattered signal by MATLAB software. A low-cost realistic homogeneous breast phantom with tumor material is developed and measured to test the capability of the imaging system to detect the breast tumors. A post-processing delay-multiply-and-sum (DMAS) algorithm is used to process the recorded backscatter signal to get an image of the breast phantom, and to accurately identify the existence and located area of multiple breast tumor tissues.
One of the major causes of death all over the world is heart disease or cardiac dysfunction. These diseases could be identified easily with the variations in the sound produced due to the heart activity. These sophisticated auscultations need important clinical experience and concentrated listening skills. Therefore, there is an unmet need for a portable system for the early detection of cardiac illnesses. This paper proposes a prototype model of a smart digital-stethoscope system to monitor patient's heart sounds and diagnose any abnormality in a real-time manner. This system consists of two subsystems that communicate wirelessly using Bluetooth low energy technology: A portable digital stethoscope subsystem, and a computer-based decision-making subsystem. The portable subsystem captures the heart sounds of the patient, filters and digitizes, and sends the captured heart sounds to a personal computer wirelessly to visualize the heart sounds and for further processing to make a decision if the heart sounds are normal or abnormal. Twenty-seven t-domain, f-domain, and Mel frequency cepstral coefficients (MFCC) features were used to train a public database to identify the best-performing algorithm for classifying abnormal and normal heart sound (HS). The hyper parameter optimization, along with and without a feature reduction method, was tested to improve accuracy. The cost-adjusted optimized ensemble algorithm can produce 97% and 88% accuracy of classifying abnormal and normal HS, respectively.
Cameron Highland is a popular tourist hub in the mountainous area of Peninsular Malaysia. Most communities in this area suffer frequent incidence of debris flow, especially during monsoon seasons. Despite the loss of lives and properties recorded annually from debris flow, most studies in the region concentrate on landslides and flood susceptibilities. In this study, debris-flow susceptibility prediction was carried out using two data mining techniques; Multivariate Adaptive Regression Splines (MARS) and Support Vector Regression (SVR) models. The existing inventory of debris-flow events (640 points) were selected for training 70% (448) and validation 30% (192). Twelve conditioning factors namely; elevation, plan-curvature, slope angle, total curvature, slope aspect, Stream Transport Index (STI), profile curvature, roughness index, Stream Catchment Area (SCA), Stream Power Index (SPI), Topographic Wetness Index (TWI) and Topographic Position Index (TPI) were selected from Light Detection and Ranging (LiDAR)-derived Digital Elevation Model (DEM) data. Multi-collinearity was checked using Information Factor, Cramer's V, and Gini Index to identify the relative importance of conditioning factors. The susceptibility models were produced and categorized into five classes; not-susceptible, low, moderate, high and very-high classes. Models performances were evaluated using success and prediction rates where the area under the curve (AUC) showed a higher performance of MARS (93% and 83%) over SVR (76% and 72%). The result of this study will be important in contingency hazards and risks management plans to reduce the loss of lives and properties in the area.
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.
Ionic polymer-metal composites (IPMC) are smart material transducers that bend in response to low-voltage stimuli and generate voltage in response to bending. IPMCs are mechanically compliant, simple in construction, and easy to cut into desired shape. This allows the designing of novel sensing and actuation systems, e.g., for soft and bio-inspired robotics. IPMC sensing can be implemented in multiple ways, resulting in significantly different sensing characteristics. This paper will review the methods and research efforts to use IPMCs as deformation sensors. We will address efforts to model the IPMC sensing phenomenon, and implementation and characteristics of different IPMC sensing methods. Proposed sensing methods are divided into active sensing, passive sensing, and self-sensing actuation (SSA), whereas the active sensing methods measure one of IPMC-generated voltage, charge, or current; passive methods measure variations in IPMC impedances, or use it in capacitive sensor element circuit, and SSA methods implement simultaneous sensing and actuation on the same IPMC sample. Frequency ranges for reliable sensing vary among the methods, and no single method has been demonstrated to be effective for sensing in the full spectrum of IPMC actuation capabilities, i.e., from DC to ∼100 Hz. However, this limitation can be overcome by combining several sensing methods.
The cladded conductor is broadly utilized in engineering fields, such as aerospace, energy, and petrochemical; however, it is vulnerable to thickness loss occurring in the clad layer and nonconductive protection coating due to abrasive and corrosive environments. Such a flaw severely undermines the integrity and safety of the mechanical structures. Therefore, evaluating the thickness loss hidden inside cladded conductors via reliable nondestructive evaluation techniques is imperative. This paper intensively investigates the pulse-modulation eddy current technique (PMEC) for the assessment of thickness loss in a cladded conductor. An analytical model of the ferrite-cored probe is established for analyzing PMEC signals and characteristics of lift-off intersection (LOI) in testing signals. Experiments are conducted for evaluation of the thickness loss in cladded conductors. An inverse scheme based on LOI for estimation of the thickness-loss depth is proposed and further verified. Through simulations and experiments, it is found that the influences of the thickness loss in the clad layer and protective coating on the PMEC signals can be decoupled in virtue of the LOI characteristics. Based on LOI, the hidden thickness loss can be efficiently evaluated without much of a reduction in accuracy by using the PMEC probe for dedicated inspection of the cladded conductor.