The fabrication of an electrochemical sensor based on an iron oxide/graphene modified glassy carbon electrode (Fe3O4/rGO/GCE) and its simultaneous detection of dopamine (DA) and ascorbic acid (AA) is described here. The Fe3O4/rGO nanocomposite was synthesized via a simple, one step in-situ wet chemical method and characterized by different techniques. The presence of Fe3O4 nanoparticles on the surface of rGO sheets was confirmed by FESEM and TEM images. The electrochemical behavior of Fe3O4/rGO/GCE towards electrocatalytic oxidation of DA was investigated by cyclic voltammetry (CV) and differential pulse voltammetry (DPV) analysis. The electrochemical studies revealed that the Fe3O4/rGO/GCE dramatically increased the current response against the DA, due to the synergistic effect emerged between Fe3O4 and rGO. This implies that Fe3O4/rGO/GCE could exhibit excellent electrocatalytic activity and remarkable electron transfer kinetics towards the oxidation of DA. Moreover, the modified sensor electrode portrayed sensitivity and selectivity for simultaneous determination of AA and DA. The observed DPVs response linearly depends on AA and DA concentration in the range of 1-9 mM and 0.5-100 µM, with correlation coefficients of 0.995 and 0.996, respectively. The detection limit of (S/N = 3) was found to be 0.42 and 0.12 µM for AA and DA, respectively.
The rapid growth of wireless technology has improved the network's technology from 4G to 5G, with sub-6 GHz being the centre of attention as the primary communication spectrum band. To effectively benefit this exclusive network, the improvement in the mm-wave detection of this range is crucial. In this work, a silicon self-switching device (SSD) based full-wave bridge rectifier was proposed as a candidate for a usable RF-DC converter in this frequency range. SSD has a similar operation to a conventional pn junction diode, but with advantages in fabrication simplicity where it does not require doping and junctions. The optimized structure of the SSD was cascaded and arranged to create a functional full-wave bridge rectifier with a quadratic relationship between the input voltage and outputs current. AC transient analysis and theoretical calculation performed on the full-wave rectifier shows an estimated cut-off frequency at ~12 GHz, with calculated responsivity and noise equivalent power of 1956.72 V/W and 2.3753 pW/Hz1/2, respectively. These results show the capability of silicon SSD to function as a full-wave bridge rectifier and is a potential candidate for RF-DC conversion in the targeted 5G frequency band and can be exploited for future energy harvesting application.
Quorum sensing regulates bacterial virulence determinants, therefore making it an interesting target to attenuate pathogens. In this work, we screened edible, endemic plants in Malaysia for anti-quorum sensing properties. Extracts from Melicope lunu-ankenda (Gaertn.) T. G. Hartley, a Malay garden salad, inhibited response of Chromobacterium violaceum CV026 to N-hexanoylhomoserine lactone, thus interfering with violacein production; reduced bioluminescence expression of E. coli [pSB401], disrupted pyocyanin synthesis, swarming motility and expression of lecA::lux of Pseudomonas aeruginosa PAO1. Although the chemical nature of the anti-QS compounds from M. lunu-ankenda is currently unknown, this study proves that endemic Malaysian plants could serve as leads in the search for anti-quorum sensing compounds.
Klebsiella pneumoniae is one of the most common Gram-negative bacterial pathogens in clinical practice. It is associated with a wide range of disorders, ranging from superficial skin and soft tissue infections to potentially fatal sepsis in the lungs and blood stream. Quorum sensing, or bacterial cell-cell communication, refers to population density-dependent gene expression modulation. Quorum sensing in Proteobacteria relies on the production and sensing of signaling molecules which are mostly N-acylhomoserine lactones. Here, we report the identification of a multidrug resistant clinical isolate, K. pneumoniae strain CSG20, using matrix-assisted laser desorption ionization-time-of-flight (MALDI-TOF) mass spectrometry. We further confirmed quorum sensing activity in this strain with the use of high resolution tandem liquid chromatography quadrupole mass spectrometry and provided evidence K. pneumoniae strain CSG20 produced N-hexanoyl-homoserine lactone (C6-HSL). To the best of our knowledge, this is the first report on the production of N-hexanoylhomoserine lactone (C6-HSL) in clinical isolate K. pneumoniae.
Quorum sensing (QS), acts as one of the gene regulatory systems that allow bacteria to regulate their physiological activities by sensing the population density with synchronization of the signaling molecules that they produce. Here, we report a marine isolate, namely strain T47, and its unique AHL profile. Strain T47 was identified using 16S rRNA sequence analysis confirming that it is a member of Vibrio closely clustered to Vibrio sinaloensis. The isolated V. sinaloensis strain T47 was confirmed to produce N-butanoyl-L-homoserine lactone (C4-HSL) by using high resolution liquid chromatography tandem mass spectrometry. V. sinaloensis strain T47 also formed biofilms and its biofilm formation could be affected by anti-QS compound (cathechin) suggesting this is a QS-regulated trait in V. sinaloensis strain T47. To our knowledge, this is the first documentation of AHL and biofilm production in V. sinaloensis strain T47.
In the bacteria kingdom, quorum sensing (QS) is a cell-to-cell communication that relies on the production of and response to specific signaling molecules. In proteobacteria, N-acylhomoserine lactones (AHLs) are the well-studied signaling molecules. The present study aimed to characterize the production of AHL of a bacterial strain A9 isolated from a Malaysian tropical soil. Strain A9 was identified as Burkholderia sp. using matrix-assisted laser desorption ionization-time-of-flight mass spectrometry and 16S rDNA nucleotide sequence analysis. AHL production by A9 was detected with two biosensors, namely Chromobacterium violaceum CV026 and Escherichia coli [pSB401]. Thin layer chromatography results showed N-hexanoylhomoserine lactone (C6-HSL) and N-octanoylhomoserine lactone (C8-HSL) production. Unequivocal identification of C6-HSL and C8-HSL was achieved by high resolution triple quadrupole liquid chromatography-mass spectrometry analysis. We have demonstrated that Burkholderia sp. strain A9 produces AHLs that are known to be produced by other Burkholderia spp. with CepI/CepR homologs.
Fault Tolerant Control (FTC) systems are crucial in industry to ensure safe and reliable operation, especially of motor drives. This paper proposes the use of multiple controllers for a FTC system of an induction motor drive, selected based on a switching mechanism. The system switches between sensor vector control, sensorless vector control, closed-loop voltage by frequency (V/f) control and open loop V/f control. Vector control offers high performance, while V/f is a simple, low cost strategy with high speed and satisfactory performance. The faults dealt with are speed sensor failures, stator winding open circuits, shorts and minimum voltage faults. In the event of compound faults, a protection unit halts motor operation. The faults are detected using a wavelet index. For the sensorless vector control, a novel Boosted Model Reference Adaptive System (BMRAS) to estimate the motor speed is presented, which reduces tuning time. Both simulation results and experimental results with an induction motor drive show the scheme to be a fast and effective one for fault detection, while the control methods transition smoothly and ensure the effectiveness of the FTC system. The system is also shown to be flexible, reverting rapidly back to the dominant controller if the motor returns to a healthy state.
The detection of Pb(II) ions in a river using the surface plasmon resonance (SPR)-based silver (Ag) thin film technique was successfully developed. Chitosan-graphene oxide (CS-GO) was coated on top of the Ag thin film surface and acted as the active sensing layer for Pb(II) ion detection. CS-GO was synthesized and characterized, and the physicochemical properties of this material were studied prior to integration with the SPR. In X-ray photoelectron spectroscopy (XPS), the appearance of the C=O, C-O, and O-H functional groups at 531.2 eV and 532.5 eV, respectively, confirms the success of CS-GO nanocomposite synthesis. A higher surface roughness of 31.04 nm was observed under atomic force microscopy (AFM) analysis for Ag/CS-GO thin film. The enhancement in thin film roughness indicates that more adsorption sites are available for Pb(II) ion binding. The SPR performance shows a good sensor sensitivity for Ag/CS-GO with 1.38° ppm-1 ranging from 0.01 to 5.00 ppm of standard Pb(II) solutions. At lower concentrations, a better detection accuracy was shown by SPR using Ag/CS-GO thin film compared to Ag/CS thin film. The SPR performance using Ag/CS-GO thin film was further evaluated with real water samples collected from rivers. The results are in agreement with those of standard Pb(II) ion solution, which were obtained at incidence angles of 80.00° and 81.11° for local and foreign rivers, respectively.
Magnetoactive (MA) foam, with its tunable mechanical properties and magnetostriction, has the potential to be used for the development of soft sensor technology. However, researchers have found that its mechanical properties and magnetostriction are morphologically dependent, thereby limiting its capabilities for dexterous manipulation. Thus, in this work, MA foam was developed with additional capabilities for controlling its magnetostriction, normal force, storage modulus, shear stress and torque by manipulating the concentration of carbonyl iron particles (CIPs) and the magnetic field with regard to morphological changes. MA foams were prepared with three weight percentages of CIPs, namely, 35 wt.%, 55 wt.% and 75 wt.%, and three different modes, namely, zero shear, constant shear and various shears. The results showed that the MA foam with 75 wt.% of CIPs enhanced the normal force sensitivity and positive magnetostriction sensitivity by up to 97% and 85%, respectively. Moreover, the sensitivities of the storage modulus, torque and shear stress were 8.97 Pa/mT, 0.021 µN/mT, and 0.0096 Pa/mT, respectively. Meanwhile, the magnetic dipolar interaction between the CIPs was capable of changing the property of MA foam from a positive to a negative magnetostriction under various shear strains with a low loss of energy. Therefore, it is believed that this kind of highly sensitive MA foam can potentially be implemented in future soft sensor systems.
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 proliferation of mobile devices such as smartphones and tablets with embedded sensors and communication features has led to the introduction of a novel sensing paradigm called mobile crowd sensing. Despite its opportunities and advantages over traditional wireless sensor networks, mobile crowd sensing still faces security and privacy issues, among other challenges. Specifically, the security and privacy of sensitive location information of users remain lingering issues, considering the "on" and "off" state of global positioning system sensor in smartphones. To address this problem, this paper proposes "SenseCrypt", a framework that automatically annotates and signcrypts sensitive location information of mobile crowd sensing users. The framework relies on K-means algorithm and a certificateless aggregate signcryption scheme (CLASC). It incorporates spatial coding as the data compression technique and message query telemetry transport as the messaging protocol. Results presented in this paper show that the proposed framework incurs low computational cost and communication overhead. Also, the framework is robust against privileged insider attack, replay and forgery attacks. Confidentiality, integrity and non-repudiation are security services offered by the proposed framework.
Over-the-air dissemination of code updates in wireless sensor networks have been researchers' point of interest in the last few years, and, more importantly, security challenges toward the remote propagation of code updating have occupied the majority of efforts in this context. Many security models have been proposed to establish a balance between the energy consumption and security strength, having their concentration on the constrained nature of wireless sensor network (WSN) nodes. For authentication purposes, most of them have used a Merkle hash tree to avoid using multiple public cryptography operations. These models mostly have assumed an environment in which security has to be at a standard level. Therefore, they have not investigated the tree structure for mission-critical situations in which security has to be at the maximum possible level (e.g., military applications, healthcare). Considering this, we investigate existing security models used in over-the-air dissemination of code updates for possible vulnerabilities, and then, we provide a set of countermeasures, correspondingly named Security Model Requirements. Based on the investigation, we concentrate on Seluge, one of the existing over-the-air programming schemes, and we propose an improved version of it, named Seluge++, which complies with the Security Model Requirements and replaces the use of the inefficient Merkle tree with a novel method. Analytical and simulation results show the improvements in Seluge++ compared to Seluge.
To assist in the broadcasting of time-critical traffic information in an Internet of Vehicles (IoV) and vehicular sensor networks (VSN), fast network connectivity is needed. Accurate traffic information prediction can improve traffic congestion and operation efficiency, which helps to reduce commute times, noise and carbon emissions. In this study, we present a novel approach for predicting the traffic flow volume by using traffic data in self-organizing vehicular networks. The proposed method is based on using a probabilistic generative neural network techniques called deep belief network (DBN) that includes multiple layers of restricted Boltzmann machine (RBM) auto-encoders. Time series data generated from the roadside units (RSUs) for five highway links are used by a three layer DBN to extract and learn key input features for constructing a model to predict traffic flow. Back-propagation is utilized as a general learning algorithm for fine-tuning the weight parameters among the visible and hidden layers of RBMs. During the training process the firefly algorithm (FFA) is applied for optimizing the DBN topology and learning rate parameter. Monte Carlo simulations are used to assess the accuracy of the prediction model. The results show that the proposed model achieves superior performance accuracy for predicting traffic flow in comparison with other approaches applied in the literature. The proposed approach can help to solve the problem of traffic congestion, and provide guidance and advice for road users and traffic regulators.
This paper presents a novel approach to predicting self-calibration in a pressure sensor using a proposed Levenberg Marquardt Back Propagation Artificial Neural Network (LMBP-ANN) model. The self-calibration algorithm should be able to fix major problems in the pressure sensor such as hysteresis, variation in gain and lack of linearity with high accuracy. The traditional calibration process for this kind of sensor is a time-consuming task because it is usually done through manual and repetitive identification. Furthermore, a traditional computational method is inadequate for solving the problem since it is extremely difficult to resolve the mathematical formula among multiple confounding pressure variables. Accordingly, this paper describes a new self-calibration methodology for nonlinear pressure sensors based on an LMBP-ANN model. The proposed method was achieved using a collected dataset from pressure sensors in real time. The load cell will be used as a reference for measuring the applied force. The proposed method was validated by comparing the output pressure of the trained network with the experimental target pressure (reference). This paper also shows that the proposed model exhibited a remarkable performance than traditional methods with a max mean square error of 0.17325 and an R-value over 0.99 for the total response of training, testing and validation. To verify the proposed model's capability to build a self-calibration algorithm, the model was tested using an untrained input data set. As a result, the proposed LMBP-ANN model for self-calibration purposes is able to successfully predict the desired pressure over time, even the uncertain behaviour of the pressure sensors due to its material creep. This means that the proposed model overcomes the problems of hysteresis, variation in gain and lack of linearity over time. In return, this can be used to enhance the durability of the grasping mechanism, leading to a more robust and secure grasp for paralyzed hands. Furthermore, the exposed analysis approach in this paper can be a useful methodology for the user to evaluate the performance of any measurement system in a real-time environment.
This study presents a new technique to improve the indoor localization of a mobile node by utilizing a Zigbee-based received-signal-strength indicator (RSSI) and odometry. As both methods suffer from their own limitations, this work contributes to a novel methodological framework in which coordinates of the mobile node can more accurately be predicted by improving the path-loss propagation model and optimizing the weighting parameter for each localization technique via a convex search. A self-adaptive filtering approach is also proposed which autonomously optimizes the weighting parameter during the target node's translational and rotational motions, thus resulting in an efficient localization scheme with less computational effort. Several real-time experiments consisting of four different trajectories with different number of straight paths and curves were carried out to validate the proposed methods. Both temporal and spatial analyses demonstrate that when odometry data and RSSI values are available, the proposed methods provide significant improvements on localization performance over existing approaches.
We performed a comparative study to select the efficient mother wavelet (MWT) basis functions that optimally represent the signal characteristics of the electrical activity of the human brain during a working memory (WM) task recorded through electro-encephalography (EEG). Nineteen EEG electrodes were placed on the scalp following the 10-20 system. These electrodes were then grouped into five recording regions corresponding to the scalp area of the cerebral cortex. Sixty-second WM task data were recorded from ten control subjects. Forty-five MWT basis functions from orthogonal families were investigated. These functions included Daubechies (db1-db20), Symlets (sym1-sym20), and Coiflets (coif1-coif5). Using ANOVA, we determined the MWT basis functions with the most significant differences in the ability of the five scalp regions to maximize their cross-correlation with the EEG signals. The best results were obtained using "sym9" across the five scalp regions. Therefore, the most compatible MWT with the EEG signals should be selected to achieve wavelet denoising, decomposition, reconstruction, and sub-band feature extraction. This study provides a reference of the selection of efficient MWT basis functions.
The deployment of optical network infrastructure and development of new network services are growing rapidly for beyond 5/6G networks. However, optical networks are vulnerable to several types of security threats, such as single-point failure, wormhole attacks, and Sybil attacks. Since the uptake of e-commerce and e-services has seen an unprecedented surge in recent years, especially during the COVID-19 pandemic, the security of these transactions is essential. Blockchain is one of the most promising solutions because of its decentralized and distributed ledger technology, and has been employed to protect these transactions against such attacks. However, the security of blockchain relies on the computational complexity of certain mathematical functions, and because of the evolution of quantum computers, its security may be breached in real-time in the near future. Therefore, researchers are focusing on combining quantum key distribution (QKD) with blockchain to enhance blockchain network security. This new technology is known as quantum-secured blockchain. This article describes different attacks in optical networks and provides a solution to protect networks against security attacks by employing quantum-secured blockchain in optical networks. It provides a brief overview of blockchain technology with its security loopholes, and focuses on QKD, which makes blockchain technology more robust against quantum attacks. Next, the article provides a broad view of quantum-secured blockchain technology. It presents the network architecture for the future research and development of secure and trusted optical networks using quantum-secured blockchain. The article also highlights some research challenges and opportunities.
With continuous advancements in Internet technology and the increased use of cryptographic techniques, the cloud has become the obvious choice for data sharing. Generally, the data are outsourced to cloud storage servers in encrypted form. Access control methods can be used on encrypted outsourced data to facilitate and regulate access. Multi-authority attribute-based encryption is a propitious technique to control who can access encrypted data in inter-domain applications such as sharing data between organizations, sharing data in healthcare, etc. The data owner may require the flexibility to share the data with known and unknown users. The known or closed-domain users may be internal employees of the organization, and unknown or open-domain users may be outside agencies, third-party users, etc. In the case of closed-domain users, the data owner becomes the key issuing authority, and in the case of open-domain users, various established attribute authorities perform the task of key issuance. Privacy preservation is also a crucial requirement in cloud-based data-sharing systems. This work proposes the SP-MAACS scheme, a secure and privacy-preserving multi-authority access control system for cloud-based healthcare data sharing. Both open and closed domain users are considered, and policy privacy is ensured by only disclosing the names of policy attributes. The values of the attributes are kept hidden. Characteristic comparison with similar existing schemes shows that our scheme simultaneously provides features such as multi-authority setting, expressive and flexible access policy structure, privacy preservation, and scalability. The performance analysis carried out by us shows that the decryption cost is reasonable enough. Furthermore, the scheme is demonstrated to be adaptively secure under the standard model.
Vehicular ad hoc networks (VANET) are also known as intelligent transportation systems. VANET ensures timely and accurate communications between vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) to improve road safety and enhance the efficiency of traffic flow. Due to its open wireless boundary and high mobility, VANET is vulnerable to malicious nodes that could gain access into the network and carry out serious medium access control (MAC) layer threats, such as denial of service (DoS) attacks, data modification attacks, impersonation attacks, Sybil attacks, and replay attacks. This could affect the network security and privacy, causing harm to the information exchange within the network by genuine nodes and increase fatal impacts on the road. Therefore, a novel secure trust-based architecture that utilizes blockchain technology has been proposed to increase security and privacy to mitigate the aforementioned MAC layer attacks. A series of experiment has been conducted using the Veins simulation tool to assess the performance of the proposed solution in the terms of packet delivery ratio (PDR), end-to-end delay, packet loss, transmission overhead, and computational cost.
The stepping forces of normal and Freund Complete Adjuvant (FCA)-induced arthritic rats were studied in vivo using a proposed novel analgesic meter. An infrared charge-coupled device (CCD) camera and a data acquisition system were incorporated into the analgesic meter to determine and measure the weight of loads on the right hind paw before and after induction of arthritis by FCA injection into the paw cavity. FCA injection resulted in a significant reduction in the stepping force of the affected hind paw. The stepping force decreased to the minimum level on day 4 after the injection and then gradually increased up to day 25. Oral administration of prednisolone significantly increased the stepping forces of FCA-induced arthritic rats on days 14 and 21. These results suggest that the novel device is an effective tool for measuring the arthritic pain in in vivo studies even though walking is a dynamic condition.