Several studies have been conducted using both visual and thermal facial images to identify human affective states. Despite the advantages of thermal facial images in recognizing spontaneous human affects, few studies have focused on facial occlusion challenges in thermal images, particularly eyeglasses and facial hair occlusion. As a result, three classification models are proposed in this paper to address the problem of thermal occlusion in facial images, with six basic spontaneous emotions being classified. The first proposed model in this paper is based on six main facial regions, including the forehead, tip of the nose, cheeks, mouth, and chin. The second model deconstructs the six main facial regions into multiple subregions to investigate the efficacy of subregions in recognizing the human affective state. The third proposed model in this paper uses selected facial subregions, free of eyeglasses and facial hair (beard, mustaches). Nine statistical features on apex and onset thermal images are implemented. Furthermore, four feature selection techniques with two classification algorithms are proposed for a further investigation. According to the comparative analysis presented in this paper, the results obtained from the three proposed modalities were promising and comparable to those of other studies.
Reaching a flat network is the main target of future evolved packet core for the 5G mobile networks. The current 4th generation core network is centralized architecture, including Serving Gateway and Packet-data-network Gateway; both act as mobility and IP anchors. However, this architecture suffers from non-optimal routing and intolerable latency due to many control messages. To overcome these challenges, we propose a partially distributed architecture for 5th generation networks, such that the control plane and data plane are fully decoupled. The proposed architecture is based on including a node Multi-session Gateway to merge the mobility and IP anchor gateway functionality. This work presented a control entity with the full implementation of the control plane to achieve an optimal flat network architecture. The impact of the proposed evolved packet Core structure in attachment, data delivery, and mobility procedures is validated through simulation. Several experiments were carried out by using NS-3 simulation to validate the results of the proposed architecture. The Numerical analysis is evaluated in terms of total transmission delay, inter and intra handover delay, queuing delay, and total attachment time. Simulation results show that the proposed architecture performance-enhanced end-to-end latency over the legacy architecture.
A printed compact monopole antenna based on a single negative (SNG) metamaterial is proposed for ultra-wideband (UWB) applications. A low-profile, key-shaped structure forms the radiating monopole and is loaded with metamaterial unit cells with negative permittivity and more than 1.5 GHz bandwidth of near-zero refractive index (NZRI) property. The antenna offers a wide bandwidth from 3.08 to 14.1 GHz and an average gain of 4.54 dBi, with a peak gain of 6.12 dBi; this is in contrast to the poor performance when metamaterial is not used. Moreover, the maximum obtained radiation efficiency is 97%. A reasonable agreement between simulation and experiments is realized, demonstrating that the proposed antenna can operate over a wide bandwidth with symmetric split-ring resonator (SSRR) metamaterial structures and compact size of 14.5 × 22 mm2 (0.148 λ0 × 0.226 λ0) with respect to the lowest operating frequency.
A multiband coplanar waveguide (CPW)-fed antenna loaded with metamaterial unit cell for GSM900, WLAN, LTE-A, and 5G Wi-Fi applications is presented in this paper. The proposed metamaterial structure is a combination of various symmetric split-ring resonators (SSRR) and its characteristics were investigated for two major axes directions at (x and y-axis) wave propagation through the material. For x-axis wave propagation, it indicates a wide range of negative refractive index in the frequency span of 2-8.5 GHz. For y-axis wave propagation, it shows more than 2 GHz bandwidth of near-zero refractive index (NZRI) property. Two categories of the proposed metamaterial plane were applied to enhance the bandwidth and gain. The measured reflection coefficient (S11) demonstrated significant bandwidths increase at the upper bands by 4.92-6.49 GHz and 3.251-4.324 GHz, considered as a rise of 71.4% and 168%, respectively, against the proposed antenna without using metamaterial. Besides being high bandwidth achieving, the proposed antenna radiates bi-directionally with 95% as the maximum radiation efficiency. Moreover, the maximum measured gain reaches 6.74 dBi by a 92.57% improvement compared with the antenna without using metamaterial. The simulation and measurement results of the proposed antenna show good agreement.
Recent brain imaging findings by using different methods (e.g., fMRI and PET) have suggested that social anxiety disorder (SAD) is correlated with alterations in regional or network-level brain function. However, due to many limitations associated with these methods, such as poor temporal resolution and limited number of samples per second, neuroscientists could not quantify the fast dynamic connectivity of causal information networks in SAD. In this study, SAD-related changes in brain connections within the default mode network (DMN) were investigated using eight electroencephalographic (EEG) regions of interest. Partial directed coherence (PDC) was used to assess the causal influences of DMN regions on each other and indicate the changes in the DMN effective network related to SAD severity. The DMN is a large-scale brain network basically composed of the mesial prefrontal cortex (mPFC), posterior cingulate cortex (PCC)/precuneus, and lateral parietal cortex (LPC). The EEG data were collected from 88 subjects (22 control, 22 mild, 22 moderate, 22 severe) and used to estimate the effective connectivity between DMN regions at different frequency bands: delta (1-3 Hz), theta (4-8 Hz), alpha (8-12 Hz), low beta (13-21 Hz), and high beta (22-30 Hz). Among the healthy control (HC) and the three considered levels of severity of SAD, the results indicated a higher level of causal interactions for the mild and moderate SAD groups than for the severe and HC groups. Between the control and the severe SAD groups, the results indicated a higher level of causal connections for the control throughout all the DMN regions. We found significant increases in the mean PDC in the delta (p = 0.009) and alpha (p = 0.001) bands between the SAD groups. Among the DMN regions, the precuneus exhibited a higher level of causal influence than other regions. Therefore, it was suggested to be a major source hub that contributes to the mental exploration and emotional content of SAD. In contrast to the severe group, HC exhibited higher resting-state connectivity at the mPFC, providing evidence for mPFC dysfunction in the severe SAD group. Furthermore, the total Social Interaction Anxiety Scale (SIAS) was positively correlated with the mean values of the PDC of the severe SAD group, r (22) = 0.576, p = 0.006 and negatively correlated with those of the HC group, r (22) = -0.689, p = 0.001. The reported results may facilitate greater comprehension of the underlying potential SAD neural biomarkers and can be used to characterize possible targets for further medication.
In recent years, fiber Bragg gratings (FBGs) are becoming increasingly attractive for sensing applications in biomechanics and rehabilitation engineering due to their advantageous properties like small size, light weight, biocompatibility, chemical inertness, multiplexing capability and immunity to electromagnetic interference (EMI). They also offer a high-performance alternative to conventional technologies, either for measuring a variety of physical parameters or for performing high-sensitivity biochemical analysis. FBG-based sensors demonstrated their feasibility for specific sensing applications in aeronautic, automotive, civil engineering structure monitoring and undersea oil exploration; however, their use in the field of biomechanics and rehabilitation applications is very recent and its practicality for full-scale implementation has not yet been fully established. They could be used for detecting strain in bones, pressure mapping in orthopaedic joints, stresses in intervertebral discs, chest wall deformation, pressure distribution in Human Machine Interfaces (HMIs), forces induced by tendons and ligaments, angles between body segments during gait, and many others in dental biomechanics. This article aims to provide a comprehensive overview of all the possible applications of FBG sensing technology in biomechanics and rehabilitation and the status of ongoing researches up-to-date all over the world, demonstrating the FBG advances over other existing technologies.
The distribution of interface stresses between the residual limb and prosthetic socket of a transtibial amputee has been considered as a direct indicator of the socket quality fit and comfort. Therefore, researchers have been very interested in quantifying these interface stresses in order to evaluate the extent of any potential damage caused by the socket to the residual limb tissues. During the past 50 years a variety of measurement techniques have been employed in an effort to identify sites of excessive stresses which may lead to skin breakdown, compare stress distributions in various socket designs, and evaluate interface cushioning and suspension systems, among others. The outcomes of such measurement techniques have contributed to improving the design and fitting of transtibial sockets. This article aims to review the operating principles, advantages, and disadvantages of conventional and emerging techniques used for interface stress measurements inside transtibial sockets. It also reviews and discusses the evolution of different socket concepts and interface stress investigations conducted in the past five decades, providing valuable insights into the latest trends in socket designs and the crucial considerations for effective stress measurement tools that lead to a functional prosthetic socket.
This study presents the first investigation into the capability of fiber Bragg grating (FBG) sensors to measure interface pressure between the stump and the prosthetic sockets of a trans-tibial amputee. FBG element(s) were recoated with and embedded in a thin layer of epoxy material to form a sensing pad, which was in turn embedded in a silicone polymer material to form a pressure sensor. The sensor was tested in real time by inserting a heavy-duty balloon into the socket and inflating it by using an air compressor. This test was conducted to examine the sensitivity and repeatability of the sensor when subjected to pressure from the stump of the trans-tibial amputee and to mimic the actual environment of the amputee's Patellar Tendon (PT) bar. The sensor exhibited a sensitivity of 127 pm/N and a maximum FSO hysteresis of around ~0.09 in real-time operation. Very good reliability was achieved when the sensor was utilized for in situ measurements. This study may lead to smart FBG-based amputee stump/socket structures for pressure monitoring in amputee socket systems, which will result in better-designed prosthetic sockets that ensure improved patient satisfaction.
In this paper, we propose an easy-to-implement passive liquid valve (PLV) for the microfluidic compact-disc (CD). This valve can be implemented by introducing venting chambers to control the air flow of the source and destination chambers. The PLV mechanism is based on equalizing the main forces acting on the microfluidic CD (i.e., the centrifugal and capillary forces) to control the burst frequency of the source chamber liquid. For a better understanding of the physics behind the proposed PLV, an analytical model is described. Moreover, three parameters that control the effectiveness of the proposed valve, i.e., the liquid height, liquid density, and venting chamber position with respect to the CD center, are tested experimentally. To demonstrate the ability of the proposed PLV valve, microfluidic liquid switching and liquid metering are performed. In addition, a Bradford assay is performed to measure the protein concentration and evaluated in comparison to the benchtop procedure. The result shows that the proposed valve can be implemented in any microfluidic process that requires simplicity and accuracy. Moreover, the developed valve increases the flexibility of the centrifugal CD platform for passive control of the liquid flow without the need for an external force or trigger.
In this study, porous silicon (PSi) was prepared and tested as an extended gate field-effect transistor (EGFET) for pH sensing. The prepared PSi has pore sizes in the range of 500 to 750 nm with a depth of approximately 42 µm. The results of testing PSi for hydrogen ion sensing in different pH buffer solutions reveal that the PSi has a sensitivity value of 66 mV/pH that is considered a super Nernstian value. The sensor considers stability to be in the pH range of 2 to 12. The hysteresis values of the prepared PSi sensor were approximately 8.2 and 10.5 mV in the low and high pH loop, respectively. The result of this study reveals a promising application of PSi in the field for detecting hydrogen ions in different solutions.
Zinc oxide (ZnO) nanorods (NRs) have been synthesized via the hydrothermal process. The NRs were grown over a conductive glass substrate. A non-enzymatic electrochemical sensor for hydrogen peroxide (H₂O₂), based on the prepared ZnO NRs, was examined through the use of current-voltage measurements. The measured currents, as a function of H₂O₂ concentrations ranging from 10 μM to 700 μM, revealed two distinct behaviours and good performance, with a lower detection limit (LOD) of 42 μM for the low range of H₂O₂ concentrations (first region), and a LOD of 143.5 μM for the higher range of H₂O₂ concentrations (second region). The prepared ZnO NRs show excellent electrocatalytic activity. This enables a measurable and stable output current. The results were correlated with the oxidation process of the H₂O₂ and revealed a good performance for the ZnO NR non-enzymatic H₂O₂ sensor.
The functional connectivity (FC) patterns of resting-state functional magnetic resonance imaging (rs-fMRI) play an essential role in the development of autism spectrum disorders (ASD) classification models. There are available methods in literature that have used FC patterns as inputs for binary classification models, but the results barely reach an accuracy of 80%. Additionally, the generalizability across multiple sites of the models has not been investigated. Due to the lack of ASD subtypes identification model, the multi-class classification is proposed in the present study. This study aims to develop automated identification of autism spectrum disorder (ASD) subtypes using convolutional neural networks (CNN) using dynamic FC as its inputs. The rs-fMRI dataset used in this study consists of 144 individuals from 8 independent sites, labeled based on three ASD subtypes, namely autistic disorder (ASD), Asperger's disorder (APD), and pervasive developmental disorder not otherwise specified (PDD-NOS). The blood-oxygen-level-dependent (BOLD) signals from 116 brain nodes of automated anatomical labeling (AAL) atlas are used, where the top-ranked node is determined based on one-way analysis of variance (ANOVA) of the power spectral density (PSD) values. Based on the statistical analysis of the PSD values of 3-level ASD and normal control (NC), putamen_R is obtained as the top-ranked node and used for the wavelet coherence computation. With good resolution in time and frequency domain, scalograms of wavelet coherence between the top-ranked node and the rest of the nodes are used as dynamic FC feature input to the convolutional neural networks (CNN). The dynamic FC patterns of wavelet coherence scalogram represent phase synchronization between the pairs of BOLD signals. Classification algorithms are developed using CNN and the wavelet coherence scalograms for binary and multi-class identification were trained and tested using cross-validation and leave-one-out techniques. Results of binary classification (ASD vs. NC) and multi-class classification (ASD vs. APD vs. PDD-NOS vs. NC) yielded, respectively, 89.8% accuracy and 82.1% macro-average accuracy, respectively. Findings from this study have illustrated the good potential of wavelet coherence technique in representing dynamic FC between brain nodes and open possibilities for its application in computer aided diagnosis of other neuropsychiatric disorders, such as depression or schizophrenia.
Traditional parallel computing for power management systems has prime challenges such as execution time, computational complexity, and efficiency like process time and delays in power system condition monitoring, particularly consumer power consumption, weather data, and power generation for detecting and predicting data mining in the centralized parallel processing and diagnosis. Due to these constraints, data management has become a critical research consideration and bottleneck. To cope with these constraints, cloud computing-based methodologies have been introduced for managing data efficiently in power management systems. This paper reviews the concept of cloud computing architecture that can meet the multi-level real-time requirements to improve monitoring and performance which is designed for different application scenarios for power system monitoring. Then, cloud computing solutions are discussed under the background of big data, and emerging parallel programming models such as Hadoop, Spark, and Storm are briefly described to analyze the advancement, constraints, and innovations. The key performance metrics of cloud computing applications such as core data sampling, modeling, and analyzing the competitiveness of big data was modeled by applying related hypotheses. Finally, it introduces a new design concept with cloud computing and eventually some recommendations focusing on cloud computing infrastructure, and methods for managing real-time big data in the power management system that solve the data mining challenges.
This paper presents a comparison between the electroencephalogram (EEG) channels during scoliosis correction surgeries. Surgeons use many hand tools and electronic devices that directly affect the EEG channels. These noises do not affect the EEG channels uniformly. This research provides a complete system to find the least affected channel by the noise. The presented system consists of five stages: filtering, wavelet decomposing (Level 4), processing the signal bands using four different criteria (mean, energy, entropy and standard deviation), finding the useful channel according to the criteria's value and, finally, generating a combinational signal from Channels 1 and 2. Experimentally, two channels of EEG data were recorded from six patients who underwent scoliosis correction surgeries in the Pusat Perubatan Universiti Kebangsaan Malaysia (PPUKM) (the Medical center of National University of Malaysia). The combinational signal was tested by power spectral density, cross-correlation function and wavelet coherence. The experimental results show that the system-outputted EEG signals are neatly switched without any substantial changes in the consistency of EEG components. This paper provides an efficient procedure for analyzing EEG signals in order to avoid averaging the channels that lead to redistribution of the noise on both channels, reducing the dimensionality of the EEG features and preparing the best EEG stream for the classification and monitoring stage.
Biosignal analysis is one of the most important topics that researchers have tried to develop during the last century to understand numerous human diseases. Electroencephalograms (EEGs) are one of the techniques which provides an electrical representation of biosignals that reflect changes in the activity of the human brain. Monitoring the levels of anesthesia is a very important subject, which has been proposed to avoid both patient awareness caused by inadequate dosage of anesthetic drugs and excessive use of anesthesia during surgery. This article reviews the bases of these techniques and their development within the last decades and provides a synopsis of the relevant methodologies and algorithms that are used to analyze EEG signals. In addition, it aims to present some of the physiological background of the EEG signal, developments in EEG signal processing, and the effective methods used to remove various types of noise. This review will hopefully increase efforts to develop methods that use EEG signals for determining and classifying the depth of anesthesia with a high data rate to produce a flexible and reliable detection device.
Wi-Fi has shown enormous potential for indoor localization because of its wide utilization and availability. Enabling the use of Wi-Fi for indoor localization necessitates the construction of a fingerprint and the adoption of a learning algorithm. The goal is to enable the use of the fingerprint in training the classifiers for predicting locations. Existing models of machine learning Wi-Fi-based localization are brought from machine learning and modified to accommodate for practical aspects that occur in indoor localization. The performance of these models varies depending on their effectiveness in handling and/or considering specific characteristics and the nature of indoor localization behavior. One common behavior in the indoor navigation of people is its cyclic dynamic nature. To the best of our knowledge, no existing machine learning model for Wi-Fi indoor localization exploits cyclic dynamic behavior for improving localization prediction. This study modifies the widely popular online sequential extreme learning machine (OSELM) to exploit cyclic dynamic behavior for achieving improved localization results. Our new model is called knowledge preserving OSELM (KP-OSELM). Experimental results conducted on the two popular datasets TampereU and UJIndoorLoc conclude that KP-OSELM outperforms benchmark models in terms of accuracy and stability. The last achieved accuracy was 92.74% for TampereU and 72.99% for UJIndoorLoc.
For highly sensitive pH sensing, an electrolyte insulator semiconductor (EIS) device, based on ZnO nanorod-sensing membrane layers doped with magnesium, was proposed. ZnO nanorod samples prepared via a hydrothermal process with different Mg molar ratios (0-5%) were characterized to explore the impact of magnesium content on the structural and optical characteristics and sensing performance by X-ray diffraction analysis (XRD), atomic force microscopy (AFM), and photoluminescence (PL). The results indicated that the ZnO nanorods doped with 3% Mg had a high hydrogen ion sensitivity (83.77 mV/pH), linearity (96.06%), hysteresis (3 mV), and drift (0.218 mV/h) due to the improved crystalline quality and the surface hydroxyl group role of ZnO. In addition, the detection characteristics varied with the doping concentration and were suitable for developing biomedical detection applications with different detection elements.
Routing in Vehicular Ad hoc Networks (VANET) is a bit complicated because of the nature of the high dynamic mobility. The efficiency of routing protocol is influenced by a number of factors such as network density, bandwidth constraints, traffic load, and mobility patterns resulting in frequency changes in network topology. Therefore, Quality of Service (QoS) is strongly needed to enhance the capability of the routing protocol and improve the overall network performance. In this paper, we introduce a statistical framework model to address the problem of optimizing routing configuration parameters in Vehicle-to-Vehicle (V2V) communication. Our framework solution is based on the utilization of the network resources to further reflect the current state of the network and to balance the trade-off between frequent changes in network topology and the QoS requirements. It consists of three stages: simulation network stage used to execute different urban scenarios, the function stage used as a competitive approach to aggregate the weighted cost of the factors in a single value, and optimization stage used to evaluate the communication cost and to obtain the optimal configuration based on the competitive cost. The simulation results show significant performance improvement in terms of the Packet Delivery Ratio (PDR), Normalized Routing Load (NRL), Packet loss (PL), and End-to-End Delay (E2ED).
Several researchers have proposed secure authentication techniques for addressing privacy and security concerns in the fifth-generation (5G)-enabled vehicle networks. To verify vehicles, however, these conditional privacy-preserving authentication (CPPA) systems required a roadside unit, an expensive component of vehicular networks. Moreover, these CPPA systems incur exceptionally high communication and processing costs. This study proposes a CPPA method based on fog computing (FC), as a solution for these issues in 5G-enabled vehicle networks. In our proposed FC-CPPA method, a fog server is used to establish a set of public anonymity identities and their corresponding signature keys, which are then preloaded into each authentic vehicle. We guarantee the security of the proposed FC-CPPA method in the context of a random oracle. Our solutions are not only compliant with confidentiality and security standards, but also resistant to a variety of threats. The communication costs of the proposal are only 84 bytes, while the computation costs are 0.0031, 2.0185 to sign and verify messages. Comparing our strategy to similar ones reveals that it saves time and money on communication and computing during the performance evaluation phase.
Wireless Body Area Networks (WBANs) are single-hop network systems, where sensors gather the body's vital signs and send them directly to master nodes (MNs). The sensors are distributed in or on the body. Therefore, body posture, clothing, muscle movement, body temperature, and climatic conditions generally influence the quality of the wireless link between sensors and the destination. Hence, in some cases, single hop transmission ('direct transmission') is not sufficient to deliver the signals to the destination. Therefore, we propose an emergency-based cooperative communication protocol for WBAN, named Critical Data-based Incremental Cooperative Communication (CD-ICC), based on the IEEE 802.15.6 CSMA standard but assuming a lognormal shadowing channel model. In this paper, a complete study of a system model is inspected in the terms of the channel path loss, the successful transmission probability, and the outage probability. Then a mathematical model is derived for the proposed protocol, end-to-end delay, duty cycle, and average power consumption. A new back-off time is proposed within CD-ICC, which ensures the best relays cooperate in a distributed manner. The design objective of the CD-ICC is to reduce the end-to-end delay, the duty cycle, and the average power transmission. The simulation and numerical results presented here show that, under general conditions, CD-ICC can enhance network performance compared to direct transmission mode (DTM) IEEE 802.15.6 CSMA and benchmarking. To this end, we have shown that the power saving when using CD-ICC is 37.5% with respect to DTM IEEE 802.15.6 CSMA and 10% with respect to MI-ICC.