Search

In this paper, an optimal design approach for a permanent magnet synchronous machine (PMSM), as an electric vehicle (EV) traction machine is proposed in such a way that a high power density machine can be achieved. A surface mounted PMSM is selected for its high efficiency and torque density. The machine parameters and the design constraints are established based on the EV characteristics. Guided by the analytical results of the design approach, a finite element study is performed on the machine to verify the relevance of the proposed approach. Then, a new optimization method, combining particle swarm optimization (PSO) and chaos theory, is applied for the machine design parameters in order to maximize the power density. This algorithm, referred to as CPSO, utilizes the chaos system properties such as its sensitivity to the initial conditions and its dynamical behaviour to enhance the global search capabilities of the PSO algorithm. The simulation results show the effectiveness of this algorithm compared to other meta-heuristic algorithms in increasing the machine power density. Furthermore, it is found that the optimized PMSM offers a 41.60% improvement in power while respecting the EV characteristics.

The study attempts to propose a conceptual framework for evaluating public healthcare service quality in the Indian context. First, it aims to construct and validate the Public HealthCare Service Quality (PubHCServQual) scale for three decision models; Collective Decision Model (CDM), Independent Urban Decision Model (IUDM), and Independent Rural Decision Model (IRDM). Second, the study estimates and validates the relative importance of service quality constructs. Lastly, it evaluates Aggregate Risk Measures (ARM) by incorporating concepts of Fuzzy Analytic Hierarchy Process (FAHP). Based on the importance of factors and the probability of occurrence of dissatisfaction due to factors, the aggregate risk was evaluated for CDM, IUDM, and IRDM, using FAHP. The study successfully constructed and validated the PubHCServQual scale which is original for CDM, IUDM, and IRDM. The study directs the management of public healthcare facilities towards improvements and creates the value of service quality.

In recent years, there has been a significant increase in the development of autonomous vehicles. One critical task for ensuring their safety and dependability, is obstacle avoidance in challenging weather conditions. However, no studies have explored the use of data augmentation to generate training data for Deep learning (DL) models aimed at navigating obstacles in extreme weather conditions. This study makes a substantial contribution to the field of autonomous vehicle obstacle avoidance by introducing an innovative approach that utilizes a Generative Adversarial Network (GAN) model for data augmentation, with the objective of enhancing the accuracy of DL models. The use of a GAN model to generate a training dataset and integrate images depicting challenging weather conditions has been pivotal in enhancing the accuracy of the DL models. The extensive training dataset, consisting of 64,336 images, was created using three cameras installed in VSim-AV, an autonomous vehicle simulator, thereby ensuring a diverse and comprehensive dataset for training purposes. Three DL models (ResNet50, ResNet101, and VGG16 transfer learning) were trained on this dataset both before and after applying the data augmentation techniques. The performance of the augmented models was evaluated in a real-time environment using the autonomous mode of the VSim-AV simulator. The testing phase resulted in the highest accuracy rate of 97.2% when employing Resnet101 following the implementation of GAN. It was observed that the autonomous car could navigate without any collisions, showcasing a remarkable reaction time of 0.105 seconds, thus affirming the effectiveness of the approach. The comparison between the original and augmented datasets demonstrate the originality and value of this study, showcasing its significant contribution to the advancement of autonomous vehicle obstacle avoidance technology. This paper makes significant advances to the field of autonomous vehicle navigation by exploiting Generative Adversarial Networks (GANs) to improve obstacle avoidance capabilities in severe weather conditions, hence increasing safety and dependability in real-world applications.

This paper focuses on investigating uplink (UL) data transmission in cell-free massive MIMO based on orthogonal frequency division multiplexing (CF-mMIMO-OFDM) systems, taking into account the effects of hardware impairments (HWIs). Specifically, the HWIs arise from nonlinear distortions (NLD) caused by power amplifiers (PAs) at user equipments (UEs). These NLD have a significant impact on both channel estimation and data transmission in UL CF-mMIMO-OFDM. To mitigate NLD while maintaining a good power-efficiency, we propose a successive NLD cancellation approach that is adequate for CF-mMIMO-OFDM. Specifically, a novel frequency-domain channel estimation method is introduced that incorporates NLD cancellation. This method aims to accurately estimate the channel despite the presence of hardware impairments. Additionally, a joint multi-user (MU) combining and NLD cancellation scheme is proposed to mitigate the NLD impact on data detection. Note that three combining schemes are adopted, namely maximum-ratio (MR), zero-forcing (FZF), and partial-FZF (PFZF). Most-importantly, the proposed techniques are designed to be implemented in a distributed and scalable manner, highlighting the advantages of CF-mMIMO-OFDM systems. The performance of the proposed techniques are evaluated with simulations when considering the combining schemes. Results show the capability of our proposed NLD cancellation approach to improve both channel estimation and data detection, especially when leveraging the good features of PFZF combing scheme. For objective comparison purpose, we derived closed-form expressions on UL spectral-efficiency (SE) performance of an UL CF-mMIMO-OFDM system in presence of ideal and nonlinear PA.

This paper provides a unique multi-disciplinary survey of standalone solutions for clean and sustainable water access in Africa and proposes a three-segment architecture, comprised of an UV-LED-based water disinfection unit, a solar-powered battery, and a wireless positioning system. In addition, our paper also provides initial snapshot results from all the three segments of the proposed architecture, as well as two examples of the water analysis and user-survey-based results collected from Togo, Africa. We believe that the features of our envisaged system such as long battery life, low maintenance cost, ability to significantly increase the quality and disinfect at least 1.5 l/min of water and ability to offer wireless positioning without the need to access a cellular or WiFi network, for accurate annotation of water sources, localization of misplaced or lost disinfection systems, and user standalone positioning capabilities, are attractive features towards improving the quality of life of people with limited access to potable water or to water that fulfills international quality criteria.

We have developed a low-cost, high-accuracy, and energy-efficient wearable tag sensor for apnea detection. The sensor can detect different types of breathing problems by monitoring the small movements of the chest wall compartments during each respiration cycle. This tag sensor sends also apnea events, digital respiration rate, and patient posture data using an ultra high radio frequency identification (UHF RFID) reader. The reader is based on the recent AS3993 chip connected to a Raspberry Pi 4 controller, which acts as a local server and is connected to the cloud to share acquired data with the treating doctor. A sleep disorder detection and classification with several positions using a long short-term memory (LSTM) network algorithm is implemented in real-time on the embedded arm microcontroller STM32F407. The proposed apnea detection method exhibits low error, enabling it to meet clinical requirements. The accuracy of apnea events and position detection were triggered in over 93% of cases. We have also evaluated six different classification techniques optimized by considering the proposed feature extraction and regularization of classifier parameters. [ABSTRACT FROM AUTHOR]

Goal: The evaluation of respiratory events using audio sensing in an at-home setting can be indicative of worsening health conditions. This paper investigates the use of image-based transfer learning applied to five audio visualizations to evaluate three classification tasks (C1: wet vs. dry vs. whooping cough vs. restricted breathing; C2: wet vs. dry cough; C3: cough vs. restricted breathing). Methods: The five visualizations (linear spectrogram, logarithmic spectrogram, Mel-spectrogram, wavelet scalograms, and aggregate images) are applied to a pre-trained AlexNet image classifier for all tasks. Results: The aggregate image-based classifier achieved the highest overall performance across all tasks with C1, C2, and C3 having testing accuracies of 0.88, 0.88, and 0.91 respectively. However, the Mel-spectrogram method had the highest testing accuracy (0.94) for C2. Conclusions: The classification of respiratory events using aggregate image inputs to transfer learning approaches may help healthcare professionals by providing information that would otherwise be unavailable to them.

This paper introduces a new approach to providing secure physical-layer massive multiple-input multiple-output (MIMO) based communications that can improve the energy efficiency of the system. This is achieved by synthesizing orthogonal artificial noise (AN) that has to be constrained to lie in the null space of the legitimate users’ channels while it should lie in the range space of the eavesdropper’s channel. In addition, this AN reduces the peak-to-average power ratio (PAPR) of the transmit signal. Indeed, low PAPR signals are preferable and more efficient for low-cost hardware, thus improving the energy consumption of massive MIMO systems. In this paper, we propose a new PAPR-aware precoding scheme based on the use of AN to enhance the secrecy performance of massive MIMO while reducing the PAPR of the transmit signal and guaranteeing excellent transmission quality for legitimate users. The scheme is formulated as a convex optimization problem that can be resolved via steepest gradient descent (GD). Accordingly, we developed a new iterative algorithm, referred to as PAPR-Aware-Secure-mMIMO, that makes use of instantaneous information to solve the optimization problem. Simulation results show the efficiency of our proposed algorithm in terms of PAPR reduction and secrecy, which is also studied with respect to power distribution between useful signal and AN, PAPR targets and the number of BS antennas.

Mobile devices have become very important for our daily needs. The user authentication protocols with the key agreement are required to deal with the security issues that arise from the use of mobile devices through Internet applications. However, existing user authentication protocols are only suitable if the client and the server use a similar cryptographic approach. Therefore, it is important to develop an authentication protocol for mobile environments with heterogeneous cryptographic approaches. In this paper, an efficient user authentication and key agreement protocol is proposed for a heterogeneous client-server mobile environment. The security of the proposed scheme is formally proved under the ${q}$ -strong Diffie-Hellman problem ( ${q}$ -SDH), the ${q}$ -bilinear Diffie-Hellman inversion problem ( ${q}$ -BDHI), and the modified bilinear Diffie-Hellman inversion problem (mBDHI), respectively. Our scheme has reasonable processing costs and communication costs on the client and server sides. Moreover, our scheme is suitable for applications that use different cryptographic approaches. In particular, the proposed protocol can work when the client applies the identity-based cryptosystem and the server applies the certificateless cryptosystem.

The computational cloud aims to move traditional computing from personal computers to cloud providers on the internet. Cloud security represents an important research area. Confidentiality, integrity, and availability are the main cloud security characteristics addressed. Cloud providers apply dynamic load balancing and reactive fault tolerance techniques to build secure cloud services to achieve high service availability. Dynamic cloud load balancing approaches distribute submitted tasks to virtual machines during tasks execution and the load of virtual machines is updated based on the system’s state. Reactive cloud fault tolerance is activated for system process failures after failure effectively happens. Reactive cloud fault tolerance handles failure after the fault has occurred. Despite the significance of dynamic load balancing and reactive fault tolerance techniques and mechanisms, few reviews focus on these approaches in a systematic, unbiased method focusing on integrating cloud dynamic load balancing and reactive fault tolerance techniques. This paper conducts a systematic literature review of the existing literature concerning reactive fault tolerance, dynamic load balancing, and their integration in their basic approaches, types, frameworks, and future directions.

Digital technology advancement and the Internet of Things (IoT) are playing a major role to take a big leap towards achieving Industry 4.0. Cloud-based data management and big data analytics have given rise to adopt Cloud Enterprise Resource Planning (CERP). The CERP has become a significant tool for the success of the information management system (IMS) which is ultimately responsible for the success of any organization. The selection of CERP depends on many critical success factors (CSFs) that must be considered while evaluating and selecting a CERP. In this work, identified CSFs of CERP are modeled using a multi-criteria decision-making (MCDM) approach. The Analytic Hierarchy Process (AHP) and Fuzzy Analytic Hierarchy Process (FAHP) based modelling have been carried out to derive the ranking of the CSFs responsible for the CERP. The group decision-making (GDM) based AHP has also been adopted to build the decision-making model. The paper models 5 Dimensions and 20 sub-criteria factors to provide the prioritized rank of Dimensions and CSFs. The AHP and FAHP models identify the ranking of the 5 Dimensions as Organizational Behavior, Cloud ERP Essentials, Technological Advancement, Innovational Ideas, and Environmental Impact.

In the last few years, most of the public universities in the Kingdom of Saudi Arabia (KSA) have adopted electronic learning (e-learning) as an evolutionary educational platform both by choice and by demand. Despite its many advantages, it has increased the ethical and moral challenges faced in the form of the violations of academic integrity that exist in traditional education. Universities all over the world are struggling to overcome these issues. Keeping this in mind, the purpose of this paper is to investigate and priorities the factors of violating academic integrity within Saudi Arabian universities. Based on the previously published literature and using Delphi - based discussions with e-Learning experts, this paper has identified twelve major factors which were related to e-Learning environment, Awareness on Academic Integrity, and e-Learning guiding principles. Analytic Hierarchy Process (AHP) was used to prioritize these factors. It was concluded that inappropriate guidelines provided to students was the most contributing factor while Shortage of feedbacks was least contributing factor among all the identified factors.

Polysomnography (PSG) is the standard test for diagnosing sleep apnea. However, the approach is obtrusive, time-consuming, and with limited access for patients in need of sleep apnea diagnosis. In recent years, there have been many attempts to search for an alternative device or approach that avoids the limitations of PSG. Pressure-sensitive mats (PSM) have proven to be able to detect central sleep apneas (CSA) and be a potential alternative for PSG. In the current study, we combine advanced machine learning approaches with a practical unobtrusive home monitoring device (PSM) to detect CSA events from data collected nocturnally and unattended. Two deep learning methods are implemented for the automatic detection of CSA events: a temporal convolutional network (TCN) and a bidirectional long short-term memory (BiLSTM) network. The deep learning models are compared to a classical machine learning approach (linear support vector machine, SVM) and a simple threshold-based algorithm. Considering the characteristics of each method, we choose strategies, including resampling and weighted cost-functions, to optimize the methods and to perform CSA detection as anomaly detection in an imbalanced data set. We evaluate the performance of all models on a database containing 7 days of data from 9 elderly patients. From the resulting 63 days, data from 7 patients (49 days) are devoted to training for optimizing hyperparameters, and data from 2 patients (14 days) are devoted to testing. Experimental results indicate that the best-performing model achieves an accuracy of 95.1% through training an BiLSTM network. Overall, the implemented deep learning methods achieve better performance than the conventional classification approach (SVM) and the simple threshold-based method, and show good potential for the use of PSM for practical unobtrusive monitoring of CSA.

The current pandemic associated with the novel coronavirus (COVID-19) presents a new area of research with its own set of challenges. Creating unobtrusive remote monitoring tools for medical professionals that may aid in diagnosis, monitoring and contact tracing could lead to more efficient and accurate treatments, especially in this time of physical distancing. Audio based sensing methods can address this by measuring the frequency, severity and characteristics of the COVID-19 cough. However, the feasibility of accumulating coughs directly from patients is low in the short term. This article introduces a novel database (NoCoCoDa), which contains COVID-19 cough events obtained through public media interviews with COVID-19 patients, as an interim solution. After manual segmentation of the interviews, a total of 73 individual cough events were extracted and cough phase annotation was performed. Furthermore, the COVID-19 cough is typically dry but can present as a more productive cough in severe cases. Therefore, an investigation of cough sub-type (productive vs. dry) of the NoCoCoDa was performed using methods previously published by our research group. Most of the NoCoCoDa cough events were recorded either during or after a severe period of the disease, which is supported by the fact that 77% of the COVID-19 coughs were classified as productive based on our previous work. The NoCoCoDa is designed to be used for rapid exploration and algorithm development, which can then be applied to more extensive datasets and potentially real time applications. The NoCoCoDa is available for free to the research community upon request.

This paper studies the performance of enhanced multicarrier waveforms in the presence of a nonlinear memoryless high power amplifier (HPA). In particular, we focus on the in-band non-linear distortions (NLD) and their effects on symbol-error-rate. First, we introduce a theoretical characterization of NLD caused by HPAs. Two HPA models are considered in this paper: 1) a 4-GHz realistic HPA, modeled using a polynomial model, and this model exhibits both amplitude-to-amplitude (AM/AM) and amplitude-to-phase distortions and 2) a Rapp model, which exhibits only the AM/AM distortion. Second, we present further discussions and comparisons on the different waveforms: the cyclically prefixed orthogonal frequency division multiplexing (OFDM), the weighted overlap and add OFDM, the universal filtered multicarrier, the filtered OFDM, and the filter bank multicarrier based on offset-QAM. Then, we provide insights into the impact of in-band non-linear distortions caused by HPAs. The simulation and theoretical results are shown to be in agreement for different waveforms and various input back-off values.

We investigate the peak-to-average power ratio (PAPR) reduction problem in orthogonal frequency-division multiplexing-based massive multi-user (MU) multiple-input multiple-output (MIMO) downlink systems. In this paper, we develop a downlink transmission scheme that performs jointly MU precoding and PAPR reduction (PP) by exploiting the excess degrees of freedom offered by equipping the BS by a large number of antennas. Specifically, the joint MU precoding and PAPR reduction scheme is formulated as a simple convex optimization problem solved via steepest gradient descent (GD) approach. Then, we develop a novel algorithm, referred to as MU-PP-GDm, to reduce the PAPR of the transmitted signals by exploiting the high-dimensional null space of the MIMO channel matrix while maintaining an excellent transmission quality. The simulation results show that the proposed MU-PP-GDm has low computational complexity and can achieve substantial PAPR performance with a fast convergence rate.

The extremely diverse service requirements is an important challenge for the upcoming fifth-generation (5G) wireless communication technologies. Orthogonal frequency division multiplexing (OFDM)-based massive multiple-input multiple-output (MIMO) and mixed numerologies transmission are proposed as solutions. This paper investigates the use of spatial multiplexing of users, sharing the same bandwidth, whose associated numerologies are different. We first introduce a precoding design that aims to manage the mixed numerologies spectrum sharing (SS) transmission. Then, we analyse the inter-numerology interference (INI) and derive the theoretical expressions of its radiation pattern in massive MIMO-OFDM downlink systems. We demonstrate that by using the proposed precoding scheme and considering two groups of users using two different numerologies, INI appears only in frequency selective channels. Besides, the transmission of users using numerology with large subcarrier spacing (SCS) is always with the best quality, only users using the numerology with small SCS suffer from INI. In that case, INI increases due to the difference in SCS, channel selectivity and power allocation. Based on the derived INI, in closed-form expressions, the precoding scheme is improved and a new INI cancellation scheme is introduced. Our analysis demonstrates that the INI theoretical model matches the simulation results, and the introduced INI cancellation based precoder efficiently mitigates the INI and enhances the performance of massive MIMO-OFDM downlink systems.

Electronic health records (EHRs) replaced the old paper-based systems to make patient data more accurate, reliable, and more accessible. Yet, the EHRs system requires high transmission cost, energy, and waste of time for both doctors and patients. Furthermore, EHRs security presents a serious issue threatening the patient's privacy. Most of the third-party hosting systems have some issues related to the users' privacy and data security. Hence, it is necessary to restrict the access control policies and develop efficient mechanisms for cloud-based EHRs data. In this paper, a sensitive and energetic access control (SE-AC) mechanism is proposed for managing the cloud-hosted EHRs and providing a fine-grained access control even in critical situations. The proposed mechanism ensures the confidentiality of the patient's data, where only authorized individuals to have permission to be able to edit or review certain of the patient's data. Each EHR data is encrypted by the managing authority before submitting to the cloud storage. The requesting user can get dynamically changing permissions based on authentication and context attributes. In addition, seven major aspects have been quantified to assess the operation of any access control that could be deployed in the Internet-of-Thing (IoT). The security analysis indicates that the SE-AC mechanism is secure and will prevent any unauthorized access. The results show exceptional compatibility and performance with different setups and configuration.

Cloud computing has been regarded as one of the significant Information Technology (IT) tools. Many sectors are adopting cloud computing services for its business support. It has also become a new IT paradigm that has transformed the E-Learning system to become more user-friendly. As a result, the E-Learning usage is growing rapidly and being preferred over the conventional teaching-learning process in a big way. This revolutionary change is attributed to the advancement in digital technology. The transformation in digital technology has made the teaching-learning process flexible, easy and convenient for effective knowledge transfer. The cloud-based E-Learning process depends upon many factors of different dimensions that are of significant importance for cloud-based-E-Learning success. Hence they must be studied to successfully analyze their level of importance and fulfill Cloud-based E-Learning positive effectiveness. The current research provides a detailed literature review for cloud-based E-Learning Critical Success Factors (CSFs) of teaching-learning process. Further, the research employs the combinatorial approach to evaluate the diversified dimensions and CSFs of cloud-based E-Learning that helps in quantifying and comparing the influence of various dimensions and CSFs of cloud-based-E-Learning. Four dimensions and fourteen factors have been identified through in-depth literature review and later on evaluated for the prioritization using a combinatorial approach. The influence of such dimensions and factors will help various stakeholders to plan their strategy and resources for the betterment of knowledge transfer through cloud-based-E-Learning.

Cloud computing has been widely applied in numerous applications for storage and data analytics tasks. However, cloud servers engaged through a third party cannot be fully trusted by multiple data users. Thus, security and privacy concerns become the main obstructions to use machine learning services, especially with multiple data providers. Additionally, some recent outsourcing machine learning schemes have been proposed in order to preserve the privacy of data providers. Yet, these schemes cannot satisfy the property of public verifiability. In this paper, we present an efficient privacy-preserving machine learning scheme for multiple data providers. The proposed scheme allows all participants in the system model to publicly verify the correctness of the encrypted data. Furthermore, a unidirectional proxy re-encryption (UPRE) scheme is employed to reduce the high computational costs along with multiple data providers. The cloud server embeds noise in the encrypted data, allowing the analytics to apply machine learning techniques and preserve the privacy of data providers' information. The results and experiments tests demonstrate that the proposed scheme has the ability to reduce computational costs and communication overheads.

This paper details an experimental testbed conceived for studying the capability of new multicarrier waveforms to accommodate 5G requirements. Testbed experiments are done with an implementation of cyclically prefixed orthogonal frequency division multiplexing (CP-OFDM) and its most promising enhancements, i.e., weighted overlap and add based OFDM (WOLA-OFDM) and block-filtered OFDM (BF-OFDM), with configurable universal software radio peripherals (USRPs)-based software defined radio prototype. These experiments are done in a realistic laboratory-like environment, where capabilities of the selected waveforms to accommodate 5G requirements are evaluated while focusing on the optimization of the energy efficiency. On one hand, we provide details and deign guidance to improve energy efficiency and robustness of the studied waveforms through new approaches of digital predistortion (DPD) and peak-to-average power ratio (PAPR) reduction in the presence of RF power amplifier (RF PA). In particular, we focus on the mitigation of in-band and out-of-band non-linear distortions and their effects on power spectrum density (PSD) and bit error rate, respectively. It has been demonstrated that the combination of PAPR reduction and DPD allows the transmitter to significantly improve the spectrum localization without sacrificing the in-band and out-of-band waveform quality, while achieving high power efficiency, thus operating the PA very close to its saturation region, as well. On another hand, we address the impact of the lack of synchronism between transmitters on the performance of the selected waveforms, which is of special relevance for future 5G massive machine type communications applications. Experimental results show that BF-OFDM and WOLA-OFDM would permit the accommodation of 5G requirements when RF PA issues are tackled. In some specific scenarios, ideal spectrum utilization can be realized by these waveforms, using only one tone as guard band while keeping good energy efficiency.

In this paper, we address the problem of confidentiality of keyframes, which are extracted from diagnostic hysteroscopy data using video summarization. We propose an image color coding method aimed at increasing the security of keyframes extracted from diagnostic hysteroscopy videos. In this regard, we use a 2-D logistic map to generate the cryptographic keys sequences, which relies on mixing and cascading the orbits of the chaotic map in order to generate the stream keys for the encryption algorithm. The encrypted images produced by our proposed algorithm exhibit randomness behavior, providing a high-level of security for the keyframes against various attacks. The experimental results and security analysis from different perspectives verify the superior security and high efficiency of our proposed encryption scheme compared to other state-of-the-art image encryption algorithms. Furthermore, the proposed method can be combined with mobile-cloud environments and can be generalized to ensure the security of cloud contents as well as important data during transmission.

During the past few years, we are witnessing the emergence of 5G and its high-level performance targets. Waveform (WF) design is one of the important aspects for 5G that received considerable attention from the research community in recent years. To find an alternative to the classical orthogonal frequency division multiplexing (OFDM), several multicarrier approaches addressing different 5G technical challenges, have been proposed. In this paper, we focus on critical machine-type communications (C-MTC), which is one of the key features of the foreseen 5G system. We provide a comparative performance study of the most promising multicarrier WFs. We consider several C-MTC key performance indicators: out-of-band radiations, spectral efficiency, end-to-end physical layer latency, robustness to time and frequency synchronization errors, power fluctuation, and transceiver complexity. The investigated multicarrier WFs are classified into three groups based on their ability to keep the orthogonality: in the complex domain, e.g., most of the OFDM-inspired WFs, in the real domain like offset-quadrature amplitude modulation (QAM)-based techniques, and non-orthogonal WFs like generalized frequency division multiplexing and filter bank-based multicarrier-QAM. Finally, the performances of these WFs are thoroughly discussed in order to highlight their pros and cons and permit a better understanding of their capabilities in the context of C-MTC.