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This Special Issue is assembled from a selection of highly-rated technical papers presented at the 4th IEEE Electron Devices Technology and Manufacturing Conference 2020, EDTM 2020. It took place during April 6-21, 2020 and signified the 1st of its kind as a virtual conference financially-sponsored by the Electron Devices Society (EDS). The original EDTM 2020 was planned as a full three-day event at the Hotel Equatorial , Penang, Malaysia from March 16 to 18, 2020. However, due to the COVID-19 pandemic, EDTM 2020 was held as a virtual event .

Industries also evolved much since 1700. There have been four industrial revolutions since the 1700s. The first industrial revolution in 1780 was about steam engines, textile industries, and mechanical engineering. The second in 1840 was about steel industries. The third in 1900 was about electricity and automobiles, whereas, the fourth industrial revolution was about the IT industry, and it is generally accepted that the fourth industrial revolution has just begun [1]. As such, the term “Industry 4.0” was pinned by the German government in 2011 [2]. Industry 4.0 or Fourth Industrial Revolution is all about the Internet of Things and services (IoTS), cyber-physical systems (CPSs), and interaction and exchange of data through the Internet or cloud computing. During 1960, one system can perform only one task at a time. Multiple systems needed to run multiple tasks simultaneously [3]. Now moving forward to the present 2020, the single system can perform multiple tasks within a few seconds. Such technology is achieved by scientific advancements like the Internet, web services, Internet of things, and cloud computing. Like the industrial revolutions, there have been several improvements and developments in computing, processing, and accessing the stored data. The evolution of computing is provided in Figure 13.1.

Sleep apnea is a growing sleep disorder issue and estimate to affect 7% of the adult population in Malaysia. In this study, the electrical activity of the brain is studied using Electroencephalogram (EEG). The data obtained was then decomposed using three methods; Empirical Mode Decomposition (EMD), Bivariate EMD and finally Ensemble EMD. The Index of Orthogonatility (IO) was obtained which shows EMD performed the most poorly, EEMD the best and Bivariate in between. The performance of EMD greatly improves when the number of samples was greatly decreased and very high peaks and more complex parts of the signal were excluded in the analysis. Segmentation was also conducted and the segmentation error revealed when an Event Related Potential (ERP) has happened which is when apnea occurred.

In this research, the electroencephalogram (EEG) signals for sleep apnea were extracted and processed using Blind Source Separation approach. To identify the EEG features, 13 Independent Component Analysis methods were adopted to analyse the data extraction performance. All the EEG signals on Obstructive Sleep Apnea Syndrome (OSAS) were recorded using 10-20 international electrode placement system. The experiment was conducted based on a 20-min sleep recording during rapid eye movement sleep characterized by rapid saccadic movements of the eyes and non-rapid eye movement sleep. Seven electrode positions were identified to record the EEG signals, with a sampling time of 100 Hz. The result was investigated using the proposed 13 ICA algorithms to understand the important EEG signals and features for every process. The wavelets denoising results were obtained to evaluate the robustness of the proposed wavelets denoising algorithms. According to the performance analysis, the proposed wavelets denoising technique could be used to investigate the recorded EEG signals with lower signal amplitude.

To detect the sleep apnea electroencephalogram (EEG) signal, several feature extraction and optimisation methods were investigated in this chapter. The sleep apnea signals were acquired in this experiment. This chapter researched on the abnormalities in EEG for those who suffered from sleep apnea. The statistical correlation measurement of the EEG brain signals was analysed mainly to identify the abnormalities and specific features of patients who suffered from sleep apnea. The features and characteristics of the EEG signals were measured using the fundamental section of the Hilbert-Huang transform (HHT) decomposition method to breakdown EEG sleep apnea data into finite and smaller components. Based on this research, the fundamental empirical mode decomposition (EMD), bivariate decomposition and white noise-based ensemble EMD (EEMD) to the targeted EEG data method were investigated to obtain instantaneous frequency data. All these three methods were used to analyse the extracted sleep apnea EEG signals and features.

In this chapter, four experiments were reported. The first study is to identify factors of seating comfort. Next, the survey of relationship between seat location and sitting posture is reported. It is followed by the validation of aircraft cabin simulator. Lastly, the validation experiment of smart neck support system is described. The validation experiment is to validate the developed smart systems for neck support in a simulated “real life” setting. The aim of a developed smart system is to support the passenger's head and reduce passenger neck muscle stress during air travel adaptively. Experimentally measurements were obtained using attached EMG electrodes.

In this paper, a low cost mobile foot pressure scanning device has been developed which has linked with the Wireless Sensor Networks (WSN) for continuous monitoring of human foot plantar-pressure. The device was attached to test subjects who then went through a regimen of varied walking, jumping and turning action on the ground. Data was collected simultaneously through WSN device. In this project, the hardware and software system have been developed for detecting, collecting, transmitting and finally analyzing the collected data. To estimate the peak pressure points on the foot sole, several experiments were conducted.

In this paper, a health care system is introduced which called “Remote Upper Limb Tracking System (RULTS)” for indoor used. This system uses gyro sensor, light sensor and temperature sensor to accomplish the task which are tracking position of upper limb motion and provide ambient data to the users. In addition, the position of patient can also be tracked in this system to prevent some emergency situations. Indoor position tracking systems are essential to support new types of applications for domestics and elderly care services. Our purpose is to provide a low cost and an efficient health care system.

In this paper, the Wireless Sensor Network(WSN) has been developed for location tracking purposes. The two possible techniques of localization, namely the RSSI based and the hop count based, were investigated. For RSSI based technique, the principle and methodology was presented. The experiment was carried out to investigate the performance between the RSS and hop count technique. A set of nesC programme was designed to review the structural requirement. The performance analyses of both techniques in localization were investigated.

Seismic tremors are a sudden vibration or trembling in the Earth that can be felt more than a few hundreds of kilometers. Quakes can twist the ground, making structures and non-structures crumple. Lives might be lost because of building deformation. Malaysia is a nation with low seismic movement. Be that as it may, Malaysia is surrounded by Philippines and Indonesia, which is two of the most seismically dynamic nations and certain level of surface waves, could be felt in our nation. Seismic tremor occasions have turned out to be more regular in this area. Seismic zone mapping for school located in Malaysia particularly in Johor Bahru can assist engineers to predict earthquake risk in building planning. Johor Bahru is situated in southern of peninsular Malaysia and is one of the urban areas with high population. School structures are considered as an essential structure since it contains a high numbers of pupils and teacher most of the time. The seismic zone mapping can recognize high possibly dangerous quake influenced area and essential risk mitigation method can be prepared. The seismic zone mapping not just ready to give designer to recognize region of higher earthquake risk but it can also be a good reference for future seismic safe building advancement.

Human spine consists of 33 vertebrae. 7 cervical, 12 thoracic, 5 lumbar, 5 fused sacral vertebrae and 4 fused coccygeal vertebrae. These vertebrae generally increase in size from cervical to lumbar. Similar to the size, weight bearing capacity of the vertebrae is also higher. This weight bearing capacity of the vertebrae depends on both material properties (Bone mineral density) and geometric properties (connectivity of bone elements). Both are inter-related and together called as mechanical properties of vertebrae. When there is a structural change in the alignment of vertebrae due to congenital or neuromuscular or degenerative, it affects the mechanical properties of vertebrae. In this study we are going to deal with the mechanical properties of vertebrae affected by spinal deformities like Scoliosis and Kyphosis.

This paper is addressed to analyze the performance of various CSMA protocols under disaster scenario. The performance is measured in terms of the total duration to recover, the mean delay and the throughput when a network disaster takes place. Our model includes the widely used BEB algorithm and is implemented for CSMA/CD, CSMA/CA, CSMA/RI, CSMA/PRI, and CSMA/PR in the purpose of performance comparison. It's observed that CSMA/PR is able to offer satisfying performance for small-size packets, while CSMA/RI is better for large-size packets. Also whenever the network involves priority packets, CSMA/PRI would be an optimal solution.

Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) is one of the basic Media Access Control (MAC) protocols specified in the IEEE 802.11 to support many stations to share a channel in the network. This paper analyzes how the performance of CSMA/CA is affected by the back-off time. We propose to use random back-off time and simulation results show that it can achieve better performance with shorter delay. In the second part of the paper, we implement multichannel CSMA/CA through simulation and use Markov chain analysis to model the performance. It has been found both random back-off time and multi-channel protocols offer improvements over a system using original CSMA/CA. Therefore, we combine the two methods mentioned above and simulation shows the performance is further improved with for multi-channel CSMA/CA system random back-off interval.

In this paper, the processing and characterization of aircraft defect signal data have been explored and studied. The signal data was collected using the nondestructive ultrasonic inspection technique, Pitch/Catch swept method. Defects of different size and depths were studied and signal processing techniques were used to identify features that provide the most distinction among the defects. The discussion in this paper is only limited to the parts of the classification process that pertain to data collection, pre-processing, and feature extraction.