It has been claimed that the adoption of a smart and sustainable city concept is one of the most effective ways to eliminate the risk of unsustainable living. Since the sixties, the Kingdom of Saudi Arabia was transformed from a country of traditional settlements to a modern urban setting. However, this development has been based on conventional methods of city construction and building practices. Therefore, the main aim of this primary research is to establish the fundamental aspects of developing a sustainable and smart city framework that can be applied to most Saudi Arabian cities and societies. A qualitative research methodology is adopted to determine end-user requirements, with more than 500 well-informed participants from 17 different cities. Subsequently, the main contributions here can be summaries as follows: (a) evaluating public willingness regarding smart sustainable city movements, (b) establishing requirements for a sustainable and smart city framework based on end-user perspectives, (c) providing a decision makers with a robust generic framework of sustainable development across Saudi Arabian cites, and (d) an Internet of Thing (IoT) architecture for SSC Adoption and implementation along with an integrated socio-organizational, financial, and technical generic framework for smart sustainable city adoption in the Saudi Arabian context.

Most methods for sparse representation are designed to be used in the original space. However, their performance is not always satisfactory especially when training samples are limited. According to the previous studies, more information can be obtained from samples in the feature space than those in the original space. The authors propose a novel kernel difference maximisation-based sparse representation method, and its remarkable performance in face recognition is demonstrated by the experiments. The proposed method converts all the samples into the feature space, and a test sample can be denoted as a representation with all the training samples’ linear combinations. Besides, a novel solution scheme for sparse representation is utilised to obtain the regularisation-based sparse solution. Finally, the classification of the test sample can be easily judged according to the representation result. The representation results of test samples from different classes obtained by their method are very different, making the classification of test samples easier. Besides, the proposed method is simpler than the related methods and does not require dictionary learning.

In order to solve the problem of network congestion caused by a large number of data requests generated by intelligent vehicles in LTE-V network, a brand-new fog server with fog computing function is deployed on both the cellular base stations and vehicles, and an LTE-V-fog network is constructed to deal with delay-sensitive service requests in the Internet of vehicles. The weighted total cost combines delay and energy consumption is taken as the optimisation goal. First a reinforcement learning algorithm Q-learning based on Markov decision process is proposed to solve the problem for minimising weighted total cost. Furthermore, this study specifically explains the setting method of three elements for reinforcement learning-state, action and reward in the fog computing system. Then for reducing the scale of problems and improving efficiency, the authors set up a pre-classification process before reinforcement learning to control the possible values of actions. However, considering that as the number of vehicles in system increases, Q-learning method based on recorded Q values may fall into a dimensional disaster. Therefore, the authors propose a deep reinforcement learning method, deep Q-learning network (DQN), which combines deep learning and Q-learning. Experimental results show that the proposed method has advantages.

Partial discharge (PD) detection is useful to the online monitoring of high-voltage cables, which are affected by periodic narrowband interference and random white noise. To suppress the influence of complex noise signals on the measurement of PD signals, a novel noise reduction method based on improved empirical wavelet transform (IEWT) and kurtosis for the PD signals of high-voltage cables is proposed. First, empirical wavelet function (EWF) signals arranged in order of frequency are obtained by decomposing the signal with IEWT. Then, the kurtosis criterion is introduced, and the reconstructed inherent modal function is adaptively screened. Finally, the improved threshold function is used in eliminating residual noise information in the reconstructed signal. Compared with the noise reduction methods based on EWT and EMD, the proposed noise reduction method can effectively suppress noise information in PD and has good practicability.

Hybrid electric vehicles (HEVs) are becoming a more promising means of transportation mainly because of environmental issues and depletion of fossil fuel resources. This study deals with the basic theoretical knowledge for describing their behaviour in acceleration, cruising, deceleration and control strategies. Preliminary design calculations for a series HEV bus are carried out in MATLAB environment using a backward model. In addition to this, the rule-based algorithm is implemented to compare fuel consumption, battery's state of charge (SOC) and energy-saving possibilities. The model is firstly tested for highway and city drive cycles for SOC limits of 0.6 and 0.7. Further, prolonged simulations are conducted for both the highway and city drive cycles for four different SOC limits and three parameters of the hybrid vehicle (SOC of battery, fuel power and average charging power) are observed and compared with each other. The performance indexes of both drive cycles are estimated and it is found that higher performance indexes are obtained using power-split mode and greater SOC lower limit as internal combustion engine is more efficient when operated in this mode.

Load shedding (LS) is implemented by distribution utilities (DUs) in addressing power supply insufficiency problems to avoid DU system damages. This is commonly implemented by the installation of LS relays in every DU feeder. However, in either scheduled or unscheduled supply disruptions, a huge amount of unnecessary de-loading is taking place in a feeder level LS implementation. In addition, the consumers connected to a de-loaded feeder are in total blackout, that is, consumers have no choice over which appliances to spare from being de-loaded. This study proposes an LS implementation that replaces feeder-level de-loading by a finer consumer-appliance-level de-loading and allows consumers to have some control over their de-loading. In this method, consumers can set an appliance priority level to their selected connected loads at a given time, to avoid a total blackout. Furthermore, to deal with the enormous data involved in this proposed method, both centralised and distributed optimisation approaches are employed to expedite the system processing response. Simulations are conducted to verify the proposed method's functionality. Lastly, economic analysis is done to assess the proposed method's viability.

Solar photovoltaic (PV) is a prominent technology for the generation of electricity and its utility is on the rise. The PV-based generation facilities are susceptible to faults which if mismanaged, can result in an interruption in the supply of load demand and damage to the system. Faults in PV systems are caused by a broad range of reasons and hence, it is crucial to situate a fault-tolerant system for reliable operation. This study proposes a fault-tolerant control strategy for power electronics inverters for the integration of PV systems into power systems. This is a supervisory mechanism designed to aid PV systems to continue their operation during faults. A computer simulation verifies the performance of the proposed control strategy under a series of common fault conditions assuming a wide range of configurations for the PV system and load variations.

In a distributed coherent aperture radar, the time delay differences and the phase differences among received echoes from each pair of transmitter–receiver should be estimated and then can be compensated to realise coherent processing. Most of the existing algorithms improve the outputs of the matched filtered signals of each transmitter–receiver pair separately. In this study, a structure of equal time delay differences existing in the signal model can be exploited to jointly perform coherence parameter estimation. With this idea, a structure-based joint estimation algorithm is presented to enhance the performance of the coherence parameter estimation. Simulation results verify that this algorithm can have better estimation performance compared with some existing algorithms in the case of low signal-to-noise ratios. Especially, its performance advantage is more significant in the multiple nearfield target scenarios.

Matrix converter has become one of the research hotspots of power electronic converter due to its excellent input and output characteristics and adjustable power factor. To study the stability of the matrix converter grid-connected system, the harmonic linearisation method is adopted to model the positive and negative sequence output impedance models of the matrix converter grid-connected system. The stability of the system is analysed by judging whether the plot of the ratio of the input impedance to the positive and negative sequence output impedance satisfies the Nyquist stability criterion. Also, an impedance remodelling method based on the positive sequence impedance is proposed to improve the stability of the system. Finally, the simulation verification is carried out by MATLAB/Simulink. The simulation results prove the correctness of the theoretical analysis and the feasibility of the impedance remodelling method.

The application of non-invasive devices in personal health care is becoming more and more widespread, especially sleep quality, which is a critical part of personal health because it is often associated with many diseases. Using some sensor devices such as pressure sensors, photoplethysmography and heart rate devices, the authors can collect a lot of physiological signals. In this work, they provide a method of fuzzy inference to evaluate the sleep phase, which uses the values of heart rate, heart rate variation, and body movement as input parameters that collected by the sensor devices. This method has been applied to the actual product. The results show that the measurement results of this method are consistent with polysomnography, which is recognised as the best method for measuring sleep quality currently. At the same time, the device can make some additional contributions to monitoring personal health. Combining personal activity information collected by GPS with heart rate information collected by heart rate sensors and using process mining to analyse those data, they can provide good recommendations for personal health care.