To increase torque and power conversion of permanent magnet synchronous motors (PMSMs) at high-speed operation, the DC-link voltage of the inverter must be fully utilized during steady-state and transient operations. This paper proposes the incorporation of a harmonic reference generator (HRG) with a pulse clipping (PC) scheme into a model predictive control framework to achieve highest power output. The HRG calculates flux references in the stator-fixed coordinate system for an underlying continuous control set model predictive flux control (CCS-MPFC). These reference trajectories contain the harmonic flux component induced by the voltage hexagon in the overmodulation range and specific switching states (e.g., six-step operation) of the inverter are ensured by the proposed PC scheme. This enables a seamless transition to the overmodulation range including six-step operation and increases the drive’s power conversion to its maximum extent. Since the flux differential equation in the stator-fixed coordinate system, used as motor model for the HRG and CCS-MPFC, is able to represent PMSMs with linear and nonlinear magnetization, the proposed approach is well suited for the control of PMSMs with nonlinear magnetization. Extensive transient and steady-state experimental investigations on a highly utilized PMSM in the entire modulation and speed range prove the performance of the proposed approach.

Coal-fired power plants (CFPP) are a significant resource for flexibility regulation of power systems containing a high proportion of renewable energy (RE). This paper presents an extended study of the technology for co-firing coal and ammonia, which can decarbonize CFPP. The excess RE produces green hydrogen, converted into green ammonia for CFPP combustion. Moreover, this system has energy storage features that can cope with short-term fluctuations and store and utilize RE seasonally. In this paper, this system’s complex coupled thermal and electrical rocesses are simplified and characterized as a mixed integer linear programming (MILP) model for the first time. Furthermore, the operation of the green ammonia co-firing system is simulated at two time scales of five days and a full year. Finally, sensitivity and efficiency analyses are performed. The results shows that as the maximum co-firing ratio of ammonia and coal increases, the total operation cost (TOC), carbon emission (CE), and RE curtailment shows a decreasing trend. Therefore, the ratio of ammonia-coal co-firing is recommended to be 25% to compromise the TOC and exhaust gas emission. In this situation, the TOC and CE will decrease by 8.59% and 12.74%, respectively, compared with the scenario without co-firing technology. The overall efficiency of green ammonia synthesis and combustion for generation is about 13.92%.

This paper develops a low-complexity near-optimal non-coherent receiver for a multi-level energy-based coded modulation system. Inspired by the turbo processing principle, we incorporate the fundamentals of bit-interleaved coded modulation with iterative decoding (BICM-ID) into the proposed receiver design. The resulting system is called bit-interleaved coded energy-based modulation with iterative decoding (BICEM-ID) and its error performance is analytically studied. Specifically, we derive upper bounds on the average pairwise error probability (PEP) of the non-coherent BICEM-ID system in the feedback-free (FF) and error-free feedback (EFF) scenarios. It is revealed that the definition of the nearest neighbors, which is important in the performance analysis in the FF scenario, is very different from that in the coherent BICM-ID counterpart. The analysis also reveals how the mapping from coded bits to energy levels influences the diversity order and coding gain of the BICEM-ID systems. A design criterion for good mappings is then formulated and an algorithm is proposed to find a set of best mappings for BICEM-ID. Finally, simulation results corroborate the main analytical findings.

In this paper, the outage probability of maritime free-space optical communication system is analyzed. Maritime turbulence channel is modeled by Lognormal distribution. Channel model includes the combined effect of turbulence, pointing error, angle-of-arrival (AOA) fluctuations, and attenuation. Pointing error and AOA fluctuations are assumed to be Beckmann and Rayleigh distributed, respectively. Turbulence power spectrum is considered to present the Kolmogorov characteristics and Rytov variance of a propagating Gaussian beam is obtained. The probability density function, cumulative distribution function and outage probability of maritime communication channel are obtained analytically. Outage performance of a maritime communication link is given depending on various parameters of the maritime environment, the Gaussian beam, and the communication system.

Reconfigurable planar antennas are essential in multifunctional wireless communication devices. This paper presents the design of a frequency reconfigurable patch antenna printed on a composite substrate made of a dielectric and a magnetic metamaterial (MMM). The MMM comprises magnetized ferrite slabs with embedded split-ring resonators (SRR). The constructive coupling between the resonances of the magnetized ferrites and SRR is optimized to achieve promising reconfigurable properties. The proposed frequency reconfigurable antenna demonstrates a 158% increase in the frequency tuning range. Furthermore, the simulated results are experimentally verified, and the composite substrate shows slight effects on the antenna radiation characteristics.

Orthogonal frequency division multiplexing (OFDM) is widely used and works efficiently for the communication, but emerging applications requires OFDM to be flexible to meet sensing requirements. The time-frequency waveform design of OFDM for dual-functional radar-communications (DFRC) is critical to achieve the future communication and sensing requirements. Therefore, we propose a novel method to minimize Cram\’{e}r-Rao bounds (CRBs) of the delay and Doppler estimation to improve radar performance of an OFDM DFRC system. Although some methods are proposed in the literature to improve the CRBs, these methods either require feedforward signaling or subcarrier reservation. However, it is possible to exploit the constellation extension of quadrature amplitude modulation (QAM)  to achieve lower CRBs without these requirements. Therefore, the proposed method provides a transparent communication along with the CRB minimization for conventional OFDM systems. For the evaluation of the proposed method, CRB and symbol error rate (SER) are considered in the simulation results. Furthermore, the theoretical SER  analysis of the proposed method is derived to understand the effects of CRB minimization on the communication performance.

An improved planar microwave sensor comprising a resistor-embedded top microstrip line and a ground etched complementary split-ring resonator (CSRR) is presented to assess the quality of recycled cooking oils. The proposed sensor designed on a 1.6 mm Taconic substrate (εr=2.2) exhibits a reasonably higher value of sensitivity and an improved quality factor, thereby facilitating the detection of even slight variations in electrical and chemical properties of low-loss dielectric samples. The enhanced performance of the proposed sensor is attributed to incorporating a specific value of the lumped resistor on the top side of a microstrip line, which helps in coupling the maximum electric field to the CSRR and providing an extreme improvement in the S21 notch (-50 dB) near resonance. The proposed sensor is designed and simulated using the full wave simulator, the CST MWS. The equivalent circuit model is developed using the ADS to understand the behavior of the circuit in detail. An analytical formulation is implemented using MATLAB, which provides a definite relationship between the small value of the resistor connected on the top of the microstrip line, and the S21 value near resonance. After optimizing various parameters of the sensor including the lumped resistor, it is fabricated and tested. Several fresh and recycled cooking oil samples are measured, and it is observed that their acid values get degraded from 1.5 to even 9.4 indicating the toxic nature of the recycled oils and their adverse effect on human health. The acid value of the oil samples in the present situation is obtained under a controlled chemical environment by developing an empirical relationship between this parameter and the normalized frequency and the S21 notch. The proposed sensor provides substantially high sensitivity (fractional sensitivity of 9.5%) for a unit change in oil rancidity in terms of frequency (~25 MHz/mgKOHgm-1) and S21 notch (~ 3.33 dB/mgKOHgm-1).

Three different deep learning models were designed in this paper, to predict the electric fields of single nanoparticles, dimers, and nanoparticle arrays. For single nanoparticles, the prediction error was 4.4%, respectively. For dimers with strong couplings, a sample self-normalization method was proposed, and the error was reduced by an order of magnitude compared with traditional methods. For nanoparticle arrays, the error was reduced from 28.8% to 5.6% compared with previous work. Numerical tests proved the validity of the proposed deep learning models, which have potential applications in the design of nanostructures.

This work is under review of IEEE Transactions on Parallel and Distributed Systems.

An intelligible step-by-step Reinforcement Learning (RL) problem formulation and the availability of an easy-to-use demonstrative toolbox for students at various levels (e.g., undergraduate, bachelor, master, doctorate), researchers and educators. This tool facilitates the familiarization with the key concepts of RL, its problem formulation and implementation. The results demonstrated in this paper are produced by a Python program that is released open-source, along with other lecture materials to reduce the learning barriers in such innovative research topic in robotics. The RL paradigm is showing promising results as a generic purpose framework for solving decision-making problems (e.g., robotics, games, finance). In this work, RL is used for solving a robotics 2D navigational problem where the robot needs to avoid collisions with obstacles while aiming to reach a goal point. A navigational problem is simple and convenient for educational purposes, since the outcome is unambiguous (e.g., the goal is reached or not, a collision happened or not). Thus, the intent is to accelerate the adoption of RL techniques in the field of mobile robotics. Motivate and promote the adoption of RL techniques to solve decision-making problems, specifically in robotics. Due to a lack of accessible educational and demonstrative toolboxes concerning the field of RL, this work combines theoretical exposition with an accessible open-source graphical interactive toolbox to facilitate the apprehension. This study aims to reduce the learning barriers and inspire young students, researchers and educators to use RL as an obvious tool to solve robotics problems.

Impairment in persons with multiple sclerosis (PwMS) can often be attributed to symptoms of motor instability and fatigue. Symptom monitoring and queued interventions often target these symptoms. Clinical metrics are currently limited to objective physician assessments or subjective patient reported measures. Recent research has turned to wearables for improving the objectivity and temporal resolution of assessment. Our group has previously observed wearable assessment of supervised and unsupervised standing transitions to be predictive of fall-risk in PwMS. Here we extend the application of standing transition quantification to longitudinal home monitoring of symptoms. Subjects (N=23) with varying degrees of MS impairment were recruited and monitored with accelerometry for a total of ~6 weeks each. These data were processed using a deep learning activity classifier to isolate periods of standing transition from which descriptive features were extracted for analysis. Participants completed daily and biweekly assessments describing their symptoms. From these data, Canonical Correlation Analysis was used to derive digital phenotypes of MS instability and fatigue. We find these phenotypes capable of distinguishing fallers from non-fallers, and further that they demonstrate a capacity to characterize symptoms at both daily and sub-daily resolutions. These results represent promising support for future applications of wearables, which may soon augment or replace current metrics in longitudinal monitoring of PwMS.

Physical layer security (PLS) can be adopted for efficient key sharing in secured wireless systems. The random nature of the wireless channel and channel reciprocity (CR) are the main pillars for realizing PLS techniques. However, for applications that involve air-to-air (A2A) transmission, such as unmanned aerial vehicle (UAV) applications, the channel does not generally have sufficient randomness to enable reliable key generation. Therefore, this work proposes a novel system design to mitigate the channel randomness constraint and enable high-rate secret key generation (SKG) process. The proposed system integrates physically unclonable functions (PUFs) and CR principle to securely exchange secret keys between two nodes. Moreover, an adaptive and controllable artificial fading (AF) level with interleaving is used to mitigate the impact of low randomness variations in the wireless channel. The proposed system can operate efficiently even when the channel is nearly flat or time invariant. Consequently, the time required for generating and sharing a key is significantly shorter than conventional techniques. We also propose a novel bit extraction scheme that reduces the number of overhead bits required to share the intermediate keys. The obtained Monte Carlo simulation results show that a key agreement can be reached at the first trial for moderate and high signal-to-noise ratios (SNRs), which is substantially faster than other PLS techniques. Moreover, the results show that inducing AF into static channels reduces the mismatch ratio between the generated secret sequences and degrades the eavesdropper’s capability to predict the secret keys.

5G and beyond networks are expected to support a wide range of services, with highly diverse requirements. Yet, the traditional “one-size-fits-all” network architecture lacks the flexibility to accommodate these services. In this respect, network slicing has been introduced as a promising paradigm for 5G and beyond networks, supporting not only traditional mobile services, but also vertical industries services, with very heterogeneous requirements. Along with its benefits, the practical implementation of network slicing brings a lot of challenges. Thanks to the recent advances on Machine Learning (ML), some of these challenges have been addressed. In particular, the application of ML approaches is enabling the autonomous management of resources, in the network slicing paradigm. Accordingly, this paper presents a comprehensive survey on contributions on ML in network slicing, identifying major categories and sub-categories in the literature. Key takeaways are also presented and open research challenges are discussed, together with potential solutions.

The sixth generation (6G) of wireless networks are envisioned to support a plethora of human-centric applications and offer connectivity to a massive number of devices with diverse requirements, thus enabling massive Machine Type Communications. Nevertheless, with the rapid growth of the number of connected devices as well as the ever-increasing network traffic, network energy consumption has become a major challenge. Additionally, 6G is expected to catalyze the emergence of new applications that are characterized by their harsh environmental conditions, with ultra-small and low-cost wireless devices. Therefore, there is a pressing need for developing sustainable solutions that take into consideration all these requirements in order to realize the full potential of 6G networks. Within this context, zero-energy devices (ZEDs) have emerged as a prominent solution for the next generation green communication architecture. Such devices eliminate the need for recharging plugins and replacing batteries by integrating disruptive technologies, such as radio frequency energy harvesting, backscatter communications, low power computing, and ultra-low power receivers. Motivated by this, this article provides an in-depth review of the existing literature on the newly emerging ZEDs for future networks. We further identify different relevant use cases and provide an extensive overview on the key enabling technologies and their requirements for realizing ZED-empowered networks. Finally, we discuss potential future research directions and challenges that are envisioned to enhance the performance and efficiency of ZED-based networks.

The South African rail industry is seeking a critical measure to monitor and save energy usage in the freight rail sector. The rail industry is experiencing increasing operational costs and high energy consuming driving records  along various coal lines. Eco-driving is a modern and efficient way of driving that emphasizes fuel efficiency, speed, and safety. This study provides an algorithm to find the optimal trajectory for a freight train hauling a load over a specific distance. Optimised speed profile is composed of optimal acceleration, coasting, and deceleration. The Freight Eco-Driving Energy Optimiser (FEDEO) solution is not yet applied to freight trains globally, especially in Africa. In this study, the eco-driving strategy of a freight train is formulated as an optimisation problem, whose objective function is the energy cost. The decision variables are the tractive and braking effort notches, and the speed, acceleration and distance limits are formulated as constraints. The formulated eco-driving problem is solved by Mixed-Integer Non-Linear Programming (MINLP) from the Opti-Solver toolbox. The FEDEO algorithm is applied to a train consisting of eight 19E locomotives with two-hundred CCR-9 wagons, over a distance of 90.64 km. The results show up to 34.76% reduction in energy costs.

Background: Energy and matter is two basic forms of existence in a universe. So, there are two types of interaction in a universe. One is about between energies which takes a form of heat, and the other is about energy and matter which takes a form of work. As of the discussion between energies, interaction could be described. On the other hand, interaction between energy and matter takes another subject. In this paper, symmetrical identitcalness would be discussed between these two forms - heat and work. Methods: By showing mathematical identity of principle between heat and work, law of interaction between energy and matter could be described. Using category theory, way of expression pattern for addressing any reality would be addressed. By using dimensional metric for logical expression, logical structure would be in an expression of number in real number. By exemplifying constructing and deconstructing logical structure by basic matrix decomposition methods, explanation for transforming dimensionality by factorizing matrix would be introduced. And identity between heat and work by this mechanism would be shown. Results: Methods of decomposing matrix could be explained as a basic way of operator that could be adapted to transforming dimensionality. And this transformation gives justification of notation for heat and work. Conclusions: It could be natural to say there is a universal expression that is consists of numerical ordinals and cardinals. And both heat and work are identical as an expression form of energy and matter. Nuclear fusion could be introduced by simulating universe.

Deep convolutional neural networks (CNNs) generate intensive inter-layer data during inference, which results in substantial on-chip memory size and off-chip bandwidth. To solve the memory constraint, this paper proposes an accelerator adopted with a compression technique that can reduce the inter-layer data by removing both intra- and inter-channel redundant information. Principal component analysis (PCA) is utilized in the compression process to concentrate inter-channel information. The spatial differences, truncation, and reconfigurable bit-width coding are implemented inside every feature map to eliminate the intra-channel data redundancy. Moreover, a particular data arrangement is introduced to enhance data continuity to optimize PCA analysis and improve compression performance. A CNN accelerator with the proposed compression technique is designed to support the on-the-fly compression process by pipelining the reconstruction, CNN computation, and compression operation. The prototype accelerator is implemented using 28-nm CMOS technology. It achieves 819.2GOPS peak throughput and 3.75TOPS/W energy efficiency with 218.5mW. Experiments show that the proposed compression technique achieves a compression ratio of 21.5%~43.0% (8-bit mode) and 9.8%~19.3% (16-bit mode) on state-of-the-art CNNs with a negligible accuracy loss.

Flexible characteristics, tunable parameters, and improved power density are the desired attributes that have led to widespread research on active capacitors and inductors (ACI) in power conversion systems. However, the two-terminal realisation of ACI is challenging due to different trade-offs between power density, device stress, and reliability of the added components. Moreover, the requirement of handling second-harmonic ripple makes bulk electrolytic capacitors nearly indispensable in two-terminal ACIs. Thus, the scope of power density improvement becomes constrained. Hence, active capacitors requiring minimum or no bulk dc-link capacitance are seen as an attractive technology in recent literatures. However, they can mimic either inductive or capacitive behaviour with a single converter unit, restricting their adaptability across different applications. This limitation is addressed in this work with the proposed unified active capacitor and inductor (UACI), realised without any bulk dc capacitor. It emulates variable capacitance and inductance while offering a smooth transition from one characteristic to another. High effective impedance due to a small susceptance at transition duty prevents undesirable current overshoot during the transition. A laboratory-scale prototype of the proposed ACI is fabricated, and the hardware is verified up to +/-450 VAR.