This article presents a robust model predictive control (R-MPC)-based scheduling approach for electric vehicle (EV) charging service at a station with power supply from a photovoltaic (PV) system and grid. The designed algorithm aims to maximize the operational revenue while handling high power loads in the grid, intermittent solar power supply, charging demands uncertainties, and online computational burdens. To this end, the scheduling problem is formulated as a mixed-integer nonlinear program (MINLP) with solar power generation and charging demands models embedded. The time-of-use price is employed for cost calculation. The resulting optimization problem then can be further simplified as a mixed-integer linear program (MILP). The proposed R-MPC is evaluated in the IEEE-13 node test feeder with simulated power load profiles and real charging demands data for demonstrations. The results show that R-MPC achieves near-optimal profit and meets all charging requests even under high uncertainties.

Orthogonal frequency-division multiplexing (OFDM) is used in third and fourth generation and is being planned for fifth-generation (5G) systems. OFDM requires that the received subcarriers be orthogonal. However, various factors, such as residual carrier frequency offset due to Doppler shift in mobile fading channels, can lead to a loss in orthogonality between subcarriers, which results in intersymbol interference (ISI), intercarrier interference (ICI), and bit error rate (BER) degradation. In developing the conjugate cancelation (CC) scheme for mitigating these ISI and ICI of OFDM systems, we integrate the CC scheme together with a space-time (ST) system and form an STCC multiple-input-multiple-output (MIMO) scheme. This STCC system improves the BER without expending power or bandwidth or complexity. Additionally, a Walsh-Hadamard (WH) transform is employed as a precoder for this STCC system and forms a novel WHSTCC scheme. It provides a low peak-to-average power ratio (PAPR) at the transmitter and a significant diversity gain at the receiver. Simulations indicate that both STCC and WHSTCC schemes outperform the regular CC and ST systems in mobile fading channels. Both schemes are simple and are backward compatible, and they can serve as the baseband building block for reconfigurable 5G multiuser MIMO systems.

Carrier frequency offset, time variations due to Doppler shift or phase noise lead to a loss in the orthogonality between subcarriers of Orthogonal frequency division multiplexing (OFDM) systems and results in inter-carrier interference (ICI). Further developing the parallel cancellation (PC) scheme to mitigate the ICI of OFDM systems, we investigate the characteristics of this two-branch OFDM symbol-based PC scheme in details. Moreover, with known channel state information, we show that this PC scheme is the same as the optimal maximal ratio combining (MRC) technique for transmitter diversity, but with ICI mitigation capability. Additionally, we integrate this PC scheme into a space-time (ST) coded system to form a simple space-time parallel cancellation (STPC) scheme. This STPC scheme not only inherits advantages of the conventional PC scheme, such as backward compatibility with the existing OFDM systems, low receiver complexity, and two-branch diversity, but also provides better bit error rate (BER) performance with lower error floor, especially in slow and fast frequency selective fading channels at a high signal-noise-ratio (SNR) without increasing computational load.

This paper presents the feature extraction, selection and K-Nearest Neighbors (K-NN) algorithm to classify behaviors of sharks based on the data collected by tri-axial acceleration data loggers (ADLs). Because these behaviors are hard to observe in the wild and do not occur frequently, being able to adequately classify them is extremely challenging. In the proposed scheme, we first employ several transformations to enrich the static and dynamic acceleration data. Then, the enhanced data is converted from time to the frequency domain through the fast Fourier transform (FFT) for noise removal. A modified K-NN approach integrated with feature selection is developed and conducted on the frequency domain data to improve the F1-score for minority classes. The acceleration data of California horn sharks (Heterodontus francisci) gathered through ADLs mounted on the first dorsal fin is used to demonstrate our algorithm. A comparison study shows that the features extracted and selected by our proposed scheme can significantly improve the performance of classification on the imbalanced dataset.

Because of serious challenges such as air pollution, global warming, and fossil fuels limitations, renewable energy sources such as photovoltaic (PV) systems are increasingly being integrated into the power systems around the world. In this article, a new grid-tied system is proposed for PV applications which consists of an improved flyback DC-DC converter and a new switched-capacitor (SC) based multilevel inverter. Over previously introduced topologies in the same class, the proposed SC-based multilevel inverter has many advantages like reduced number of power switches and DC voltage sources, and also increased number of voltage levels produced at the output. In the proposed structure, one of the most important problems of the capacitive switching inverters, i.e. the occurrence of the impact currents during charging capacitors, is solved by using an inductor with a parallel diode in the capacitive charging current path. This increases the efficiency of the converter and reduces the charging current stress on the capacitors. To validate the performance of the proposed topology, comprehensive experiments and comparisons have been performed and presented. In the experiments, a current controller is used to control the amount of active and reactive power injected to the grid by the proposed grid-tied 17-levels inverter.