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Identifying tiny objects with extremely low resolution is generally considered a very challenging task even for human vision, due to limited information presented inside the object areas. There have been very limited attempts in recent years to deal with low-resolution recognition. The existing solutions rely on either generating super-resolution images or learning multi-scale features. However, their performance improvement becomes very limited, especially when the resolution becomes very low. In this paper, we propose a Representation Learning Generative Adversarial Network (RL-GAN) to generate super image representation that is optimized for recognition. Our solution deals with the classical vision task of object recognition in the distance. We evaluate our idea on the challenging task of low-resolution object recognition. Comparison of experimental results conducted on public and our newly created WIDER-SHIP datasets demonstrate the effectiveness of our RL-GAN, which improves the classification results significantly, with 10-15% gain on average, compared with benchmark solutions.

Big data analytics (BDA) is a systematic approach for analyzing and identifying different patterns, relations, and trends within a large volume of data. In this paper, we apply BDA to criminal data where exploratory data analysis is conducted for visualization and trends prediction. Several the state-of-the-art data mining and deep learning techniques are used. Following statistical analysis and visualization, some interesting facts and patterns are discovered from criminal data in San Francisco, Chicago, and Philadelphia. The predictive results show that the Prophet model and Keras stateful LSTM perform better than neural network models, where the optimal size of the training data is found to be three years. These promising outcomes will benefit for police departments and law enforcement organizations to better understand crime issues and provide insights that will enable them to track activities, predict the likelihood of incidents, effectively deploy resources and optimize the decision making process.

In this paper, a space-time line code (STLC) is combined with offset spatial modulation (OSM) for reaping both their advantages toward efficient multiple-input multiple-output (MIMO) transmission. Specifically, the spatial diversity offered by STLC increases the resilience against fading channels, while the simplified radio frequency (RF) switching processing merits of OSM inherently are preserved. As a further advancement in the pursuit of enhancing the performance of index detection, angle rotation is conceived and optimized for various transmit conditions. Subsequently, based on the approximation of the Q-function, an upper bound for the average bit error rate (BER) of STLC-OSM is derived in the context of an equispaced (ES) angle arrangement. Eventually, we demonstrate that STLC-OSM achieves considerable BER performance while achieving a unique single RF structure with a low implementation cost. Simulation results confirm the tightness of the derived BER bound and the advantages of the proposed scheme.

Many graph construction methods for clustering cannot consider both local and global data structures in the construction of initial graph. Meanwhile, redundant features or even outliers and data with important characteristics are addressed equally in the graph optimization process. These lead to the learned representation graph may not capture the optimal structure when clustering. This paper proposes a novel model for clustering, named adaptive graph construction and low-rank representation of weighted noise (ACLWN), to overcome these problems. ACLWN is composed of an adaptive representation graph construction model named ARG, and an adaptive weighted sparse representation graph learning model named AWSG. In ARG, manifold learning and sparse representation are employed to capture the local structure of data. In AWSG, an adaptive weighted matrix is proposed to strengthen the important features and improve the robustness of the low-dimensional representation graph. Moreover, constraints such as non-negative low-rank, sparsity and distance regularization terms are imposed to capture the local and global structures of data. Comprehensive experimental results show that our method outperforms the compared state-of-the-art methods. The low-dimensional representation graph constructed by ACLWN is more suitable for clustering.

Orthogonal Time Frequency Space (OTFS) as a two-dimensional modulation scheme designed in the delay-Doppler domain, is realized by inverse symplectic finite Fourier transform (ISFFT) and symplectic finite Fourier transform (SFFT), for combating the high Doppler channels toward future wireless communications. Meanwhile, generalized spatial modulation (GSM) offers an efficient implementation for multi-input multi-output (MIMO) systems, by activating only part of the transmit antennas to alleviate inter-channel interference (ICI). In this paper, the combination of the above two concepts is exploited, for bridging their unique advantages. On the other hand, an iterative detector based on generalized approximate message passing (GAMP) is also developed for GSM-OTFS. Simulation results demonstrate that the proposed GAMP detector can achieve better performance than the conventional minimum mean square error (MMSE) detector.

The increasing amount of network data and the development of big data technology have brought development opportunities and challenges to recommendation systems. Model-based collaborative filtering has become a mainstream algorithm in recommendation systems. For example, the Funk singular- value decomposition (FunkSVD) algorithm. However, in big data calculations, data sparseness and iterative oscillations often affect the accuracy of the FunkSVD algorithm. Moreover, when the data volume is in units of GB or more, the FunkSVD algorithm runs slowly and is ineffective. Therefore, we propose an improved FunkSVD algorithm (IFABG) based on the root-mean-square prop (RMSProp) and graphics processing unit (GPU) to solve this problem. First, we used RMSProp algorithm to improve the traditional FunkSVD algorithm, alleviate data sparseness and iterative shock, and improve the prediction accuracy of the algorithm. Next, we implemented parallelization of the improved FunkSVD algorithm in the GPU, which increased the calculation speed of the algorithm. Finally, we verify the IFABG algorithm using the Movielens dataset. The experimental results show that the IFABG algorithm is very suitable for processing sparse data, and it alleviates the iterative shock. The prediction accuracy rate is approximately 30% higher than that of the traditional FunkSVD algorithm. The experimental results also demonstrate that the IFABG algorithm has a good acceleration effect. For the same size dataset, the IFABG algorithm is faster than the traditional FunkSVD, and the acceleration ratio can be as high as 19.27.

Advanced wireless communications to support high vehicular speeds in the scenario of intelligent mobility have attracted considerable research attentions in both academia and industry. Multi-dimensional index modulation, which exploits transmission media resources for combining the concept of index modulation to achieve higher throughput with lower energy consumption, is regarded as a potential candidate for addressing the challenges in intelligent mobility communications. However, for the multi-dimensional index modulation system used in the mobile environment, the inter-carrier-interference (ICI) imposes a significant adverse impact on the system, especially in the context of multicarrier transmission. Therefore, by focusing on the two-dimensional index modulation system, the ICI impact on space-frequency index modulation is studied in this paper. Thanks to the grouping strategies and the Gaussian approximation to model the ICI, the approximate theoretical bounds for the error performance of the two dimensional systems are derived. Moreover, a novel robust space-frequency index modulation scheme is also developed. Finally, simulation results are presented to validate the system performances and the theoretical derivations.

In this paper, we investigate the impact of imperfect angle estimation (IAE) on spatial and directional modulation (SDM) systems, assuming that the signal experiences line of sight (LoS) propagation. In SDM systems with IAE, the variation is analyzed in detail, when there is a mismatch between the beamforming and precise channel matrices. Based on the union bound and statistics theory, the average bit error probabilities (ABEPs) for both the legitimate user and eavesdropper are derived. In addition, the ergodic rate is also quantified with IAE. Simulation results are presented to show that the achieved theoretical ABEPs are useful in quantifying the potential performance penalty. We also show that the mismatch between the beamforming and precise channel matrices will become greater with the increase in direction measurement error (DME), which affects the detection for both the legitimate user and eavesdropper. On the other hand, due to the effect of IAE, the SDM requires more signal-to-noise ratio (SNR) gain to achieve a stable ergodic secrecy rate.

Because traditional single-channel speech enhancement algorithms are sensitive to the environment and perform poorly, a speech enhancement algorithm based on attention-gated long short-term memory (LSTM) is proposed. To simulate human auditory perceptual characteristics, the algorithm divides the frequency band according to the Bark scale. Based on these bands, bark frequency cepstral coefficients (BFCCs), their derivative features and pitch-based features are extracted. Furthermore, considering that different noises have different influence on the clean speech, the attention mechanism is applied to screen out the information less polluted by noise, which is helpful to reconstruct the clean speech. To adaptively reallocate the power ratio of the speech and noise during the construction of the ratio mask, the ideal ratio mask (IRM) with the inter-channel correlation (ICC) is adopted as the learning target. In addition, to improve the performance of the network, the algorithm introduces a multiobjective learning strategy to jointly optimize the networks by using a voice activity detector (VAD). Subjective and objective experiments show that the proposed algorithm outperforms other baseline algorithms. In real-time experiment, the proposed algorithm maintains high real-time performance and fast convergence speed.

Recently, a class of novel spatial modulation (SM) schemes, termed offset spatial modulation (OSM), were developed to reduce the radio frequency (RF) switching frequency, in the context of multiple-input single-output (MISO) transmission. In this paper, the structure of original OSM is extended to adapt multiple-input multiple-output (MIMO) systems by introducing multiple receive antennas (MRA). The detailed system design is presented and further developed for offset space shift keying (OSSK). Furthermore, the theoretical bounds of bit-error rate (BER) performance for the proposed OSM-MRA and OSSK-MRA systems are both quantified. It is demonstrated that the proposed OSM-MRA and OSSK-MRA systems outperform their counterparts including conventional OSM and OSSK with improved spectral efficiency.

Extreme disasters may cause the power supply to the distribution system (DS) to be interrupted. The DS is forced to operate in island mode and forms an islanded microgrid (MG). In order to improve the post-disaster resilience of the DS and to provide longer power supply for as many loads as possible with limited generation resources, this paper proposes a multi-agent deep reinforcement learning (DRL) method which realizes a dual control on the source and load sides of the MG. The problem of resilience improvement is converted to a sequential decision making problem, where the objective is to maximize the cumulative MG utility value over the power outage duration. A multi-agent DRL model is proposed to solve the sequential decision making problem. A dual control policy including energy storage management and load shedding strategy is put forward to maximize the utility value of the MG. A reinforcement learning (RL) environment based on OpenAI and OpenDSS for islanded MG is constructed as a simulator, which has a general interface compatible with, and also can be published to, OpenAI Gym. Numerical simulations are performed for an MG equipped with wind turbines, diesel generators, and storage devices to validate the effectiveness of the proposed method. The influences of available generation resources and power outage duration on the control policy are discussed, which validates the strong adaptability of the proposed method in different conditions.

In this paper, a pair of clipping interference cancellation detectors based on symbol feedback and soft information are proposed for the generalized space-time-frequency index modulation (GSTFIM) systems. Specifically, the clipping interference is reconstructed by the prior information of the GSFTFIM symbol in these two detectors, then an iterative algorithm is developed for elimination and compensation of the clipping interference. The difference of these two proposed detectors lies in that the symbol feedback based interference cancellation (SFIC) detector completes the iteration through the hard decision symbol, while the soft information based interference cancellation (SIIC) detector iterates by the mean and variance of the estimated symbols. Simulation results show that the proposed two detectors can obtain flexible tradeoff between performance and complexity, compared to the traditional maximum likelihood (ML) detector with clipping interference.

In this work, heat treatment was performed on three retired 110 kV AC cables with service years of 0, 15 and 30, and the effects on the thermal and electrical performance of the cable insulation were investigated. First, each cable with a length approximately of 5 m was prepared and cut into five equal segments. Four segments of each cable were annealed at a temperature of 90, 95, 100, or 105 °C by building a small circuit to simulate cable operation. Then, a section at the middle of each segment was cut out, and the insulation layer was peeled away. The peeled-off layers from the inner, middle and outer positions were selected as the test samples. Subsequently, Fourier transform infrared spectrometry (FTIR), and differential scanning calorimetry (DSC) were performed, and the DC conduction current and dielectric breakdown strength were measured. The best thermal properties of the highest melting point, crystallinity, and smallest melting range were found when the cables were annealed at 100, 105, and 105 °C for the inner, middle, and outer positions, respectively. The highest dielectric breakdown strength and lowest electrical conductivity were found at temperatures of 95 or 100 °C for the inner and middle positions, respectively, and at 105 °C for the outer position. The FTIR results showed that thermal annealing for hundreds of hours did not adversely affect the molecular chains. It is found that this type of heat treatment could be a feasible method to rejuvenate retired cables, and a conservative temperature of 95 °C is regarded as the optimum annealing temperature.

In this paper, a novel framework to efficiently design the waveforms toward heterogeneous networks (HetNets) is proposed. The proposed scheme combines the index modulation (IM) concept with multiple-input multiple-output (MIMO) and orthogonal frequency-division multiplexing (OFDM) techniques and jointly considers the resources on both spatial and frequency domains to optimize the transmission signals. Furthermore, a generalized union bound expression is derived first to disclose the potential diversity gain for the spatial-frequency index modulation (SFIM) system and based on the developed bound different impacts of activating spatial and frequency domains resources for transmission are analyzed. Following the theoretical insights, two novel transmit schemes, termed as enhanced SFIM with non-overlap (ESFIM-NO) and enhanced SFIM with overlap (ESFIM-O), toward higher diversity gains are proposed. We show that the proposed methods can significantly improve the diversity of both index and amplitude phase modulation (APM) domains for achieving higher reliability for the entire HetNets. Finally, the simulation results are given to verify the theoretical analysis and system error performance.

Further research on retired cable rejuvenation via heat treatment was investigated in this paper. Three retired cables with service year of 7, 15 and 30 years were used and annealed at different temperatures following two different heat treatment methods. In the first test, two short cables with a length of 5 m were prepared and cut into five equal segments. Four segments of each cable were annealed at 90, 95, 100, and 105 °C to simulate the cable operation. Then, the cross-linked polyethylene (XLPE) from the cable insulation was peeled, and the peels from the inner, middle and outer positions were selected and subjected to the differential scanning calorimetry (DSC), DC conduction current, and dielectric breakdown strength measurements. The thermal and electrical performance of these samples were analyzed, and the optimum annealing temperature was determined. In the second test, three identical long cables were connected to a transformer, joints, and terminals to build two experimental circuits. The cables were annealed at the optimum temperature determined in the first test. The same measurements were performed on XLPE samples taken from the same positions as those in the first test. The results showed that the short cables exhibited the best thermal and electrical performance when annealed at 95 °C. The three long cables annealed at 95 °C verified that the thermal and electrical properties of the cable insulation were significantly improved. The results of the two different annealing methods, which closely represent the actual cable operations, verified the feasibility that old cables without overheating history could be rejuvenated via heat treatment.

In this paper, we consider unmanned aerial vehicle (UAV)-enabled two-way relaying communications between two robot swarms in the absence of communication infrastructures in remote areas or post-disaster rescues. To be more specific, UAV is employed as the relay to expand the communication range between two disconnected ground robot swarms, due to its high maneuverability and flexible deployment. Meanwhile, the two-way relaying mode adopted can improve the transmission performance in terms of delay and throughput in comparison to the conventional one-way relaying, owing to the ability of allowing devices to exchange information simultaneously. In addition, the UAV's trajectory and power allocation are jointly optimized to maximize the sum-rate of the uplink and downlink, where the joint optimization problem is decoupled into two sub-problems to address the non-convexity. Limited to the non-convex formation of the objective function for the trajectory optimization sub-problem, we firstly handle the non-convexity based on successive convex approximation method, and then alternating optimization framework is carried out to obtain the joint suboptimal solution. Numerical results exhibit significant throughput gains of the proposed scheme as compared to other benchmark schemes.

To improve the generalization ability of speech enhancement algorithms for unlabeled noisy speech, a speech enhancement transfer learning model based on the feature-attention multi-kernel maximum mean discrepancy (FA-MK-MMD) is proposed. To obtain a representation of the shared subspace (the part related with clean speech in the feature extracted by shared encoder) between source domain (speech with known noise and labels) and target domain (speech with unknown noise and no labels), the algorithm takes MK-MMD as loss function for reducing distribution differences between these two domains, which could improve the adaptability to the unknown noise. Furthermore, considering that different noise have different influence on the representation of shared subspace, the attention mechanism is applied to feature dimension to screen out the information less polluted by noise, which is helpful for reconstructing the clean speech. In the term of speech with unknown noise and no labels, the experiments demonstrate that the proposed algorithm has improved the frequency-weighed segmental signal-to-noise ratio (fwsegSNR), the perceptual evaluation of the speech quality (PESQ) and the short time objective intelligibility (STOI) compared with the baseline algorithm.

In this paper, a novel offset-based space-frequency index modulation (OSFIM) design is developed, for the sake of improving the bit error ratio (BER) performance. The proposed OSFIM scheme utilizes the channel state information (CSI) to precode the transmit signals so as to activate the optimal space-frequency resource units for offset-aided index mapping. Furthermore, aiming at enlarging the minimum Euclidean distance of the received signal, we further propose a modified scheme termed as enhanced OSFIM (E-OSFIM). Through theoretical analysis, the analytical BER upper bound for E-OSFIM is also quantified. Finally, simulation results show that, compared with conventional SFIM and offset SM-OFDM (OSM-OFDM), the proposed schemes can significantly improve the BER performance when the freedom of spatial degrees is large, while the proposed E-OSFIM scheme outperform its counterparts in terms of the BER performance.

Due to the broadcast nature of millimeter wave (mmWave) communications, physical layer security has always been a fundamental but challenging concern. Fortunately, the recent advance of intelligent reflecting surface (IRS) introduces another dimension for mmWave secure communications by reconfiguring the transmission environments. In this article, we investigate the physical layer security issue for IRS-aided mmWave communications. Specifically, two scenarios are considered, i.e., with and without the knowledge of the eavesdropper's channel. When the eavesdropper's channel is known, under the unit modulus constraints at the IRS and the transmit power constraint at the access point (AP), the secrecy rate is maximized by jointly designing the beamforming vector of the AP and the phase shifts of the IRS. We propose an alternating optimization algorithm for improving the secrecy rate to solve the non-convex optimization problem. In particular, the phase shift matrix of the IRS is firstly optimized based on the manifold optimization algorithm. Under the given phase shift matrix of the IRS, the original problem is transformed into a difference of convex (DC) programming problem. To solve this problem, we use the successive convex approximation (SCA)-based method to transform the original problem into a convex approximation problem, and the transmit beamforming vector is obtained by solving the convex approximation problem. In addition, when the eavesdropper's channel is unknown, under the transmit power constraint and the minimum user rate constraint, an artificial-noise (AN)-aided scheme is proposed to jam the eavesdropper by maximizing the AN power. Numerical results evaluate that the performance of our proposed schemes is better than that of the conventional scheme in terms of secrecy rate. Moreover, simulations also demonstrate that the secrecy behaviors of the IRS-aided mmWave communication system are superior to the mmWave communications without IRS.

The optimization of the secrecy rate of the distributed intelligent reflecting surfaces (IRSs)-assisted millimeter wave (mmWave) communication system is studied in this paper. In this system, the users are served by multiple IRSs, and by adjusting the switch state of each IRS and optimizing the phase shift of the IRS to maximize the secrecy rate of the system. The problem is described as a joint IRS control, transmit beamforming design problem. Our objective is to maximize the secrecy rate of the distributed IRSs-aided system under the transmit power constraint of the access point (AP). Since the problem is non-convex, one alternating optimization algorithm is proposed for solving this problem. We use the successive convex approximation (SCA) algorithm to solve it, and the solution that is obtained by the SCA algorithm guarantees to converge to a stable point that satisfies the Karush-Kuhn-Tucker (KKT) conditions. Moreover, by using the dual method, we obtain the IRS switch. For the IRS phase shifts optimization problem, a low-complexity majorization-minimization (MM) algorithm is used to solve this problem. Numerical results show that the proposed scheme can achieve higher secrecy rate compared with the single IRS-aided scheme.

In this article, we investigate the recently developed space time line code (STLC) scheme, which utilizes the channel state information (CSI) for encoding at the transmiter to achieve full spatial diversity gain. The theoretical analysis of the bit error rate (BER) performance for STLC is first evaluated. Moreover, aiming at alleviating the cost of radio frequency (RF) chains, antenna selection at both the transmitter and receiver sides is developed by minimizing the derived instantaneous BER. Furthermore, in the context of a flat Rayleigh fading channel, we derive an upper bound for quantifying the BER performance of STLC with both transmit and receive antenna selection. Finally, simulation results validate the accuracy of the above theoretical analysis results and show the performance improvement of the developed antenna selection schemes.

This paper reveals a correlation between morphology and thermal parameters on cross-linked polyethylene (XLPE) cable with different insulating states. Several cables were selected to detect the physicochemical and thermal parameters of the XLPE. The results show that the cable ampacity is determined by the thermal parameters, which are deeply subjected to the morphology of the XLPE. The molecular chain and crystal structure of the XLPE have a close connection with the thermal resistivity. The physicochemical parameters of carbonyl index (CI) and unsaturated band index (UBI) from Fourier transform infrared spectrum (FTIR) and melting range (Rm) from differential scanning calorimetry (DSC) can be the indicator to evaluate the diversity of the thermal resistivity. The change of thermal capacity is governed by the crystal distribution of the XLPE. The physicochemical parameters of crystallinity (χ) and lamellar thickness (L) from DSC can be the indicator to evaluate the change of the thermal capacity. In addition, FWHM of the crystallization peak ΔW, crystalline rate index (T0-TP) and cross-linking degree (G) can also be the indicator of the thermal parameters. Finally, this paper proposes a more accurate on-line monitoring method for electric power industry by detecting thermal parameters to diagnose the operating cables in the practical application.

Millimeter-wave (mm-wave) communication has been widely applied in the small cellular networks, and it is not difficult to find that mm-wave plays an important role in improving the capacity and data transmission rate of the 5G network systems. Nevertheless, mm-waves also have some disadvantages, such as large propagation loss and weak penetration blockage. Although the extremely high direction of the mm-wave can compensate for the propagation loss, highly directional transmissions of the mm-wave result in conventional omnidirectional transmission cell discovery schemes may fail in mm-waves; thus, we need to redesign the cell discovery scheme of mm-wave. In addition, the overhead of mm-wave cell discovery is also large. In order to reduce the overhead and provide a reasonable solution for the mm-wave base station (BS) discovery, we propose an analysis framework that utilizes out-of-band (OOB) information assistance. First, we consider the BS discovery problem as a hypothesis test problem. We analyze the statistical properties of the generalized logarithm likelihood ratio detector and study the detection performance of the detector. Through analysis and calculation, we are able to find that the OOB scheme can not only improve the detection performance but also reduce the system overhead. Then, we also analyze the performance of the OOB scheme in the case of multiple UEs. Finally, we use the angle information provided by the OOB information to predesign the mm-wave codebooks to achieve the optimal expected mode. Analysis of the simulation results demonstrates the effectiveness of the OOB information-assisted scheme.

Through virus-based reconnaissances, the attacker may compromise critical power substations and induce power outages. Meanwhile, the power system operator may conduct defensive measures to prevent virus-spreading. In this paper, a graphical evolutionary game model is developed to solve the equilibrium of competitions between virus propagations and countermeasures, which helps to evaluate the cyber-security risk of power systems under long-term virus-based intrusions. In the proposed game model, the virus is supposed to propagate itself in a cyber network connecting cyber nodes belonging to power substations. Each cyber node may play as an attacker or a defender according to its state. A death-birth rule is adopted to model probabilistic strategies and state transfers, which reflects the bounded rationality of the viruses. Then, an evolution dynamic equation is derived to obtain the portion of the infected substations when the equilibrium is reached, and simulation algorithm is also given to estimate the infection probability of each substation under the persistent virus-based intrusion. Assuming a virus may trip all breakers in the infected substation, the cyber-security risk is evaluated to identify system-level vulnerabilities. The IEEE 118-bus system with a layered cyber network is used for case studies. Test results validate the proposed model and provide further insights into the virus-based intrusion.

Searchable symmetric encryption (SSE) has been applied in constructing secure cloud storage for its simplicity, high-efficiency, and practicality. Recent works show that forward privacy has become a fundamental property required for SSE, and several forward secure dynamic SSE schemes supporting single-keyword search have been proposed. However, to the best of our knowledge, there is no forward secure searchable encryption scheme supporting conjunctive-keyword search. In this paper, we propose two forward secure SSE schemes with conjunctive-keyword search functionality. Technically, in our basic scheme, we introduce a bloom filter on the client side to check the existence of keywords in a file. This also makes it easy to delete a file in locality rather than on the server side. In order to avoid the false positive probability of the bloom filter and reduce the redundant search results, we further propose a construction by applying secret-key inner-product encryption. Both of our schemes achieve sub-linear efficiency and one-round communication in the search protocol. Moreover, our methods are generic, as they can be applied to any forward secure single-keyword searchable encryption scheme to obtain a conjunctive-keyword one.

In this paper, we investigate a class of new multiple-input multiple-output (MIMO) transmission schemes, dubbed space time line code (STLC), in the context of different mobility situations. We extend its original structure into space and frequency domains and propose a novel frequency line code (SFLC) for combining with orthogonal frequency division multiplexing (OFDM) in order to achieve potential diversity gains in time-varying multipath fading channels. Specifically, at the transmitter, the STLC/SFLC-OFDM scheme exploits the channel state information (CSI) to linearly encode successive modulated symbols in the time/frequency domain to achieve full diversity gain. At the receiver, it can retrieve the transmit symbols without full CSI, thanks to its special structure. We analyse the impact of time-variant channels to STLC and SFLC to show the expected performance degradation in practical channel, especially in the case of different mobile speeds. Our analysis and simulations show that the BER performance degrades with increasing correlation coefficient, which makes STLC-OFDM more robust in channels with abundant multipath spread, and SFLC-OFDM more robust in channels with high mobility speed. Meanwhile, a guideline is provided for switching when the communication environment changes. Finally, we carry out the practical implementation of STLC-OFDM and SFLC-OFDM schemes and characterize their performance with both computer simulations and an experimental testbed.

Automotive camera modules are critical components for vision, detection, and recognition in an advanced driver assistance system. They usually consist of housing, a lens, a lens holder, a harness, seals, and printed circuit boards, which are assembled through screws, adhesive, and connectors. Due to a small and complex geometry of the components, the component material failure is a critical issue. In this paper, the lens holder material failure is investigated using the root cause analysis and experimental validation. First, a fishbone diagram model is created to analyze the potential causes and an action checklist is employed to identify the root cause. Second, the structure of the lens holder is optimized according to the finite-element analysis result. The numerical results show that the strength of the optimized lens holder is improved by nearly 35%. Finally, the torque test is carried out to validate the optimized structure experimentally. The presented analysis and validation procedure can be used to solve material failure issues for small-scale automotive components effectively.

Improving the operational efficiency of wind turbines is an important way to enhance the competitiveness of wind power generation among new energy sources. This paper presents a new method for online monitoring and optimization of the operational efficiency of wind turbines. The randomness of the wind speed is analyzed in the form of wind speed variation using historical data, revealing the distribution pattern of wind speed randomness. The fluctuation amplitude of the output power of wind turbines is analyzed in the form of power variation under different wind speed modes. The level of active power is classified by quantiles based on historical data. The sliding window method is adopted to eliminate the jumping fluctuation of the efficiency level caused by the randomness of the wind speed. To identify and explain the reasons for the deviation of the operating efficiency, the benchmark values of operational parameters are obtained from historical operation data by data mining. The low operating efficiency level is adjusted according to the benchmark values of operating parameters. The model has been verified to effectively monitor the operating efficiency of wind turbines and provide operational optimization measures to improve efficiency.

Distributed state estimation (DSE) is effective for the analysis of multi-region state estimation problems, avoiding integrating complicated data sets and models. In this work, the peer-to-peer decomposition of the Karush-Kuhn-Tucker (KKT) condition is applied to formulate the fully distributed fast decoupled state estimation (FD2SE) algorithm. Then, a fixed-matrix form data-exchanging interface incorporating the fast decoupled algorithm is designed, reducing the communication costs and allowing it to be used for various system operating conditions. Further, a convergence detecting method for dispatch centers is proposed in order to infer, locally and asynchronously, about the global convergence. Case studies carried out on IEEE systems and a real power grid of China verify the accuracy and efficiency of the FD2SE algorithm.

In this paper, the performance of several annealing methods on three retired cables and the annealing effects on the improvement in the thermal and electrical properties of cross-linked polyethylene (XLPE) insulation were discussed. The cable insulation layer was peeled, and the peels near the inner semi-conductive layer were used as the test samples. Isothermal treatment and heat recycling treatment were performed at temperatures of 85, 90, 95, and 100 °C, and the temperatures were held in the heat recycling treatment for 8, 16, and 24 h, respectively. Each heat recycling treatment was repeated 20 times, and the duration of the isothermal treatment was the same as that for the heat recycling treatment with a 24 h temperature holding hour. Then, Fourier transform infrared spectroscopy (FTIR) and differential scanning calorimetry (DSC) were performed, and the dielectric spectrum, DC conduction current, and dielectric breakdown strength EB were measured. The results showed that damage involving molecular changes is linearly related to the cable service year. As the annealing temperature increases, the melting range and electrical conductivity decrease; the melting point, crystallinity, lamellar thickness, and dielectric breakdown strength increase; and the optimal values appear for the samples annealed at 95 °C. With an increased temperature holding hour, the peels annealed at 95 °C exhibit a decreased melting range and electrical conductivity and an increased crystallinity, melting point, lamellar thickness, and dielectric breakdown strength. As a result, the two different treatments are verified to effectively improve the thermal and electrical properties for the XLPE as early research on cable rejuvenation by heat treatment.

Directional modulation (DM) refers to a class of efficient techniques for enhancing the physical layer security of wireless communications. However, conventional DM approaches cannot guarantee the transmission security when eavesdroppers are located close to the legitimate user. In this paper, to mitigate this problem, a new DM scheme with cooperative receivers (DM-CR) is proposed for secrecy communications. Specifically, the legitimate user employs multiple cooperative receivers, and a predefined distortion factor set is introduced to the beamforming vector. The proposed DM-CR scheme is able to guarantee transmission security regardless of the direction or location of the eavesdroppers. Furthermore, the average bit error probability of the proposed DM-CR scheme is investigated. Both analytical and simulation results are presented to show that the proposed DM-CR scheme significantly outperforms the benchmark in terms of the bit error rate, so as to provide secure transmission toward future wireless communications.

To acquire precise channel state information in the forthcoming 5G frequency division duplexing (FDD) communication system, we study the virtual angular domain channel common sparsity in the massive multiple-input multiple-output orthogonal frequency division multiplexing downlink transmission system. Here, we propose an off-line learning algorithm, which is based on expectation maximization and generalized approximate message passing (GAMP) algorithms. Besides, the learning algorithm utilizes the common sparsity to obtain the support information of the sparse channel vectors. This process is defined as zero partition. With this learned information, the sparse channel vectors are estimated using GAMP again. We prove that when the learned support information is correct, the zero partition-enhanced GAMP algorithm will achieve better sparsity and indeterminacy trade-off performance when compared with the original GAMP algorithm. Based on these results, our proposed channel estimation (CE) solution has two stages. In the first stage, because of the practical propagation characteristics, the zero partition learning algorithm is amended to ensure the correctness of the learned result. In the second stage, channel vectors on the virtual angular domain are estimated with the proposed zero partition-enhanced GAMP algorithm. Analysis and numerical results prove that our CE solution will consume less pilots to ensure the estimation accuracy, when compared with other existed FDD downlink CE solution.

Prefix-based multicarrier transmission is in wide application in current wireless communications, at the expense of extra redundancy and reduced spectral efficiency. In this contribution, we propose a novel hybrid prefix (HP) structure, which can offer higher spectral efficiency for orthogonal frequency division multiplexing (OFDM) based future wireless communications, such as 5G and beyond systems. Compared with the conventional prefix aided OFDM scheme, the proposed HP-OFDM scheme is capable of reducing the average length of the prefix with the aid of a proposed low-complexity detector for efficient signal restoration. We also quantify the signal to interference plus noise and the achievable rate of the proposed HP-OFDM scheme through a theoretical analysis. Furthermore, the bit-error rate (BER) of the proposed HP-OFDM is also theoretically derived. Our scheme results demonstrate that HP-OFDM is capable of reducing the prefix overhead by 50% compared with prefix-aided OFDM, while achieving a BER comparable to that of prefix-aided OFDM.

Precoding aided spatial modulation (PSM) constitutes a flexible closed-loop multiple input multiple output transmission scheme, which benefits from both low cost and low complexity at the receiver side. In PSM, a single receive antenna is activated and a number of bits are conveyed by the index of the receiving antenna, in addition to the bits conveyed by a conventional amplitude-phase modulation symbol. In this paper, bit error rate (BER) of the PSM orthogonal frequency division multiplex (PSM-OFDM) system is investigated and the optimal power allocation between the data and the pilot symbols by minimizing the upper bound for the BER with imperfect channel estimation is derived. Furthermore, we prove that the proposed optimal power allocation scheme can be applied to all generalized linear interpolation techniques with the minimum mean square error channel estimation. Simulation results demonstrate that the proposed optimal power allocation scheme provides significant improvements in terms of the average BER performance for the PSM-OFDM system compared with its equal-power-allocation counterpart.

Deploying charging stations (CSs) catering to the demand from electric vehicles plays an important role in modernizing energy infrastructures. In this paper, a bi-level optimal allocation model for allocating fast CSs is proposed aiming to maximize the CS investor benefit (upper layer sub-problem) by optimally allocating CSs with the coordinated determination of the expected efficiency of charging service supply (lower layer sub-problem). The efficiency is formulated as a charging performance index in terms of user satisfaction degree, which mathematically couples the upper level and lower level subproblems. The proposed nonlinear bi-level model is reduced to a single-layer optimization model under the Karush-Kuhn-Tucker optimality conditions. An improved dynamic differential evolution algorithm with an adaptive update strategy is proposed to solve this single-layer optimization model. The proposed method is validated using a realistic case. The test results show that the proposed methodology is able to co-ordinate both objectives of interest, i.e., allocating CSs network with the maximized benefits and optimizing the fast charging service efficiency at the same time.

Generalized spatial modulation (GSM), where both the transmit antenna combination (TAC) index and the amplitude phase modulation (APM) symbols convey information, is a novel low-complexity and high efficiency multiple-input multiple-output (MIMO) technique. In conventional GSM (C-GSM), the legitimate TACs are selected randomly to transmit the APM symbols. However, the number of the TACs must be a power of two, hence the excess TACs are discarded, resulting in wasting some resource. To address these issues, in this paper, an optimal TAC set-aided enhanced GSM (E-GSM) scheme is proposed, where the optimal TAC set is selected with the aid of the channel state information (CSI) by maximizing the minimum Euclidean distance. Furthermore, a hybrid mapping GSM (HM-GSM) scheme operating without CSI knowledge is investigated, where the TAC selection and bit-to-TAC mapping are both taken into consideration for optimizing the average Hamming distance. Finally, an enhanced high throughput GSM (E-HT-GSM) scheme is developed, which makes full use of all the TACs. This scheme is capable of achieving an extra one bit transmission rate per time slot. Moreover, the rotated phase is employed and optimized for the reused TACs. Our simulation results show that the proposed E-GSM system and HM-GSM system are capable of outperforming the C-GSM system. Furthermore, the E-HT-GSM system is capable of obtaining one extra bit transmission rate per time slot compared with the C-GSM system.

Index modulation (IM) techniques appear as competitive candidates for next-generation (5G and beyond) wireless networks due to the attractive advantages they offer in terms of spectral and energy efficiency as well as hardware simplicity. IM is a highly spectrum- and energy-efficient yet simple digital modulation technique, which utilizes the indices of the building blocks of the corresponding communication systems to convey additional information bits. IM systems provide alternative ways to transmit information in contrast to traditional digital modulation schemes that rely on the modulation of the amplitude/phase/frequency of a sinusoidal carrier signal for transmission, as widely considered in the field of communications over the past 50 years. Radically, IM schemes have the ability to map information bits by altering the on/off status of their transmission entities such as transmit antennas, subcarriers, radio frequency (RF) mirrors, transmit light emitting diodes, relays, modulation types, time slots, precoder matrices, dispersion matrices, spreading codes, signal powers, loads and so on. In other words, IM creates completely new dimensions for data transmission. Since the initial skepticism of both academia and industry on the potential and applicability of IM technologies has now gone away, we strongly believe that IM is not another simple digital modulation alternative, but rather can be a game-changing communication paradigm whose time has come!

The false data injection (FDI) attack is a potential threat to the security of smart grids, and therefore, such threats should be assessed carefully. This paper proposes a self-governing FDI attack method with exploitation and exploration mechanisms and then evaluates its threat to power systems. The attack is executed by viruses embedded in monitoring systems in substations. First, the FDI attack is formulated as a partially observable Markov decision process. Then, an improved online reinforcement learning method called nearest sequence memory Q-learning is adopted to make the attack more effective. Finally, propagation, an inherent property of viruses, is described using a propagation-evolution model that serves as the exploration mechanism for the proposed FDI attack. To validate the proposed attack method, cosimulations of daily operations of the IEEE 39-bus system are performed in which both the automatic voltage control system and the proposed FDI attack are modeled. Test results show that the proposed FDI method can cause voltage collapse even if only a few substations are infected.

Pre-coding aided spatial modulation (PSM) constitutes a flexible closed-loop multiple-input multiple-output transmission scheme, which attracts substantial research interest recently. In this paper, an upper bound of the average bit error probability (ABEP) for the PSM system based on the minimum mean square error (MMSE) pre-coding is investigated. In addition, a theoretical analysis is given based on the assumption that the numbers of transmit and receive antennas are large but their ratio θ is constrained. Furthermore, the theoretical analysis shows that the ABEP upper bound of the PSM system based on MMSE pre-coding depends only on ratio θ. Simulation results demonstrate that the upper bound is asymptotically tight as the signal-to-noise ratio increases, and the proposed system outperforms its benchmark that is based on zero-forcing pre-coding.

In this paper, a novel unified power allocation (PA) framework is proposed for receive (pre-coding aided) spatial modulation (RSM). We find that the PA matrix design can be formulated as a non-convex quadratically constrained quadratic program (QCQP) problem, whose solution is generally intractable. To tackle this problem, we propose a pair of solvers having different trade-offs in terms of bit-error-rate and complexity. Specifically, we first propose a successive convex approximation (SCA) method, to convert the non-convex QCQP problem under consideration into a series of linear convex subproblems, where the latter can be easily solved by the classic polynomial-time-based optimization method, i.e., the interior point method. To further reduce the computational complexity, we propose an augmented Lagrangian multiplier (ALM) method, which transforms the challenging non-convex constrained PA optimization problem into its unconstrained counterpart, which can be efficiently solved by an iterative manner. Our simulation results show that both the proposed SCA and ALM methods are capable of substantially improving the system error performance compared with the conventional RSM system without PA as well as conventional PA-aided RSM schemes.

Millimeter-wave (mm-wave) communication is a practicable scheme for big data communication, such as next-generation cellular communication. However, because mm-wave frequencies have an extremely large path loss, in order to mitigate the mm-wave path loss, a large number of antennas are packed for beamforming. Conventional multiple-input multiple-output (MIMO) beamforming uses digital processing, which leads to needing extremely giant energy. Hybrid analog/digital beamforming could serve as an awesome technique to reduce the cost. It is essential for achieving high beamfroming gain that accurate mm-wave channel information should be attained at the transmitter. This paper concentrates on the channel state information (CSI) acquirement problem in mm-wave communication systems with massive MIMO arrays. Because of the CSI acquirement, a method of significant overhead, we consider an accurate channel estimation scheme with low overhead. Not only do we in this paper propose using support information extracted at Sub-6GHz to aid the mm-wave CSI acquirement but also introduce a strategy used in the mm-wave channel estimation. We, in particular, formulate mm-wave CSI acquirement as a compressive sensing problem and obtain the CSI by using Bernoulli-Gaussian generalized approximate message passing (BG-GAMP) algorithm. We also extend the BG-GAMP algorithm with support distribution information from sub-6-GHz channel. Furthermore, based on the K nearest neighbor idea, we redesign the BG-GAMP algorithm depending on sub-6-GHz support distribution information. Details of BG-GAMP with the nearest neighbor learning algorithm that is built on support distribution information from sub-6-GHz channel would also be revealed. Simulation results show the out-of-band information aided mm-wave channel estimation is capable of reducing the pilot overhead greatly and channel estimation accuracy improved as well.

Despite the widespread use of multi-physiological parameters for deception detection, they have been severely restricted due to the high degree of cooperation in contacting-detection. Therefore, a noncontacting method is proposed for deception detection using acoustic features as an input and convolutional bidirectional long short-term memory (LSTM) as a classifier. The algorithm extracts frame-level acoustic features whose dimension dynamically varies with the length of speech, in order to preserve the temporal information in the original speech. Bidirectional LSTM was applied to match temporal features with variable dimension in order to learn the context dependences in speech. Furthermore, the convolution operation replaces multiplication in the traditional LSTM, which is used to excavate time-frequency mixed data. The average accuracy of the experiment on Columbia-SRI-Colorado corpus reaches 70.3%, which is better than the previous works with non-contacting modes.

What is index modulation (IM)? This is an interesting question that we have started to hear more and more frequently over the past few years. The aim of this paper is to answer this question in a comprehensive manner by covering not only the basic principles and emerging variants of IM, but also reviewing the most recent as well as promising advances in this field toward the application scenarios foreseen in next-generation wireless networks. More specifically, we investigate three forms of IM: spatial modulation, channel modulation and orthogonal frequency division multiplexing (OFDM) with IM, which consider the transmit antennas of a multiple-input multiple-output system, the radio frequency mirrors (parasitic elements) mounted at a transmit antenna and the subcarriers of an OFDM system for IM techniques, respectively. We present the up-to-date advances in these three promising frontiers and discuss possible future research directions for IM-based schemes toward low-complexity, spectrum- and energy-efficient next-generation wireless networks.

Electrification of transportation has drawn increasing attention for the sake of low-pollution emission and high-fuel economy. Scientific scale forecasting of electric vehicles (EVs) is fundamental to promote EVs' integration for transportation and electric power industries. To capture the evolution pattern, a system dynamics (SD) approach is proposed to simulate and forecast the scale of the EVs. The proposed SD model can integrate various factors and quantify their relationship by comprehensive reasoning. Causal loop diagrams are designed to describe the relationship between factors and variables, and their quantifications are formulated by different business models and surveys. The main procedure for simulation includes survey, problem analysis, variable definition, feedback analysis, and model building. The effectiveness of the SD approach is verified by case studies. Furthermore, sensitivity analysis of the key factors, such as fuel price, subsidy policy, and so on, is also conducted.