A novel method is proposed for spectrally  shaping the beamforming weights quantization error at the  sub-array layer where the in-band distortion intends to  move into a position where the sub-array factor has high  attenuation. To do that, the sub-array factor or the  composite sub-array factor is tiled by the periodicity of the  ultimate-layer array factor. Then, the digital filter layout is  designed based on the superposition of all tiles intersecting  with the visible region of the overall array factor (O-AF),  and the O-AF is spectrally shaped to minimize the number  of bits quantifying the beamforming weights. The method is  investigated for analog, digital, and hybrid array  beamforming with multi-layer overlapped sub-array  systems of different sizes and shapes. Its performance is  promising and potent in alleviating the overall array factor  distortion in all cases.

The growing influence of digitalization and the widespread availability of data from electrical systems have unlocked significant potential for data-driven analysis. This has led to substantial opportunities for enhancing power system performance, reliability, and overall operational efficiency. One concrete illustration of these data-driven analyses involves assessing and predicting harmonic distortion using statistical learning models, notably linear regression. The implementation of linear regression models may present various challenges, especially when dealing with time series data, which is the case of harmonic measurements too. One of the main issues is the presence of autocorrelation within error terms. In this situation, the conventional linear regression approach (ordinary least square) tends to lose its reliability due to the introduction of biased standard errors impacting the accuracy of the analysis. Experts in statistics have developed various methods to handle such situations, where the error terms show autocorrelation. Interestingly, these methods have been somehow ignored or perhaps sparsely discussed by power system engineers. The primary objective of this paper is to contribute to narrowing this gap. It does so by presenting the application of the Cochrane-Orcutt method in the harmonic data analysis context. The Cochrane-Orcutt method represents a well-known econometric technique used to consider serial correlations within the error terms of a linear model and can also be applied in the harmonic analysis domain. This paper is supported at first by an illustrative example based on simulated data, designed to provide a clear insight into the subject matter. Additionally, a case study with real-world measurement data is also presented, to further enhance the understanding.

As social networks continue to gain widespread popularity, an urgent requirement arises to automatically identify and detect offensive language and hate speech. While there is a wealth of research and datasets available for English in this domain, there is currently a scarcity of research and datasets focused on identifying hate speech and offensive language in Persian text. This article introduces a 3-class dataset named Pars-HaO, consisting of 8013 tweets, to fill the gap in existing research. We collected the dataset by combining comments from pages that are more exposed to hate speech and using a keyword-based approach. Three annotators then labeled the tweets. In this study, we employed a combination of the Convolutional Neural Network (CNN) model and four widely recognized machine learning models, namely Support Vector Machine (SVM) and Logistic Regression (LR), Random Forest (RF), and Decision Tree (DT) as a baseline. Then, we compared the base models with Long Short-Term Memory(LSTM), Bidirectional LSTM (BiLSTM), and CNN models, each trained using the output of the last hidden state of Bidirectional Encoder Representations from Transformers (BERT). Experimental results on the Pars HaO dataset demonstrated that the BERT with BiLSTM technique yielded the best outcome, achieving a macro F1-score of 70%. Â

This study delineates the convergence of blockchain technology with the Internet of Things (IoT), offering a critical analysis of the current state-of-the-art, prevailing challenges, and potential future directions. Initially, a comprehensive overview of the existing literature is provided, highlighting the progressive integration of blockchain with IoT networks. Subsequently, a meticulous evaluation of the present challenges, including security concerns and scalability issues, is conducted. Furthermore, this research explores the promising prospects of blockchain-enabled IoT systems, emphasizing innovations in data security, privacy, and decentralized control. Through a systematic approach, this investigation not only underscores the significant advancements in the field but also proposes potential strategies to overcome identified hurdles. The findings of this study aspire to pave the way for the development of more secure, scalable, and efficient blockchain-based IoT networks, fostering a new era of technological synergy.

Microsoft Kinect camera can capture depth images of the subject during surveillance of Human Activity Recognition (HAR) and subsequently obtain the skeletal data. Several studies have attempted to use and analyse human actions based on skeletal data and other complex feature representation extraction methods. Most authors have proposed obtaining Spatio-temporal information as one of the extraction methods. Therefore, this study automatically extracts the Spatio-temporal information from the skeletal data by using an Imaging time series (ITS) method called Recurrence Plots (RP) to transform the skeleton joint coordinates into 2D images. The raw data are preprocessed and partitioned into three-channel matrices (R, G, B) before applying the principal component analysis (PCA). The generated RP images are used as input to Convolutional Neural Network (CNN) to distinguish between different activities. The proposed method uses the UTD-MHAD dataset for benchmarking and shows that our approach outperforms previous studies with a maximum accuracy of 92.6%.

Media-based modulation (MBM) is a novel technique for embedding information in the channel states via intentional perturbations of the transmission media. This article provides an overview of MBM and its benefits while highlighting relevant challenges and future research directions. We explain how MBM differs from source-based modulation and how it addresses issues in legacy multiple-input multipleoutput (MIMO) systems, such as deep fades and MIMO diversity-multiplexing trade-off. We demonstrate how MBM works in harmony with other index modulations and improves upon them by providing similar advantages with a more compact transmitter. Numerical results (simulation and analytical) support these claims and include outage comparison with legacy MIMO systems, comparisons with other state-of-the-art modulation schemes, and a performance example showcasing transmitting 32 bits of information in a single channel use with an excellent symbol error rate of SER ~ 1e-5  at “energy per bit to noise power spectral density ratio” of Eb=N0 ~ 3:5 dB. The article continues with methods to address the issues of receiver training and decoding for large constellation sets. A number of other research questions, such as pulse shaping to limit bandwidth expansion due to the time-varying nature of MBM and the effect of forward error correcting codes on MBM diversity order are discussed. We present an RF transceiver structure that generates independent propagation paths for embedding information. Fabrication and testing of the transceiver structure show close agreement between simulation and measurement. There are inherent connections between MBM and intelligent reflecting surface (IRS). These connections, including the application of MBM in beamforming, are discussed. We present a solution that involves the integration of a filtering radiating patch within the MBM walls to restrict bandwidth expansion. Lastly, we delve into several specific application domains for MBM.

The reliability and accuracy of detecting faults in induction motors are crucial for reducing maintenance costs, increasing safety, and improving efficiency in industrial applications. This paper proposes a method to enhance the reliability and accuracy of detecting short-circuit faults (SCF) in three-phase induction motors (IMs) by fusing data with the Extended Kalman Filter (EKF). The method addresses the challenge of selecting appropriate variances of measurement noise and process noise affecting EKF convergence by proposing a new algorithm for updating the process noise covariance based on EKF convergence conditions in decentralized data integration (DI). The proposed algorithm ensures convergence and improves fault detection accuracy, as demonstrated by comparing it to using only one estimator. This method shows promise in improving the reliability and accuracy of fault detection in induction motors, leading to significant benefits in industrial applications

5G NR V2X has been designed to support advanced connected and automated driving V2X services. These services are  characterized by variable traffic patterns that can generate packet  collisions in decentralized systems where vehicles autonomously  select their radio resources like 5G NR V2X mode 2. 5G NR V2X  introduces a re-evaluation mechanism at the MAC layer to detect and avoid possible packet collisions before a vehicle transmits in  selected resources. Most of the studies conducted to date on 5G NR  V2X do not consider the re-evaluation mechanism despite being a  mandatory MAC feature. This paper advances the state of the art  with an in-depth analysis and evaluation of the operation and performance of re-evaluation in 5G NR V2X mode 2 under different traffic patterns and mode 2 configurations. The study shows that re-evaluation is effective in avoiding collisions with  periodic traffic but its effectiveness decreases with aperiodic  traffic and of variable size. The study also shows that re-evaluation  is effective in avoiding collisions generated by the retransmission  of packets. However, its overall impact on the performance of 5G  NR V2X mode 2 is small, while it can have a relevant  implementation cost due to the frequent re-evaluation checks and resource reselections. This raises questions on the current design of the re-evaluation mechanism that is a mandatory feature in 5G NR V2X mode 2.

Reliance on commercial virtual private networks has increased manifold in the recent past. This paradigm shift of global internet users is primarily driven by their need of online privacy, censorship circumvention, and accessing geo-filtered content among other motivations. Variety of VPN services underscored by their opaque nature captures huge user base which is technically unsophisticated, and has very limited means to verify claims of a given VPN service. Capitalizing on extensive literature and online review, we have selected a sample of 60 x VPNs (from categories of free as well as paid services) which adequately represents the commercial VPN market from cost-quality perspective. An elaborate eco-system analysis reveals significant variations against the claims of service providers. Our crystallized infrastructure testing methodology executed over selected VPNs reveals: (1) Anonymity index of free versus paid VPNs differs widely at all levels i.e., IP, subnet, country and ISP; (2) 54% of selected VPN’s server locations in attractive geographic locations are virtual instead of physical; (3) 4/10 VPN tested services have been found to share their vantage points at subnet level; and (4) Selective co-location of 22% vantage points have also been observed.

MIMO radars with electromagnetic vector sensor (EMVS) antennas (also known as EMVS-MIMO radars) have been investigated intensively for target direction estimation in recent years. Many existing works on this topic are based on an unrealistic assumption that each EMVS antenna transmits six identically polarized and mutually orthogonal waveforms. Furthermore, target-fluctuation induced non-coherent backscattering in polarization has been entirely overlooked. This paper aims to develop a general transmit-receive signal model for EMVS-MIMO radar direction estimation. Specifically, by taking transmit polarization agility and/or polarization non-coherent backscattering into account, the collected pulse signals are no longer of complete polarization, but of incomplete polarization. By regarding an incomplete polarized (IP) signal as two incoherent completely polarized (CP) signals with the same azimuth-elevation directions and drawing upon the idea of ESPRIT, a new azimuth-elevation direction estimation algorithm is thus derived. The advantage of the new algorithm is that it can provide closed-form, automatically paired azimuth-elevation direction estimates and require no information on the location/displacement of the transmit-receive antennas. Finally, simulations are conducted to demonstrate the effectiveness of the proposed algorithm.

In this paper, by taking advantage of physical unclonable functions (PUFs), we introduce a novel design that provides physical security to CRYSTALS-Dilithium. After discussing the advantages of our design compared to the original design, we implemented it on two different architectures, ARMv7 and ARMv8. Our findings demonstrate substantial enhancements in both security and performance over the reference work.

This study investigates the feasibility of using Raspberry Pi-equipped Unmanned Aerial Vehicles (UAVs) for object detection. The research focuses on analyzing the impact of the distance between the UAV and the target person on the accuracy of object detection. Experimental results reveal a decrease in detection accuracy as the distance between the UAV and the target person increases, emphasizing the critical role of distance in efficient object detection systems. By combining drones, Raspberry Pi, and machine learning algorithms, this research showcases the potential of advanced object detection systems. The findings contribute to the growing application of UAV technology by refining and optimizing UAV-based object detection systems.

Preprint submitted to IOP Journal of Neural Engineering Objective. Individuals with Amyotrophic lateral sclerosis (ALS) progressively lose muscle functionality and therefore experience both an increased need for assistive robot technologies and a reduced ability to control such robots. While these individuals may use high-performing control systems, such as tongue control, at the beginning of their disease progression, they will eventually be restricted to a lower-performing control system, such as brain control. However, an adaptive multimodal control interface framework consisting of combinations of tongue control and noninvasive brain control can utilize the residual tongue functionality to optimize the control performance throughout the disease progression. Approach. To investigate this concept, a new adaptive tongue-brain multimodal control framework for manual and continuous control of a 7-degree-of-freedom robot arm is developed, based on a prior validation study. The new framework focuses on improved visual feedback, as individuals with ALS specifically requested this in a previous validation study, and consists of four subsystems: the first uses full tongue control; the second and third use hybrid tongue and noninvasive brain control, with a decreasing need for tongue functionality; and the fourth uses noninvasive brain control only. The framework was evaluated with three participants with ALS. Main results. All participants were succesful with all subsystems. One user could no longer efficiently use the full tongue control interface but achieved good results with the third and fourth subsystems. The second participant achieved significantly better results with the subsystems that included some tongue control, thereby showing the advantage of including some tongue control. The third participant performed well with all subsystems showing the ideal performance progression between each subsystem. Moreover, all participants, including the two with good tongue control, chose a multimodal control interface as their favorite. Significance. The results indicate that individuals with ALS prefer interfaces that combine multiple control modalities.

A robust approach for cyber-security of industrial control systems (ICSs) that utilize programmable logic controllers (PLCs) to control critical industrial processes is demonstrated in this paper. For example, industrial water/chemical tank-based system, the liquid level sensor measurement output data may be compromised or manipulated by attackers, this can cause tank overflow, unregulated, or even malfunction. We proposed a general-purpose method called Dynamic Watermarking (DW) to secure ICSs. The basic idea of DW is that it adds a private random signal watermark on the control signal from the controller, then this watermark signal propagates through the plant and adequately converted then comes back to the attack detector. The attack detector at the actuator side can detect any malicious activity in the system in real time. The proposed method is experimentally tested and validated with several cyber?attack scenarios on a laboratory scale water tank level control system controlled by an Allen-Bradley Micro820 PLC.

With the passage of time, the ability of intelligent software in making predictions of seemingly random, chaotic events increases. Most real-world uses of multi-layer perceptrons are in highly chaotic environments. Such environments include the prediction of economic trends and weather phenomena to the modelling of human behaviour. This study aims to analyse the power of Multi-Layer Perceptrons (MLPs) in predicting the outcome of sporting events, in particular, a football (soccer) game. Any sporting event is dominated by chaos, where small changes in the environment can drastically affect the outcome. This study does the same by predicting, based on multi-dimensional data, the point return of English Premier League (EPL) players in the official Fantasy Premier League forum (FPL). The study uses MLPs as a means to optimise FPL team selection and thus predicts an average point return for the team through the summation of point average predictions for each player individually. This is then compared with the actual FPL gameweek return of the selected team and evaluated. The study found that an MLP can predict the return of each player with an RMSE of approximately 2 points. The study shows the extent of the capability of MLPs in finding ordered patterns in chaotic events.

High penetration of renewable energy resources is widely connected to the grid via grid-following converters, which may threaten the transient voltage stability of the system. However, most researchers only pay attention to the transient voltage stability of inductive motors in the electromechanical transient time scale, and the electromagnetic dynamics of voltage source converters (VSCs) are ignored for simplicity. Therefore, the potential transient voltage instability risk of the system in the electromagnetic transient time scale is concealed. To fill this gap, the transient voltage instability mechanisms for grid-following VSCs with various control strategies are revealed, which can be concluded that the positive-feedback process after the state variable exceeds the unstable equilibrium point is the main cause. Then, the effect of the current limitation of VSCs on the transient voltage stability and transient performance of the system is theoretically illustrated. On this basis, the indexes for transient voltage stability margin considering the active power recovery process are presented, and a robust current limitation based stability enhancement scheme is proposed to stabilize the system. Simulation results verify the correctness of the theoretical analysis and the effectiveness of the proposed control strategies.

This paper presents a logical modeling of a multi-process monitoring system that uses a formal model to verify the correctness and efficiency of the system. The system consists of multiple processes that run in parallel and communicate with each other through private channels. The system uses an accelerated kernel interpreter to execute the connection models on different subsets of the multi-process data. The paper describes the main operations and functions of the system, such as allocating, setting up, switching, and running the multi-process connections. The paper also provides several assertions that check the validity of the parameters and the results of the system. The paper demonstrates how the logical modeling of the multi-process monitoring system can improve the reliability and performance of the system.

A pdf of an article submitted to IEEE Circuits and Systems. A method of describing a constant phase element with the SPICE family of circuit simulators.