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Digital processing of electroencephalography (EEG) signals has now been popularly used in a wide variety of applications such as seizure detection/prediction, motor imagery classification, mental task classification, emotion classification, sleep state classification, and drug effects diagnosis. With the large number of EEG channels acquired, it has become apparent that efficient channel selection algorithms are needed with varying importance from one application to another. The main purpose of the channel selection process is threefold: (i) to reduce the computational complexity of any processing task performed on EEG signals by selecting the relevant channels and hence extracting the features of major importance, (ii) to reduce the amount of overfitting that may arise due to the utilization of unnecessary channels, for the purpose of improving the performance, and (iii) to reduce the setup time in some applications. Signal processing tools such as time-domain analysis, power spectral estimation, and wavelet transform have been used for feature extraction and hence for channel selection in most of channel selection algorithms. In addition, different evaluation approaches such as filtering, wrapper, embedded, hybrid, and human-based techniques have been widely used for the evaluation of the selected subset of channels. In this paper, we survey the recent developments in the field of EEG channel selection methods along with their applications and classify these methods according to the evaluation approach.

This paper presents a patient-specific epileptic seizure predication method relying on the common spatial pattern- (CSP-) based feature extraction of scalp electroencephalogram (sEEG) signals. Multichannel EEG signals are traced and segmented into overlapping segments for both preictal and interictal intervals. The features extracted using CSP are used for training a linear discriminant analysis classifier, which is then employed in the testing phase. A leave-one-out cross-validation strategy is adopted in the experiments. The experimental results for seizure prediction obtained from the records of 24 patients from the CHB-MIT database reveal that the proposed predictor can achieve an average sensitivity of 0.89, an average false prediction rate of 0.39, and an average prediction time of 68.71 minutes using a 120-minute prediction horizon.

Modern home automation systems include features that enhance security, such as cameras and radars. This paper proposes an innovative home security system that can detect burglars by analyzing acoustic signals and instantly notifying the authorized person(s). The system architecture incorporates the concept of the Internet of Things (IoT), resulting in a network and a user-friendly system. The proposed system uses an adaptive detection algorithm, namely the “short-time-average through long-time-average” algorithm. The proposed algorithm is implemented by an IoT device (Arduino Duo) to detect people’s acoustical activities for the purpose of home/office security. The performance of the proposed system is evaluated using 10 acoustic signals representing actual events and background noise. The acoustic signals were generated by the sounds of keys shaking, the falling of a small object, the shrinking of a plastic bag, speaking, footsteps, etc. The effects of different algorithms’ parameters on the performance of the proposed system have been thoroughly investigated.

Ahead-of-time forecasting of the output power of power plants is essential for the stability of the electricity grid and ensuring uninterrupted service. However, forecasting renewable energy sources is difficult due to the chaotic behavior of natural energy sources. This paper presents a new approach to estimate short-term solar irradiance from sky images. The proposed algorithm extracts features from sky images and use learning-based techniques to estimate the solar irradiance. The performance of proposed machine learning (ML) algorithm is evaluated using two publicly available datasets of sky images. The datasets contain over 350,000 images for an interval of 16 years, from 2004 to 2020, with the corresponding global horizontal irradiance (GHI) of each image as the ground truth. Compared to the state-of-the-art computationally heavy algorithms proposed in the literature, our approach achieves competitive results with much less computational complexity for both nowcasting and forecasting up to 4 h ahead of time.

This paper demonstrates an intruder detection system using a strain-based optical fiber Bragg grating (FBG), machine learning (ML), and adaptive thresholding to classify the intruder as no intruder, intruder, or wind at low levels of signal-to-noise ratio. We demonstrate the intruder detection system using a portion of a real fence manufactured and installed around one of the engineering college’s gardens at King Saud University. The experimental results show that adaptive thresholding can help improve the performance of machine learning classifiers, such as linear discriminant analysis (LDA) or logistic regression algorithms in identifying an intruder’s existence at low optical signal-to-noise ratio (OSNR) scenarios. The proposed method can achieve an average accuracy of 99.17% when the OSNR level is <0.5 dB.

A Rotman lens is a wideband true-time delay device. Due to its simplistic structure with wave/signal routing capabilities, it has been widely utilized as a beamforming device in numerous communication systems. Since the basic Rotman lens design incorporates multiple input, output, and dummy ports, in this study, and for the first time, we utilized a Rotman lens as a sensor. The main idea was to gather abundant information from available Rotman lens ports to obtain better sensing performance. The realized lens is optimized to work in the millimeter wave (mmW) band from 27 to 29 GHz with a focus on a central frequency of 28 GHz. The design has a footprint of 140 × 103 × 0.8 mm3. The polarity correlator was used to characterize the material under investigation.

Terahertz (THz) frequencies, spanning from 0.1 to 1 THz, are poised to play a pivotal role in the development of future 6G wireless communication systems. These systems aim to utilize photonic technologies to enable ultra-high data rates—on the order of terabits per second—while maintaining low latency and high efficiency. In this work, we present a novel photonic method for generating sub-THz vector signals within the THz band, employing a semiconductor optical amplifier (SOA) and phase modulator (PM) to create an optical frequency comb, combined with in-phase and quadrature (IQ) modulation techniques. We demonstrate, both through simulation and experimental setup, the generation and successful transmission of a 0.1 THz vector. The process involves driving the PM with a 12.5 GHz radio frequency signal to produce the optical comb; then, heterodyne beating in a uni-traveling carrier photodiode (UTC-PD) generates the 0.1 THz radio frequency signal. This signal is transmitted over distances of up to 30 km using single-mode fiber. The resulting 0.1 THz electrical vector signal, modulated with quadrature phase shift keying (QPSK), achieves a bit error ratio (BER) below the hard-decision forward error correction (HD-FEC) threshold of 3.8 × 10−3. To the best of our knowledge, this is the first experimental demonstration of a 0.1 THz photonic vector THz wave based on an SOA and a simple PM-driven optical frequency comb.

This paper exploits for the first time the use of machine learning (ML) based techniques to identify complex structured light patterns under free space optics (FSO) jamming attacks for secure FSO-based applications. Five M-ary modulation schemes, construed using Laguerre and Hermite Gaussian (LG and HG) mode families, were used in this investigation. These include 8-ary LG, 8-ary superposition-LG, 16-ary HG, 16-ary LG and superposition-LG, and 32-ary LG and superposition-LG and HG formats. The work was conducted using experimental demonstrations for two different jammer positions. The convolutional neural network (CNN)-based ML method was utilized to differentiate between the stressed mode patterns. The experimental results show a 100% recognition accuracy for 8-ary LG, 8-ary superposition-LG, and 16-ary HG at 1, −2, and −2 dB signal-to-jammer ratios (SJR), respectively. For SJR values < 0 dB, the standard LG modes are the most affected by jamming and are not recommended for data transmission in such an environment. Besides, the accuracy of determining the jammer direction of arrival was investigated using CNN and a simpler classifier based on linear discriminant analysis (LDA). The results show that advanced networks (e.g., CNN) are required to achieve reliable performance of 100% direction determination accuracy, at −5 dB SJR, as opposed to 97%, at 2 dB SJR, for a simple LDA classifier.

This paper presents two efficient frameworks for seizure detection and prediction that depend on statistical analysis. The common thread between them is the selection of certain attributes extracted from the electroencephalography (EEG) signals and the derivation of probability density functions (PDFs) of these attributes in two different types of activities of EEG signals. The first framework is for seizure detection based on scale-invariant feature transform (SIFT). Its idea is to choose some segments for normal and seizure activities. These segments are transformed to 2D matrices to be treated with the well-known SIFT. The feature key-points are extracted from those 2D matrices. The parameter that is used to discriminate between normal and seizure activities is the number of key-points. The PDFs of the number of key-points in cases of normal and seizure activities are estimated and a threshold value is used to classify any new segment as either a seizure or a normal segment. The other framework is for seizure prediction. The discrimination in this case is between normal and pre-ictal activities. A statistical treatment is performed on five attributes extracted from the wavelet transforms of different EEG segments for normal and pre-ictal activities. These attributes are amplitude, local mean, local variance, median and derivative. All PDFs of these attributes are estimated for normal and pre-ictal activities. Thresholds are set for all attributes. Decisions are taken based on these thresholds. Finally, a majority-voting strategy is used to merge decisions taken for all attributes.

We present a 4 × 4 slot-coupled Vivaldi antenna (SCVA) array unit cell, which offers wide bandwidth and high gain (~23 dBi) at the millimeter wave (mmW) frequencies of 28 GHz and 38 GHz. A single SCVA element is first presented, which has a bandwidth of 25–40 GHz with an average gain of ~13 dBi at the frequencies of interest. This antenna element is then used to design a 1 × 4 linear SCVA array matched to a 50 Ω impedance via a modified Wilkinson power divider (WPD). Next, the 1 × 4 linear array is used to construct a 4 × 4 antenna array unit cell. The proposed 4 × 4 antenna array unit cell is fabricated, and the characteristics of its elements (i.e., the single SCVA, 1 × 4 linear array, and WPD) are thoroughly investigated. Further, the 4 × 4 array is tested for signal reception of various digital modulation formats at lab environment using high-speed digital signal oscilloscope. In particular, a 2.5 Gbps data rate is successfully transmitted achieving receiver sensitivity of −50 dBm at 2 × 10−3 bit error rate (BER) for 32 quadrature amplitude modulation (QAM) with a system baud rate of 500 MHz. The wide bandwidth and high gain along with the excellent performance of the proposed 4 × 4 antenna array unit cell makes it an excellent candidate for future 5G wireless communication applications.