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One important aspect of radar systems is the transmit waveform, which plays a key role in defining system’s detection capability and target resolution. Waveforms with good autocorrelations and increased bandwidth are preferred for this purpose. However, waveforms with large bandwidths may cause spectral interference with neighboring channels. As a result, it is crucial to establish frequency stopbands within the spectrum of transmit waveform to mitigate potential interference. While it’s easy to independently design waveforms with either good autocorrelation or specific frequency stopbands, designing radar waveforms that excel in both aspects simultaneously is a difficult task. In this paper, we address this challenge by optimizing radar waveform with dual objectives: minimizing autocorrelation sidelobes to enhance system performance and managing spectral characteristics to expand bandwidth while avoiding interference with other frequency bands. We first transform the dual-objective function into a single-objective function encompassing both correlation and stopband properties. We propose a novel algorithm to solve this problem and rigorously demonstrate its convergence through mathematical proof, providing a robust foundation for practical implementation. We evaluate the algorithm’s performance in challenging scenarios and demonstrate its effectiveness compared to recent approaches in the literature.

Sub-terahertz (sub-THz) waves, operating in the 0.1-1 THz band, offer significant potential for next-generation 6G systems. These systems stand to benefit from photonic technologies that enable terabits-per-second communication with low latency and high throughput. This paper introduced, for the first time, a semiconductor optical amplifier (SOA) as a fast and reliable photonic-based technique for sub-THz wavelength conversion in fiber-based networks. The wavelength conversion leveraged nonlinear phenomena in the SOA, including cross-gain modulation, self-phase modulation, and four-wave mixing effects. Numerical simulations demonstrated an all-optical wavelength conversion capable of handling 100-GHz sub-THz signals with 16-ary quadrature amplitude modulation formats, achieving a data rate switching of 5 Gbps. Key system parameters were optimized, resulting in a 28.7 dB suppression ratio for the generated optical single sideband (OSSB) signal. Additionally, a proof-of-concept laboratory experiment validated the THz signal-switching concept. The proposed system incorporated a hybrid fiber/wireless channel and utilized various measures for performance evaluation.

In the ever-evolving landscape of modern technology, integrating communication and sensing systems has become increasingly essential for a wide range of applications, from military and defense to autonomous vehicles and beyond. The integration offers a convergence of capabilities that enhances operational efficiency and provides adaptability in complex environments. In this paper, we develop, in simulation and experiment, an integrated communication and sensing system, exploring the cutting-edge utilization of spread spectrum and radio-over-fiber (RoF) photonic technologies. RoF technology inherits the benefits of optical fibers, which include low attenuation and longer reach distance compared to other media. First, we consider the integration of communication and sensing functions using a spread spectrum–binary phase-shift keying waveform. In this integrated system, the sensing function is performed using a radar system. The performance of the proposed system is evaluated in terms of the peak-to-sidelobe ratio of the radar correlator output and the bit error rate for the communication system. The results are obtained through extensive MATLAB simulations. Next, we consider the realization of the proposed integrated communication and sensing system using photonics technology. This phase commences with the utilization of specialized photonics-based software for extensive simulations at different fiber lengths, which is an essential foundational step toward the practical implementation of the proposed system using photonics. Lab experiments are also presented to validate the simulation results.

In this paper, we present a simulation-based study for the coexistence of an International Mobile Telecommunication (IMT) system with a Digital Terrestrial Television Broadcasting (DTTB) system. The study considers the coexistence of the two systems in the sub-700 MHz band and presents interference analysis results for locations in the Kingdom of Saudi Arabia (KSA). The study includes different terrains in which each of the two systems is deployed, e.g., mountains, sea, desert, etc. Using an advanced terrain-aware simulation software (HTZ from Advanced Topographic and Digital Imaging (ATDI), the coexistence problem is studied in these cases which represent some of the most common types of terrains. The goal is to quantify the level of interference in each of the case studies and compare it with the protection ratio in terms of either the carrier-to-interference-plus noise $C/(I+N)$ or interference-to-noise $(I/N)$ ratios, depending on the simulation parameters and following the standard approach used in such studies. These two measures help in providing the proper recommendations on the coexistence of the IMT and DTTB systems. This study is structured into two main parts. The first part focuses on studying the interference induced by IMT base station (BS) and IMT user equipment (UE) on the DTTB receiver in the sub−700 MHz band (from 614 to 694 MHz) which corresponds to the 5G N71 band, whereas the second part considers the interference induced by DTTB system on IMT BS and IMT UE. The investigation has revealed that in most of those situations, separation distances (exclusion zones) are found to be relatively within a reasonable range from the border compared with the size of the whole network. This indicates that IMT network could be deployed in many locations close to the DTTB network with tolerable effect. The most challenging case is the effect of DTTB system on the up-link (UL) of IMT network, which necessitates the use of additional mitigation techniques to reduce the interference.

Structured light is gaining importance in free-space communication. Classifying spatially-structured light modes is challenging in a dusty environment because of the distortion on the propagating beams. This article addresses this challenge by proposing a deep learning convolutional autoencoder algorithm for modes denoising followed by a neural network for modes classification. The input to the classifier was set to be either the denoised image or the latent code of the convolutional autoencoder. This code is a low-dimensional representation of the inputted images. The proposed machine learning (ML) models were trained and tested using laboratory-generated mode data sets from the Laguerre and Hermite Gaussian mode bases. The results show that the two proposed approaches achieve an average classification accuracy exceeding 98%, and both are better than the classification accuracy reported recently (83–91%) in the literature.

Code division multiple access (CDMA) is a class of techniques allowing multiple users to transmit simultaneously to meet the demand of the recently unprecedented revolution in wireless services. In CDMA systems, a unique pseudo-random (PN) code is assigned to each user to spread the signal at the transmitter side and facilitate signal’s recovery at the receiver side. In particular, a CDMA receiver initially seeks synchronization or acquisition of PN code. This paper focuses on the development and analysis of a new adaptive serial search acquisition method for direct sequence (DS)-CDMA systems in Rayleigh slowly fading channels. The proposed method makes use of smart antenna and switched constant false alarm (S-CFAR) processor for PN code acquisition. The smart antenna and S-CFAR processor are employed to improve the PN acquisition performance by adjusting a decision threshold according to the environment. The performance of proposed acquisition system has been evaluated in terms of probability of false alarm, probability of detection, and mean acquisition time. Closed form expressions for the probability of false alarm and probability of detection have been derived. Further, numerical and simulation results have been presented to show the performance of proposed PN acquisition system in comparison with other existing CFAR-based PN acquisition schemes.

The problem of automatic modulation format identification (MFI) is one of the main challenges in adaptive optical systems. In this work, we investigate MFI in super-channel optical networks. The investigation is conducted by considering the classification of seven multiplexed channels of 20 Gbaud, each with six commonly used modulation formats, including polarization division multiplexing (PDM)-BPSK, PDM-QPSK, and PDM-MQAM with (M = 8, 16, 32, 64). The classification performance is assessed under different values of optical signal-to-noise ratio (OSNR) and in the presence of channel interference, channel chromatic dispersion, phase noise, and $1^{st}$ polarization mode dispersion (PMD). Furthermore, the effect of fiber nonlinearity on the MFI accuracy is investigated. A well-established machine learning algorithm based on histogram features and a convolutional neural network has been used in this investigation. Results indicate that accurate identification accuracy can be achieved within the OSNR range of practical systems and that the MFI accuracy of side subcarriers outperforms that of middle subcarriers at a fixed value of OSNR. The results also show that the MFI accuracy of PDM-16QAM and PDM-64QAM are affected more by channel interference than the other modulation formats, especially when the ratio of the subcarrier bandwidth to subcarriers spacing is $\geq$ 1.4. Finally, laboratory experiments have been conducted for validation purposes. The experimental results were found in good agreement with those achieved by simulation.

In this article, we propose, design, and numerically assess a novel parametric refractive index profile for optical fiber, which we refer to as reciprocal-inverse graded index fiber (R-IGIF). The R-IGIF features a shape parameter $\alpha$ that controls the refractive index behavior (i.e. concave vs convex). We performed a systematic scanning of parameter' effects; using finite element method in Comsol Multiphysics, in order to design R-IGIFs that handle robust orbital angular momentum (OAM) channels (i.e. low radial modes). The designed fibers possess high inter-channels separation ($\Delta n_{eff} \geq 1\times$10−4) that outperforms standard step index fiber and inverse parabolic graded index fiber; hence, reduces channels crosstalk. Numerical simulations show that the supported OAM modes are of high purity ($\geq\! 99.9\%$), enabling low-level intrinsic crosstalk ($\leq\! -30\,dB$). Further investigations considered metrics over the C and L ITU-T bands including the cut-offs wavelengths, chromatic dispersion, differential group delay, effective area, and nonlinearity coefficient. Results show potential capability to achieve stable data channels transmission. Further assessments have been carried out under practical condition (i.e. bending) by evaluating the $\Delta n_{eff}$, the intra-mode walk-offs, and the confinement loss. The findings show great resilience to bending effects. Therefore, based on these deep physical investigations of modes carrying data, the R-IGIF could find application in next generation space division multiplexing SDM-Networks.

Forecasting the output power of renewable energy sources is of an utmost importance for maintaining the stability of the electricity grid and ensuring uninterrupted service. Internet-of-Things (IoT) devices equipped with sky imagers are often used to estimate solar irradiance whose value is essential for forecasting solar energy. This paper considers distributed sky imagers whose data are transmitted through the Internet to a cloud for possible storage, forecasting, and/or performing other signal processing operations. The data comprise a collection of images, and this makes their streaming to the cloud a burden, i.e., they require a large bandwidth. In an attempt to deal with that, an IoT system is proposed in this paper such that it collects, compresses, transmits, and reconstructs those images with as little bandwidth as possible. More specifically, an IoT device multiplexes the collected images and compresses them using a deep learning algorithm, the encoder of a convolutional autoencoder (CAE) in particular. It, then, transmits the compressed images to a cloud server where they could be decompressed with high-fidelity using the decoder of the CAE. As the core to the proposed system, the performance of the CAE algorithm is evaluated using a publicly available dataset of sky images. Results indicate that the images can be compressed to 2% of their original size (i.e., compression ratio of 98%) and reconstructed again with high fidelity, i.e., average structural similarity index measure (SSIM) of 99%. Hardware implementation of the CAE algorithm has also been attempted using a Raspberry Pi platform representing the IoT device with an average processing speed of 6.6681 seconds per frame. This speed well meets practical requirements given the low frame-rate of sky imagers.

Fire detection has been an issue of interest to researchers due to its significant damage to lives and property within a very short time. One of the recent solutions developed to detect fire is to use Internet-of-Things (IoT) devices equipped with cameras for surveillance. The captured videos of surroundings may be processed by the IoT devices themselves or at the cloud. The latter case is required if the detection algorithm is computationally demanding. However, the use of cloud has a flaw. In fact, using the cloud could pose the threat of having the privacy of a place violated, either through hacking or unauthorized access to the footage of the place where the cloud is installed. In this paper, a fire detection system that preserves the privacy of surroundings, while maintaining a high level of accuracy for fire detection is proposed. The proposed system makes use of the cloud for fire detection; and that is achieved by sending to the cloud features extracted from the video captured by the IoT device, instead of sending the actual footage. Binary video descriptors and Convolutional Neural Network (CNN) have been used to develop the fire detection algorithm. The video descriptors are used to extract features, while the CNN is used for classification. Videos with real fire and non-fire scenes have been used in this development. Results show that the performance of proposed fire detection algorithm can achieve 97.5% classification accuracy, that outperforms the state-of-the art algorithms which make direct use of raw videos. Therefore, the proposed fire detector is as reliable as other available systems, with the advantage of having a privacy-preserving capability. It is also demonstrated that the proposed video descriptors can be implemented for real-time processing using an IoT device, Raspberry Pi 4 platform, with an average processing speed of 100ms per frame, which well satisfies practical needs.

Direct Detection (DD) optical performance monitoring (OPM), Modulation Format Identification (MFI), and Baud Rate Identification (BRI) are envisioned as crucial components of future-generation optical networks. They bring to optical nodes and receivers a form of adaptability and intelligent control that are not available in legacy networks. Both are critical to managing the increasing data demands and data diversity in modern and future communication networks (e.g., 5G and 6G), for which optical networks are the backbone. Machine learning (ML) has been playing a growing role in enabling the sought-after adaptability and intelligent control, and thus, many OPM, MFI, and BRI solutions are being developed with ML algorithms at their core. This paper presents a comprehensive survey of the available ML-based solutions for OPM, MFI, and BFI in non-coherent optical networks. The survey is conducted from a machine learning perspective with an eye on the following aspects: (i) what machine learning paradigms have been followed; (ii) what learning algorithms are used to develop DD solutions; and (iii) what types of DD monitoring tasks have been commonly defined and addressed. The paper surveys the most widely used features and ML-based solutions that have been considered in DD optical communication systems. This results in a few observations, insights, and lessons. It highlights some issues regarding the ML development procedure, the dataset construction and training process, and the solution benchmarking dataset. Based on those observations, the paper shares a few insights and lessons that could help guide future research.

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.

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

In this work, we propose and theoretically/numerically investigate a novel parametric refractive index profile for few mode fibers, referred to as a hyperbolic tangent few mode fiber (HTAN-FMF). To the best of our knowledge, the HTAN-FMF is adopted for the first time, to simultaneously maximize the inter-channels separation and OAM purity. The proposed FMF achieves a wide separation between cylindrical vector modes (≥3 × 10-4 between TE0,1, HE2,1, and TM0,1). This enables low-level crosstalk and clearly overcomes recently reported levels in literature. On the other hand, simulations show that the generated and evaluated OAM modes are mostly high pure (≥ 99.9%) leading to intrinsic crosstalk as minimum as -30 dB during propagation through HTAN-FMF. Other propagation performance metrics of the proposed fiber including chromatic dispersion and differential mode delay are also evaluated over the C ITU-T band showing that HTAN-FMF has the potential to achieve stable OAM modes transmission. Bending insensitivity is numerically proved showing that the proposed FMF has the potential to enhance the transmission capacity and the spectral efficiency in next generation OAM mode division Multiplexing (OAM-MDM) systems.

Filtering of unwanted signals has a great impact on the performance of EEG signal processing applied to neurological disorders diagnosis. It is so difficult to remove undesirable noises using static filtering approaches as the performance of such techniques is strongly relying on specific EEG signal sub-bands, whose locations differ from one subject to another. In this paper, we present a novel dynamic filtering approach, which makes use of Finite and Infinite Impulse Response (FIR and IIR) filters along with a Recurrent Neural Networks using a Gated-Recurrent Unit (RNN-GRU), to identify and preprocess the most informative sub-bands pertaining to a particular neurological disorder. This combination of RNN with GRU requires more hidden layers than for conventional NN structures, and therefore offers much higher capacity to learn fitting and extract features from highly complex EEG data recording to afford better harmonization of the diagnosis process. Followed by an Independent Component Analysis (ICA) algorithm, all extracted features become independent to facilitate classification of clinical disorders using Convolutional Neural Network (CNN). The proposed diagnosis system achieves an average of 100% classification accuracy for epilepsy according to an offline diagnosis process using Bonn and MIT datasets, and when the same system is applied to autism provides an average accuracy of 99.5% using KAU dataset. The presented dynamic deep-learning approach applied to EEG classification pipeline, which includes artifact removal, feature extraction and classification, leads to significant improvements in the accuracy of the diagnosis classification regarding the targeted neurological pathologies.

Distinctive phonetic features have an important role in Arabic speech phoneme recognition. In a given language, distinctive phonetic features are extrapolated from acoustic features using different methods. However, exploiting lengthy acoustic features vector in the sake of phoneme recognition has a huge cost in terms of computational complexity, which in turn, affects real time applications. The aim of this work is to consider methods to reduce the size of features vector employed for distinctive phonetic feature and phoneme recognition. The objective is to select the relevant input features that contribute to the speech recognition process. This, in turn, will lead to a reduced computational complexity of recognition algorithm, and an improved recognition accuracy. In the proposed approach, genetic algorithm is used to perform optimal features selection. Therefore, a baseline model based on feedforward neural networks is first built. This model is used to benchmark the results of proposed features selection method with a method that employs all elements of a features vector. Experimental results, utilizing the King Abdulaziz City for Science and Technology Arabic Phonetic Database, show that the average genetic algorithm based phoneme overall recognition accuracy is maintained slightly higher than that of recognition method employing the full-fledge features vector. The genetic algorithm based distinctive phonetic features recognition method has achieved a 50% reduction in the dimension of the input vector while obtaining a recognition accuracy of 90%. Moreover, the results of the proposed method is validated using Wilcoxon signed rank test.

5G wireless networks promise to provide massive bandwidth for various types of connections. In such networks, the backhaul/fronthaul sections should be easy to deploy and support the required high bandwidth. To improve the free space optic (FSO) link bandwidth so that it can replace fiber cables and support 5G networks, all-optical FSO systems were proposed which exploit advanced modulation formats in the transmitter side and coherent detection in the receiver side. However, such links will suffer much under harsh outdoor environment, especially under fog and dust conditions than traditional FSO links that have limited bandwidth. Effect of fog on such links has been investigated in the literature. However, the dust effect is not covered. In this paper, we first experimentally analyzed the effects of dust storms on the performance of an all-optical FSO link carrying a 1-Gbaud/16-quadrature amplitude modulation 5G signal. The results demonstrate that the all-optical FSO link is significantly affected by low visibility range, with severe bit-error-rate (BER), and error vector magnitude (EVM) limits appearing at a 50-m visibility range for a 2.7-m channel length. For a visibility range greater than 200-m, the BER and EVM were improved to 10-9 and 5.5% of the root mean square, respectively. Furthermore, the analysis showed that the dust storm condition introduces flat fading over the frequency range under study, i.e., 21-29 GHz. Second, a comparison between FSO and radio frequency (RF) channels under the same dusty conditions were performed. The results showed that the effects of the dust storm are negligible for the RF link which makes it suitable as a backup for FSO link under severe dust conditions. Finally, a hybrid cascaded FSO/RF link was installed and analyzed in terms of visibility range, BER, and EVM.

In this paper, we address the problem of modulation format identification (MFI) for few mode fiber (FMF) transmission in elastic optical networks (EONs). The MFI accuracy is studied under different FMF channel conditions including mode coupling (MC), optical signal-to-noise ratio (OSNR), and chromatic dispersion (CD). Artificial neural network, trained using features extracted from the asynchronous in-phase quadrature histogram (IQH), is proposed to investigate the identification accuracy. Extensive simulation results have been conducted to identify six modulation schemes widely used in polarization division multiplexing coherent optical networks. This includes PDM-BPSK, PDM-QPSK, PDM-8QAM, PDM-16QAM, PDM-32QAM, and PDM-64QAM transmitted at 10 Gbaud network transmission speed. The results show that the proposed MFI achieves a successful average identification accuracy exceeding 98% in the presence of low MC when the incoming signal OSNR is greater than 20 dB. However, the effect of high MC and CD = 1100 ps/nm reduces the average accuracy to 90%. Further, the MFI accuracy is investigated under different symbol rates such as 14 and 20 Gbaud.

In this paper, and for the first time in literature, optical performance monitoring (OPM) of super-channel optical networks is considered. In particular, we propose a novel machine learning OPM technique based on the use of transformed in-phase quadrature histogram (IQH) features and support vector regressor (SVR) to estimate different optical parameters such as optical signal-to-noise ratio (OSNR) and chromatic dispersion (CD). Two transformation methods, the two-dimensional (2D) discrete Fourier transform (DFT) and 2D discrete cosine transform (DCT), are applied to the IQH to extract features with a considerably reduced dimensionality. For the purpose of simulation, the OPM of a 7 × 20 Gbaud dual-polarization–quadrature phase shift keying (DP-QPSK) is considered. Simulations reveal that it can accurately estimate the various optical parameters (i.e., OSNR and CD) with a coefficient of determination value greater than 0.98. In addition, the effectiveness of proposed OPM scheme is examined under different values of polarization mode dispersion and frequency offset, as well as the utilization of different higher order modulation formats. Moreover, proof-of-concept experiments are performed for validation. The results show an excellent matching between the simulation and experimental findings.

In this paper, we develop new classification and estimation algorithms in the context of free space optics (FSO) transmission. Firstly, a new classification algorithm is proposed to address efficiently the problem of identifying structured light modes under jamming effect. The proposed method exploits support vector machine (SVM) and the histogram of oriented gradients algorithm for the classification task within a specific range of signal-to-jamming ratio (SJR). The SVM model is trained and tested using experimental data generated using different modes of the structured light beam, including the 8-ary Laguerre Gaussian (LG), 8-ary superposition-LG, and 16-ary Hermite Gaussian (HG) formats. Secondly, a new algorithm is proposed using neural networks for the sake of predicting the value of SJR with promising results within the investigated range of values between −5 dB and 3 dB.

Real-time optical performance monitoring (OPM) is of the utmost importance in adaptive optical networks to enable awareness of channel conditions and to achieve high quality of service. In single-mode fiber (SMF)-based networks, optical signal-to-noise ratio (OSNR) and chromatic dispersion (CD) monitoring have been extensively studied in the literature. In this work, we consider OPM in few-mode fiber (FMF) networks employing non-coherent detection. OPM in such networks is a challenging task, as FMF has an additional performance-limiting impairment over SMF, namely mode coupling (MC). Here, we propose an OPM scheme to estimate three FMF channel parameters: OSNR within the range of 8 to 20 dB, CD within the range of 160 to 1120 ps/nm, and different levels of MC. The proposed scheme uses a stacked auto-encoder (AE) to extract features with reduced dimensionality compared to the original data. These features are used to train an artificial neural network (ANN) regressor. Simulation results show that the proposed OPM scheme can accurately estimate the OSNR, CD, and MC with root mean square error (RMSE) values of 0.0015 dB, 0.28 ps/nm, and 7.88 × 10−6, respectively. The performance of proposed OPM scheme is also evaluated against different types of features commonly used in literature.

Epilepsy is one of the most serious disorders that affect patients' daily lives. When seizures occur, patients cannot control their behaviors, which can lead to serious injuries. With the great advances in recording both electroencephalogram (EEG) and magnetoencephalography (MEG) signals, it has become possible to analyze these signals in an automated manner for information extraction to help in seizure detection and prediction. Both EEG and MEG recordings of epilepsy patients contain spikes that can be used for the localization of epileptogenic zones, efficient onset detection, and even, in some cases, prediction. In this paper, we consider the characteristics of EEG and MEG spikes, present a discussion of the importance of spike detection in both signal modalities, and provide a review of spike detection algorithms. Since EEG signals have been widely used for decades, most of the algorithms presented in this paper cover the EEG spike detection methods. Few works in the literature are dedicated to MEG spike detection. Nevertheless, we assert that with some modifications, a considerable number of EEG spike detection algorithms can be applied to MEG signals. We classify the spike detection algorithms according to the domain used for processing the signal. Finally, we conclude with future research directions and open problems in this area.

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.

Chipless radio frequency identification (RFID) technology recently observed a growing interest, mainly because of its wide area of applications, and huge potential market, with the advent of Internet of Things era. Recently, in the demonstration of high capacity chipless RFID tags, ultra-wideband technology has been proposed. It can enable development of robust chipless RFID systems with the promising features of low cost, compact, and lightweight. In response, we propose a novel scheme of broadband chipless RFID tagging that is based on slot coupled tapered slot antenna (TSA) loaded with a set of resonators, referred to as multi-resonators filters (MRF). Using numerical simulations, out of 256 combinations, randomly selected 14 different 8-bit MRF circuits operating over the frequency band 4 to 9 GHz are designed and their spectral and time domain responses under short and open terminations are recorded. The time domain signatures, generated due to high impedance mismatches along the microstrip lines of MRFs terminated with open and short circuits, are quantified by finding the cross-correlation among the signals and that is done by calculating the pulse fidelity factor. The designed MRFs are integrated with TSA to develop chipless RFID tags, referred to as MRF-TSA tags. Our designed TSA operates over 3.5-18 GHz band with an average gain that exceeds 6.5 dBi. The retransmitted time domain signals from the MRF-TSA tags are modulated by loading the antenna with MRFs terminated with either short or open loads. The recorded time domain signals are reasonably distinguishable since 85% (96%) of fidelity factor values among the 14 different MRF-TSA tags with open/short termination are less than 0.70 (0.8), respectively. Finally, for verification purposes, two different MRF-TSA based chipless tags are designed and fabricated. RFID monostatic measurements based on the use of vector network analyzer are also reported.

Millimeter waves (MMWs), operating at 30-300 GHz band, are very promising to the next-generation 5G wireless communication systems, enabling data rates of multi Gbps per user. Photonic technology is increasingly considered to play a key role in a wide range of MMW devices, modules, and subsystems that are essential to successfully build next generation MMW-based 5G networks. This work considers the switching function of MMWs exploiting nonlinearity in photonic devices. In this paper, we perform a systematic investigation of the optimum operating conditions that enable an optical single sideband wavelength conversion, by exploiting the nonlinear effects in a semiconductor optical amplifier (SOA). This principle of switching carefully exploits SOA's four-wave mixing, cross-gain modulation in addition to self-phase modulation effects. The key parameters under investigation include the injection current, the wavelength spacing between the probe and the pump signals in addition to their respective powers. We experimentally determine the optimal operating conditions that maximize the sideband suppression ratio and simultaneously reduce the useless left sideband signal intensity, leading to a dispersion free transmission in optical fiber. Further, we experimentally demonstrate a photonic-based MMW switch of MMW signals having 30 GHz frequency and carrying 3 Gbaud/QPSK modulated signals. A 14-dB sideband suppression ratio of modulated signal is reported.

Epilepsy is a brain disorder that may strike at different stages of life. Patients' lives are extremely disturbed by the occurrence of sudden unpredictable epileptic seizures. A possible approach to diagnose epileptic patients is to analyze magnetoencephalography (MEG) signals to extract useful information about subject's brain activities. MEG signals are less distorted than electroencephalogram signals by the intervening tissues between the neural source and the sensor (e.g., skull, scalp, and so on), which results in a better spatial accuracy of the MEG. This paper aims to develop a method to detect epileptic spikes from multi-channel MEG signals in a patient-independent setting. Amplitude thresholding is first employed to localize abnormalities and identify the channels where they exist. Then, dynamic time warping is applied to the identified abnormalities to detect the actual epileptic spikes. The sensitivity and specificity of proposed detection algorithm are 92.45% and 95.81%, respectively. These results indicate that the proposed algorithm can help neurologists to analyze MEG data in an automated manner instead of spending considerable time to detect MEG spikes by visual inspection.

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.

Epilepsy is a brain disorder, where patients' lives are extremely disturbed by the occurrence of sudden unpredictable seizures. This paper develops patient-independent signal processing techniques based on common spatial patterns and linear discriminant analysis to detect epileptic activities (spikes) from multi-channel brain signal recordings. In contrast to current existing studies which heavily rely on the analysis of electroencephalogram (EEG) data for the detection of epileptic activities, this research work considers magnetoencephalography (MEG) recordings for the detection of epileptic spikes. This requires careful development since unlike EEG spikes, MEG spikes do not have well-defined morphological characteristics. Due to the recent advances in MEG technology, it became possible to consider MEG signals to detect and analyze epileptic activities, but efforts to develop signal processing tools in this area are still in its outset, as compared with those devoted to EEG signal processing.

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.

In this paper, a new dense dielectric (DD) patch array antenna prototype operating at 28 GHz for future fifth generation (5G) cellular networks is presented. This array antenna is proposed and designed with a standard printed circuit board process to be suitable for integration with radio frequency/microwave circuitry. The proposed structure employs four circular-shaped DD patch radiator antenna elements fed by a 1-to-4 Wilkinson power divider. To improve the array radiation characteristics, a ground structure based on a compact uniplanar electromagnetic bandgap unit cell has been used. The DD patch shows better radiation and total efficiencies compared with the metallic patch radiator. For further gain improvement, a dielectric layer of a superstrate is applied above the array antenna. The measured impedance bandwidth of the proposed array antenna ranges from 27 to beyond 32 GHz for a reflection coefficient (S11) of less than -10 dB. The proposed design exhibits stable radiation patterns over the whole frequency band of interest, with a total realized gain more than 16 dBi. Due to the remarkable performance of the proposed array, it can be considered as a good candidate for 5G communication applications.

This article proposes receiver spatial diversity propagation path-loss channel models based on real-field measurement campaigns that were conducted in a line-of-site (LOS) and non-LOS (NLOS) indoor laboratory environment at 2.4 GHz. We apply equal gain power combining (EGC), coherent and noncoherent techniques, on the received signal powers. Our empirical data is used to propose spatial diversity propagation path-loss channel models using the log-distance and the floating intercept path-loss models. The proposed models indicate logarithmic-like reduction in the path-loss values as the number of diversity antennas increases. In the proposed spatial diversity empirical path-loss models, the number of diversity antenna elements is directly accounted for, and it is shown that they can accurately estimate the path-loss for any generalized number of receiving antenna elements for a given measurement setup. In particular, the floating intercept-based diversity path-loss model is vital to the 3GPP and WINNER II standards since they are widely utilized in multi-antenna-based communication systems.