The trade-off between more user bandwidth and quality of service requirements introduces unprecedented challenges to the next generation smart optical networks. In this regard, the use of optical performance monitoring (OPM) and modulation format identification (MFI) techniques becomes a common need to enable the development of next-generation autonomous optical networks, with ultra-low latency and selfadaptability. Recently, machine learning (ML)-based techniques have emerged as a vital solution to many challenging aspects of OPM and MFI in terms of reliability, quality, and implementation efficiency. This paper surveys ML-based OPM and MFI techniques proposed in the literature. First, we address the key advantages of employing ML algorithms in optical networks. Then, we review the main optical impairments and modulation formats being monitored and classified, respectively, using ML algorithms. Additionally, we discuss the current status of optical networks in terms of MFI and OPM. This includes standards, monitoring parameters, and the available commercial products with their limitations. Second, we provide a comprehensive review of the available ML-based techniques for MFI, OPM, and joint MFI/OPM, describing their performance, advantages, and limitations. Third, we give an overview of the exiting ML-based OPM and MFI techniques for the emerging optical networks such as the new fiber-based networks that use future space division multiplexing techniques (e.g. few-mode fiber), the hybrid radioover-fiber networks, and the free space optical networks. Finally, we discuss the open issues, potential future research directions, and recommendations for the potential implementation of MLbased OPM and MFI techniques. Some lessons learned are presented after each section throughout the paper to help the reader identifying the gaps, weaknesses, and strengths in this field.

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.

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 presents a sensitive microwave near-field sensor based on utilizing a three-dimensional capacitor within a planar split-ring resonator. The planar resonator is etched in the ground plane of a microstrip line, where the resonator's length is relatively smaller than the guided wavelength. The sensor was fabricated utilizing PCB technology and used to detect the presence of dielectric fluids. The numerical analysis shows that the three-dimensional capacitor significantly enhances the electric field in the sensing area (free space) leading to an increase in the sensitivity. The electric field enhancement was quantitatively investigated using numerical analysis. The analysis shows that a loaded-quality factor of 1556 can be achieved using a three-dimensional capacitor with a length of 29 mm, which is significantly higher than what can be achieved by using planar resonators working in the same range of frequency. In addition, the utilization of the high-dielectric substrate can enhance coupling to the resonator. The proposed sensor was experimentally tested on two fluids, chloroform and dichloromethane with the relative permittivity of 4.81 and 8.93, respectively. The proposed sensor shows higher sensitivity of 700% and 374.5% in detecting the presence of chloroform and dichloromethane, respectively. Furthermore, the proposed sensor was utilized to detect the changes in fluid levels.