Optical multi-band (OMB) systems exploit the low-loss spectrum of the single mode fiber (SMF) and are key enablers to increase the transportation capacity and node connectivity of already deployed systems. The realization of OMB systems is mainly based on the technological advances on the component and system level, and for this purpose, a broad gamut of various structural elements, such as transceivers, amplifiers, filters, etc. have been commercialized already or are close to commercialization. This wide range of options, which aid in unlocking the concurrent transmission in all amplification bands, is reviewed here for the first time, whilst their pros and cons as well as their limitations are discussed. Furthermore, the needs for additional components in order to fully exploit the ≈390 nm low-loss wavelength range of SMF, which spans from 1260 to 1650 nm, are highlighted. Finally, based on a physical layer formalism, which incorporates the impact of the most important physical layer constraints for an OMB system, the attainable capacity and transparent reach of each amplification band are quantified.

We introduce the concept of all-anisotropic spheroidal photonic antennas, where the nanoantenna is formed by a uniaxial anisotropic crystal, and study their modeling and optimization. For this purpose, we develop, for the first time, a rigorous theory for the electromagnetic (EM) scattering by anisotropic spheroids, using a full-wave method which features spheroidal eigenvectors with a set of discrete wavenumbers for the expansion of the fields in the region of anisotropy. We fully validate our theory by comparisons with the commercial HFSS software, as well as with a general purpose discrete dipole approximation method. Our implementation turns out to be fast, and serves as an efficient tool for modeling the resonant properties of spheroidal photonic antennas. In this context, we show that geometrical tailoring, by employing high-aspect ratio prolate and oblate spheroids, allows for optimizing the nanoantenna and achieving maximal forward-to-back scattering or maximal forward-scattering with reduced back-scattering. In addition, we demonstrate how the use of anisotropic materials enables us to set this optimum state at smaller wavelengths, even close to ultraviolet (UV), as compared to isotropic dielectrics, and we reveal the key role of the incident field's polarization in achieving optimized operation for both prolate and oblate configurations.

The paper describes the design and implementation of a virtual lab for on-line lab training on optical communications, part of the ‘Optical Communications’ course offered at the Dept. of Electrical & Electronic Engineering Educators, School of Pedagogical & Technological Education (ASPETE), Athens, Greece. The virtual lab is based on the OptiSystem 16 software tool while the on-line component was implemented by using the MS-Teams tool of MS-Office. The lab includes a synchronous and an asynchronous part. Since the virtual lab was offered for the first time, students’ response was evaluated on the basis of the submitted lab reports to allow for proper corrective actions during the subsequent academic year. Results were encouraging in that 79% of the students achieved a passing grade, however, 60% of students either failed or attained a low passing grade, which leaves room for improvement. Further analysis revealed a lack of a proper background in mathematics, physics and essential signal quantities. Future actions will include an investigation of the optimum combination between the physical and the simulation lab, development of short notes and multiple-choice questions, a closer association between the lab work and relevant small tasks/projects and a more systematic feedback process.

The article describes an Arduino-based, portable, remotely usable weather monitoring station capable of measuring temperature, relative humidity, pressure, and carbon monoxide (CO) concentration and transmitting the collected data to the Cloud through the mobile telephony network. The main modules of the station are as follows: a DHT11 sensor for temperature and relative humidity sensing, a BMP180 sensor for pressure monitoring (with temperature compensation), a MQ7 sensor for the monitoring of the CO concentration, an Arduino Uno board, a GSM SIM900 module, and a buzzer, which is activated when the temperature exceeds 35 °C. The station operates as follows: the Arduino Uno board gathers the data collected by the sensors and, by means of the GSM SIM900 module, it transmits the data to the Cloud by using the mobile telephony network as well as the ThingSpeak software which is an open-code IoT application that, among others, enables saving and recovering of sensing and monitoring data. The main novelty of this work is the combined use of the GSM network and the Cloud which enhances the portability and usability of the proposed system and enables remote collection of data in a straightforward way. Additional merits of the system are the easiness and the low cost of its development (owing to the easily available, low-cost hardware combined with an open-code software) as well as its modularity and scalability which allows its customization depending on specific application it is intended for. The system could be used for real-time, remote monitoring of essential environmental parameters in spaces such as farms, warehouses, rooms etc.

We investigate the impact of dataset augmentation and synthetic generation techniques on the accuracy of supervised audio classification based on state-of-the-art neural networks used as classifiers. Dataset augmentation techniques are applied upon the raw sound and its transformed image format. Specifically, sound augmentation techniques are applied prior to spectral-based transformation and include time stretching, pitch shifting, noise addition, volume controlling, and time shifting. Image augmentation techniques are applied after the transformation of the sound into a scalogram, involving scaling, shearing, rotation, and translation. Synthetic sound generation is based on the AudioGen generative model, triggered through a series of customized prompts. Augmentation and synthetic generation are applied to three sound categories: (a) human sounds, (b) animal sounds, and (c) sounds of things, with each category containing ten sound classes with 20 samples retrieved from the ESC-50 dataset. Sound- and image-orientated neural network classifiers have been used to classify the augmented datasets and their synthetic additions. VGGish and YAMNet (sound classifiers) employ spectrograms, while ResNet50 and DarkNet53 (image classifiers) employ scalograms. The streamlined AI-based process of augmentation and synthetic generation, enhanced classifier fine-tuning and inference allowed for a consistent, multicriteria-comparison of the impact. Classification accuracy has increased for all augmentation and synthetic generation scenarios; however, the increase has not been uniform among the techniques, the sound types, and the percentage of the training set population increase. The average increase in classification accuracy ranged from 2.05% for ResNet50 to 9.05% for VGGish. Our findings reinforce the benefit of audio augmentation and synthetic generation, providing guidelines to avoid accuracy degradation due to overuse and distortion of key audio features.

We present the optical modelling of a mesoporous fibre perovskite solar cell (PSC). It was conducted by means of the transmission line method (TLM), which was used to calculate the efficiency and short-circuit photo-current density of the cell. The TLM was first applied for a planar mesoporous PSC and verified with the experimental results from the literature. Numerical calculations for both planar and fibre PSC were conducted and analysed regarding their efficiency in terms of optical simulation. The importance of choosing the thin-film layers’ materials and thickness was demonstrated, and a potential improvement using anti-reflection coatings was also examined.