We develop a deep neural network (DNN) modeling methodology to predict the radiated emissions of a shielding enclosure in terms of its aperture attributes including aperture shape, size, pitch, and quantity. The target structure is the inside of a three-dimensional (3D) enclosure comprised of perfect electric conductor (PEC) boundaries with dimensions of a desktop personal computer (PC) containing thermal dissipation apertures on the surface of its back panel. The DNN model is developed to compute the radar cross section (RCS) as a function of aperture attributes and to enable the efficient assessment of the PC’s electromagnetic compatibility (EMC). To generate training data for machine learning (ML), we implement the modified equivalent current approximation (MECA) method and validate it against analytical methods and a commercial field-solver. We use MECA to compute RCS data for approximately 55,000 experiments across a wide range of aperture attributes. We examine numerous DNN models across parameters such as number of layers and nodes per layer, activation function, optimization algorithm, loss function, batch size, and epoch, to identify the optimal DNN model based on (a) accuracy, (b) computation time, and (c) memory usage. Results show excellent agreement between MECA and DNN predictions for previously unseen cases.

In the present scenario, Women journalists have played a significant role in shaping the media industry, from reporting on important events to advocating for gender equality and social justice. However, despite progress in recent years, women journalists still need to overcome several challenges and threats in the industry. Gender discrimination is one major challenge faced by women journalists today. This can take many forms, from being paid less than their male counterparts to being passed over for promotion. Women journalists may also face bullying and harassment in the workplace, which can make it difficult to do their job effectively. Another challenge women journalist's face is the underrepresentation of women in decision-making roles. Women are often excluded from leadership positions in media organizations, making it difficult for them to have their voices heard and advance in their careers. Women journalists have made significant contributions to the media industry and continue to do so. They bring a unique perspective to news coverage and have helped to create a more diverse and inclusive media landscape. As the industry continues to evolve, ensuring women have equal opportunities to succeed and thrive as journalists are important. This review paper highlights the opportunities and challenges for women journalists in the media industry.

Millimeter-wave (mmWave) beamspace massive multiple-input multiple-output (mMIMO) system with lens antenna array can minimize transceiver hardware complexity without compromising performance. However, the number of supported portable user terminals (PUTs) cannot exceed the number of radio frequency (RF) blocks accessible at the same time, frequency, and coding resources. As a result, we propose the integration of rate-splitting multiple access (RSMA) into a beamspace mMIMO system to support a larger number of PUTs than the number of available RF blocks while minimizing intra-beam interferences. Moreover, orthogonal random precoding (ORP) is utilized in the downlink of the beamspace mMIMO system to mitigate the inter-beam interferences and extend the cell coverage area. Then, we develop an optimization problem to optimize the system’s overall throughput while keeping the minimum needed throughput and power budget in consideration. The nonconvex optimization issue is then turned into a convex optimization problem using the successive convex approximation approach. Following that, we offer an alternating method to solve the approximate optimization issue and select an optimal solution. Furthermore, the suggested method’s effectiveness is evaluated in terms of total throughput, energy efficiency, and cell coverage area. Finally, numerical results confirm the superior performance of the proposed method over benchmark techniques in terms of sum throughput, energy efficiency, and cell coverage area.

This letter introduces an attention-based unsupervised generative adversarial network, named Illu-GAN, for low- light camera image enhancement. Many images captured by camera sensors suffer from inadequate lighting conditions or over-/under- exposure. Previous low-light enhancement methods are mostly based on supervised learning-based and heavily rely on paired data. Existing unsupervised methods only utilize information in spatial domain and take no account of frequency information, which leads to results of poor quality. In this letter, we propose a new network, Illu- GAN, to incorporate the features from frequency and spatial domains to better guide the network through a Wavelet Transform module. Extensive experiments with Illu-GAN on various benchmarking datasets demonstrate qualitatively and quantitatively the improvement with respect to state-of-the-art methods. The qualitative and quantitative results demonstrate that the newly proposed Illu-GAN can generate more natural enhanced images, less noisy, and with better generalisation ability.

Softwarization and virtualization of 5G radio access network components can bring cloud-like benefits to mobile network operators. In cloud and edge computing environments, these benefits are typically achieved through Virtual Machines (VMs) and containers, with the latter option gaining traction thanks to its relatively lightweight requirements. Currently available open-source container platform(s), however, do not achieve a full-fledged containerized mobile network protocol stack, as they fail to support successful live migration of containers that execute specific 5G software functionalities. The contribution of this paper is to present a proof-of-concept implementation that completes the live migration of a containerized 5G Central Unit (CU) module between two servers without causing permanent service disruption to the mobile user.  The authors’ implementation makes use of an open-source migration software (CRIU) that has been modified to support a key protocol (SCTP) that the CU module requires to exchange signaling messages with other 5G modules. The authors’ implementation is experimentally tested in a federated testbed environment with four container migration techniques. A performance comparison reveals that the end-user service recovery time due to the CU live migration can be reduced by up to 36% when applying the hybrid migration technique compared to the conventional cold migration technique.

LIDO is a Metamodeling language for developing IoT solutions. It is a set of concepts of high-level abstraction. LIDO make it possible that people with different technical backgrounds and IoT culture can come together around the development of IoT solutions. LIDO allow people of different disciplines, having different viewpoints to come together to discuss and reflect on the modeling and implementation of an IoT solution. This article presents the philosophy underlying LIDO that makes it a universal language allowing sustainable development.

Modern mobile communication systems exploit complicated modulations to increase spectrum efficiency and larger antenna arrays to suppress co-channel interferences. However, these techniques have also caused surges of power consumption and hardware cost, making it unsustainable to squeeze gains from the radio-frequency electronics. To circumvent the dilemma, optical mobile communication (OMC) systems exploit wideband lasers to carry data, and do not necessarily involve high-order modulation and oversized arrays. In OMC systems, parallel laser beams can achieve data rate of Tbps, connection density of thousands of links/m$^2$, and energy efficiency of Gbits/J, with low cost and low signal processing complexity, as long as the challenges in receiver tracking, channel decorrelation and anti-blocking can be tackled. To this end, this work presents a new OMC system architecture and discusses enabling technologies of practical OMC systems. A prototype is built to verify the feasibility of several key concepts. It is shown that a practical OMC system requires collaboration of fast and accurate positioning systems, rapid laser steerers, optical reflective intelligent surfaces and decorrelation optics.

As deep neural networks (DNNs) are being applied to a wide range of edge intelligent applications, it is critical for edge inference platforms to have both high-throughput and low-latency at the same time. Such edge platforms with multiple DNN models pose new challenges for scheduler designs. First, each request may have different service level objectives (SLOs) to improve quality of service (QoS). Second, the edge platforms should be able to efficiently schedule multiple heterogeneous DNN models so that system utilization can be improved. To meet these two goals, this paper proposes BCEdge, a novel learning-based scheduling framework that takes adaptive batching and concurrent execution of DNN inference services on edge platforms. We define a utility function to evaluate the trade-off between throughput and latency. The scheduler in BCEdge leverages maximum entropy-based deep reinforcement learning (DRL) to maximize utility by 1) co-optimizing batch size and 2) the number of concurrent models automatically. Our prototype implemented on different edge platforms shows that the proposed BCEdge enhances utility by up to 37.6% on average, compared to state-of-the-art solutions, while satisfying SLOs.

We  consider active hypothesis testing with multiple heterogeneous agents. Each agent has access to its own set of experiments, has different action costs and forms its own beliefs. Additionally,  each experiment carries a global cost, and the agents must try to keep the expected cumulative cost below a certain threshold. We study a centralized and a decentralized scenario. Under the centralized scenario, the agents  are instructed how to act by a central controller. Under the decentralized scenario they communicate and exchange information over a directed graph. For each scenario we propose  separate deep reinforcement learning algorithms based on proximal policy optimization. Solutions to the decentralized problem start from fully decentralised training, and progressively introduce two levels of centralisation during training.  We assess the proposed algorithms in an example of anomaly detection over sensor networks, considering three different decentralised communication settings. We infer that all algorithms achieve the required accuracy level considerably faster than a single deep reinforcement learning agent, while satisfying the expected cost constraints when required. Under the assumption that the communication  graph is fully connected, the decentralised agents perform just as well, and sometimes better than the centralised  controller. Out of all decentralised algorithms, the one that uses a global critic is by far the best performing and can compete with the central controller even when the communication graph is not complete.

In this paper, two open problems of the existing bounded UDE-based robust control method are investigated: the input constraint with multiple control inputs, and the performance degradation caused by the introduced additional time-varying control variable. A modified bounded UDE-based robust control method is developed for the MIMO system with the inputs constrained by a general positive defined matrix, which is a more general condition of the norm constraint for control inputs. A nonlinear designed is further proposed to modified the direct multiplication of the additional time-varying variable to mitigate the performance degradation of the existing bounded UDE-based robust control, and the guidance of the nonlinear design procedures is provided. The proposed design is applied to the vector control of a PMSM drive system, and the simulation studies are provided to validate the proposed design under voltage constraint of the vector control with both normal operation and voltage constraint triggered.

The work is devoted to the search for new vacuum-plasma coatings with high hardness to increase the durability of the compressor blades of the GTE of aircraft engines Ti-Al-N-based vacuum-plasma coatings obtained by Avinit technologies, which ensure the application of hard, high-quality coatings with dramatically reduced micro-arc damage, were selected as candidates. Avinit multilayer coatings have higher functional characteristics than TiN (microhardness, crack resistance, temperature resistance, erosion and corrosion resistance) and may be promising for applying erosion-resistant coatings for compressor blades. Avinit technologies are technologically closest to the vacuum-plasma technologies used in industrial production for applying TiN protective coatings. New multi-layered 2D nanocomposite wear-resistant ion-plasma hard coatings Avinit (TiN-AlN)n have been developed. The created software products made it possible to reach a qualitatively new level in terms of further modification and improvement of the designs of Avinit functional coatings, stability of technologies and improvement of their quality control when applying such coatings for use in the production of compressor blades of gas turbine engines of aircraft engines. Special attention is paid to methods of preliminary ion-plasma treatment of surfaces before coating. Metallographic studies of the chemical and phase composition and structure of Avinit (TiN-AlN)n coatings have been carried out. The thickness of the coatings is 7–9 μm, the microhardness is 34–35 GPa (compared to the serially used TiN coating: 27.4 GPa). The use of three-stage ion-plasma treatment in Avinit technologies using a double vacuum-arc discharge followed by the application of strengthening coatings in a single technological cycle eliminates the formation of cracks and ensures the production of tightly bonded, high-quality coatings of a given composition with the maximally reduced share of the droplet component. The developed coatings (TiN-AlN)n were applied to experimental batches of working compressor blades of GTE aircraft engines for bench tests.

Rapid progress in various fields of science and technology in recent years has caused various adverse and predictable negative effects on the environment. Today, one of the most important biological problems is environmental electromagnetic pollution. The intensity of its electromagnetic radiation has become so widespread that it is currently known as one of the hidden and silent forms of pollution. One means of communication that is an inseparable part of daily life is the mobile phone. Research and surveys show that mobile phone waves have a negative effect on the human body. Our goal in this article is to study the effects of mobile phone electromagnetic waves on the human eye. In order to check the effect of these waves, the specific absorption rate (SAR) criterion is used. Because the rate of specific absorption in the eye is determined according to the absorption of energy in the eye tissues and the conversion of this absorbed energy into thermal energy, they increase the temperature of the eye tissues. In other words, the temperature distribution in the eye directly correlates with the rate of specific absorption. The results have shown that the tissue of the eye is susceptible. When the eye is exposed to waves, the eye’s temperature increases, and the antenna angle relative to the eye is effective in the rate of specific absorption. According to studies, the conclusion of this research indicates that without observing safety points, including maintaining distance, they can cause dangerous bodily complications.

Multispectral imaging camera is an important tool for two-dimensional (2-D) and  3-D spectroscopic analysis. First deployment was in space for remote sensing application, and it is now available for applying on sites or installing with drones. In this work, we propose an affordable palm-size wide-band 3-D multispectral imaging camera, covering broad blue, green, red, near infrared, and long wave infrared regions. Our palm-size 3-D multispectral imaging camera module is also embedded with our own designed software for capturing or recording  2-D spectral images with depth information from each spectral band simultaneously. These spectral images and depth information can be combined into 2-D and 3-D fused spectral images. Promising applications are in industry, agriculture, environment, public health, and security.

Fuzzy Cognitive Maps (FCMs) is a graph-based methodology successfully applied for knowledge representation of complex systems modelled through an interactive structure of nodes connected with causal relationships. Due to their flexibility and inherent interpretability, FCMs have been used in various modelling and prediction tasks to support human decisions. However, one of the main limitations of FCMs is that they may unintentionally absorb spurious correlations presented in the collected data, resulting in poor prediction accuracy and interpretability. This article proposes a novel framework for constructing FCMs based on Liang-Kleeman Information Flow (L-K IF) analysis to address this limitation. The novelty of the proposed approach is the identification of actual causal relationships from the data using an automatic causal search algorithm. The actual causal relationships are then imposed as constraints in the FCM learning procedure to rule out spurious correlations and improve the aggregate predictive and explanatory power of the model. Numerical simulations were conducted to demonstrate the effectiveness of the proposed approach by comparing it with state-of-the-art FCM-based models.

Speech Command Recognition (SCR) is rapidly gaining prominence due to its diverse applications, such as virtual assistants, smart homes, hands-free navigation, and voice-controlled industrial machinery. In this paper, we present a data-centric approach to creating SCR systems for low-resource languages, particularly focusing on the Kazakh language. By leveraging synthetic data generated by Text-to-Speech (TTS) and data extracted from a large-scale speech corpus, we successfully created the Kazakh language equivalent of the Google Speech Commands dataset. Moreover, we also compiled the Kazakh Speech Commands dataset with data collected from 119 participants. This dataset was used to benchmark the performance of the Keyword-MLP model trained using our synthetic dataset. The results showed that the model achieves 89.79% accuracy for the real-world data demonstrating the efficacy of our approach. Our work can serve as a recipe for creating customized speech command datasets, including for low-resource languages, obviating the need for laborious and costly human data collection.

Abstract—Artificial intelligence (AI) has rapidly evolved over the past few decades, leading to the development of innovative technologies that have transformed various industries. One of the latest inventions in this field is the autonomous vehicle, which has the potential to revolutionize transportation and mobility. In addition to autonomous vehicles, AI has also led to the development of innovative technologies in the healthcare industry, such as AI-powered medical imaging systems. This paper provides a review of the background of AI and the latest advancements in autonomous vehicles and healthcare inventions. It explores the underlying technologies, such as machine learning and computer vision, and the methods used to develop autonomous vehicles and healthcare inventions. The paper also examines the benefits and challenges associated with autonomous vehicles and healthcare inventions and provides insights into future research directions. Overall, this paper highlights the significant impact of AI on transportation, healthcare, and other industries, and the potential of autonomous vehicles and healthcare inventions to create a safer and more efficient mobility and healthcare system.

A hyperspectral sensing system operating in the SWIR band (900-1700nm) was developed for drone-based detection of plastic litter in the environment. The detection software is based on a multiple linear classifier trained on examples, that detects plastics and transmits information to a ground station that visualizes on a map the location of points surveyed, labeled as plastics where applicable.

This article addresses switching noise propagation and its suppression in multi-rate control systems, where the inductor current is oversampled, digitally filtered and the control execution is decimated to single(double)-sampled pulse-width modulation (PWM). The operating points where the sampling instants occur near the switching ones are critical for switching noise sensitivity. It is shown that, when the number of the corrupted samples is high with respect to the number of samples used for filtering, typically considered moving average filters (MAFs) bring a detrimental impact. As an alternative, median filter (MED) is proposed. To decouple the ranking within MED from the switching ripple, repetitive ripple removal is added beforehand. Multi-rate PWM control with the proposed feedback filtering outperforms state-of-the-art double-sampled PWM by eliminating the well-known switching noise sensitivity for small and large duty cycles. In addition, noise-sensitive operating point regions that appear with MAF are successfully suppressed.