Internet-of-Things (IoT) technology has received much attention due to its great potential to interconnect billions of devices in a broad range of applications. IoT networks can provide high-quality services for a large number of users and smart objects. On the other hand, massive connectivity in IoT networks brings problems associated with spectral congestion. This issue can be solved by applying cognitive radio (CR) and non-orthogonal multiple access (NOMA) techniques. In this respect, this paper studies the performance of cooperative CR-NOMA enabled IoT networks over a generalized α - μ fading channel model. Closed-form analytical expressions of the end-to-end outage probability (OP) for the secondary NOMA users are derived using the Meijer's G-function with a consideration of the impacts of the interference temperature constraint, primary interference, residual hardware impairments and imperfect successive interference cancellation. Moreover, to acquire some useful insights on the system performance, asymptotic closed-form OP expressions are provided. Additionally, the impact of α and μ fading parameters on the outage performance is examined and, as a result, it is concluded that the system performance sufficiently improves as α and/or α increase. Furthermore, the outage performance of the proposed system model is shown to outperform that of an identical IoT network operating on orthogonal multiple access. Finally, the provided closed-form OP expressions are validated with Monte Carlo simulations.

Unobserved fruit crop illnesses are a major threat to agricultural productivity worldwide and frequently cause farmers to suffer large financial losses. Manual field inspection-based disease detection techniques are time-consuming, unreliable, and unsuitable for extensive monitoring. Deep learning approaches, in particular convolutional neural networks, have shown promise for automated plant disease identification, although they still face significant obstacles. These include poor generalization across complicated visual backdrops, limited resilience to different illness sizes, and high processing needs that make deployment on resource-constrained edge devices difficult. We suggest a Hybrid Multi-Scale Neural Network (HMCT-AF with GSAF) architecture for precise and effective fruit crop disease identification in order to overcome these drawbacks. In order to extract long-range dependencies, HMCT-AF with GSAF combines a Vision Transformer-based structural branch with multi-scale convolutional branches to capture both high-level contextual patterns and fine-grained local information. These disparate features are adaptively combined using a novel HMCT-AF with a GSAF module, which enhances model interpretability and classification performance. We conduct evaluations on both PlantVillage (controlled environment) and CLD (real-world in-field conditions), observing consistent performance gains that indicate strong resilience to natural lighting variations and background complexity. With an accuracy of up to 93.79%, HMCT-AF with GSAF outperforms vanilla Transformer models, EfficientNet, and traditional CNNs. These findings demonstrate how well the model captures scale-variant disease symptoms and how it may be used in real-time agricultural applications using hardware that is compatible with the edge. According to our research, HMCT-AF with GSAF presents a viable basis for intelligent, scalable plant disease monitoring systems in contemporary precision farming.

The paper considers the main ways of describing the process that characterizes the arrival of packets to a multiservice node of a telecommunications network. The features of the process under consideration are best represented by the cumulative distribution function A(t). It determines the distribution of the interval size between the moments of arrival of neighboring packets to the multiservice node. These intervals are random values. If it is not possible to perform measurements that allow the choosing of the A(t) function, then the distribution law of random variables is selected based on reasonable assumptions. For telephone switching nodes, the Poisson flow hypothesis was used, which is often similar to the symmetric distribution of the number of calls at time interval t. The results of traffic measurements for multiservice switching nodes showed that the studied distribution is inherently asymmetric. This paper mainly considers the possibility of choosing the A(t) function based on the measurement results presented in a form of the histogram a(t), which contains a series of values. This histogram allows us to obtain the desired distribution as a stepwise function by integration of the a(t). Practical interest is associated with the possibility of reducing the number of readings used to assess the A(t) function. The methods used by some authors are based on the application of arbitrarily chosen functions A(t) with so-called heavy tails. The proposed approach is based on real distributions defined at a finite time interval. As a result of this research, a methodology has been developed to accurately describe the process of packet arrival at the input of the multiservice node. The proposed methodology is based on analytical methods. It guarantees error minimization when investigating the probabilistic characteristics of a switching node in a multiservice network.

This paper evaluates the simultaneous impact of antenna correlation and imperfect channel state information (CSI) on the sum rate achievable in the compound multiple-input multiple-output (MIMO) Broadcast Channel (BC). The considered correlation model incorporates the correlation due to the proximity of antenna elements at both transmitter and receiver sides. We analyse the achievable sum rate and DoF region and characterize the corresponding losses according to different CSI mismatch and correlation modes.

Одной из задач Экологического кодекса Республики Казахстан является работы по обеспечению постоянного и систематического сбора, накопления, хранения и анализа экологической информации, в том числе с использованием современных цифровых технологий. Поэтому исследования, связанные с разработкой цифровых систем мониторинга качества воздуха на наличие вредных примесей, являются актуальными. Результаты экспериментальных исследований по созданию газоаналитической системы типа «электронный нос», сбору сенсорных откликов на присутствие в воздухе образцов газа, обработке экспериментальных данных и идентификации исследуемых газов на основе методов машинного обучения представлены в работе. Показано, что «электронный нос» позволяет выявить присутствие в окружающем воздухе молекул исследуемых газов, в том числе при низких их концентрациях. Ансамбль сенсорных откликов обеспечивает цифровой отпечаток летучих соединений. Это позволяет обучить классификатор газов на основе методов искусственного интеллекта. Получены оценки производительности сенсорной газоаналитической системы при выявлении вредных примесей в воздухе и их идентификации с использованием алгоритмов машинного обучения.

The need to ensure LTE network coverage in sparsely populated and rural areas of Europe (ITU Region 1) has led to a massive use of 800 MHz band (band 20) with its good characteristics of radio wave propagation in LTE networks. However, the frequency band of 800 MHz called “digital dividend” in Region 1 is used on a primary basis not only by the terrestrial mobile service but also by air-traffic control radars (ATCR) that can lead to the creation of harmful interferences at the receivers’ input of ATCR. Such scenarios of mutual interferences became possible after granting licenses for LTE-800 frequencies to operators in such countries as Azerbaijan, Kazakhstan, Russia and other CIS countries, so this problem should be solved by operators at the deployment of LTE-800 networks in airports and areas close to them. So far, for such scenarios the ITU and CEPT have not formulated criteria for interference protection. The proposed protection criteria for receivers of ATCR from user devices’ interferences of LTE-800 networks were tested by experimental studies and can provide a solution to the electromagnetic compatibility (EMC) problem in a complex electromagnetic environment of modern airports and cross-order coordination of 800 MHz frequency bands in Region 1.