Radio Propagation Models

In this study a comparative analysis of various empirical models for estimating radio wave propagation path losses with those measured experimentally for the rural area in Erbil city is presented. In Gazna village near the center of the Erbil city, one of the Korek telecommunication towers is selected for the purpose of comparative analysis and seven different empirical models were utilized. The implemented models are free space model, Electronic Communication Committee (ECC-33), Stanford Interim University (SUI), Optimization Cost-231, Okumura-Hata, Egli, and Ericson models. The data were collected at operating frequencies of 1800 MHz and 2100 MHz using drive test equipment with Sony-Ericson mobile phone to measure received signal strength. Generally, the analyzed results, which are based on the Mean Absolute Percentage Error (MAPE) values, shows that the Egli and ECC-33 overestimated while both FSPL and Okamura-Hata are underestimated path loss values. In addition, the SUI and Optimized-Cost-231 models are providing minimum MAPE path loss values which are 0.49 and 0.55 at 1800 MHz and 0.07 and 0.16 at 2100 MHz, respectively. Therefore, in order to improve network performance and accurate estimation of financial feasibility, these two models especially SUI model can be used successfully and confidently in the design of the wireless communication system in the rural area of Erbil city in Kurdistan region of Iraq.

Wireless network parameters such as transmitting power, antenna height, and cell radius are determined based on predicted path loss. The prediction is carried out using empirical or deterministic models. Deterministic models provide accurate predictions but are slow due to their computational complexity, and they require detailed environmental descriptions. While empirical models are less accurate, Machine Learning (ML) models provide fast predictions with accuracies comparable to that of deterministic models. Most Empirical models are versatile as they are valid for various values of frequencies, antenna heights, and sometimes environments, whereas most ML models are not. Therefore, developing a versatile ML model that will surpass empirical model accuracy entails collecting data from various scenarios with different environments and network parameters and using the data to develop the model. Combining datasets of different sizes could lead to lopsidedness in accuracy such that the...

There is an increased exploration of the potential of wireless communication networks in the automation of daily human tasks via the Internet of Things. Such implementations are only possible with the proper design of networks. Path loss prediction is a key factor in the design of networks with parameters such as cell radius, antenna heights, and the number of cell sites that can be set. As path loss is affected by the environment, satellite images of network locations are used in developing path loss prediction models such that environmental effects are captured. We developed a path loss model based on the Extreme Gradient Boosting (XGBoost) algorithm, whose inputs are numeric (non-image) features that influence path loss and features extracted from images composed of four tiled satellite images of points along the transmitter to receiver path. The model can predict path loss for multiple frequencies, antenna heights, and environments such that it can be incorporated into Radio Pla...

Wireless Mesh Networks (WMNs) are part of wireless technologies that are known for their flexibility and extended coverage. Wireless applications have reached their peak in applications related to various fields such as healthcare, image processing, and so on. However, delay and energy efficiency are considered the two aspects that diminish the performance of WMNs. To overcome the aforementioned issues, this research introduces an effective routing method using Multi-Objective Dwarf Mongoose Optimization Algorithm (MO-DMOA). The MO-DMOA performs routing by considering the multiple paths using an enriched population resource. The nomadic behavior of MO-DMOA helps in detecting the optimal routing path with minimized over-exploitation. The proposed MO-DMOA is evaluated with different routing schemes such as Load Balance and Interference Avoid-Partially Overlapped Channels Assignment (LBIA-POCA) framework, and Multi-Objective Dyna Q-based Routing (MODQR). The outcomes obtained through the experimental analysis show that the proposed approach acquires a better throughput of 13.5 × 10 5 kbps for 22 flows, whereas the existing LBIA-POCA achieves a throughput 60× 10 3 kbps.

Path loss propagation is a vital concern when designing and planning networks in mobile communication systems. Propagation models such as the empirical, deterministic and theoretical models, which possess complex, inconsistent, time-consuming and non-adaptable features, have proven to be inefficient in designing of wireless systems, thereby resulting in the need for a more reliable model. Artificial Intelligence methods seem to overcome the drawbacks of the propagation models for predicting path loss. In this paper, the ANFIS approach to path loss prediction in the GSM and WCDMA bands is presented for selected urban areas in Nigeria. Furthermore, the effects of the number of Membership Functions (MFs) are investigated. The prediction results indicated that the ANFIS model outperformed the Hata, Cost-231, Egli and ECC-33 models in both Kano and Abuja urban areas. In addition, an increase in the number of MFs conceded an improved RMSE result for the generalized bell-shaped MF. The gen...

The correct capture of forest operations information carried out in forest plantations can help in the management of mechanized harvesting timber. Proper management must be able to dimension resources and tools necessary for the fulfillment of operations and helping in strategic, tactical, and operational planning. In order to facilitate the decision making of forest managers, this work aimed to analyze the performance of machine learning algorithms in estimating the productivity of timber harvesters. As predictors of productivity, we used the availability of hours of machine use, individual mean volumes of trees, and terrain slopes. The dataset was composed of 144,973 records, carried out over a period of 28 months. We tested the predictive performance of 24 machine learning algorithms in default mode. In addition, we tested the performance of blending and stacking joint learning methods. We evaluated the model's fit using the root mean squared error, mean absolute error, mean absolute percentage error, and determination coefficient. After cleaning the initial database, we used only 1.12% to build the model. Learning by blending ensemble stood out with a determination coefficient of 0.71 and a mean absolute percentage error of 15%. From the use of data from machine learning algorithms, it became possible to predict the productivity of timber harvesters. Testing a variety of machine learning algorithms with different dynamics contributed to the machine learning technique that helped us reach our goal: maximizing the model's performance by conducting experimentation.

The paper presents a proposed data-driven path loss prediction over conventional models for wireless communication design, especially in the setting of dynamic signal variations in advanced Fifth Generation (5G) technologies. The research analyzes key parameters to train a multi-layered neural network using a robust data-driven model based on neural network architecture. Significant correlations show that environmental factors and path parameters affect path loss. The proposed three-layer model, trained over 150 epochs, outperforms traditional models in Mean Absolute Error, Mean Squared Error, and Root Mean Squared Error. With accuracy measures, it outperforms Okumura-Hata, Costa 231, and Egli models. The study uses TensorFlow and Python to explore 5G wireless communication parameter-path loss interactions for optimum network design and resource allocation. The simulation-based neural network path loss prediction method improves accuracy and reduces estimation mistakes. Future research includes enhanced machine learning techniques and 5G's impact on path loss prediction.

Feature extraction plays a significant part in computer vision tasks. In this paper, we propose a method which transfers rich deep features from a pretrained model on face verification task and feeds the features into Bayesian ridge regression algorithm for facial beauty prediction. We leverage the deep neural networks that extracts more abstract features from stacked layers. Through simple but effective feature fusion strategy, our method achieves improved or comparable performance on SCUT-FBP dataset and ECCV HotOrNot dataset. Our experiments demonstrate the effectiveness of the proposed method and clarify the inner interpretability of facial beauty perception.

The received signal strength of any cellular network system at the user equipment terminal is dependent on the propagation environment and the path loss model used during the network planning stage. One of the most commonly used empirical model for predictive path loss analysis and estimation is the COST231 model. Despite the popularity of this model, its precision performance is usually poor when engaged for propagation prediction modelling outside the intended environment wherein it was originally developed. To cater for such limitation, an adaptive path loss model for optimal path loss prediction is developed in this paper using a hybrid Wavelet-Genetic genetic algorithm (Wavelet-GA). For the purpose of comparative analysis between wavelet-GA, GA, and COST231 models, three evaluation indicators based on root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (R), were engaged. The results showed that the estimated RMSEs attained with the proposed wavelet

The frequent poor service network experienced by some mobile phone users within some deadlock areas in Nigeria is an issue which has been identified by different researchers due to wrong positioning and planning of the evolved NodeB (eNodeB) transmitter using existing propagation loss models. To effectively contribute towards this potential issue constantly experienced in some part of Nigeria, an adaptive hybrid propagation loss model that is based on wavelet transform and genetic algorithm methods has been developed for cellular network planning and optimization, with the capacity to resolve the problems absolutely. First, the signal strengths were measured within four selected eNodeB cell sites in long term evolution (LTE) at 2600MHz using drive-test method. Secondly, the measured data were denoised through wavelet tools. Thirdly, COST231 model was optimize and deduced to generic model with parameters. Fourthly, genetic optimization algorithm automatically developed the propagation loss models for denoised signal data (designated as wavelet-GA model) and unprocessed signal data (designated as GA model). The hybrid wavelet-GA propagation loss model, GA propagation loss model, and COST231 propagation loss model were compared based on three error metrics such as root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (R). The developed hybrid wavelet-GA model estimated the lowest RMSEs of 2.8813 dB, 3.9381 dB, 4.7643 dB, 6.9366 dB, whereas, COST231 model gave highest value of RMSE. The developed hybrid wavelet-GA model also derived the least value of MAE as compared with COST231 and the GA models, such as, 2.2016 dB, 2.8672 dB, 3.4766 dB, 5.8235 dB. The correlation coefficients were also compared, and it showed that the developed hybrid wavelet-GA model were 90.04%, 78.61%, 92.21% and 91.23% for the four cell sites. The developed hybrid wavelet-GA model was also validated to account for the performance level by checking for the correlation coefficient using another measured signal data from different eNodeB cell sites other than the once used for the developed of the hybrid wavelet-GA model. It was noticed that the developed hybrid wavelet-GA propagation loss model is 97.41% valid. Existing standard COST231 model are not able to predict propagation loss with high level of accuracy, as such not efficient to be applied within part of Port Harcourt, Nigeria. The proposed hybrid wavelet-GA model has proven to achieve high performance level and it is relevant to be utilized for cellular network planning and optimization. In future purposes, more regions and locations should be considered to form a broader view in the development of more robust propagation loss models.

One sort of wireless ad-hoc network is called MANET (Mobile Ad-hoc Network), which is an autonomous network made up of wireless nodes and routers connected by wireless connections. Transmission of data in effective way is important. The DOF-MEN (Direction Oriented Forwarding through Minimum Number of Edge Nodes) protocol is the protocol which lessens the amount of messages for route discovery. It attempts to choose just single node as the next following hop. The node's address is added by the sender. Therefore, only the chosen node will receive and subsequently transmit the data. This protocol increases the throughput, Packet Delivery Ratio, and cuts down on the Routing Overhead. Several aspects affect the routing protocol's execution accuracy in mobile ad hoc networks (MANET). The propagation model is a crucial thing to be considered among many. MANET experiences an enormous loss in performance due to blockage between transmission devices and fluctuation in the receiver's signal intensity. Therefore, it's crucial to determine how Radio Propagation Model and study of performance of routing protocol in MANET. Here we do simulation and analysis using NS2 (Network Simulator 2). The outcome demonstrates that the MANET routing protocol's performance is more influenced by the propagation model.

Negli anni ’80 furono lanciati e realizzati programmi di cooperazione europea - quali ESPRIT e RACE - per la ricerca precompetitiva nell’ informatica e nelle telecomunicazioni. L’articolo ricorda il contributo fornito da alcune di queste ricerche sia all’innovazione nell’ICT, sia all’effettiva evoluzione delle reti
broadband e ultrabroadband

Deep learning (DL) is a combination of AI and ML approaches that can intelligently examine very large datasets efficiently. It includes various approaches based on the dataset, such as CNN, RNN, MLP, and others. To thoroughly comprehend the working methodology and algorithms of various approaches, we have created an environment to evaluate their efficacy. For this purpose, consider the DOG image dataset with 20,000 images of 120 breeds of dogs.

In questo articolo viene presentato GrainLab, software per la sintesi granulare in tempo reale. Nella sua implementazione si è prestata particolare attenzione alla scalabilità garantendo l'accesso al maggior numero di parametri, all'usabilità fornendo un'interfaccia grafica semplice e accessibile, e all'efficienza ottimizzando l'algoritmo di generazione dei grani. Il software è gratuito e open source, quindi facilmente modificabile e adattabile a esigenze specifiche.

Questo articolo presenta l’implementazione software di un sistema automatizzato di routing audio dinamico. In base a specifiche politiche di gestione, controllabili dall’utente, e al segnale audio in ingresso al sistema, DynAudioRouter è in grado di reindirizzare dinamicamente i segnali in ingresso attivi verso le uscite. Nato per un contesto di improvvisazione tra strumenti acustici e live electronics, il sistema è scalabile e open source, quindi facilmente adattabile a situazioni diverse quali la spazializzazione del suono, le installazioni multimediali e il mixaggio.

This article provides novel insights on Hardy-Littlewood's conjecture (infinity and distribution of twin primes) and on Goldbach's conjecture; this work is primarily based on two primality theorems of congruence and of compcongruence. Study results in demonstration of Hardy-Littlewood and the Goldbach conjectures.The approach taken also opens up new areas of possible research in the field of Number Theory. Nell'articolo viene sviluppato uno studio sulla congettura di Hardy-Littlewood (infinità e distribuzione dei primi gemelli) e sulla congettura di Goldbach; esso è basato prioritariamente su due teoremi di primalità della congruenza e della compcongruenza. Lo studio perviene alla dimostrazione delle congetture di Hardy-Littlewood e di Goldbach. Lo studio, oltre ai risultati raggiunti, apre nuovi ambiti di possibile ricerca nel campo della Teoria dei numeri. Dato un numero N0  N, un numero n0  N, minore di N0 e tale che (N0-n0) sia dispari si chiama Prisotto di N0 se risulta che n0 ≢ N0(mod pi) ∀ pi ∈ ℙ(√(N 0)). Corollario 1.2.3 ∀ N0, n0 ∈ N con N0 ≥ 9, 0 ≤ n0 ≤ N0-pmax e pari se N0 è dispari o viceversa, con ℙ(√(0)) insieme dei numeri primi dispari ≤ √(0) e con pmax numero primo più alto di ℙ(√(0)), condizione necessaria e sufficiente affinché N0-n0 sia un numero primo è che n0 sia un numero prisotto di N0. Dim. Sostituendo ℙ(√(N 0)) a ℙ(√(N 0 − n 0)), a differenza del teorema (1.2.1), i numeri n0 minori di N0 ed appartenenti all'intervallo [N0-pmax., N0-3] non vengono considerati in quanto presentano tutti almeno una classe di congruenza mod pj, con pj  ℙ(√(N 0)), uguale a quella di pari modulo di N0. Infatti per gli n0  [N0-pmax., N0-3] , N0-n0 apparterrà all'intervallo [3, pmax] e quindi sarà uguale ad un numero primo o composto appartenente a questo intervallo; nel primo caso in base all' aritmetica modulare se N0-n0 = pj, con pj  ℙ(√(N 0)) ⸦ [3, pmax.] questo implica che [N0] mod pj-[n0] mod pj = [pj] mod pj = [0] da cui la congruenza mod pj di n0 con N0; se invece N0-n0 è uguale ad un numero composto m* pj , con pj  ℙ(√(N 0)) ⸦ [3, pmax.], si avrà che [N0] mod pj-[n0] mod pj = [m] mod pj * [pj] mod pj = [m] mod pj *[0] = [0] da cui la congruenza mod pj di n0 con N0. Viceversa se N0-n0 è un numero primo, appartenente all'intervallo ] pmax, N0], esso in quanto primo non sarà divisibile per nessun altro numero primo dispari inferiore o uguale di pmax e quindi della √(0) e pertanto N0 ed n0 risulteranno sempre non congrui ∀ ∈ ℙ(√(0)). Si è posto N 0 ≥ 9 in quanto per valori inferiori pmax non sarebbe definito.

Simulation is one of the most powerful tools we have for evaluating the performance of Opportunistic Networks. In this survey, we focus on available tools and models, compare their performance and precision and experimentally show the scalability of different simulators. We also perform a gap analysis of state-of-the-art Opportunistic Network simulations and sketch out possible further development and lines of research. This survey is targeted at students starting work and research in this area while also serving as a valuable source of information for experienced researchers.

Nel presente elaborato, partendo dalle basi fornite nel corso di "Teoria dei Circuiti Digitali", viene affrontato il problema della realizzazione di un moltiplicatore a 4bit con ritardi fino alla sua rappresentazione di Wallace. Verrà utilizzato il programma SPICE nella versione commerciale PSpice per l'esecuzione delle simulazioni, essendo diventato negli anni uno standard per la realizzazione di simulazioni circuitali. Partendo dalla scelta delle singole porte e dai loro ritardi dichiarati e simulati, si realizzeranno i vari componenti che costituiscono un moltiplicatore per poi giungere alla sua forma completa. Questo procedimento sarà svolto ponendo sempre particolare attenzione ai tempi di propagazione dei segnali, cercando dove possibile l'ottimizzazione. Di volta in volta sarà controllata la presenza di casi in cui i ritardi misurati differiscano dai valori attesi ideali; scegliendo in ogni occasione, se necessario, i componenti e il circuito che sintetizzano nel modo migliore il moltiplicatore di Wallace.

For many developing nations, agriculture is the fundamental source of economic engine. Without a significant increase in food production, the rise in global population during the 21st century would not have been conceivable. Thus, a key issue in agricultural expansion is the accurate forecast of healthy crop output / crop type utilizing machine learning (ML) algorithms. In this paper, the aim is to identify the most effective model that can help farmers in predicting the crop yield / crop type for the benefit of farmers. Seven popular algorithms such as K-Nearest Neighbor (KNN), Random Forest (RF) Classifier, Extreme Gradient Boosting(XGBoost), Light Gradient Boosting Machine (LightGBM), Artificial Neural Network (ANN), Support Vector Machine(SVM) and Multi-layer Perceptron (MLP) are investigated on the numerical crop dataset and performances are studied. Experimental results reveal that XGBoost gives the highest accuracy among the seven. Subsequently, Another crop image dataset is explored to identify crop type using transfer learning in convolutional neural networks (CNN). Different models for crop image classification with weights trained on ImageNet such as VGG-19 (Visual Geometry Group-19), VGG-16, Inception v3, ResNet-50 and EfficientNet-B0 are studied. Results based on the accuracy and kappa coefficient show that VGG-19 outperforms other techniques.

 In this paper, two crop datasets are investigated.  First one is numerical data which is downloaded crop dataset from   https://github.com/Shrey-B/AV-Janatahack-Machine-Learning-in-Agriculture obtained from US field data collection. The dataset contains 88858 labeled samples, eight features and three classes.  Second one is a collection of crop image data.  This dataset is downloaded from https://www.kaggle.com/datasets/aman2000jaiswal/agriculture .  Crop image dataset contains 1005 samples having five type of images namely maize, wheat, jute, rice and sugarcane. Each sample crop image consists of 224 × 224 pixels) of all category.    

Nel mercato mondiale delle sorgenti laser, i laser a semiconduttore (LS) hanno di gran lunga i volumi di vendita maggiori, e trovano applicazioni nei più svariati campi, dalla lettura/scrittura di dischi ottici, alla sensoristica, alle telecomunicazioni ottiche. Lo sviluppo delle tecniche di misura optoelettroniche ha consentito di indagare nel dettaglio le caratteristiche ed i

In the field of wireless communication and network planning, accurate path loss predictive modelling plays a vital role in understanding the behavior of signal propagation in diverse environments. Traditional empirical models have been widely used for path loss estimation, but they often lack the flexibility to adapt to complex scenarios. On the other hand, machine learning techniques have shown great potential in various domains, including wireless communication. This paper aims to present a hybrid empirical and machine learning approach for efficient path loss predictive modelling. By combining the strengths of empirical models and machine learning algorithms, we can enhance the accuracy and adaptability of path loss predictions. The following sections provide an overview of path loss modelling, explore traditional empirical techniques, discuss the application of machine learning approaches, and outline the methodology for the hybrid approach, along with evaluation and analysis. Finally, we conclude with a summary of findings and suggest future directions for research in this field.