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This study was conducted for six cities in southern Algeria, where the accuracy of three models—support vector machines (SVM), artificial neural networks (ANN) and a novel hybrid firefly algorithm-based model (FFA-ANN)—were investigated when estimating global solar irradiation throughout an eleven-year period, utilizing nine input parameters as input data. The goal of our novel suggested a hybrid FFA-ANN model, where we relied on the optimization Firefly algorithm to enhance the ANN model created. Despite the fact that the ANN and SVM models produced promising results, our suggested FFA-ANN hybrid model outperformed the stand-alone ANN-based model using three statistical factors—correlation coefficient, relative root mean squared error and mean absolute percent error—with the best values of (R = 0.9321, rRMSE = 9.35% and MAPE = 6.29%). The findings demonstrated that FFA-ANN was preferable to the optimized SVM and ANN models when forecasting daily global solar irradiation in all zones. Furthermore, after comparing the combinations, the study's findings showed that the ANN model depended on: Extraterrestrial solar irradiation (H0), declination and average temperature (Tavg) together with relative humidity (RH) as inputs in order to estimate daily sun radiation. Thus, the findings of this study suggest that in regions with dry climates and other places with comparable climates, the created model may be used to estimate daily global solar radiation whenever data is accessible.

Abstract An asymmetric cryptosystem is presented for encrypting multiple images in gyrator transform domains. In the encryption approach, the devil’s spiral Fresnel lens variable pure phase mask is first designed for each image band to be encrypted by using devil’ mask, random spiral phase and Fresnel mask, respectively. Subsequently, a novel random devil’ spiral Fresnel transform in optical gyrator transform is implemented to achieved the intermediate output. Then, the intermediate data is divided into two masks by employing random modulus decomposition in the asymmetric process. Finally, a random permutation matrix is utilized to obtain the ciphertext of the intact algorithm. For the decryption approach, two divided masks (private key and ciphertext) need to be imported into the optical gyrator input plane simultaneously. Some numerical experiments are given to verify the effectiveness and capability of this asymmetric cryptosystem.

This article presents a Johansen test assessing the predetermined long-term relationships of datasets from a photovoltaic (PV) plant to predict the power output of an island zone. The goal was to use Johansen’s model to predict the PV power generation in the island of Mauritius. In this article, time series using an on-site measurement dataset have been used to design an original prediction model, the Johansen model for PV power output. This model is trained to predict random monthly, weekly, and daily PV power outputs in different seasons and years. The experimental results demonstrate that the Johansen model is a powerful medium-term predicting tool.

Since 2007 Reunion Island, a French overseas region located in the Indian Ocean, aims to achieve energy self-sufficiency by 2030. The French government has made this insular zone an experimental territory for renewable energy resources (RES) by implementing great powers photovoltaic (PV) plants. However, the performance of PV conversion is highly climate dependent, and there have been many research contributions to show that the two main factors that influence PV cell efficiency are solar radiation and cell temperature. Moreover, considering the high variability of environmental factors on PV plants, the high penetration of PV in electric systems may threaten the stability and reliability of the electrical power grid. In this study, a linear relation analysis of time series data collected over one year is performed in order to investigate the dependent variable of PV power output from explanatory variables such as solar irradiance, cell temperature, wind speed and humidity. The originality of this paper is to apply cointegration methods, usual tools of econometrics, to PV systems. More precisely, this research work lies in the use a robust statistical method to model a vector cointegrating relationship linking the PV power output and the four environmental parameters mentioned above, to make accurate forecasts in a tropical area. The Johansen vector error correction model (VECM) cointegration approach is used to determine the most appropriate PV power output forecasting when the desired model is concerned with N explanatory variables and for N > 2. This long run equilibrium relationship has been tested over many years of data and the outcome is more than reliable when comparing the model to measured data.

In this work, a dielectric resonator antenna (DRA) is miniaturized by using Artificial Magnetic conductor (AMC) surface without disturbing other important parameters. The design has three main features: (i) simple micro-strip line feeding, (ii) use of parasitic metallic strip to achieve impedance matching, and (iii) use of AMC surface for design miniaturization. The miniaturization is performed for dielectric resonator antenna at 3.5 GHz. Without changing the basic characteristic of the antenna such as gain, resonance frequency and efficiency, the size of the antenna was reduced by 85%. The AMC surface and DRA both are positioned on the FR4 substrate. The AMC surface consists of small patches of copper. DRA is mounted on the AMC surface, which significantly reduces the overall DRA volume. Nine AMC patches were introduced with a small gap between them. AMC patches were shorted with the ground metal with the help of small metallic vias. For an overall performance analysis, the design was fabricated, and measured results were taken. The fabricated design covers a bandwidth of 180 MHz for -10 dB reference value of the reflection coefficient, which is mainly used for 5G wireless application. The design has 14.2% impedance bandwidth. Based on the analysis made for the proposed design, it is found that this simple technique highly reduces the DR volume (85%) and ground surface (15.5%) while maintaining the overall performance of the square DRA.

A novel optical signature and authentication cryptosystem is proposed by applying triangular association encryption algorithm (TAEA) and 3D Arnold transform in Gyrator domains. Firstly, a triangular association encryption algorithm (TAEA) is designed, which makes it possible to turn the diffusion of pixel values within bands into the diffusion within and between bands. Besides, the image signature function is considered and utilized in the proposed cryptosystem. Without the image signature, the original image cannot be restored even if all of the keys are obtained. Moreover, the image integrity authentication function is provided to prevent pixel values from being tampered with. Through the numerical simulation of various types of attacks, the effectiveness and capability of the proposed hyperspectral data signature and authentication cryptosystem is verified.

Radiomics with deep learning models have become popular in computer-aided diagnosis and have outperformed human experts on many clinical tasks. Specifically, radiomic models based on artificial intelligence (AI) are using medical data (i.e., images, molecular data, clinical variables, etc.) for predicting clinical tasks such as autism spectrum disorder (ASD). In this review, we summarized and discussed the radiomic techniques used for ASD analysis. Currently, the limited radiomic work of ASD is related to the variation of morphological features of brain thickness that is different from texture analysis. These techniques are based on imaging shape features that can be used with predictive models for predicting ASD. This review explores the progress of ASD-based radiomics with a brief description of ASD and the current non-invasive technique used to classify between ASD and healthy control (HC) subjects. With AI, new radiomic models using the deep learning techniques will be also described. To consider the texture analysis with deep CNNs, more investigations are suggested to be integrated with additional validation steps on various MRI sites.

Power output from photovoltaic (PV) systems in outdoor conditions is substantially influenced by climatic parameters such as solar irradiance and temperature. PV manufacturers always provide technical specifications in laboratory conditions but reliable relationship for the power output must be determined for accurate prediction under real operating conditions. For the present study, solar irradiance G, temperature T and electrical power output P data under real conditions are methodically and regularly inscribed in dataloggers. Hence, in this paper, we investigate rigorous and robust statistical methods for small sample such as Augmented Dickey-Fuller and Engle Granger for stationary series to determine the estimate regression between variables P, G & T. A first regression of power output P time series variable on solar irradiance G time series has shown spurious results and thus spurious regression. The first differences of such time series are stationary and a regression is proposed whereas temperature variable is identified as not significant and where autocorrelation of residuals is suspected. Finally, the novelty of this paper is the Engle & Granger method that is used to provide a relationship between variables P and G in a difference level. A final relationship without suspicious heteroscedasticity has been determined. Our model is formulated on the basis of PV real conditions statistical approach and is more realistic than steady approach models.

Abstract Background Emerging evidence suggests the presence of neuroanatomical abnormalities in subjects with autism spectrum disorder (ASD). Identifying anatomical correlates could thus prove useful for the automated diagnosis of ASD. Radiomic analyses based on MRI texture features have shown a great potential for characterizing differences occurring from tissue heterogeneity, and for identifying abnormalities related to these differences. However, only a limited number of studies have investigated the link between image texture and ASD. This paper proposes the study of texture features based on grey level co-occurrence matrix (GLCM) as a means for characterizing differences between ASD and development control (DC) subjects. Our study uses 64 T1-weighted MRI scans acquired from two groups of subjects: 28 typical age range subjects 4–15 years old (14 ASD and 14 DC, age-matched), and 36 non-typical age range subjects 10–24 years old (20 ASD and 16 DC). GLCM matrices are computed from manually labeled hippocampus and amygdala regions, and then encoded as texture features by applying 11 standard Haralick quantifier functions. Significance tests are performed to identify texture differences between ASD and DC subjects. An analysis using SVM and random forest classifiers is then carried out to find the most discriminative features, and use these features for classifying ASD from DC subjects. Results Preliminary results show that all 11 features derived from the hippocampus (typical and non-typical age) and 4 features extracted from the amygdala (non-typical age) have significantly different distributions in ASD subjects compared to DC subjects, with a significance of p

Abnormal cell (ABC) is a markedly heterogeneous tissue area and can be categorized into three main types: benign hyperplasia (BH), carcinoma (Ca), and intraepithelial neoplasia (IN) or precursor cancerous lesion. In this study, the goal is to determine and characterize the continuum of colorectal cancer by using a 3D-texture approach. ABC was segmented in preprocessing step using an active contour segmentation technique. Cell types were analyzed based on textural features extracted from the gray level cooccurrence matrices (GLCMs). Significant texture features were selected using an analysis of variance (ANOVA) of ABC with a p value cutoff of p

An encryption algorithm for hyperspectral data in fractional Fourier domain is designed. Firstly, the original hyperspectral image is separated into single bands and then each pair of bands are regarded as the real and imaginary part of a complex function by using an affine transform. Subsequently, the complex functions are encoded and transformed in fractional Fourier domain (FrFT). The parameters in affine transform and FrFT serve as the key of the encryption system. The proposed encryption scheme can not only protect the image information in spatial domains but also the spectrum information in spectral domains. Various experiments are given to demonstrate the validity and capability of the proposed encryption scheme.

PURPOSE:This paper proposes to characterize the continuum of colorectal cancer (CRC) using multiple texture features extracted from multispectral optical microscopy images. Three types of pathological tissues (PT) are considered: benign hyperplasia, intraepithelial neoplasia and carcinoma. MATERIALS AND METHODS:In the proposed approach, the region of interest containing PT is first extracted from multispectral images using active contour segmentation. This region is then encoded using texture features based on the Laplacian-of-Gaussian (LoG) filter, discrete wavelets (DW) and gray level co-occurrence matrices (GLCM). To assess the significance of textural differences between PT types, a statistical analysis based on the Kruskal-Wallis test is performed. The usefulness of texture features is then evaluated quantitatively in terms of their ability to predict PT types using various classifier models. RESULTS:Preliminary results show significant texture differences between PT types, for all texture features (p-value < 0.01). Individually, GLCM texture features outperform LoG and DW features in terms of PT type prediction. However, a higher performance can be achieved by combining all texture features, resulting in a mean classification accuracy of 98.92%, sensitivity of 98.12%, and specificity of 99.67%. CONCLUSIONS:These results demonstrate the efficiency and effectiveness of combining multiple texture features for characterizing the continuum of CRC and discriminating between pathological tissues in multispectral images.

Approaches for the design of fault tolerant Network-on-Chip (NoC) for use in System-on-Chip (SoC) reconfigurable technology using Field-Programmable Gate Array (FPGA) technology are challenging, especially in Multiprocessor System-on-Chip (MPSoC) design. To achieve this, the use of rigorous formal approaches, based on incremental design and proof theory, has become an essential step in the validation process. The Event-B method is a promising formal approach that can be used to develop, model and prove accurately SoC and MPSoC architectures. This paper proposes a formal verification approach for NoC architecture including the dependability constraints relating to the choice of the path routing of data packets and the strategy imposed for diversion when faulty routers are detected. The formalization process is incremental and validated by correct-by-construction development of the NoC architecture. Using the concepts of graph colouring and B-event formalism, the results obtained have demonstrated its efficiency for determining the bugs, and a solution to ensure a fast and reliable operation of the network when compared to existing similar methods.

Background: Colorectal cancer (CRC) is the third most common cancer among men and women. Its diagnosis in early stages, typically done through the analysis of colon biopsy images, can greatly improve the chances of a successful treatment. This paper proposes to use convolution neural networks (CNNs) to predict three tissue types related to the progression of CRC: benign hyperplasia (BH), intraepithelial neoplasia (IN), and carcinoma (Ca). Methods: Multispectral biopsy images of thirty CRC patients were retrospectively analyzed. Images of tissue samples were divided into three groups, based on their type (10 BH, 10 IN, and 10 Ca). An active contour model was used to segment image regions containing pathological tissues. Tissue samples were classified using a CNN containing convolution, max-pooling, and fully-connected layers. Available tissue samples were split into a training set, for learning the CNN parameters, and test set, for evaluating its performance. Results: An accuracy of 99.17% was obtained from segmented image regions, outperforming existing approaches based on traditional feature extraction, and classification techniques. Conclusions: Experimental results demonstrate the effectiveness of CNN for the classification of CRC tissue types, in particular when using presegmented regions of interest.

We present a new reliable Network-on-Chip (NoC) suitable for Dynamically Reconfigurable Multiprocessors on Chip systems. The proposed NoC is based on routers performing online error detection of routing algorithm and data packet errors. Our work focuses on adaptive routing algorithms which allow to bypass faulty components or processor elements dynamically implemented inside the network. The proposed routing error detection mechanism allows to distinguish routing errors from bypasses of faulty components. The new router architecture is based on additional diagonal state indications and specific logic blocks allowing the reliable operation of the NoC. The main originality in the proposed NoC is that only the permanently faulty parts of the routers are disconnected. Therefore, our approach maintains a high run time throughput in the NoC without data packet loss thanks to a self-loopback mechanism inside each router.

We present a methodology based on self-organization to manage resources in networked embedded systems based on reconfigurable hardware. Two points are detailed in this paper, the monitoring system used to analyse the system and the Local Marketplaces Global Symbiosis (LMGS) concept defined for self-organization of dynamically reconfigurable nodes.

Summary: New cognitive radio (CR) systems require high throughput and bandwidth. Hence, CR users need to detect wide frequency bands of the radio spectrum to exploit unused frequency channels. This paper proposes a new wideband spectrum sensing (WBSS) detection approach based on machine learning (ML) for scanning subchannels. The originality of the proposed approach is to detect spectrum opportunities using a narrowband spectrum sensing (NBSS) method‐based support vector machine (SVM) classification and two features: energy and goodness of fit (GoF). The simulation results show that the proposed WBSS approach‐based ML presents a higher probability of detection than the WBSS approach‐based conventional detectors, even at low signal‐to‐noise ratio (SNR). Finally, the software defined radio (SDR) implementation validates the proposed WBSS approach for real detection scenarios. [ABSTRACT FROM AUTHOR]

When designing solar systems and assessing the effectiveness of their many uses, estimating sun irradiance is a crucial first step. This study examined three approaches (ANN, GA-ANN, and ANFIS) for estimating daily global solar radiation (GSR) in the south of Algeria: Adrar, Ouargla, and Bechar. The proposed hybrid GA-ANN model, based on genetic algorithm-based optimization, was developed to improve the ANN model. The GA-ANN and ANFIS models performed better than the standalone ANN-based model, with GA-ANN being better suited for forecasting in all sites, and it performed the best with the best values in the testing phase of Coefficient of Determination (R = 0.9005), Mean Absolute Percentage Error (MAPE = 8.40%), and Relative Root Mean Square Error (rRMSE = 12.56%). Nevertheless, the ANFIS model outperformed the GA-ANN model in forecasting daily GSR, with the best values of indicators when testing the model being R = 0.9374, MAPE = 7.78%, and rRMSE = 10.54%. Generally, we may conclude that the initial ANN stand-alone model performance when forecasting solar radiation has been improved, and the results obtained after injecting the genetic algorithm into the ANN to optimize its weights were satisfactory. The model can be used to forecast daily GSR in dry climates and other climates and may also be helpful in selecting solar energy system installations and sizes. [ABSTRACT FROM AUTHOR]