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Abstract Transform-domain audio watermarking systems are more robust than time-domain systems. However, the main weakness of these systems is their high computational cost, especially for long-duration audio signals. Therefore, they are not desirable for real-time security applications where speed is a critical factor. In this paper, we propose a fast watermarking system for audio signals operating in the hybrid transform domain formed by the fractional Charlier transform (FrCT) and the dual-tree complex wavelet transform (DTCWT). The central idea of the proposed algorithm is to parallelize the intensive and repetitive steps in the audio watermarking system and then implement them simultaneously on the available physical cores on an embedded systems cluster. In order to have a low power consumption and a low-cost cluster with a large number of physical cores, four Raspberry Pis 4B are used where the communication between them is ensured using the Message Passing Interface (MPI). The adopted Raspberry Pi cluster is also characterized by its portability and mobility, which are required in watermarking-based smart city applications. In addition to its resistance to any possible manipulation (intentional or unintentional), high payload capacity, and high imperceptibility, the proposed parallel system presents a temporal improvement of about 70%, 80%, and 90% using 4, 8, and 16 physical cores of the adopted cluster, respectively.

Car manufacturers around the globe are in a race to design and build driverless cars. The concept of driverless is also being applied to any moving vehicle such as wheelchairs, golf cars, tourism carts in recreational parks, etc. To achieve this ambition, vehicles must be able to drive safely on streets stay within required lanes, sense moving objects, sense obstacles, and be able to read traffic signs that are permanent and even temporary signs. It will be a completely integrated system of the Internet of Things (IoT), Global Positioning System (GPS), Machine Learning (ML)/Deep Learning (DL), and Smart Technologies. A lot of work has been done on traffic sign recognition in the English language, but little has been done for Arabic traffic sign recognition. The concepts used for traffic sign recognition can also be applied to indoor signage, smart cities, supermarket labels, and others. In this paper, we propose two optimized Residual Network (ResNet) models (ResNet V1 and ResNet V2) for automatic traffic sign recognition using the Arabic Traffic Signs (ArTS) dataset. Additionally, the authors developed a new dataset specifically for Arabic Traffic Sign recognition consisting of 2,718 images taken from random places in the Eastern province of Saudi Arabia. The optimized proposed ResNet V1 model achieved the highest training and validation accuracies of 99.18% and 96.14%, respectively. It should be noted here that the authors accounted for both overfitting and underfitting in the proposed models. It is also important to note that the results achieved using the proposed models outperform similar methods proposed in the extant literature for the same dataset or similar-size dataset.

This research provides an illustration of a straightforward, budget-friendly, and low-profile multiple-input multiple-output (MIMO) antenna that is able to function at 5.9 GHz and has lowered mutual coupling between the antenna parts. The frequency spectrum of this antenna for automotive communications ranges from 5.85 GHz to 5.95 GHz. It is created specifically for this range. The suggested antenna has not one but two distinct radiating components, each of which is slit in order to give a significant amount of isolation between the radiators. The MIMO antenna has a total dimension of 60 × 87 mm2, which is notably tiny for Vehicular to anything (V2X) applications. Additionally, the MIMO antenna is printed on a 0.6 mm thick flexible polymide substrate. The most important takeaway from this study is that it is possible to achieve extremely low isolation (less than 28 dB) over the entire operational range. The suggested antenna for V2X communications has a radiation efficiency of around 95%, which allows it to achieve an Envelope Correlation Coefficient (ECC) of less than 0.001. The suggested configuration is analyzed in terms of S-parameters, radiation properties such as a two-dimensional radiation pattern, efficiency, and gain, as well as MIMO parameters. With such low-profile design the designed antenna achieves significant improvement in isolation radiation performance in the desired band of operation without adopting decoupling mechanism proves that the conformal antenna that is suggested in this study has been shown to be an important contender for vehicular communications.

Spam is some unwanted material that may be put in the form of images. While many machine learning approaches are effective at detecting textual spam, this is not true for image spam. In this paper, a new framework for identifying image spams is proposed. The images are divided into two categories: undesirable material contained in the form of images which is referred to as a spam image, whereas anything else is referred to as a ham image. Our proposed methodology is based on applying different pre-trained deep learning models, including InceptionV3, Densely Connected Convolutional Networks 121(DenseNet121), Residual Networks (ResNet50), Visual Geometry Group (VGG16) and MobileNetV2, to filter out the unwanted spam images. Different standard test datasets such as Dredze Dataset, Image Spam Hunter (ISH) Dataset and Improved Dataset are utilized in this paper for performance testing. Furthermore, transfer learning and data augmentation are employed to address the issue of a shortage of labeled data. In our implementation, the fully connected (FC) layer in the aforementioned pre-trained models is replaced with a Support Vector Machine (SVM) classifier, resulting in an improved accuracy. The obtained results reveal that ResNet50 model yields the best performance achieving 99.87% accuracy, 99.88% area under the curve (AUC), 99.98% sensitivity, 99.79% precision, 98.99% F1 score and a computational testing time of in the order of one to two seconds for the ISH dataset.

The recycling process is controversial on a worldwide scale since it is based on the concept of sustainability and minimizing the environmental footprint. From this perspective, a practical framework for managing the 2D nesting waste is presented in this paper, which is a crucial phase in many production processes. Deep Learning (DLs) models are used to detect (i.e., segmentation) and categorize (i.e., classification) waste area sizes to enhance the circular economy via recycling procedures. Moreover, the apparel industry for having huge 2D nesting waste is implemented in this paper. Furthermore, data augmentation is performed in this paper to overcome the lack of datasets. The segmentation stage is applied based on the SegNet deep convolutional neural network (DCNN). In addition to, the classification stage based on the DLs, ResNet34, InceptionV3, and DenseNet121 is implemented to classify our datasets. The experimental results demonstrate that the proposed framework outperforms other existing techniques, where the Area Under the Curve (AUC) of 99.87 %, detection success rate (DSR) of 99.88 %, sensitivity of 99.98 %, precision of 99.98 %, F1-score of 98.99 %, mean square error (MSE) from 0.01 % to 2.49 %, efficiency of 97.41 %, and computational time of 3.9 s. Therefore, our proposed framework achieves the best performance compared with the literature. The future work will focus on enhancing the circular economy by utilizing the 3D nested sheet waste.

Mode division multiplexing (MDM) is an emerging information transmission technique in which multiple data signals can be transmitted simultaneously on different modes of a single wavelength laser beam over a free-space channel. MDM is a potential technique for the realization of high-speed spectral efficient communication links for future generations of wireless networks. We present a novel MDM-based free-space optics (FSO) system. The system integrates a 3D hybrid modulation scheme produced by combining carrier suppressed-non-return-to-zero (CSNRZ), differential quadrature phase-shift keying (DQPSK), and polarization shift keying (PolSK) modulation schemes for beyond 100 Gbps applications. Three unrelated 40 Gbps data signals are modulated and transmitted on one optical carrier utilizing three distinct signal properties: amplitude (CSNRZ), phase (DQPSK), and polarization state (PolSK). The proposed 3D modulation scheme offers a high-capacity system, where each channel transmits 4 Gbps as compared to 1 Gbps in the case of OOK modulation. MDM using distinct Hermite Gaussian modes: (HG00 and HG01) of a laser beam is incorporated to boost the spectral efficiency and information rates of the FSO link. The proposed 120 Gbps single-channel MDM-FSO link performance is examined under the impact of different levels of dust and fog environmental conditions using quality factors and received eye diagrams as the performance metrics. This system achieved optimal performances up to 1250 m (very light dust), 540 m (light dust), 170 m (moderate dust), 750 m (low fog), and 425 m (medium fog). In the worst-case scenario, the system manages to work up to a 67 m range in dense dust with a maximum attenuation of 297.38 dB/km and a 200 m distance in heavy fog with only 90 dB/km attenuation which is less than 1/3rd of the attenuation measured for dense dust event. In addition, our results and the case studies confirm that dust introduces greater signal attenuation than fog. Therefore, an encounter with a dust environment should be considered as the bottleneck issue for FSO links. The creative contribution of this paper is to put forward a bandwidth-efficient MDM-FSO-enabled B5G system that could be deployed in harsh and challenging locations at reduced visibility. This is expected to be further technically sustainable owing to the use of advanced 3D hybrid optical orthogonal modulation and therefore find use in implementing 5G and 6G cellular and data networks.

Prediction of wireless channel scenarios is fundamental for modern wireless communication systems with diverse propagation conditions. Moreover, the type of data extracted from a wireless communication channel impulse response (CIR) is complex. In recent research, machine learning (ML) techniques have proven their success in classification problems of wireless communication scenarios and provide reasonable results. In this paper, a new enhanced feature selection method is proposed to improve the training model and classification performance of the conventional model. This improvement is achieved based on the concept of regularization in which the selection of the best features is considered before training the model under any propagation environment. The adoption of regularization leads to a high Total Explained Variance (TEV) during the process of kernel Principal Component Analysis (k-PCA). As a consequence, two principal features are used instead of three. The proposed model has high generalization ability since it reduces the features dimensionality (computational complexity) and, generally, enhances the ML classification performance. Experimental simulation is executed to compare the proposed model and the conventional one in terms of accuracy, precision and recall. The accuracy is increased from 97% to 99%, from 96% to 99%, from 89% to 97% and from 90% to 98% for k-nearest neighbor (k-NN), support vector machine (SVM), k-Means and Gaussian mixture model (GMM), respectively.

This paper surveys a new class of electromagnetic materials. The emphasis is on the bianisotropic medium; a complex material which has never been applied in antenna design. In this context, we investigate the behavior of the magnetoelectric elements of the gyrotropic reciprocal bianisotropic medium used as a grounded substrate by studying their effects on the input impedance and mutual coupling of a printed dipole antenna. Numerical integrative equation calculations are carried out using the spectral method of moments (SMoM) based on the analytical derivation of the appropriate spectral Green’s functions for the considered dipole configuration. The obtained results reveal a change in the sign of the real part of the input impedance. This is mainly due to the complex property of the considered medium. The medium has also minimized the input impedance ∼4 times (1kΩ); this can help in device matching. This medium has shown a significant impact on the input impedance as well as the antenna array coupling. The design miniaturization possibility is an important result of this research in the design of antenna arrays.

A novel 120 Gbps free space optics (FSO) transmission scheme is introduced by combining orbital angular momentum (OAM) multiplexed signals with spectral amplitude coded (SAC)-optical code division multiplexing access (OCDMA) technique. Four OAM beams, each carrying three independent channels with 10 Gbps data rate, are used for increasing the capacity of the FSO system to 120 Gbps. Enhanced double weight (EDW) codes are employed for the SAC-OCDMA system. The proposed system is simulated and its performance is compared for the twelve channels under different weather conditions including clear air (CA), varying levels of rain, haze and fog conditions. The obtained results reveal that longer propagation distances between transmitter and receiver are possible with a bit error rate (BER) of ∼ 10-5. The possible distances are, respectively, 300 m, 160 m, 200 m, and 150 m, under CA, heavy rain (HR), heavy haze (HH), and heavy fog (HF), with a system capacity of 120 Gbps.

In order to address the growing demands for both enhanced security levels and increased transmission capacity, this research proposes a novel approach for free space optical (FSO) transmission. The proposed design incorporates an identity column shift matrix (ICSM) code to ensure robust security. Additionally, capacity enhancement is achieved through the utilization of a 4-level quadrature amplitude modulation (4-QAM) scheme in conjunction with an orthogonal frequency division multiplexing (OFDM) modulator. The performance of the system is evaluated across three channels, each transmitting data at a rate of 20 Gb/s, while operating in an FSO channel that is subjected to varying atmospheric attenuation effects and atmospheric turbulence. Real meteorological data from three different cities [Alexandria, Egypt; Jeddah, Kingdom of Saudi Arabia (KSA); and Hyderabad, India], situated across two continents, are incorporated to demonstrate the practicality of implementing the proposed model in real-world environments. The experimental results reveal that an increase in atmospheric turbulence leads to a higher bit error rate (BER) and lower received optical power (ROP), resulting in degraded data reception. Furthermore, the study examines the impact of weather conditions, indicating that the longest and shortest propagation ranges of 12.5 and 0.286 km, respectively, are achieved under clear weather and heavy dust storms. These conditions yield an ROP of −9.5 dBm and a log (BER) of approximately −2.5. Conversely, in the presence of strong turbulence, the performance further deteriorates. The proposed model demonstrates its ability to transmit a message signal over a distance of 0.8375 km, with a log (BER) of −2.6 under weak atmospheric turbulence. However, under strong atmospheric turbulence at the same distance, the log (BER) increases to −0.5. Regarding specific cities, the FSO range for transmitting information signals is found to be 9.58 km in Jeddah, which decreases to 6.58 km in Alexandria and 5.17 km in Hyderabad due to the increased atmospheric attenuation in these cities.

Cancelable biometric recognition techniques play a vital role in the privacy and security of remote surveillance systems to keep the genuine users’ confidential data safe and away from intruders. This research work presents an efficient cancelable biometric recognition framework that exploits an irreversible hybrid encryption algorithm. It incorporates Deoxyribonucleic, Ribonucleic Acid sequence (DNA and RNA) encryption technique, and an evolutionary optimization technique, namely Genetic Algorithms (GAs). These techniques are employed to create completely deformed templates from their original ones. Hence, the main contribution is introducing a novel biometric security framework that achieves unique randomness characteristics using RNA and DNA sequences and the evolutionary GA technique. The proposed framework produces entirely deformed biometric templates by ciphering the main discriminative features of the biometric traits of the authorized clients. It is firstly initialized by creating several encrypted biometric images for the original users with the logistic map. After that, the initially encrypted images are transformed into vectors of a binary array. Then, they are converted to their corresponding introns, and exons, and consequently, their relevant codons are stored in the cloud database. These relevant codons are replaced by new ones after generating encrypted RNA lists. The utilized encryption key for each template is extracted from the original biometric image through excessive permutations between pixels. The GA optimization technique is applied to select the most convenient biometric features. Finally, after employing the GA-based cross-over and mutation operations, the chosen features are used to generate the cancelable biometric traits. To assess the proposed framework, six different biometric databases are considered. These databases are Olivetti Research Laboratory (ORL) Faces (gray), CASIA v.5 Faces (color), UPOL Iris (gray), Indian Institute of Technology Delhi (IIT Delhi) Ear (color and gray), Fingerprint, and CASIA Palmprint (color and gray). The security performance of the proposed encryption algorithm is compared to those of recent studies in this field, such as Optical Scanning Holography (OSH) and Double Random Phase Encoding (DRPE). The simulation results prove the superior performance of the proposed framework in terms of all adopted evaluation metrics. The proposed framework provides high Area under the Receiver Operating Characteristic (AROC) curve that reaches 0.9990, low False Acceptance Rate (FAR) of 0.0015, more uniform histograms, high correlation values for genuine users, and completely hidden biometric features. In addition, from the security perspective, the proposed framework achieves good entropy, Unified Average Changing Intensity (UACI), and Number of Pixels Change Rate (NPCR) values that reach 7.9960, 33.55%, and 99.65%, respectively.

We propose a novel design of orthogonal frequency division multiplexing-mode division multiplexing (OFDM-MDM) based radio over free space optical (RoFSO) transceiver under the dust effect. Two separate 20 Gbps-40 GHz 4-QAM data bearing optical beams are transported over two distinctive Hermite-Gaussian modes; HG00 and HG01, of a single-frequency laser to realize a single-channel 40 Gbps-80 GHz 5G communication link. The link performance and availability are investigated under the impact of clear climate and under varied dust conditions, including very light dust, light dust, moderate dust, and dense dust using signal-to-noise ratio (SNR) and total power as the key evaluation parameters. Further, we have determined the maximum reachable link distance within acceptable performance criteria, i.e., SNR 20 dB for each desert weather conditions. A higher data rate with tolerable SNR and power requirements for 5G applications has been verified.

In this work, a new framework for breast cancer image segmentation and classification is proposed. Different models including InceptionV3, DenseNet121, ResNet50, VGG16 and MobileNetV2 models, are applied to classify Mammographic Image Analysis Society (MIAS), Digital Database for Screening Mammography (DDSM) and the Curated Breast Imaging Subset of DDSM (CBIS-DDSM) into benign and malignant. Moreover, the trained modified U-Net model is utilized to segment breast area from the mammogram images. This method will aid as a radiologist's assistant in early detection and improve the efficiency of our system. The Cranio Caudal (CC) vision and Mediolateral Oblique (MLO) view are widely used for the identification and diagnosis of breast cancer. The accuracy of breast cancer diagnosis will be improved as the number of views is increased. Our proposed frame work is based on MLO view and CC view to enhance the system performance. In addition, the lack of tagged data is a big challenge. Transfer learning and data augmentation are applied to overcome this problem. Three mammographic datasets; MIAS, DDSM and CBIS-DDSM, are utilized in our evaluation. End-to-end fully convolutional neural networks (CNNs) are introduced in this paper. The proposed technique of applying data augmentation with modified U-Net model and InceptionV3 achieves the best result, specifically with the DDSM dataset. This achieves 98.87% accuracy, 98.88% area under the curve (AUC), 98.98% sensitivity, 98.79% precision, 97.99% F1 score, and a computational time of 1.2134 s on DDSM datasets.

Corona Virus Disease 2019 (COVID-19) has affected millions of people worldwide and caused more than 6.3 million deaths (World Health Organization, June 2022). Increased attempts have been made to develop deep learning methods to diagnose COVID-19 based on computed tomography (CT) lung images. It is a challenge to reproduce and obtain the CT lung data, because it is not publicly available. This paper introduces a new generalized framework to segment and classify CT images and determine whether a patient is tested positive or negative for COVID-19 based on lung CT images. In this work, many different strategies are explored for the classification task. ResNet50 and VGG16 models are applied to classify CT lung images into COVID-19 positive or negative. Also, VGG16 and ReNet50 combined with U-Net, which is one of the most used architectures in deep learning for image segmentation, are employed to segment CT lung images before the classifying process to increase system performance. Moreover, the image size dependent normalization technique (ISDNT) and Wiener filter are utilized as the preprocessing techniques to enhance images and noise suppression. Additionally, transfer learning and data augmentation techniques are performed to solve the problem of COVID-19 CT lung images deficiency, therefore the over-fitting of deep models can be avoided. The proposed frameworks, which comprise of, end-to-end, VGG16, ResNet50, and U-Net with VGG16 or ResNet50, are applied on the dataset which is sourced from COVID-19 lung CT images in Kaggle. The classification results show that using the preprocessed CT lung images as the input for U-Net hybrid with ResNet50 achieves the best performance. The proposed classification model achieves 98.98% accuracy (ACC), 98.87% area under the ROC curve (AUC), 98.89% sensitivity (Se), 97.99% precision (Pr), 97.88% F1- score, and 1.8974-second computational time.

In the fireworks industry (FI), many accidents and explosions frequently happen due to human error (HE). Human factors (HFs) always play a dynamic role in the incidence of accidents in workplace environments. Preventing HE is a main challenge for safety and precautions in the FI. Clarifying the relationship between HFs can help in identifying the correlation between unsafe behaviors and influential factors in hazardous chemical warehouse accidents. This paper aims to investigate the impact of HFs that contribute to HE, which has caused FI disasters, explosions, and incidents in the past. This paper investigates why and how HEs contribute to the most severe accidents that occur while storing and using hazardous chemicals. The impact of fireworks and match industry disasters has motivated the planning of mitigation in this proposal. This analysis used machine learning (ML) and recommends an expert system (ES). There were many significant correlations between individual behaviors and the chance of HE to occur. This paper proposes an ML-based prediction model for fireworks and match work industries in Sivakasi, Tamil Nadu. For this study analysis, the questionnaire responses are reviewed for accuracy and coded from 500 participants from the fireworks and match industries in Tamil Nadu who were chosen to fill out a questionnaire. The Chief Inspectorate of Factories in Chennai and the Training Centre for Industrial Safety and Health in Sivakasi, Tamil Nadu, India, significantly contributed to the collection of accident datasets for the FI in Tamil Nadu, India. The data are analyzed and presented in the following categories based on this study’s objectives: the effect of physical, psychological, and organizational factors. The output implemented by comparing ML models, support vector machine (SVM), random forest (RF), and Naïve Bayes (NB) accuracy is 86.45%, 91.6%, and 92.1%, respectively. Extreme Gradient Boosting (XGBoost) has the optimal classification accuracy of 94.41% of ML models. This research aims to create a new ES to mitigate HE risks in the fireworks and match work industries. The proposed ES reduces HE risk and improves workplace safety in unsafe, uncertain workplaces. Proper safety management systems (SMS) can prevent deaths and injuries such as fires and explosions.

The reliability and efficiency of free space optics (FSO) systems is significantly degraded by attenuation caused by different climate conditions and atmospheric turbulence. In this work, an FSO transmission system with high-speed transmission capability is proposed by deploying orthogonal frequency division multiplexing (OFDM) to mitigate channel fading effects. Further, we propose the hybridization of polarization division multiplexing (PDM) with CO-OFDM to improve the system capacity. We perform the bit error rate (BER) and optical signal to noise ratio (OSNR) comparative analysis of multi-level quadrature amplitude modulation (QAM) schemes, i.e., 4-QAM, 16-QAM, 32-QAM, and 64-QAM at 100, 120, 360, and 480 Gbps transmission rates, respectively. The obtained results reveal that the 4-QAM modulation scheme achieves the highest transmission range and the least OSNR requirement. In terms of spectral efficiency, 64-QAM shows the best performance. Also, the impact of different climate conditions on the system performance has been investigated. Through simulations, we demonstrate a successful transmission of 100 Gbps 4-QAM information at 15 km. Furthermore, we numerically investigated the increasing scintillation effect on the BER system performance. The proposed system not only reduces the channel fading but also improves the information rate and maximum transmission distance. The reported work can be considered for future FSO systems even in adverse climate conditions.

In this paper, we studied the probability distribution function (PDF) of the channel gain of dynamic underwater visible light communication (UVLC) model for different types of water using goodness-of-fit (GoF). We used different water channels at different system parameters with dynamic scenarios. First, the Zemax Optics Studio simulator is used to simulate dynamic UVLC channels. UVLC links are examined using Monte Carlo Ray-Tracing (MCRT) simulation for three different water channels; namely, pure sea water channel, clear ocean water channel and coastal ocean water channel at different configuration types. With the presence of blocking divers, we added a dynamic movement in a single input multiple users (SIMU) scenario. Our simulation is based on Zemax Programming Language (ZPL) in sequence with the Zemax Optics Studio. The GoF tests are used to get the degree of fitness between the simulation data and the set of well-known candidate distributions to determine the best fit. We used the R statistical programming language and applied predefined algorithms to determine the optimum degree of fitting for each statistical result. The Kolmogorov-Smirnov (KS), Chi-Square (CS), Cramer-Von-Mises (CVM) and Anderson-Darling (AD) tests are used to represent the four GoF statistical computation techniques for each channel scenario. The received power is enhanced by 35% when the detector movement area increases from 25 m2 to 100 m2 in clear ocean water channel. The obtained results reveal that the UVLC is best represented by Weibull, Gamma or Lognormal distributions.

Silicon Germanium (SiGe) solar cells with different bandgaps are used in the fabrication of the crystalline-Si/SiGe (c-Si/SiGe) double-junction cell in order to enhance the performance of the c-Si single devices. The new device is simulated with SCAPS-1D by placing the c-Si junction on the top of the SiGe solar cell. Through this simulation, one can assess the performance of the c-Si/SiGe double-junction device. The upper c-Si cell achieved an efficiency of 16.66% with 40.5 $\mu \text{m}$ total thickness, while the lower cell SiGe achieved ~ 19% efficiency. By analyzing the current matching between both cells at different bandgaps for SiGe cell (1.097 eV, 1.016 eV, and 0.928 eV), a mismatch is found. But, when applying 0.882 eV bandgap for the lower cell with absorber thicknesses of 110, 100, 90, and 6.2 $\mu \text{m}$ , a matching is obtained between the two junctions. The efficiency of the designed cell is found ~ 24.7%, with a maximum current density of 30.25 mA/cm2, achieved at an open circuit voltage of 1.01 V and a total thickness of 57.2 $\mu \text{m}$ . These results indicate that the double-junction device significantly exceeds the device performance by 7.6% with equal thickness of the optimized single-junction c-Si solar cell.

The higher bit rates, lower noise figure, decreasing the nonlinear penalty of fiber system, longer amplifier length, tight channel spacing and operating near the zero-dispersion wavelength are achieved by using Distributed Raman amplifiers (DRAs). In this paper, a new model of noise figure (NF) due to amplified spontaneous emission (ASE) is introduced for the DRA at different pump schemes: co-pumping, counter-pumping and bidirectional pumping.The variation of power conversion efficiency (PCE) with input pump power; input signal power and amplifier length for S-band is also investigated. It is found that, the NF increases exponentially with the fiber length and decreases exponentially with input signal power, power conversion efficiency (PCE) increases exponentially with the fiber length and input signal power and decreases exponentially with the input pump power. The obtained results show that, the noise figure (NF) reaches its minimum value of 8.6, 9.7 and 11 dB, while the PCE reaches its maximum value of 71%, 31% and 7% in counter-pumping, bidirectional pumping and co-pumping, respectively.

This work reports the development of an ultra-high capacity Nyquist-wavelength division multiplexing (WDM) superchannel based free space optics (FSO) transmission link. In the proposed link, 32 independent wavelength channels are multiplexed, each carrying 200 Gbps dual polarization-quadrature phase shift keying (DP-QPSK) data with 60 GHz channel spacing yield net transmission rate of 6.4 Tbps with spectral efficiency of 3.33 bits/s/Hz. The performance of the proposed FSO link has been numerically investigated under the influence of dynamic weather conditions such as clear, haze, rain, and fog conditions. Also, we investigate the BER performance of the proposed link under the influence of beam divergence angle. Further, we compare the proposed FSO link performance with previous works reported in the literature showing the superiority of our proposed link. The simulative investigation of the proposed link was carried out using Optisystem version 14 which is a powerful tool for the development and investigation of optical networks.

Recently, biometric systems are extensively and commonly utilized for authentication and verification applications. The security issue and the dependence on a specific biometric for the biometric verification process are the main challenges confronted in biometric systems. The security issue comes due to the exploitation of the original biometrics in stored servers. Therefore, if any attacks have been introduced to the stored biometrics, they will be missed indefinitely. Consequently, the stored original biometrics must be secured through maintaining and storing these templates away from exploitation in their servers. So, there is a need for designing a cancelable biometric recognition system (CBRS) that is a promising protection trend in biometric verification and authentication fields. The CBRS is based on the conversion of biometric data or its features to a different arrangement. In this article, a novel CBRS based on the suggested optical PTFT (Phase Truncated Fourier Transform) asymmetric encryption algorithm is introduced. In the proposed algorithm, two different distributions of phases in the output and Fourier planes are maintained as deciphering keys, and thus, the encryption keys will not be utilized for the decryption process. This leads to the advantage that the two ciphering keys may be utilized as public secret keys to encrypt distinct biometric images. Consequently, the suggested PTFT cryptosystem is an asymmetric encryption/decryption technique compared to the preceding related optical encryption techniques that are symmetric techniques such as Optical Scanning Holography (OSH) and Double Random Phase Encoding (DRPE). The suggested PTFT asymmetric encryption algorithm also has a wonderful practical performance in security applications. One of the main contributions of the proposed optical PTFT asymmetric encryption algorithm is that it removes the linearity features of the optical OSH and DRPE symmetric encryption algorithms through its great features of the phase truncation nonlinear operation. Subsequently, this produces an encrypted biometric template with two public keys, and the authenticated user can retrieve the original biometric template utilizing two private keys with achieving a high security and cancelability performance for the stored biometrics. To confirm the efficacy of the suggested optical encryption algorithm for developing a secure CBRS, various biometric datasets of face, ear, palmprint, fingerprint, and iris images are examined and analyzed. Extensive comparative analyses are performed amongst the suggested algorithm and the optical OSH and DRPE encryption algorithms. The experimental outcomes achieved for performance quality assessment assure that the suggested CBRS is reliable, robust, and realistic. It has great security and cancelability proficiency that expose excellent cancelable biometric recognition performance even in the existence of noise. Moreover, the performed experiments declare that the suggested CBRS guarantee an average FRR (False Reject Rate) of 0.0012, EER (Equal Error Rate) of 0.0019, and FAR (False Accept Rate) of 0.0030, and an average AROC (Areas under the Receiver Operating Characteristic) of 0.9996.

Distributed Power Generation and Energy Storage Systems (DPG-ESSs) are crucial to securing a local energy source. Both entities could enhance the operation of Smart Grids (SGs) by reducing Power Loss (PL), maintaining the voltage profile, and increasing Renewable Energy (RE) as a clean alternative to fossil fuel. However, determining the optimum size and location of different methodologies of DPG-ESS in the SG is essential to obtaining the most benefits and avoiding any negative impacts such as Quality of Power (QoP) and voltage fluctuation issues. This paper’s goal is to conduct comprehensive empirical studies and evaluate the best size and location for DPG-ESS in order to find out what problems it causes for SG modernization. Therefore, this paper presents explicit knowledge of decentralized power generation in SG based on integrating the DPG-ESS in terms of size and location with the help of Metaheuristic Optimization Algorithms (MOAs). This research also reviews rationalized cost-benefit considerations such as reliability, sensitivity, and security studies for Distribution Network (DN) planning. In order to determine results, various proposed works with algorithms and objectives are discussed. Other soft computing methods are also defined, and a comparison is drawn between many approaches adopted in DN planning.

In this research, a one-dimensional (1D) photonic structure was employed to study the nature of both enamel and dentine teeth at the signal of 1.8 THz. A simple three layer one-dimensional crystal is chosen to avoid fabrication intricacy. The materials and methods for sample preparations are discussed. The principle of investigation of caries in the teeth relies on the amount of reflected signal from the structure. Similarly, reflectance is a function of refractive indices and thickness of each layer, the nature of both substrate and infiltrated materials, and the configuration of the structure. Apart from this, the fabrication process of one-dimensional structure and experimental set-up was proposed in this article. The numerical treatment is explained here to obtain reflectance, and subsequently, the output potential. Comparison studies on output potential between enamel and dentine are also shown through graphical representation. The output result in terms of milli-Volt (mV) were obtained at the output end and collected at the photodiode. Interesting results were also observed at the photodetector. For example; the output potential of the reflected signal is around 0.18 mV for both enamel and dentine teeth whereas the potential is more than 0.26 mV and 0.31 mV for caries in dentine and enamel, respectively. Finally, it was inferred that the nature of teeth pertaining to the caries in the enamel and dentine teeth can be investigated by identifying the amount of potential at the output end.

The modest objective is to check the integrated effect of energy storage systems (ESSs) and distributed generations (DGs) and compare the optimization of the size and location of ESS and DG to explore its challenges for smart grids (SGs) modernization. The research enlisted different algorithms for cost-effectiveness, security, voltage control, and less power losses. From this perspective, optimization of the distribution network’s energy storage and capacity are being performed using a variety of methods, including the particle swarm, ant-lion optimization, genetic, and flower pollination algorithms. The experimental outcomes demonstrate the effectiveness of these techniques in lowering distribution network operating costs and controlling system load fluctuations. The efficiency and dependability of the distribution network (DN) are both maximized by these strategies.

Choosing a proper Region of Interest (ROI) for Remote Photoplethysmography (rPPG) is essential and a challenging first step, and it has a direct effect on the accuracy and reliability of the overall heart rate (HR) algorithm. Non-skin areas have no contribution to the HR information; however, few works have tackled the issue of non-skin pixels included in the ROI. First, this paper considers improving the quality of the rPPG signal by filtering out non-skin pixels included within the ROI. The feasibility of employing skin segmentation for ROI definition is demonstrated. Then, this technique is compared with our previous real-time rPPG-based method. Moreover, we explore the effect of extracting the HR from three ROIs using signal fusion. Second, we give a comprehensive account of the examined methods in our algorithm for face detection, face tracking, skin detection, and blind signal separation. Finally, we compare our rPPG measurements with ground truth values obtained from a commercial pulse oximeter. Based on the simulation results, the proposed algorrithm significantly improves the quality of the rPPG technique.

Both light emitting diode (LED) characteristics for illumination and communication simultaneously have made visible light communication-orthogonal frequency division multiplexing (VLC-OFDM) a strong competitive to radio frequency (RF). In this juncture, to improve signal to noise ratio (SNR) and coverage contour, the wavelet-OFDM is suggested for indoor VLC systems. In this paper, a wavelet VLC-OFDM is proposed for imaging multiple-input multiple-output (MIMO) systems. The proposed wavelet-OFDM is exploited for a hybrid space-frequency domain pre-equalization technique instead of the traditional fast Fourier transform (FFT)-OFDM technique. The Meyer filter is selected and employed in the proposed technique. A comparable achievement is elaborated for several numbers of channels to achieve the enhanced performance in terms of bit rate and coverage contour. In addition, a useful comparison is executed between our wavelet VLC-OFDM and the traditional FFT-OFDM for a hybrid space-frequency domain pre-equalization technique. The simulation results emphasize the superiority point of wavelet VLC-OFDM MIMO system by improving the coverage contour by ~20% over the traditional OFDM at a 10−3 bit error rate (BER) target. Hence, the proposed technique can be potentially executed in indoor VLC-MIMO systems.

Massive multiple input multiple output (MIMO), also known as a very large-scale MIMO, is an emerging technology in wireless communications that increases capacity compared to MIMO systems. The massive MIMO communication technique is currently forming a major part of ongoing research. The main issue for massive MIMO improvements depends on the number of transmitting antennas to increase the data rate and minimize bit error rate (BER). To enhance the data rate and BER, new coding and modulation techniques are required. In this paper, a generalized spatial modulation (GSM) with antenna grouping space time coding technique (STC) is proposed. The proposed GSM-STC technique is based on space time coding of two successive GSM-modulated data symbols on two subgroups of antennas to improve data rate and to minimize BER. Moreover, the proposed GSM-STC system can offer spatial diversity gains and can also increase the reliability of the wireless channel by providing replicas of the received signal. The simulation results show that GSM-STC achieves better performance compared to conventional GSM techniques in terms of data rate and BER, leading to good potential for massive MIMO by using subgroups of antennas.

For 5G and beyond cellular communication systems, new coding and modulation techniques are suggested to reach the requirements of high data rate and quality of service. In this paper, a new space-time coded orthogonal transform division multiplexing (STC OTDM) technique is proposed for 5G applications. The proposed system is used to enhance the data rate and performance of the orthogonal transform division multiplexing (OTDM) technique. The proposed system is based on using space-time coding (STC) with OTDM to increase the system diversity and consequently the system performance. The OTDM technique is based on transmitting data on orthogonal basis functions obtained from the Singular Value Decomposition (SVD) of the channel impulse response of the desired user. Various modulation techniques like QPSK, 64-QAM, and 256-QAM are investigated using different subcarriers and channel models. The simulation results show that the proposed system achieved a better performance when compared to classical and recent multicarrier techniques. The proposed technique increases the diversity gain resulting in a decrease in the fading effect of the multipath channel and an enhancement in the bit error rate (BER) performance. The proposed technique also provides a secure data transmission to the desired user as his data is sent on the basis functions extracted from his own channel impulse response that cannot be decoded by other users.

Free Space Optics (FSO) Communication has attracted the attention of the researchers in the last decade due to its high data rate, security, and low cost. Relay-assisted techniques are used to divide the distance to shorter hops in order to mitigate the effects of turbulence, weather attenuation, pointing error, and geometric loss. Choosing an active relay per time slot has been proven to enhance the performance of the system and decrease the loading effect on the system when compared to all active relays. This paper investigates the best relay that can be selected according to the source to relay (S-R) channel coefficient, relay to destination (R-D) channel coefficient, and source to destination (S-D) channel coefficient. A comprehensive comparison is applied to the three following cases: (a) Broadcast phase from source to relay to select the best (Proactive-Relay); (b) Broadcast phase from relay to destination after broadcasting to all relays then select (Reactive-relays); and, (c) Direct link from source-to-best relay-to-destination to conclude which method is better for different scenarios, such as turbulence regime, number of relays, different pointing error effect, and severity of S-R as compared to R-D and vice versa. The selection methods regard to four aspects: (1) Number of relays (two or three relays); (2) Distance between Source-Relay and Relay-Destination (D = 400–600 m, 500–500 m, and 600–400 m); (3) The different turbulence of Log-normal channel and Gamma-Gamma channel (with a refractive index coefficient( C n 2 = 0.5 × 10−14, 2 × 10−14 and 5 × 10−14)); and finally, (4) Beam waist ω z (pointing error).

Boosting is a machine learning approach built upon the idea of producing a highly precise prediction rule by combining many relatively weak and imprecise rules. The Adaptive Boosting (AdaBoost) algorithm was the first practical boosting algorithm. It remains one of the most broadly used and studied, with applications in many fields. In this paper, the AdaBoost algorithm is utilized to improve the bit error rate (BER) of different modulation techniques. By feeding the noisy received signal into the AdaBoost algorithm, it is able to recover the transmitted data from the noisy signal. Consequently, it reconstructs the constellation diagram of the modulation technique. This is done by removing the noise that affects and changes the signal space of the data. As a result, AdaBoost shows an improvement in the BER of coherently detected binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK). The AdaBoost is next used to improve the BER of the noncoherent detection of the used modulation techniques. The improvement appears in the form of better results of the noncoherent simulated BER in comparison to that of the theoretical noncoherent BER. Therefore, the AdaBoost algorithm is able to achieve a coherent performance for the noncoherent system. The AdaBoost is simulated for several techniques in additive white Gaussian noise (AWGN) and Rayleigh fading channels so, as to verify the improving effect of the AdaBoost algorithm.

In this paper, the performance of a spectral amplitude coding-optical code division multiple access (SAC-OCDMA) system is investigated utilizing a single photodiode (SPD) detection technique. The proposed system uses enhanced double weight (EDW) codes as signature codes with three simultaneous users to overcome both phase-induced intensity noise (PIIN) and multiple access interference (MAI). In addition, a dispersion compensating fiber (DCF) is used in order to decrease the group velocity dispersion (GVD) caused in the single mode fiber. An erbium-doped fiber amplifier (EDFA) is used to overcome the attenuation. The use of both DCF and EDFA leads to an appreciable enhancement in the system performance. The system performance is evaluated through its bit error rate (BER), Q-factor, and received power. A comparison between the EDW codes and modified double weight (MDW) codes on the SAC-OCDMA system is demonstrated. Simulation is carried out through Optisystem ver. 7. The simulation results show that: (a) using an avalanche photodiode (APD) over PIN photodiode allows data transmission over longer distances; (b) the use of DCF improves the system BER;(c) using MDW codes gives better BER than using EDW codes.

Free-space optical (FSO) communication systems have attracted considerable research interest owing to its high transmission rates and its efficient solution for the last mile problem. Atmospheric turbulence degrades the performance of FSO systems. To estimate the received signal, Channel State Information (CSI) or blind detection is used. The blind detection estimates the received signal without the need for pilot signals which reduce the throughput and increase the complexity of the signal. In this paper, we have proposed a relay technique which utilizes the blind detection method for FSO communications. This proposed technique would improve the performance of the FSO system compared to the direct link method. It is observed that the proposed method achieves a signal-to-noise ratio (SNR) close to the SNR reported by CSI method maintaining the same average bit error rate, provided that only a small observation window is employed. Moreover, Monte Carlo simulation results are further provided to demonstrate the validity of the derived approximated average bit error rate.

In this paper, a comprehensive study on erbium-doped fiber amplifier (EDFA) characteristics under temperature variation has been performed. The rate and propagation equations that characterize EDFA performance pumped at 980 nm and 1480 nm in the forward direction are solved numerically. The Boltzmann distribution between the pump and the gain wavelength is taken into account, and is found to be effective when pumping only at 1480 nm. In addition, a full comparison between the effect of temperature on some of the EDFA characteristics such as the maximum peak gain, optimum fiber length, saturation input power, and saturation output power has been carried out. The temperature variation in the range from −40 °C to +80 °C is taken into account.

In this work, different FBG temperature sensors are designed and evaluated with various apodization profiles. Evaluation is done under a wide range of controlling design parameters like sensor length and refractive index modulation amplitude, targeting a remarkable temperature sensing performance. New judgment techniques are introduced such as apodization window roll-off rate, asymptotic sidelobe (SL) decay level, number of SLs, and average SL level (SLav). Evaluation techniques like reflectivity, Full width at Half Maximum (FWHM), and Sidelobe Suppression Ratio (SLSR) are also used. A “New” apodization function is proposed, which achieves better performance like asymptotic decay of 18.4 dB/nm, high SLSR of 60 dB, high channel isolation of 57.9 dB, and narrow FWHM less than 0.15 nm. For a single accurate temperature sensor measurement in extensive noisy environment, optimum results are obtained by the Nuttall apodization profile and the new apodization function, which have remarkable SLSR. For a quasi-distributed FBG temperature sensor the Barthann and the new apodization profiles obtain optimum results. Barthann achieves a high asymptotic decay of 40 dB/nm, a narrow FWHM (less than 25 GHZ), a very low SLav of −45.3 dB, high isolation of 44.6 dB, and a high SLSR of 35 dB. The new apodization function achieves narrow FWHM of 0.177 nm, very low SL of −60.1, very low SLav of −63.6 dB, and very high SLSR of −57.7 dB. A study is performed on including an unapodized sensor among apodized sensors in a quasi-distributed sensing system. Finally, an isolation examination is performed on all the discussed apodizations and a linear relation between temperature and the Bragg wavelength shift is observed experimentally and matched with the simulated results.

A numerical investigation of the performance characteristics of erbium doped fiber amplifier using different host materials is presented. The emission and absorption curves of each of these hosts are fitted to Guassian fitting parameters. A software program is then implemented to calculate the gain coefficient, gain spectrum and the equivalent input noise factors in forward and reverse directions. The hosts under consideration are: almino-germanosilicate, bismuth, LiNbO3, tellurite, sodium niobium phosphate, oxyfluoride silicate, Al2O3 and fluoride phosphate glasses. The corresponding gain covers the 1450-1650 nm wavelength range.