Design and performance evaluation of a novel broadband THz modulator based on graphene metamaterial for emerging applications

Introduction: Metamaterials consist of periodic arrangements of artificial subwavelength units that possess electromagnetic properties not present in natural media. It has attracted more interest due to its ability to alter electromagnetic radiation in a flexible manner, which has resulted in the development of multiple radio frequency devices based on metamaterials. Metamaterials with the required frequency band for electric or magnetic resonance can be made using unit cell structure. The incident electromagnetic wave will enter the metamaterials and be kept there in the absence of reflection.Methods: This paper proposes a novel broadband THz absorber filter based on graphene for emerging applications. The proposed structure comprised of three parts. The top layer consists of graphene, the middle layer consists of dielectric and the bottom layer is made up of gold.Results: The proposed structure is experimentally designed and validated using the COMSOL simulator.Discussion: Simulation results show that the proposed absorber has better performance as compared with existing methods

Empowering the Internet of Things Using Light Communication and Distributed Edge Computing

With the rapid growth of connected devices, new issues emerge, which will be addressed by boosting capacity, improving energy efficiency, spectrum usage, and cost, besides offering improved scalability to handle the growing number of linked devices. This can be achieved by introducing new technologies to the traditional Internet of Things (IoT) networks. Visible light communication (VLC) is a promising technology that enables bidirectional transmission over the visible light spectrum achieving many benefits, including ultra-high data rate, ultra-low latency, high spectral efficiency, and ultra-high reliability. Light Fidelity (LiFi) is a form of VLC that represents an efficient solution for many IoT applications and use cases, including indoor and outdoor applications. Distributed edge computing is another technology that can assist communications in IoT networks and enable the dense deployment of IoT devices. To this end, this work considers designing a general framework for IoT networks using LiFi and a distributed edge computing scheme. It aims to enable dense deployment, increase reliability and availability, and reduce the communication latency of IoT networks. To meet the demands, the proposed architecture makes use of MEC and fog computing. For dense deployment situations, a proof-of-concept of the created model is presented. The LiFi-integrated fog-MEC model is tested in a variety of conditions, and the findings show that the model is efficient

Empowering the Internet of Things Using Light Communication and Distributed Edge Computing

With the rapid growth of connected devices, new issues emerge, which will be addressed by boosting capacity, improving energy efficiency, spectrum usage, and cost, besides offering improved scalability to handle the growing number of linked devices. This can be achieved by introducing new technologies to the traditional Internet of Things (IoT) networks. Visible light communication (VLC) is a promising technology that enables bidirectional transmission over the visible light spectrum achieving many benefits, including ultra-high data rate, ultra-low latency, high spectral efficiency, and ultra-high reliability. Light Fidelity (LiFi) is a form of VLC that represents an efficient solution for many IoT applications and use cases, including indoor and outdoor applications. Distributed edge computing is another technology that can assist communications in IoT networks and enable the dense deployment of IoT devices. To this end, this work considers designing a general framework for IoT networks using LiFi and a distributed edge computing scheme. It aims to enable dense deployment, increase reliability and availability, and reduce the communication latency of IoT networks. To meet the demands, the proposed architecture makes use of MEC and fog computing. For dense deployment situations, a proof-of-concept of the created model is presented. The LiFi-integrated fog-MEC model is tested in a variety of conditions, and the findings show that the model is efficient

An Improved Bald Eagle Search Algorithm with Deep Learning Model for Forest Fire Detection Using Hyperspectral Remote Sensing Images

This paper presents an improved Bald Eagle Search Algorithm with Deep Learning model for forest fire detection (IBESDL-FFD) technique using hyperspectral images (HSRS). The major intention of the IBESDL-FFD technique is to identify the presence of forest fire in the HSRS images. To achieve this, the IBESDL-FFD technique involves data pre-processing in two stages namely data augmentation and noise removal. Besides, IBES algorithm with NASNetLarge method was utilized as a feature extractor to determine feature vectors. Finally, Firefly algorithm (FFA) with denoising autoencoder (DAE) is applied for the classification of forest fire. The design of IBES and FFA techniques helps to adjust optimally the parameters contained in the NSANetLarge and DAE models respectively. For demonstrating the better outcomes of the IBESDL-FFD approach, a wide-ranging simulation was implemented and the outcomes are examined. The results reported the better outcomes of the IBESDL-FFD technique over the existing techniques with maximum average accuracy of 93.75%

Microsatellite instability screening in colorectal carcinoma: immunohistochemical analysis of MMR proteins in correlation with clinicopathological features and Ki-67 protein expression

Abstract Background Defects in mismatch repair (MMR) system or microsatellite instability (MSI) and detected in colorectal carcinoma (CRC), both in sporadic and more frequently in hereditary cases. Immunohistochemistry (IHC) is the most frequent method for MMR protein deficiency screening in CRCs. In this study, we aimed to evaluate immunohistochemical expression of MMR and Ki-67 in colorectal carcinoma with clinicopathological features. Methods In this study, we evaluated the immunohistochemical expression of MMR proteins including MSH6, MSH2, PMS2 and MLH1 in 50 resection materials with colorectal carcinoma. Their expression is correlated with clinicopathological features of patients together, with Ki-67 protein expression in attempt to screen the most significant predictor of microsatellite instability. Results Of the 50 cases of cancer colon, 28% were classified as MSI-H, 20% were MSI-L, and 52% were MSS. The most frequent pattern in MSI-H tumors was concurrent loss of MSH6 and PMS2 proteins. There was a significant correlation between MMR protein expression pattern with tumor size, grade, T-classification and stage (0.015, 0.0515, 0.0162 and 0.0391), respectively. MSI-H tumors were located more frequently in right colon, early TNM stage and poorly differentiated and infrequent distant metastases. There was a significant correlation between Ki-67 high expression and MSI status patterns in their common biological aspects distinct from MSI-negative tumors. Conclusions Mismatch repair defective colorectal carcinoma has characteristics clinicopathological features different from MSS tumors. The role of immunohistochemistry (IHC) for MSI evaluation is the easiest and effective way for evaluation of MMR deficiency in colorectal carcinoma

An Efficient Deep Learning Approach for Colon Cancer Detection

Colon cancer is the second most common cause of cancer death in women and the third most common cause of cancer death in men. Therefore, early detection of this cancer can lead to lower infection and death rates. In this research, we propose a new lightweight deep learning approach based on a Convolutional Neural Network (CNN) for efficient colon cancer detection. In our method, the input histopathological images are normalized before feeding them into our CNN model, and then colon cancer detection is performed. The efficiency of the proposed system is analyzed with publicly available histopathological images database and compared with the state-of-the-art existing methods for colon cancer detection. The result analysis demonstrates that the proposed deep model for colon cancer detection provides a higher accuracy of 99.50%, which is considered the best accuracy compared with the majority of other deep learning approaches. Because of this high result, the proposed approach is computationally efficient

HLA-DRB1 alleles in Egyptian rheumatoid arthritis patients: Relations to anti-cyclic citrullinated peptide antibodies, disease activity and severity

AbstractBackgroundHuman leukocyte antigen HLA-DRB1 alleles encoding a common amino acid sequence called shared epitope in the third hypervariable region of DRB1 molecule have been identified as risk alleles for rheumatoid arthritis (RA).Aim of the workThe aim was to study HLA-DRB1 01, 04 and 10 alleles in Egyptian RA patients and determine their relation with anticyclic citrullinated peptide (anti-CCP) antibody level, disease activity, clinical and radiological severity.Patients and methodsThe study involved 40 RA patients and 20 control. Simplified disease activity index (SDAI) was calculated, clinical severity was assessed using the mechanical joint score (MJS) and radiological severity evaluated using the simple erosion narrowing score (SENS). HLA-DRB1 genotyping and anti-CCP antibodies were detected.ResultsThe mean patients’ age was 41.6±12.7years and disease duration 8.9±7.7years. The frequency of HLA DRB1 01, 04 and 10 in patients was 42.5%, 60% and 25% respectively. Of them 04 was significantly higher than in controls (p=0.013) and was associated with anti-CCP positive cases (p=0.0008) while the absence of HLA-DRB1 alleles was significantly associated with negative anti-CCP negative RA (p=0.0008). There were significant associations between HLA-DRB1 01 and 04 with SDAI (p=0.0002 and p=0.005, respectively); between HLA-DRB1 04 and 10 with SENS (p=0.002 and p=0.001 respectively) and between HLA-DRB1 01, 04 and 10 with MJS (p=0.02, p=0.03 and p=0.02, respectively).ConclusionHLA-DRB1 04 is associated with RA in Egyptian patients and is strongly associated in the production of elevated titers of anti-CCP antibodies which contribute to the development, severity and activity of the disease

A Hybrid Compressive Sensing and Classification Approach for Dynamic Storage Management of Vital Biomedical Signals

The efficient compression and classification of medical signals, particularly electroencephalography (EEG) and electrocardiography (ECG) signals in wireless body area network (WBAN) systems, are crucial for real-time monitoring and diagnosis. This paper addresses the challenges of compressive sensing and classification in WBAN systems for EEG and ECG signals. To tackle the challenges of the compression process, a sequential approach is proposed. The first step involves compressing the EEG and ECG signals using the optimized Walsh-Hadamard transform (OWHT). This transform allows for efficient representation of the signals, while preserving their essential characteristics. However, the presence of noise can impact the quality of the compressed signals. To mitigate this effect, the signals are subsequently recovered using the Sparse Group Lasso 1 (SPGL1) algorithm and OWHT, which take into account the noise characteristics during the recovery process. To evaluate the performance of the proposed compressive sensing algorithm, two metrics are employed: mean squared error (MSE) and maximum correntropy criterion (MCC). These metrics provide insights into the accuracy and reliability of the recovered signals at different signal-to-sample ratios (SSRs). The results of the evaluation demonstrate the effectiveness of the proposed algorithm in accurately reconstructing the EEG and ECG signals, while effectively managing the noise interference. Furthermore, to enhance the classification accuracy in the presence of signal compression, a local binary pattern (LBP) tehnique is applied. This technique extracts discriminative features from the compressed signals. These features are then fed into a classification algorithm based on residual learning. This classification algorithm is trained from scratch and specifically designed to work with the compressed signals. The experimental results showcase the high accuracy achieved by the proposed approach in classifying the compressed EEG and ECG signals without the need for signal recovery. The findings of this study highlight the potential of the proposed approach in achieving efficient and accurate medical signal analysis in WBAN systems. By eliminating the computational burden of signal recovery and leveraging the advantages of compressive sensing, the proposed approach offers a promising solution for real-time monitoring and diagnosis, ultimately improving the overall efficiency and effectiveness of healthcare systems

Efficient Implementation of Homomorphic and Fuzzy Transforms in Random-Projection Encryption Frameworks for Cancellable Face Recognition

To circumvent problems associated with dependence on traditional security systems on passwords, Personal Identification Numbers (PINs) and tokens, modern security systems adopt biometric traits that are inimitable to each individual for identification and verification. This study presents two different frameworks for secure person identification using cancellable face recognition (CFR) schemes. Exploiting its ability to guarantee irrevocability and rich diversity, both frameworks utilise Random Projection (RP) to encrypt the biometric traits. In the first framework, a hybrid structure combining Intuitionistic Fuzzy Logic (IFL) with RP is used to accomplish full distortion and encryption of the original biometric traits to be saved in the database, which helps to prevent unauthorised access of the biometric data. The framework involves transformation of spatial-domain greyscale pixel information to a fuzzy domain where the original biometric images are disfigured and further distorted via random projections that generate the final cancellable traits. In the second framework, cancellable biometric traits are similarly generated via homomorphic transforms that use random projections to encrypt the reflectance components of the biometric traits. Here, the use of reflectance properties is motivated by its ability to retain most image details, while the guarantee of the non-invertibility of the cancellable biometric traits supports the rationale behind our utilisation of another RP stage in both frameworks, since independent outcomes of both the IFL stage and the reflectance component of the homomorphic transform are not enough to recover the original biometric trait. Our CFR schemes are validated on different datasets that exhibit properties expected in actual application settings such as varying backgrounds, lightings, and motion. Outcomes in terms standard metrics, including structural similarity index metric (SSIM) and area under the receiver operating characteristic curve (AROC), suggest the efficacy of our proposed schemes across many applications that require person identification and verification

A Novel Dynamic Bit Rate Analysis Technique for Adaptive Video Streaming over HTTP Support

Recently, there has been an increase in research interest in the seamless streaming of video on top of Hypertext Transfer Protocol (HTTP) in cellular networks (3G/4G). The main challenges involved are the variation in available bit rates on the Internet caused by resource sharing and the dynamic nature of wireless communication channels. State-of-the-art techniques, such as Dynamic Adaptive Streaming over HTTP (DASH), support the streaming of stored video, but they suffer from the challenge of live video content due to fluctuating bit rate in the network. In this work, a novel dynamic bit rate analysis technique is proposed to model client–server architecture using attention-based long short-term memory (A-LSTM) networks for solving the problem of smooth video streaming over HTTP networks. The proposed client system analyzes the bit rate dynamically, and a status report is sent to the server to adjust the ongoing session parameter. The server assesses the dynamics of the bit rate on the fly and calculates the status for each video sequence. The bit rate and buffer length are given as sequential inputs to LSTM to produce feature vectors. These feature vectors are given different weights to produce updated feature vectors. These updated feature vectors are given to multi-layer feed forward neural networks to predict six output class labels (144p, 240p, 360p, 480p, 720p, and 1080p). Finally, the proposed A-LSTM work is evaluated in real-time using a code division multiple access evolution-data optimized network (CDMA20001xEVDO Rev-A) with the help of an Internet dongle. Furthermore, the performance is analyzed with the full reference quality metric of streaming video to validate our proposed work. Experimental results also show an average improvement of 37.53% in peak signal-to-noise ratio (PSNR) and 5.7% in structural similarity (SSIM) index over the commonly used buffer-filling technique during the live streaming of video