UWB Reflectarray Antenna for Chipless RFID Reader Gain Enhancement

The main limitation of chipless Radio Frequency Identification (RFID) systems is its short reading range which is generally less than  as the amplitude of the back scattered tag signal is inversely proportional to the fourth root of the reading distance. In this paper, a design of reflectarray (RA) antenna consisting of unified unit cell. Five different unit cells structures centered at 6GHz for chipless RFID reader applications is introduced. The proposed RA has a narrow half power beam width (HPBW) and high gain which significantly enhance the reader sensitivity, maximize the reader reading range, reduce the multipath effects, and improve the tag localization. The proposed RA is realized on a rectangular single layer Rogers RT5880 lossy substrate of thickness and relative permittivity. radiating cells or elements with uniform element spacing are arranged on the rectangular substrate of dimensions and fed by a pyramidal horn antenna with gain of   and HPBW equals 46.7°and 42.8° at E-plane and H-plane respectively. The simulation results showed that the proposed RA gives high gain up to which is greater than the feeder gain by and three times narrower HPBW of about .It operates over frequency range from to with fractional bandwidth (FBW) and has side lobe level,, which can't be achieved by the conventional antenna arrays

Efficient Multimodal Deep-Learning-Based COVID-19 Diagnostic System for Noisy and Corrupted Images

Introduction: In humanity\u27s ongoing fight against its common enemy of COVID-19, researchers have been relentless in finding efficient technologies to support mitigation, diagnosis, management, contact tracing, and ultimately vaccination. Objectives: Engineers and computer scientists have deployed the potent properties of deep learning models (DLMs) in COVID-19 detection and diagnosis. However, publicly available datasets are often adulterated during collation, transmission, or storage. Meanwhile, inadequate, and corrupted data are known to impact the learnability and efficiency of DLMs. Methods: This study focuses on enhancing previous efforts via two multimodal diagnostic systems to extract required features for COVID-19 detection using adulterated chest X-ray images. Our proposed DLM consists of a hierarchy of convolutional and pooling layers that are combined to support efficient COVID-19 detection using chest X-ray images. Additionally, a batch normalization layer is used to curtail overfitting that usually arises from the convolution and pooling (CP) layers. Results: In addition to matching the performance of standard techniques reported in the literature, our proposed diagnostic systems attain an average accuracy of 98% in the detection of normal, COVID-19, and viral pneumonia cases using corrupted and noisy images. Conclusions: Such robustness is crucial for real-world applications where data is usually unavailable, corrupted, or adulterated

Subcarrier Gain Based Power Allocation in Multicarrier Systems, Journal of Telecommunications and Information Technology, 2014, nr 1

The Orthogonal Frequency Division Multiplexing (OFDM) transmission is the optimum version of the multicarrier transmission scheme, which has the capability to achieve high data rate. The key issue of OFDMsystem is the allocation of bits and power over a number of subcarriers. In this paper, a new power allocation algorithm based on subcarrier gain is proposed to maximize the bit rate. For OFDM systems, the Subcarrier Gain Based Power Allocation (SGPA) algorithm is addressed and compared with the standard Greedy Power Allocation (GPA). The authors demonstrate by analysis and simulation that the proposed algorithm reduces the computational complexity and achieves a near optimal performance in maximizing the bit rate over a number of subcarrier

Efficient framework for brain tumor detection using different deep learning techniques

The brain tumor is an urgent malignancy caused by unregulated cell division. Tumors are classified using a biopsy, which is normally performed after the final brain surgery. Deep learning technology advancements have assisted the health professionals in medical imaging for the medical diagnosis of several symptoms. In this paper, transfer-learning-based models in addition to a Convolutional Neural Network (CNN) called BRAIN-TUMOR-net trained from scratch are introduced to classify brain magnetic resonance images into tumor or normal cases. A comparison between the pre-trained InceptionResNetv2, Inceptionv3, and ResNet50 models and the proposed BRAIN-TUMOR-net is introduced. The performance of the proposed model is tested on three publicly available Magnetic Resonance Imaging (MRI) datasets. The simulation results show that the BRAIN-TUMOR-net achieves the highest accuracy compared to other models. It achieves 100%, 97%, and 84.78% accuracy levels for three different MRI datasets. In addition, the k-fold cross-validation technique is used to allow robust classification. Moreover, three different unsupervised clustering techniques are utilized for segmentation

Deep Learning Modalities for Biometric Alteration Detection in 5G Networks-Based Secure Smart Cities

Smart cities and their applications have become attractive research fields birthing numerous technologies. Fifth generation (5G) networks are important components of smart cities, where intelligent access control is deployed for identity authentication, online banking, and cyber security. To assure secure transactions and to protect user’s identities against cybersecurity threats, strong authentication techniques should be used. The prevalence of biometrics, such as fingerprints, in authentication and identification makes the need to safeguard them important across different areas of smart applications. Our study presents a system to detect alterations to biometric modalities to discriminate pristine, adulterated, and fake biometrics in 5G-based smart cities. Specifically, we use deep learning models based on convolutional neural networks (CNN) and a hybrid model that combines CNN with convolutional long-short term memory (ConvLSTM) to compute a three-tier probability that a biometric has been tempered. Simulation-based experiments indicate that the alteration detection accuracy matches those recorded in advanced methods with superior performance in terms of detecting central rotation alteration to fingerprints. This makes the proposed system a veritable solution for different biometric authentication applications in secure smart cities

Low Complexity Greedy Power Allocation Algorithm for Proportional Resource Allocation in Multi-User OFDM Systems, Journal of Telecommunications and Information Technology, 2012, nr 4

Multi-User Orthogonal Frequency Division Multiplexing (MU-OFDM) is an efficient technique for achieving high downlink capacity in high-speed communication systems. A key issue in MU-OFDM is the allocation of the OFDM subcarriers and power to users sharing the channel. In this paper a proportional rate-adaptive resource allocation algorithm for MU-OFDM is presented. Subcarrier and power allocation are carried out sequentially to reduce the complexity. The low complexity proportional subcarriers allocation is followed by Greedy Power Allocation (GPA) to solve the rate-adaptive resource allocation problem with proportional rate constraints for MU-OFDM systems. It improves the work of Wong et al. in this area by introducing an optimal GPA that achieves approximate rate proportionality, while maximizing the total sum-rate capacity of MU-OFDM. It is shown through simulation that the proposed GPA algorithm performs better than the algorithm of Wong et al., by achieving higher total capacities with the same computational complexity, especially, at larger number of users and roughly satisfying user rate proportionality

SVD Audio Watermarking: A Tool to Enhance the Security of Image Transmission over ZigBee Networks, Journal of Telecommunications and Information Technology, 2011, nr 4

The security is important issue in wireless networks. This paper discusses audio watermarking as a tool to improve the security of image communication over the IEEE 802.15.4 ZigBee network. The adopted watermarking method implements the Singular-Value Decomposition (SVD) mathematical technique. This method is based on embedding a chaotic encrypted image in the Singular Values (SVs) of the audio signal after transforming it into a 2-D format. The objective of chaotic encryption is to enhance the level of security and resist different attacks. Experimental results show that the SVD audio watermarking method maintains the high quality of the audio signals and that the watermark extraction and decryption are possible even in the presence of attacks over the ZigBee network

An Efficient Chaotic Interleaver for Image Transmission over IEEE 802.15.4 Zigbee Network, Journal of Telecommunications and Information Technology, 2011, nr 2

This paper studies a vital issue in wireless communications, which is the transmission of images over wireless networks. IEEE ZigBee 802.15.4 is a short-range communication standard that could be used for small distance multimedia transmissions. In fact, the ZigBee network is a wireless personal area network (WPAN), which needs a strong interleaving mechanism for protection against error bursts. This paper presents a novel chaotic interleaving scheme for this purpose. This scheme depends on the chaotic Baker map. A comparison study between the proposed chaotic interleaving scheme and the traditional block and convolutional interleaving schemes for image transmission over a correlated fading channel is presented. The simulation results show the superiority of the proposed chaotic interleaving scheme over the traditional schemes