Nonlinear Equalization Structure For High-Speed ADSL In Ideal And Non Ideal Conditions

Publication in the conference proceedings of EUSIPCO, Florence, Italy, 200

Classification and Detection of Cancer in Histopathologic Scans of Lymph Node Sections Using Convolutional Neural Network

Cancer has been considered one of the major threats to the lives and health of people. The substantial clinical practices show that earlier diagnosis and detection of cancer can provide adaptable treatment methods, increase survivability, and enhance life quality. Moreover, rapid advancements in science, technology, and Computer-Aided Diagnosis systems also provide additional information for robust analysis and examination of medical images. Image processing and machine learning presented promising low-cost approaches for classifying and detecting different cancerous diseases. However, these traditional techniques need extensive pre-processing and laborious manual features extraction methods. Thus, in this paper, we presented a Convolutional Neural Network based method for the classification and detection of metastatic cancer in histopathologic images of lymph node sections. A diagnostic method of cancer in histopathologic images is time consuming and tedious for pathologists because a large tissue area has been examined, and tiny metastasis can be easily ignored. Thus the developed deep learning method can help pathologists in examining the histopathologic scans and assist in decision-making to analyze the disease and cancer staging, which will give consequential opinions in clinical diagnosis. We performed the necessary pre-processing and data augmentation steps to enhance the results and avoid overfitting. The method utilizes low dimensional representations and performs automated, categorical feature extraction and classification, which attain high accuracy for diagnosis of cancer. The method is applied to PatchCamelyon (PCam) data set. Experimental results show good performance with an accuracy rate of 0.94 for the medical image classification and detection task

Energy efficient resource allocation for re-configurable intelligent surface-assisted wireless networks

Abstract This paper focuses on energy-efficient resource allocation in reconfigurable intelligent surface (RIS)-assisted multiple-input-single-output (MISO) communication systems. Specifically, it revisits the solution to the energy efficiency (EE) problem using the alternating optimization (AO) approach. In each AO iteration, the RIS phase optimization is achieved using the gradient descent method, which unfortunately does not guarantee convergence. To overcome this limitation, we propose two alternatives: the Wolfe-based gradient-descent (GAW) EE maximization Algorithm and the trust region (TR)-based EE maximization algorithm. Additionally, we use Dinkelbach’s algorithm to obtain the optimal transmit power allocation. Our results demonstrate that the proposed methods outperform the existing approach that uses sequential fractional programming (SFP) for phase optimization and the traditional relay-based method

Energy Efficient RIS-enabled SISO-OFDMA Communication via Lower Bound Optimization

The pursuit of energy-efficient solutions in the context of Reconfigurable Intelligent Surface (RIS)-assisted wireless networks has become imperative and transformative. This paper investigates the integration of RIS into an orthogonal frequency division multiple access (OFDMA) framework for multiuser downlink communication systems. We address the challenge of jointly optimizing RIS reflection coefficients alongside OFDMA frequency and power allocations, with the aim of maximizing energy efficiency. This optimization is subject to specific quality-of-service (QoS) requirements for each user equipment (UE) and a constraint on transmission power and RIS phase shift matrix. To address this complex optimization problem, we propose a practical and low-complexity approach that derives a computationally efficient and numerically tractable lower bound on energy efficiency. Our approach strikes a balance between performance enhancement and complexity. The numerical results highlight the effectiveness of our approach, showing a substantial increase in energy efficiency compared to scenarios with random RIS integration and without RIS.</p