Near-field (NF) microwave and millimeter-wave (mm-wave) imaging, extending into the terahertz (THz) frequency range, has seen remarkable advancements across diverse applications, particularly security screening. These technologies benefit from the unique properties of microwave, mm-wave, and THz (MMT) spectra, such as penetration, nonionizing radiation, material sensitivity and the capability to operate in all weather conditions. This article provides an overview of the evolution and current state of NF radar imaging, emphasizing the critical role of signal processing in overcoming challenges related to hardware complexity, long acquisition time, and image reconstruction quality. Advanced signal processing techniques—including Fourier-based algorithms, sparse imaging, low-rank matrix recovery, and deep learning—are highlighted for their contributions to enhancing image resolution and processing efficiency. The article also discusses recent innovations in antenna technologies, aperture configurations, and scanning methods that have significantly improved NF radar imaging capabilities. Future research directions are suggested to further advance the field, highlighting the importance of continued exploration and innovation in NF MMT imaging

In recent decades, microwave imaging technology has been used in a variety of applications including security, medicine, nondestructive testing and structural health monitoring. Traditional microwave imaging systems often suffer from drawbacks such as long acquisition times and complex array structures. To address these issues, this paper introduces a panel-to-panel microwave computational imaging (CI) technique for near-field operation using dynamic metasurface antennas for both transmission and reception, enhancing system diversity and enabling real-time applications. The paper also outlines mathematical models for three-dimensional image reconstruction algorithms tailored to this scenario. The results of numerical and electromagnetic simulations show the feasibility of this approach for CI-based imaging, with both Fourier and least squares-based image reconstruction techniques.

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Machine learning (ML) techniques have been applied for radar applications in recent years. It is still changeling to classify images or objects accurately. This work has modeled 1600 reconstructed object shapes of four different objects like triangles, circles, squares, and rectangles using millimeter-wave (mmWave) FMCW radar principle based on the 2D SAR imaging technique, and the numerical analysis is performed in MATLAB. The Convolution Neural Network (CNN) technique is implemented to perform the objects' classification in a Python environment. The results give a good prospect for the study of ML techniques to classify mmWave FMCW radar data.

Millimeter-Wave FMCW radar offers highresolution imaging and object detection capabilities, making it ideal for various applications including automotive radar, industrial sensing, security systems, and Imaging. This work presents the modeling of mmWave FMCW radar system for 2D SAR imaging for different objects like square, rectangle, triangle, and circle. The main objective is to reconstruct the object shapes with high resolution by developing numerical analysis. Utilizing MATLAB, the system is modeled and analyzed to achieve high-resolution imaging. The results show good prospects for the use of mmWave FMCW radar systems for imaging multiple objects with different shapes for SAR imaging.

This study presents a numerical modeling approach that utilizes millimeter-wave (mm-Wave) Frequency-Modulated Continuous-Wave (FMCW) radar to reconstruct and classify five weapon types: grenades, knives, guns, iron rods, and wrenches. A dataset of 1000 images of these weapons was collected from various online sources and subsequently used to generate 3605 samples in the MATLAB (R2022b) environment for creating reflectivity-added images. Background reflectivity was considered to range from 0 to 0.3 (with 0 being a perfect absorber), while object reflectivity was set between 0.8 and 1 (with 1 representing a perfect electric conductor). These images were employed to reconstruct high-resolution weapon profiles using a monostatic two-dimensional (2D) Synthetic Aperture Radar (SAR) imaging technique. Subsequently, the reconstructed images were classified using a Convolutional Neural Network (CNN) algorithm in a Python (3.10.14) environment. The CNN architecture consists of 10 layers, including multiple convolutional, pooling, and fully connected layers, designed to effectively extract features and perform classification. The CNN model achieved high accuracy, with precision and recall values exceeding 98% across most categories, demonstrating the robustness and reliability of the model. This approach shows considerable promise for enhancing security screening technologies across a range of applications.

This paper introduces a 3-D near-field microwave imaging approach, combining a special 2-D multiple-input multiple-output (MIMO) structure with orthogonal coding and Fourier domain processing. The proposed MIMO coded generalized reduced dimension Fourier algorithm effectively reduces data dimensionality while preserving valuable information, streamlining image reconstruction. Through mathematical derivations, we show how the proposed approach includes phase and amplitude compensators and reduces the computational complexity while mitigating propagation loss effects. Numerical simulations confirm the approach's satisfactory performance in terms of information retrieval and processing speed. © 2024 SPIE.

Dipole antennae are commonly used radio frequency devices. They gained good prominence as a result of their efficiency, consistent performance and flexibility. Different optimization strategies such as particle swarm optimization, differential evolution and Machine Learning algorithms have been utilized in the past to design dipole antennae. This helps in creating a complete device profile and increases its efficacy. Due to the complexity of modern antennas in terms of topology and performance requirements, standard antenna design approaches are tedious and cannot be guaranteed to produce effective results. Out of the strategies that are widely being utilized, Machine Learning (ML) algorithms evolved rapidly due to their capabilities in extrapolating the dimensional and material profiles of the device. Antenna design optimization still faces several difficulties, even though machine learning-based design optimization complements traditional antenna design methodologies. The effectiveness and optimization capabilities of available ML approaches to address a wide range of antenna design problems, considering the increasingly strict specifications of current antennas, are the fundamental difficulties in antenna design optimization which need to be focused on. In our current work, the capability of ML algorithms in elucidating minor trends in device profiles is tested. A bootstrap aggregation model is proposed, concatenating Linear Regression, Support Vector Regression and Decision Tree Regression algorithms. The concatenated model was used to optimize the parameters of reflection coefficient, directivity, efficiency and operating frequency, depending on the feed length, dipole radius and dipole length of the antenna.

A kind of cancer that occurs in skin cells is called skin cancer. The body’s most significant cancer is skin cancer (Skin Cancer (Including Melanoma)-Patient Version). The two main layers of the skin are the dermis (the lower or inner layer) and the epidermis (the higher or outer layer) (Donaldson, 2022). The most typical cancer in the world is skin cancer, which is becoming more frequent (Shao et al., 2017). The three types of cancers are basal cell carcinoma, squamous cell carcinoma, and melanoma (Skin Cancer, 2006), the primary kinds of skin cancer. Although skin cancer can occur in other parts of the body (Skin Cancer- Symptoms and Causes- Mayo Clinic, 2022), these tumors most frequently affect the face, ears, arms, and hands. We train with the Convolutional Neural Network (CNN) model to recognize skin cancer and its variations. One such model is a type of neural network called Generative Adversarial Networks (GANs), intended to generate realistic synthetic data. In this study, we provide a novel GAN architecture for image generation. The foundation of a GAN is a generator and a discriminator neural network. Although the generator generates synthetic data from random noise, the discriminator uses a training dataset to assess if the created data is bogus or real. Lack of enough medical data is one of the significant obstacles to developing and training a CNN for skin cancer classification. The quantity of training data can substantially impact the network’s performance. This project’s main objective is to [6] improve classification accuracy and precision by producing high-quality, diverse skin cancer images for CNN to train on.