Advanced Near-Field Radar Imaging Approaches in Security: An overview on signal processing challenges, opportunities, and future directions
Molaei A.M., Hu S., Fusco V., Fromenteze T., Kumar R., Abbasi M.A.B., Yurduseven O.
Article, IEEE Signal Processing Magazine, 2025, DOI Link
View abstract ⏷
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.
Human Activity Classification as an Integrative approach of SAR Technique with Conditional Generative Adversarial Network
Chinnaraj G., Wadde C., Murthy P.V.R., Sree C.S., Kumar R., Bochu V.
Conference paper, 2025 IEEE Space, Aerospace and Defence Conference, SPACE 2025, 2025, DOI Link
View abstract ⏷
This paper introduces an analytical study on generating and classifying millimeter-wave frequency modulated continuous wave (mm-Wave FMCW) radar systems operating in the 77-81 GHz frequency range, utilizing Synthetic Aperture Radar (SAR) imaging techniques for human gesture such as sitting, standing, sleeping, falling, and walking for detection. In order to recreate and enhance realistic radar images, stickman images collected from online sources are used as input for creating a dataset of 5,000 SAR reconstructed images with the help of data agumentation technique and Conditional Generative Adversarial Network (CGAN). The generated dataset is classified using the EfficientNet architecture, achieving overall validation accuracy of 92.34% in recognizing human gestures. This method effectively addresses data scarcity, enhances SAR image quality, and delivers reliable classification accuracy, offering significant potential for applications in gesture detection, surveillance, and related fields.
Air-Quality Assessment by Integrating Sensors and Drone for IoT Application
Kumar S.P., Sai Kiran D.V.N., Ramana Murthy P.V., Sree Gottumukkala N., Puppala H., Kumar R.
Conference paper, 2025 IEEE Space, Aerospace and Defence Conference, SPACE 2025, 2025, DOI Link
View abstract ⏷
Emerging trends in IoT and Drone technology are revolutionizing environmental monitoring through effective data collection and analysis. This research proposes a novel geospatial data sensing platform mounted on a Unmanned Aerial Vehicles to collect selected environmental parameters including moisture, temperature, and PM2.5. The designed platform is built using Arduino Mega micro controller, PM2.5 sensor, GPS sensor, and a DHT sensor enabling to collect geospatial data. The collected data is further stored on a SD card embedded within the designed platform. The stored data can be further processed and visualized using an open source GIS environment. For demonstration, the data is collected within a University campus located in Andhra Pradesh, India. The recorded data analysis shows that the mean temperature is 39.4°C with a variance of 9.2°C, mean humidity is 29.2% with a variance of 82.0%, and mean dust concentration is 143.6 mg/m3 with a variance of 5.3 mg/m3. The applications of the developed tool can be extended to various other potential applications such as precision agriculture, climate monitoring, and disaster management.
Study of 2D Terahertz SAR Imaging Based on FMCW Radar System
Wadde C., Clemente-Arenas M., Nagireddy S., Routhu G., Kumar R.
Conference paper, 2025 IEEE Space, Aerospace and Defence Conference, SPACE 2025, 2025, DOI Link
View abstract ⏷
Terahertz (THz) systems employing frequency-modulated continuous-wave (FMCW) radar, in conjunction with the synthetic aperture radar imaging (SAR), have demonstrated significant potential across multiple applications, including medical diagnostics, security, geospatial mapping, automotive technologies, and surveillance. The research work focuses on two-dimensional (2D) SAR imaging methods, which function at two distinct THz frequency ranges: 90-96 GHz and 196-204 GHz. The primary analysis is conducted through image reconstruction by processing uniformly sampled scene data within the CST Full Wave Simulator. In both cases, the received signals are analyzed on a planar surface, incorporating both horizontal and vertical alignments. The outcomes of this study validate the feasibility of THz-based image reconstruction.
Compact Single-Layer Metasurface Absorber with Polarization Insensitivity and Wideband Characteristics
Chatla M., Burri P.K., Routhu G., Nagireddy S., Kumar R.
Conference paper, 2025 IEEE Space, Aerospace and Defence Conference, SPACE 2025, 2025, DOI Link
View abstract ⏷
This paper presents the design and experimental evaluation of a novel asymmetric metasurface absorber(MA) for wideband, polarization-insensitive operation in the X-band. The unit cell integrates an arrow-shaped resonator on an FR-4 substrate backed by a copper plate. By incorporating three strategically placed resistors, the design achieves over 80% absorption at 8-12 GHz. This absorber is suitable for applications in stealth applications and EMI shielding.
Automated Shape Classification Using SAR Imaging and Machine Learning
Wadde C., Murthy Pondala V.R., Chinnaraj G., Clemente-Arenas M., Kumar R.
Conference paper, 2025 IEEE Space, Aerospace and Defence Conference, SPACE 2025, 2025, DOI Link
View abstract ⏷
The proposed method involves generating milli-meter Wave Frequency Modulated Continuous Wave (mmWave FMCW) radar image data through MATLAB modeling, reconstructing images using SAR imaging technique, and classifying images that are cluttered with multiple object shapes such as triangles, circles, squares, donuts, T-shape, Polygon, Star, and Pentagon using a Random Forest classifier. The classifier's performance is enhanced through hyper-parameter tuning and cross-validation. The model has high rate classification for T-shape Objects of 96.94% and minimum rate classification for Pentagon as 82.35% among all 9 type of object shapes. The overall model achieving high accuracy of 0.95%. The results demonstrate good accuracy in shape classification, validating the effectiveness of the integrated SAR and machine learning approach.
2D Antenna array design using 4 x 4 Butler matrix for X-band applications
Jallepalli D.K., Prasanna Kumar S., Kumar R.
Conference paper, 2025 IEEE Space, Aerospace and Defence Conference, SPACE 2025, 2025, DOI Link
View abstract ⏷
This study presents a comprehensive design of a beam-steering two-dimensional antenna array, utilizing a Butler Matrix 4 × 4 as a feed network for X-band applications. The simulated system operates within the X-Band frequency range. Accurate beam steering at +10°, -37°, +37°, and -10° angles is realized, allowing for wide angular coverage. The design exhibits a gain of around 17.7 dBi. Due to cost-effective fabrication and easy implementation, the antenna system can be implemented in next-generation wireless communication and radar systems.
Prediction of 1D Array Antenna at V-Band Using ANN Approach
Routhu G., Sarkar M., Kumar R.
Conference paper, 2025 IEEE Space, Aerospace and Defence Conference, SPACE 2025, 2025, DOI Link
View abstract ⏷
In this paper, an machine-learning-based technique is introduced to predict the performance of a V-band 1D array antenna based on its design specifications working at the V-band frequency range. An Artificial Neural Network (ANN) is built with four input and one output dataset to efficiently design, train, and predict the V-band 1D array antenna design. The developed ANN models achieves a regression coefficient at a range between 0.98-1 with good prediction accuracy.
Thermally Tunable Bi-Functional Metasurface Based on InSb for Terahertz Applications
Charca-Benavente R., Kumar R., Rubio-Noriega R., Clemente-Arenas M.
Article, Materials, 2025, DOI Link
View abstract ⏷
In this work, we propose and analyze a thermally tunable metasurface based on indium antimonide (InSb), designed to operate in the terahertz (THz) frequency range. The metasurface exhibits dual functionalities: single-band perfect absorption and efficient polarization conversion, enabled by the temperature-dependent permittivity of InSb. At approximately 280 K, InSb transitions into a metallic state, enabling the metasurface to achieve near-unity absorptance (100%) at 0.408 THz under normal incidence, independent of polarization. Conversely, when InSb behaves as a dielectric at 200 K, the metasurface operates as an efficient polarization converter. By exploiting structural anisotropy, it achieves a polarization conversion ratio exceeding 85% over the frequency range from 0.56 to 0.93 THz, while maintaining stable performance for incident angles up to 45°. Parametric analyses show that the resonance frequency and absorption intensity can be effectively tuned by varying the InSb square size and the silica (SiO2) layer thickness, achieving maximum absorptance at a SiO2 thickness of 16 μm. The proposed tunable metasurface offers significant potential for applications in THz sensing, imaging, filtering, and wavefront engineering.
Near-Field Bistatic Microwave Imaging with Dynamic Metasurface Antennas
Molaei A.M., Hoang T.V., Fromenteze T., Skouroliakou V., Kumar R., Zhao M., Garcia-Fernandez M., Alvarez-Narciandi G., Fusco V., Yurduseven O.
Conference paper, 18th European Conference on Antennas and Propagation, EuCAP 2024, 2024, DOI Link
View abstract ⏷
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.
Application of Kirchhoff Migration Principle for Hardware-Efficient Near-Field Radar Imaging
Molaei A.M., Garcia-Fernandez M., Alvarez-Narciandi G., Kumar R., Skouroliakou V., Fusco V., Abbasi M.A.B., Yurduseven O.
Article, IEEE Transactions on Computational Imaging, 2024, DOI Link
View abstract ⏷
Achieving high imaging resolution in conventional monostatic radar imaging with mechanical scanning requires excessive acquisition time. Although real aperture radar systems might not suffer from such a limitation in acquisition time, they may still face challenges in achieving high imaging resolution, especially in near-field (NF) scenarios, due to diffraction-limited performance. Even with sophisticated electronic scanning techniques, increasing the aperture size to improve resolution can lead to complex hardware setups and may not always be feasible in certain practical scenarios. Multistatic systems can virtually increase the effective aperture but introduce challenges due to the required number of antennas and channels, making them expensive, bulky and power-intensive. An alternative solution that has been proposed in recent years is the compression of the physical layer using metasurface transducers. This paper presents a novel NF radar imaging approach leveraging dynamic metasurface antennas with multiple tuning states called masks, in a bistatic structure, using the Kirchhoff migration principle. The method involves expanding the compressed measured signal from the mask-frequency domain to the spatial-frequency domain to decode the scene's spatial content. The Kirchhoff integral is then developed based on the introduced special imaging structure to retrieve the three-dimensional spatial information of the target. Comprehensive numerical simulations analyze the masks' characteristics and their behavior under different conditions. The performance of the image reconstruction algorithm is evaluated for visual quality and computing time using both central processing units and graphics processing units. The results of computer simulations confirm the high reliability of the proposed approach in various cases. 2333-9403
Preliminary Analysis of mmWave SAR Model and Machine Learning Approach
Wadde C., Routhu G., Karvande R.S., Kumar R.
Conference paper, 2024 IEEE Space, Aerospace and Defence Conference, SPACE 2024, 2024, DOI Link
View abstract ⏷
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.
Three-dimensional near-field microwave imaging with multiple-input multiple-output coded generalized reduced dimension Fourier algorithm
Molaei A.M., Hu S., Kumar R., Yurduseven O.
Conference paper, Proceedings of SPIE - The International Society for Optical Engineering, 2024, DOI Link
View abstract ⏷
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.
Optimized Configuration of an IRNSS S-band Microstrip Patch Antenna
Conference paper, Proceedings of InC4 2024 - 2024 IEEE International Conference on Contemporary Computing and Communications, 2024, DOI Link
View abstract ⏷
This research work presents a comprehensive exploration into the optimization of an Indian Regional Navigation Satellite System (IRNSS) S-band microstrip patch antenna, catering to the increasing demand for compact antennas in satellite communication systems. Emphasizing superior performance and efficiency, the design aims to surpass existing techniques. Through meticulous parameter tuning, the antenna's capabilities have been refined for enhanced signal reception and transmission within the S-band frequency range. The optimization process entails a thorough analysis of diverse design parameters to ensure optimal radiation characteristics and impedance matching. Performance evaluations reveal good return loss, VSWR (voltage standing wave ratio), and radiation pattern stability. The proposed configuration not only meets but exceeds the requirements for satellite communication, marking a significant advancement in antenna design. Additionally, integration of supplementary filters enhances the antenna's performance by mitigating unnecessary interference, thus ensuring signal integrity. The usage additional filters to curb down the additional interference that may be introduced is a feasible way of restricting the antenna bandwidth to S-band's requirements while maintaining an excellent return loss value. These findings hold practical implications for IRNSS satellite systems, elevating their overall performance and reliability.
Modeling of mmWave FMCW Radar System for 2D SAR Imaging
Wadde C., Karvande R.S., Kumar R.
Conference paper, 2024 IEEE Space, Aerospace and Defence Conference, SPACE 2024, 2024, DOI Link
View abstract ⏷
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.
Case studies on detection using mmWave FMCW RADAR system
Prakash G.S., Chandra W., Mehta S., Kumar R.
Book chapter, Radar and RF Front End System Designs for Wireless Systems, 2024, DOI Link
View abstract ⏷
The content of this chapter provides a thoughtful analysis of case studies that highlight the detection capabilities of FMCW radar systems operating in mmWave configurations. The case studies demonstrate how mmWave FMCW radar technology may be used to detect objects, motions, and changes in both line of sight and non-line of sight settings with accuracy, and efficiency. Each case study explores the unique difficulties presented by the application environment, which can include anything from identifying impediments in automotive safety systems to detecting minute movements for vital sign monitoring in healthcare. The steps for detecting different gesture recognition using IWR 1843 BOOST FMCW radar system and its processing are focussed upon. The document highlights the technology's excellent resolution, motion sensitivity, and adaptability to a variety of challenging environments in LoS and NLoS scenarios and with the technical details of operating mmWave radars, including signal processing methods, machine learning algorithms, and mitigating interference from surrounding objects.
Preface
Editorial, Radar and RF Front End System Designs for Wireless Systems, 2024,
Radar and RF front end system designs for wireless systems
Book, Radar and RF Front End System Designs for Wireless Systems, 2024, DOI Link
View abstract ⏷
The escalating demand for advanced communication, sensing, and scanning systems across various applications as well as the urgency to comprehend the complexities of RF Frontend systems is more pronounced than ever. At the heart of this challenge lies the reconfigurability feature, playing a vital role in shaping the current trajectory of wireless technologies. The book Radar and RF Front End System Designs for Wireless Systems delves straight into this pressing issue and examines the relentless pace of innovation spurred by a myriad of configuration and design architectures. While these advancements hold great promise, they also introduce challenges that warrant thorough examination. Within the pages of this publication, a narrative unfolds that transcends theoretical discourse. The book offers a unique opportunity for academic scholars, researchers, and industry professionals to not only understand the intricacies of RF Frontend systems but also to grapple with the practical challenges posed by their rapid evolution. It becomes a guide in navigating this dynamic landscape, providing a deep exploration of the issues at hand and paving the way for informed solutions and breakthroughs. Amidst the intricacies of RF Frontend systems, the publication strategically unfolds its content across four key sections. These sections encompass RF Frontend Antenna Systems for wireless applications, Radar and Antenna Systems design and modeling, the Impact of AI/ML in Frontend System Design and Applications, and Test and Measurement for Frontend system designs. Each section serves as a gateway for scholars and professionals to delve deeper into these critical areas, providing not only theoretical insights but also practical applications that bridge the gap between academic understanding and real-world challenges.
Integrated Modeling and Target Classification Based on mmWave SAR and CNN Approach
Wadde C., Routhu G., Clemente-Arenas M., Gummadi S.P., Kumar R.
Article, Sensors, 2024, DOI Link
View abstract ⏷
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.
System Level Analysis of Computational Channel Characterization using Compressive Surfaces
Yurduseven O., Babar Abbasi M.A., Fromenteze T., Garcia-Fernandez M., Alvarez-Narciandi G., Kumar R., Fusco V.
Conference paper, 17th European Conference on Antennas and Propagation, EuCAP 2023, 2023, DOI Link
View abstract ⏷
Direction of arrival (DoA) estimation plays a crucial role in channel characterization and is a critical step to execute the necessary beam-shaping operations needed from antennas present within a wireless environment. Conventional DoA estimation frameworks rely on array-based topologies, making use of the phase difference information between the individual channels to retrieve the DoA data. Recently, the idea of computational imaging has been shown to offer a promising solution to conventional raster-scan-based techniques. This is mainly due to the physical layer compression facilitated by special types of compressive apertures (or surfaces) that leverage the idea of synthesizing quasi-orthogonal radiation patterns (modes) to probe and encode the scene information in an indirect manner and compress it into a single channel (or a reduced number of channels). The application of computational imaging to the channel characterization problem is intriguing. Yet, a system level of knowledge of the design parameters, which are key to understanding the development of compressive surfaces for computational DoA estimation, is far from comprehensive. In this paper, we demonstrate different techniques to synthesize spatio-temporally incoherent field patterns, a key requirement for computational DoA estimation, and provide a study of the different system level parameters needed to design such antennas. We show that by increasing the orthogonality of the radiated modes (and thus reducing the information redundancy), a single-channel compressive antenna can retrieve the DoA pattern of multiple far-field sources even under a signal-to-noise ratio (SNR) level of as low as 0 dB, without the necessity to use a multi-channel physical architecture.
Super-resolution Reconstruction and Denoising of 3D Millimetre-wave Images using a Complex-valued Convolutional Neural Network
Sharma R., Zhang J., Kumar R., Deka B., Fusco V., Yurduseven O.
Conference paper, 17th European Conference on Antennas and Propagation, EuCAP 2023, 2023, DOI Link
View abstract ⏷
Imaging systems leveraging millimetre-wave (mmW) frequencies have several advantages, however, such systems suffer from poor resolution images as compared to higher frequency reconstructions such as in optical regime. Also, practical radar systems are susceptible to noise such as clutter, thermal noise, motion blurs, etc. To recover the original mmW image from these poorly resolved noisy images, two individual image processing steps are required, that is, super-resolution and denoising. This paper focuses on using a complex-valued convolutional neural network (CV-CNN) to combine the two individual processing steps into one single algorithm. By designing the CV-CNN to accommodate complex-valued reconstruction data, the phase information content of the input images, along with the magnitude information, is considered in the process. A computational imaging (CI) numerical model, instead of an experimental imaging system, is used to train and test the neural network. By comparing the performance metrics of the final reconstruction images, it is observed that the developed CV-CNN can resolve and de-noise the poorly resolved noisy input mmW images to a high degree of fidelity.
Machine Learning based Low-Scale Dipole Antenna Optimization using Bootstrap Aggregation
Neelamraju P.M., Pothapragada P., Rana G., Chaturvedi D., Kumar R.
Conference paper, 2023 2nd International Conference on Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal Processing, PCEMS 2023, 2023, DOI Link
View abstract ⏷
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.
Fourier-based Image Reconstruction Algorithms for Sparse SAR Data
Skouroliakou V., Masoud Molaei A., Garcia-Fernandez M., Alvarez-Narciandi G., Kumar R., Yurduseven O.
Conference paper, 2023 34th Irish Signals and Systems Conference, ISSC 2023, 2023, DOI Link
View abstract ⏷
Frequency domain image reconstruction algorithms offer significant advantages, especially for applications for which the reconstruction time is crucial. In the frequency domain, image reconstruction can be realized using fast Fourier transformations, reducing the complexity and thus the execution time of the reconstruction algorithm. In this paper, we adopt range migration techniques to reconstruct radar images from numerical and experimental data. The aim is to examine the robustness of the range migration algorithm (RMA) as a function of sampling sparsity under varying noise levels. Considering that sampling at Nyquist rates can be quite challenging for the conventional synthetic aperture radar (SAR) acquisition, we investigate the behavior of the reconstruction algorithm for larger sampling steps.
Effect of Magnitude and Phase of Millimeter-wave Images on Classification Accuracy
Sharma R., Kumar R., Deka B., Fusco V., Yurduseven O.
Conference paper, 17th European Conference on Antennas and Propagation, EuCAP 2023, 2023, DOI Link
View abstract ⏷
Millimetre-wave (mmW) reconstructed images are of complex-valued in nature, suggesting that they contain both magnitude and phase. It is known that from the phase aspect of the reconstructed images, meaningful feature information can be extracted about the imaged objects, which in turn, is beneficial to solve computer vision problems such as classification. To this end, a comparative study is shown in this paper wherein two Convolutional Neural Network (CNN) models are considered: one trained with magnitude aspect of mmW reconstructed images, and the other is trained with both the magnitude and the phase aspects of mmW reconstructed images. After training, when these two models are tested, a higher classification accuracy is obtained in the performance of the classification model trained with both the magnitude and phase information of mmW images, as compared to the other model.
Combining Super-Resolution GAN and DC GAN for Enhancing Medical Image Generation: A Study on Improving CNN Model Performance
Vasamsetti M., Kaja P., Putta S., Kumar R.
Book chapter, GANs for Data Augmentation in Healthcare, 2023, DOI Link
View abstract ⏷
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.
MIMO Coded Generalized Reduced Dimension Fourier Algorithm for 3-D Microwave Imaging
Molaei A.M., Hu S., Kumar R., Yurduseven O.
Article, IEEE Transactions on Geoscience and Remote Sensing, 2023, DOI Link
View abstract ⏷
In this article, to accelerate data acquisition and image reconstruction procedures in a multistatic short-range microwave imaging scenario, an orthogonal coding approach with Fourier domain processing is presented. First, a special 2-D multiple-input multiple-output (MIMO) structure is introduced to fully electronically synthesize the 2-D aperture. Then, the model of the transmitted and received signals by a MIMO stepped-frequency-modulated radar is presented, with special considerations about orthogonal, balanced, and optimal sequences. On the receiver side, the backscatter frequency response extraction process is formulated with the aim of obtaining individual information of all channels. Finally, based on the introduced model, a fast Fourier-based algorithm with reduced dimensions, named MIMO coded generalized reduced dimension Fourier (CGRDF), is mathematically derived. It includes extracting phase and amplitude compensators with the aim of mapping 4-D to 2-D spatial data, transferring the backscatter transfer function from the spatial domain to the wavenumber domain, extracting the smoothing filter, compensating the curvature of the wavefront of all scatterers, and extracting the reflectivity function and an additional range compensator. The results of numerical simulations show the satisfactory and reliable performance of the proposed approach in terms of the information retrieval process and processing speed.
Analysis of 1-Bit Unit cell for Reflect Array Antenna in X-Band Applications
Chatla M., Kitti B.P., Kumar R.
Conference paper, 2023 IEEE Microwaves, Antennas, and Propagation Conference, MAPCON 2023, 2023, DOI Link
View abstract ⏷
This paper presents an analysis of a 1-bit unit cell for reflectarray antennas in X-band applications. Reflectarray antennas offer advantages such as lower profiles and lighter weights compared to traditional antennas. The unit cell is made up of four arrow-shaped structures with a single head, enabling the manipulation of electromagnetic waves and beam steering capabilities. The unit cell operates in two modes, bit-0 and bit-l. Simulation results demonstrate that the unit cell achieves a phase difference of 180 degrees for the co-polarization reflection coefficient across a wide range of frequency. Additionally, the paper discusses the potential use of tunable elements and graphene for switching operations to reduce fabrication costs. This research contributes to the development of compact and efficient reflectarray antennas for X-band applications.
Prospect of dynamic metasurface array antenna system with machine learning
Book chapter, Metamaterial Technology and Intelligent Metasurfaces for Wireless Communication Systems, 2023, DOI Link
View abstract ⏷
This chapter présents the machine learning ( ML) concept for standard RF component design in microwave frequency. It will explain the use of the deep machine learning concept for antenna and other RF components, such as RF filter, and all relevant analysis will be based on the CST simulations. The comparative study of the ML approach and the antenna design tool (such as CST) will be presented in the form of their performance. This chapter will explain the perspectives of ML in RF system design and analysis. This chapter will present the design of the antenna and filter as examples. The simulated results will be obtained by the CST MW Studio and the ML will be implemented in MATLAB.
Frequency Selective Computational Through Wall Imaging Using a Dynamically Reconfigurable Metasurface Aperture
Hoang T.V., Kumar R., Fromenteze T., Garcia-Fernandez M., Alvarez-Narciandi G., Fusco V., Yurduseven O.
Article, IEEE Open Journal of Antennas and Propagation, 2022, DOI Link
View abstract ⏷
A two-dimensional (2D) dynamically reconfigurable metasurface aperture is presented to perform frequency selective through wall imaging (TWI) with an unknown structure of the wall. Generally, in TWI, the medium properties and thickness of the wall need to be known in advance, which is not always possible. Moreover, compensating for these effects can significantly increase the computational complexity. We propose a two-stage method that leverages the concept of a dynamically reconfigurable metasurface antenna (DMA) in a narrow frequency band in which the effects of the wall are minimum to perform TWI. First, two simple probe antennas are used to evaluate the reflection response of the wall by means of a simple backscatter measurement. Based on these characteristics, a narrow band frequency selective window is identified. Second, a DMA consisting of an array of tunable metamaterial elements is used for TWI in the identified frequency selective window. The DMA aperture enables the scene information to be sampled through a set of spatio-temporally varying quasi-random modes using a single-channel transmit and receive architecture. This physical-layer compression scheme can significantly simplify the data acquisition while the quasi-random sampling of the scene information eliminates the need for conventional raster-scan based modalities.
Computational Microwave Imaging Based on a Single Electric-Field Scan
Kumar R., Alvarez-Narciandi G., Garcia-Fernandez M., Fusco V., Yurduseven O.
Conference paper, 2022 19th European Radar Conference, EuRAD 2022, 2022, DOI Link
View abstract ⏷
A scheme for reducing the need of multi-scan measurements of near electric-field information to a single-scan process is presented to simplify the construction of the sensing matrix required for performing computational microwave imaging system at K-band frequencies. For this purpose, we propose to perform only a single scan, and the obtained information is used to construct the multiple radiated near electric-field information associated to all transmit and receive units. Further, the performance of the constructed near electric-field is verified by generating an image of a test target by means of computational imaging using the scene reflectivity information. The proposed scheme helps in reducing the overall complexity of the nearfield scanning process associated to the computational imaging system.
A Compressive Sensing-Based Approach for Millimeter-Wave Imaging Compatible with Fourier-Based Image Reconstruction Techniques
Molaei A.M., Kumar R., Hu S., Skouroliakou V., Fusco V., Yurduseven O.
Conference paper, Proceedings International Radar Symposium, 2022,
View abstract ⏷
The unique characteristics of the millimeter-wave (mmW) frequency band have led to its widespread use in various fields such as communications, imaging, and wireless sensing. This paper addresses two different mmW imaging structures, monostatic and multistatic, in the face of a sparse spatial sampling scenario. By using compressive sensing theory, a solution for image reconstruction, consistent with fast Fourier-based techniques, is presented with compressed data obtained from monostatic imaging. This solution is then generalized to a multiple-input multiple-output (MIMO) imaging case using a multistatic-to-monostatic conversion. Reconstructed images from numerical and experimental data show the satisfactory performance of the presented approach.
3D SAR Imaging Radar System at Microwave Frequencies: Experimental Results
Kumar R., Molaei A.M., Fusco V., Yurduseven O.
Conference paper, 2022 19th European Radar Conference, EuRAD 2022, 2022, DOI Link
View abstract ⏷
Microwave frequencies based on frequency-modulated continuous-wave (FMCW) technique as applied to synthetic aperture radar (SAR) imaging have many potential applications related to security, automotive, mapping, medical, and surveillance. In this paper, a three-dimensional (3D) SAR imaging system at K band frequencies is presented and its effective performance is verified with the reconstructed images from a set of uniformly sampled data from the scene. The collected backscattered signals are measured over a 2D plane, in both horizontal and vertical directions, using a Nearfield Systems Inc. (NSI) measurement platform. This paper presents the complete details about the measurement steps along with the signal processing SAR technique for 3D image reconstruction. The presented work demonstrates the reconstruction of image for two different targets. The performance of the work is also verified with the 3D reconstructed image of one target in a concealed scenario.
Preliminary Study of Breast Cancer Detection Using A Computational Microwave Imaging System
Kumar R., Fusco V., Yurduseven O.
Conference paper, 2022 52nd European Microwave Conference, EuMC 2022, 2022, DOI Link
View abstract ⏷
In this paper, a preliminary study related to the detection of breast cancer based on a computational microwave imaging system is presented at K-band frequencies. In comparison to normal tissues, the different dielectric properties (permittivity and conductivity) of the malignant tissues can be exploited in order to detect the presence of tumour through a microwave imaging system. This work demonstrates the detection of breast tumour as an application of a computational imaging technique by leveraging the concept of the dynamic metasurface antenna (DMA) aperture as a transmitter. In this framework, the computational imaging aperture can provide a compact system design with a low-cost deployment for detecting tumours in early-stage, and this can speed up the screening process of the population at risk. Particularly, early detection of Stage 1 can help with the determination of the best treatment method and enhance a patient's prognosis.
3-D Super-Resolution of Coded Aperture Millimeter-Wave Images Using Complex-Valued Convolutional Neural Network
Sharma R., Zhang J., Kumar R., Deka B., Fusco V., Yurduseven O.
Article, IEEE Sensors Journal, 2022, DOI Link
View abstract ⏷
Electromagnetic (EM) waves at millimeter-wave (mmW) frequencies have found applications in a variety of imaging systems, from security screening to defense and automotive radars, with the research and development of mmW imaging systems gaining interest in recent years. Despite their significant advantages, mmW imaging systems suffer from poor resolution compared to higher frequency reconstructions, such as optical images. To improve the resolution of mmW images, various super-resolution (SR) techniques have been introduced. One such technique is the use of machine learning algorithms in the signal processing layer of the imaging system without altering any of the system's parameters. This article focuses on the use of a convolutional neural network (CNN) architecture to achieve SR when applied to 3-D mmW input images. To exploit the phase information content of the input images along with the magnitude, a complex-valued CNN is designed, which can accommodate complex-valued data. To simplify the learning process, the resolution difference between the input and output images is divided into smaller parts by using subnetworks in the CNN architecture. The trained model is tested on simulated and experimental targets. The average mean square error score and the structural similarity index obtained on a test dataset of 460 samples are 0.0127 and 0.9225, respectively. It can be inferred that the model has the capability to improve the resolution of input mmW images to a high degree of fidelity, hence paving the way for an end-to-end SR imaging system.
Development of Fast Fourier-Compatible Image Reconstruction for 3D Near-Field Bistatic Microwave Imaging With Dynamic Metasurface Antennas
Molaei A.M., Fromenteze T., Skouroliakou V., Hoang T.V., Kumar R., Fusco V., Yurduseven O.
Article, IEEE Transactions on Vehicular Technology, 2022, DOI Link
View abstract ⏷
The emerging technology of dynamic metasurface antennas (DMAs) offers a promising solution to revolutionize future wireless communications by reducing hardware costs, physical size and power consumption. Especially in the field of radar imaging, DMAs can be used as an effective alternative platform for modern computational imaging; because they can simplify the physical hardware architecture and increase the data acquisition rate. Fourier transform (FT)-based scene image reconstruction techniques are known as cost-effective computing solutions for the imaging system processing unit. However, due to the physical layer compression in DMAs and the fact that they do not produce uniform radiation patterns, the information provided by them is not compatible with Fourier-based techniques and cannot be applied directly. In this article, we first introduce a 3D near-field bistatic imaging approach using two one-dimensional (1D) DMAs as a panel-to-panel model in a Mills Cross structure. Then, based on the introduced mathematical model, we derive a Fourier-based algorithm for the image reconstruction problem. The proposed algorithm consists of five main steps: (i) pre-processing (to transfer the data provided by the transmitter and receiver DMAs to a set of equivalent spatial measurements), (ii) applying FTs to the transferred signal, (iii) filtering in the Fourier domain, (iv) a simplified interpolation, and (v) applying a 3D inverse FT to retrieve scene information. The results of numerical simulations confirm the satisfactory performance of the proposed approach.
A Dynamic Inertial Weight Strategy in Micro PSO for Swarm Robots
Bakhale M., Hemalatha V., Dhanalakshmi S., Kumar R., Siddharth Jain M.
Article, Wireless Personal Communications, 2020, DOI Link
View abstract ⏷
A relatively new area of research and development is Swarm Robotics. It is a part of the swarm intelligence field. In the proposed paper, we shall use swarm robotics in the field of defense and security, particularly for the problem of counter-improvised explosive device (IED) operations. The biggest problem in this regard is to physically detect the IEDs. We propose the use of a swarm of autonomous robots which shall be moving through the search space to collectively detect IEDs in a relatively lesser span of time with greater reliability. Since the robots are autonomous, there will not be any human contact involved, thus distancing humans from any potential IEDs or hazardous environments. The robot hardware shall be robust and able to traverse different kinds of terrains or even water bodies. A major problem of decision making for autonomous robots is localization of the robots towards the origin. Localization deals with finding its Cartesian coordinates and direction in the given coordinate system. For effective autonomous navigation of a robot, finding the position of the robot is essential at every point of time. Particle swarm optimization (PSO) is a useful method for population based global search. The proposed algorithm is an extension of micro-particle swarm optimization (µPSO) for Simultaneous Localization and Mapping. The effectiveness of this method is estimated by comparing its results with the traditional PSO and µPSO.
Recruiting Fault Tolerance Techniques for Microprocessor Security
Kumar V.B.Y., Deb S., Kumar R., Khairallah M., Chattopadhyay A., Mendelson A.
Conference paper, Proceedings of the Asian Test Symposium, 2019, DOI Link
View abstract ⏷
The growing threat of various attacks on modern microprocessors and systems calls for major design overhauls ranging from plugging micro-architectural side channels such as due to speculative execution to implementing cryptographic accelerators for side-channel and fault attack resistance. In this paper, we suggest to focus on the similarities and the differences between fault tolerance techniques and countermeasures against attacks on security sensitive systems. Modern digital circuits and systems use a diverse set of techniques to ensure operational correctness in the presence of faults. From a security perspective, the goal is to ensure a set of stated security properties hold in the presence of 'security faults' (extending the notion of conventional faults to include injected faults as well as vulnerabilities such as passive side-channels). A point of note here is that under some security faults, the operational correctness may not be compromised. This paper advocates the re-purposing of some of the known fault tolerance techniques, and show how those can be useful for enhancing security in the presence of active side-channel attacks. As a simple illustration of these ideas, we present an experimental case study in fortifying a cryptographic sub-component of a RISC-V based secure system-on-chip, against a formidable fault attack called SIFA.
