Mainuddin Sagar

Electrical Impedance Tomography (EIT) is a non-invasive and low-cost imaging modality with increasing potential for nondestructive evaluation (NDE) of structural discontinuities in conductive media. However, due to the inherently diffusive nature of the inverse problem, realistic assessment of defect detectability requires physics-consistent forward modeling and quantitative validation. In this study, a finite element method (FEM)-based framework under the Complete Electrode Model (CEM) is employed to investigate the detectability of narrow structural discontinuities and hollow inclusion using both two-dimensional (2D) and three-dimensional (3D) EIT reconstructions. A 16-electrode configuration was implemented, yielding 120 independent voltage measurements per frame in a conductive saline phantom. Conductivity distributions were reconstructed using Gauss-Newton optimization with Tikhonov Regularization. Performance was quantitatively evaluated using contrast-to-noise ratio (CNR), peak signal-to-noise ratio (PSNR). structural similarity index (SSIM), area deviation, and computational time. Results indicate that defect detectability strongly depends on conductivity contrast and geometric size. Three-dimensional reconstructions demonstrate improved volumetric localization and reduced boundary artifacts compared to 2D models, while 2D reconstruction offers significantly lower computational time & cost suitable for rapid screening applications. The results also indicate that three-dimensional EIT reconstructions significantly improve the localization of narrow structural gaps and hollow inclusions compared to two-dimensional reconstructions, particularly in conductive media such as saline considering both error margins and the visibility quality. Hollow inclusion was successfully identified in both 2D and 3D models, with 3D reconstructions providing improved internal cavity delineation. These findings provide a quantitative assessment of the trade-offs between reconstruction fidelity and computational efficiency in EIT-based structural inspection, supporting its potential application in structural health monitoring, material characterization, and conductive-environment NDE systems.

Electrical Impedance Tomography (EIT) is a non-invasive imaging technique that reconstructs interior conductivity from surface voltage measurements. This study benchmarks five finite element–based reconstruction algorithms-Back Projection (BP), Gauss–Newton (GN), Tikhonov Regularization (TR), GREIT, and EIDORS FEM-under controlled hardware conditions. A 16-electrode saline phantom integrated with multiplexers, an amplifier, a function generator, an Arduino Mega, and a DAQ was used, with chicken breast tissue inserted at four unique positions to mimic tumors. Across four excitation signals (10 kHz/1 Vpp, 50 kHz/1 Vpp, 10 kHz/5 Vpp, 50 kHz/5 Vpp), the methods yielded 80 reconstructed images. Performance was evaluated using quantitative metrics including contrast-to-noise ratio (CNR), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), root mean square error (RMSE), frequency-domain Fast Fourier Transform (FFT), spectrograms, wavelet transform, and spectral temporal energy ratio (STER) to evaluate each method’s performance and robustness. According to our investigation, there are notable variations in image fidelity based on the reconstruction technique and the signal circumstances. Back Projection exhibited artifacts and lower spatial resolution, whereas GREIT and Tikhonov showed the strongest resilience and noise suppression, with EIDORS offering advantages following them. By integrating real hardware signals with phantom experiments, this work offers a practical benchmarking framework and guidance to enhance medical and industrial EIT systems.

Electrical Impedance Tomography (EIT) provides low-cost functional impedance imaging, whereas ultrasound offers high-resolution morphological visualization. In this study, a hybrid Ultrasound–EIT (EIT–US) fusion framework is developed for soft-tissue characterization in layered ex vivo biological phantoms using controlled chicken tissue models. A custom EIT acquisition system recorded 208 boundary voltage measurements per acquisition across three tissue layers and multiple frequency–voltage conditions, with experiments repeated across three anatomical tissue configurations. Five FEM-based reconstruction methods—Back Projection (BP), Gauss–Newton (GN), Tikhonov regularization (TIK), GREIT, and a linearized EIDORS-like approach—were implemented within a stand-alone MATLAB framework. Ultrasound images acquired using a GE VScan Air CL probe (musculoskeletal mode) were integrated with EIT reconstructions through a multimodal fusion pipeline incorporating impedance imaging, ultrasound texture–echogenicity mapping, and statistical correlation analysis. Quantitative evaluation demonstrated consistent layer differentiation with moderate structural–electrical agreement ( r ≈ 0.37–0.45). ESI increased by more than 60% at the evaluated layer interfaces during the fusion approach compared to EIT-only reconstructions, along with an increase in boundary detection performance (AUC improvement of approximately 0.13–0.15). Structural similarity (SSIM) of the fused images relative to ultrasound boundaries ranged from 0.71 to 0.78, indicating preserved spatial coherence. Frequency and voltage variations influenced reconstruction stability and contrast recovery. Overall, multimodal fusion improved the structural interpretability of EIT by anchoring conductivity distributions to ultrasound-derived morphological boundaries. These results indicate that incorporating ultrasound-derived structural priors improves the interpretability of EIT reconstructions by providing spatial context to conductivity distributions. This framework provides a practical experimental approach for integrating complementary electrical and acoustic sensing modalities, with potential relevance to future low-cost and portable soft-tissue imaging systems.

Light-actuated microbots have been studied as a viable tool for interacting with micro/nano environments. Considering their applicability to a wide range of biomedical applications, novel designs, fabrication techniques, and control methodologies are being developed. Especially, micro/nanoscale three-dimensional fabrication techniques have opened many possibilities for developing microbots with complex geometries using resins as materials. Here, we developed microbots that can be actuated with tightly focused laser beams to be used in targeted drug delivery, cell poking, and cell characterization studies. These microbots were fabricated in batches using two-photon polymerization (TPP). Each microbot utilizes a deposited metal layer inside its body to manipulate convective microfluidic flows. Additionally, micro-sized end effectors allow them to make measurable physical contact with biological objects. Their expected performance was evaluated using numerical simulations with the use of multiphysics software. Furthermore, laser-induced loading and unloading of micro-sized cargo show their capability for in vitro applications.

Researchers are currently conducting several studies in the field of navigation systems and sensors. Even in the past, there was a lot of research regarding the field of velocity sensors for unmanned underwater vehicles (UUVs). UUVs have various services and significance in the military, scientific research, and many commercial applications due to their autonomy mechanism. So, it’s very crucial for the proper maintenance of the navigation system. Reliable navigation of unmanned underwater vehicles depends on the quality of their state determination. There are so many navigation systems available, like position determination, depth information, etc. Among them, velocity determination is now one of the most important navigational criteria for UUVs. The key source of navigational aids for different deep-sea research projects is water currents. These days, many different sensors are available to monitor the UUV’s velocity. In recent times, there have been five primary types of sensors utilized for UUV velocity forecasts. These include Doppler Velocity Logger sensors, paddlewheel sensors, optical sensors, electromagnetic sensors, and ultrasonic sensors. The most popular sensing sensor for estimating velocity at the moment is the Doppler Velocity Logger (DVL) sensor. DVL sensor is the most fully developed sensor for UUVs in recent years. In this work, we offer an overview of the field of navigation systems and sensors (especially velocity) developed for UUVs with respect to their use with tidal current sensing in the UUV setting, including their history, evolution, current research initiatives, and anticipated future.