arXiv subjects

Amit Kumar Mishra

Publications and source records attributed to Amit Kumar Mishra.

At least 19 recordsLinked to original sources

arXiv2026

Comparing GNSS Derived Sea Ice Drift in the Arctic and Antarctic using Rotary Spectra and Principal Component Analysis

Sea ice drift underpins air-sea-ice coupling and model evaluation, but the Southern Ocean remains chronically undersampled relative to the Arctic. This work presents a cross-polar comparison of GNSS-tracked ice-drift time series using rotary spectral analysis, principal component analysis, inter-buoy coherence, complementary shape- and amplitude-sensitive spectral differences. Arctic data are aggregated into Beaufort Gyre and Transpolar Drift seasonal composites (2017--2024); Antarctic data comprise eight heterogeneous campaigns (2000--2022) with small buoy counts and short durations. After common quality control, the clearest cross-polar similarity is spectral organisation: both regions show strong low-frequency variance and a hemisphere-appropriate rotary enhancement near the Coriolis frequency, interpreted cautiously as a combined inertial--semidiurnal response. Coherence is highest at synoptic scales and declines toward higher frequencies. Southern Ocean campaigns occupy a higher-energy envelope, so similar band structure does not imply similar drift amplitude. Principal components capture much array-scale motion, but high cumulative variance does not substitute for dense sampling of smaller-scale processes. The results provide transferable frequency-resolved benchmarks (dominant variance below 0.5 cpd and in the near-inertial band, an expanded Arctic reference, and explicit separation of spectral shape from amplitude) to inform future observing-system design without assuming identical dynamical regimes.

eess.SP↗

arXiv2025

Variational Autoencoder for Calibration: A New Approach

In this paper we present a new implementation of a Variational Autoencoder (VAE) for the calibration of sensors. We propose that the VAE can be used to calibrate sensor data by training the latent space as a calibration output. We discuss this new approach and show a proof-of-concept using an existing multi-sensor gas dataset. We show the performance of the proposed calibration VAE and found that it was capable of performing as calibration model while performing as an autoencoder simultaneously. Additionally, these models have shown that they are capable of creating statistically similar outputs from both the calibration output as well as the reconstruction output to their respective truth data. We then discuss the methods of future testing and planned expansion of this work.

cs.LG↗

arXiv2025

Theoretical Analysis for the CommSense Measurement System

Future 6G networks envisions to blur the line between communication and sensing, leveraging ubiquitous OFDM waveforms for both high throughput data and environmental awareness. In this work, we do a thorough analysis of Communication based Sensing (CommSense) framework that embeds lightweight, PCA based detectors into standard OFDM receivers; enabling real-time, device free detection of passive scatterers (e.g. drones, vehicles etc.) without any extra transmitters. Starting from a realistic three link Rician channel model (direct Tx to Rx, cascaded Tx to Scatterer and Scatterer to Rx), we compare four detectors: the full dimensional Likelihood Ratio Test (Full LRT), PCA based LRT, PCA-SVM with linear and RBF kernels. By projecting N-dimensional CSI onto a P (very less than N) principal component subspace, inference time gets reduced by an order of magnitude compared to the full LRT, while achieving optimal error rates i.e. empirical errors align tightly with the Bhattacharyya error bound and Area Under ROC Curve (AUC) approx. equal to 1 for P approx. equal to 10. From the simulated result we have shown LRT based techniques are susceptible to the parameter estimation error, where as SVM is resilient to that. Our results demonstrate that PCA driven detection when paired with lightweight SVMs can deliver fast, accurate, and robust scatterer sensing, paving the way for integrated sensing and communication (ISAC) in 6G and beyond.

eess.SP↗

arXiv2025

Dynamic Beam-Stabilized, Additive-Printed Flexible Antenna Arrays with On-Chip Rapid Insight Generation

Conformal phased arrays promise shape-changing properties, multiple degrees of freedom to the scan angle, and novel applications in wearables, aerospace, defense, vehicles, and ships. However, they have suffered from two critical limitations. (1) Although most applications require on-the-move communication and sensing, prior conformal arrays have suffered from dynamic deformation-induced beam pointing errors. We introduce a Dynamic Beam-Stabilized (DBS) processor capable of beam adaptation through on-chip real-time control of fundamental gain, phase, and delay for each element. (2) Prior conformal arrays have leveraged additive printing to enhance flexibility, but conventional printable inks based on silver are expensive, and those based on copper suffer from spontaneous metal oxidation that alters trace impedance and degrades beamforming performance. We instead leverage a low-cost Copper Molecular Decomposition (CuMOD) ink with < 0.1% variation per degree C with temperature and strain and correct any residual deformity in real-time using the DBS processor. Demonstrating unified material and physical deformation correction, our CMOS DBS processor is low-power, low-area, and easily scalable due to a tile architecture, thereby ideal for on-device implementations.

eess.SP↗

arXiv2025

Dynamic Beam-Stabilized, Additive-Printed Flexible Antenna Arrays with On-Chip Rapid Insight Generation

Conformal phased arrays promise shape-changing properties, multiple degrees of freedom to the scan angle, and novel applications in wearables, aerospace, defense, vehicles, and ships. However, they have suffered from two critical limitations. (1) Although most applications require on-the-move communication and sensing, prior conformal arrays have suffered from dynamic deformation-induced beam pointing errors. We introduce a Dynamic Beam-Stabilized (DBS) processor capable of beam adaptation through on-chip real-time control of fundamental gain, phase, and delay for each element. (2) Prior conformal arrays have leveraged additive printing to enhance flexibility, but conventional printable inks based on silver are expensive, and those based on copper suffer from spontaneous metal oxidation that alters trace impedance and degrades beamforming performance. We instead leverage a low-cost Copper Molecular Decomposition (CuMOD) ink with < 0.1% variation per degree C with temperature and strain and correct any residual deformity in real-time using the DBS processor. Demonstrating unified material and physical deformation correction, our CMOS DBS processor is low power, low-area, and easily scalable due to a tile architecture, thereby ideal for on-device implementations.

eess.SP↗

arXiv2025

Reliable Traffic Monitoring Using Low-Cost Doppler Radar Units

Road traffic monitoring typically involves the counting and recording of vehicles on public roads over extended periods. The data gathered from such monitoring provides useful information to municipal authorities in urban areas. This paper presents a low-cost, widely deployable sensing subsystem based on Continuous Wave Doppler radar. The proposed system can perform vehicle detection and speed estimation with a total cost of less than 100 USD. The sensing system (including the hardware subsystem and the algorithms) is designed to be placed on the side of the road, allowing for easy deployment and serviceability.

eess.SP↗

arXiv2025

Agile Climate-Sensor Design and Calibration Algorithms Using Machine Learning: Experiments From Cape Point

In this paper, we describe the design of an inexpensive and agile climate sensor system which can be repurposed easily to measure various pollutants. We also propose the use of machine learning regression methods to calibrate CO2 data from this cost-effective sensing platform to a reference sensor at the South African Weather Service's Cape Point measurement facility. We show the performance of these methods and found that Random Forest Regression was the best in this scenario. This shows that these machine learning methods can be used to improve the performance of cost-effective sensor platforms and possibly extend the time between manual calibration of sensor networks.

eess.SY↗

arXiv2025

Statistical Study of Sensor Data and Investigation of ML-based Calibration Algorithms for Inexpensive Sensor Modules: Experiments from Cape Point

In this paper we present the statistical analysis of data from inexpensive sensors. We also present the performance of machine learning algorithms when used for automatic calibration such sensors. In this we have used low-cost Non-Dispersive Infrared CO$_2$ sensor placed at a co-located site at Cape Point, South Africa (maintained by Weather South Africa). The collected low-cost sensor data and site truth data are investigated and compared. We compare and investigate the performance of Random Forest Regression, Support Vector Regression, 1D Convolutional Neural Network and 1D-CNN Long Short-Term Memory Network models as a method for automatic calibration and the statistical properties of these model predictions. In addition, we also investigate the drift in performance of these algorithms with time.

eess.SP↗

arXiv2023

Heart Disease Detection using Vision-Based Transformer Models from ECG Images

Heart disease, also known as cardiovascular disease, is a prevalent and critical medical condition characterized by the impairment of the heart and blood vessels, leading to various complications such as coronary artery disease, heart failure, and myocardial infarction. The timely and accurate detection of heart disease is of paramount importance in clinical practice. Early identification of individuals at risk enables proactive interventions, preventive measures, and personalized treatment strategies to mitigate the progression of the disease and reduce adverse outcomes. In recent years, the field of heart disease detection has witnessed notable advancements due to the integration of sophisticated technologies and computational approaches. These include machine learning algorithms, data mining techniques, and predictive modeling frameworks that leverage vast amounts of clinical and physiological data to improve diagnostic accuracy and risk stratification. In this work, we propose to detect heart disease from ECG images using cutting-edge technologies, namely vision transformer models. These models are Google-Vit, Microsoft-Beit, and Swin-Tiny. To the best of our knowledge, this is the initial endeavor concentrating on the detection of heart diseases through image-based ECG data by employing cuttingedge technologies namely, transformer models. To demonstrate the contribution of the proposed framework, the performance of vision transformer models are compared with state-of-the-art studies. Experiment results show that the proposed framework exhibits remarkable classification results.

cs.CV↗

arXiv2023

A Propagation-model Empowered Solution for Blind-Calibration of Sensors

Calibration of sensors is a major challenge especially in inexpensive sensors and sensors installed in inaccessible locations. The feasibility of calibrating sensors without the need for a standard sensor is called blind calibration. There is very little work in the open literature on totally blind calibration. In this work we model the sensing process as a combination of two processes, viz. propagation of the event through the environment to the sensor and measurement process in the sensor. Based on this, we propose a unique method for calibration in two flavours, viz semi-blind and completely-blind calibration. We show limited results based on simulation showing encouraging results.

eess.SP↗

arXiv2023

Analysis of Arctic Buoy Dynamics using the Discrete Fourier Transform and Principal Component Analysis

Sea-Ice drift affects various global processes including the air-sea-ice energy system, numerical ocean modelling, and maritime activity in the polar regions. Drift has been investigated via various technologies ranging from satellite based systems to ship or ice-borne processes. This paper analyses the dynamics of sea-drift in the Arctic over 2019-2021 by Fourier Analysis and Principal Component Analysis of displacement data generated from the drift tracks of Ice-Tethered Profilers. We show that the frequency characteristics of drift support the notion that it is a function of both slowly varying processes, and higher frequency, random, forcing. In addition, we show that displacement data features high correlation between deployment locations and, consequently, suggest that there is scope for the optimisation of profiler deployment locations and for the reduction in number of instruments required to capture the displacement characteristics of drift.

eess.SP↗

arXiv2023

Sensing the Environment with 5G Scattered Signals (5G-CommSense): A Feasibility Analysis

By making use of the sensors and AI (SensAI) algorithms for a specialized task, Application Specific INstrumentation (ASIN) framework uses less computational overhead and gives a good performance. This work evaluates the feasibility of the ASIN framework dependent Communication based Sensing (CommSense) system using 5th Generation New Radio (5G NR) infrastructure. Since our proposed system is backed up by 5G NR infra, this system is termed as 5G-CommSense. In this paper, we have used NR channel models specified by the 3rd Generation Partnership Project (3GPP) and added white Gaussian noise (AWGN) to vary the signal to noise ratio at the receiver. Finally, from our simulation result, we conclude that the proposed system is practically feasible.

eess.SP↗

arXiv2022

Machine Learning based Extraction of Boundary Conditions from Doppler Echo Images for Patient Specific Coarctation of the Aorta: Computational Fluid Dynamics Study

Purpose- Coarctation of the Aorta (CoA) patient-specific computational fluid dynamics (CFD) studies in resource constrained settings are limited by the available imaging modalities for geometry and velocity data acquisition. Doppler echocardiography has been seen as a suitable velocity acquisition modality due to its higher availability and safety. This study aimed to investigate the application of classical machine learning (ML) methods to create an adequate and robust approach for obtaining boundary conditions (BCs) from Doppler Echocardiography images, for haemodynamic modeling using CFD. Methods- Our proposed approach combines ML and CFD to model haemodynamic flow within the region of interest. With the key feature of the approach being the use of ML models to calibrate the inlet and outlet boundary conditions (BCs) of the CFD model. The key input variable for the ML model was the patients heart rate as this was the parameter that varied in time across the measured vessels within the study. ANSYS Fluent was used for the CFD component of the study whilst the scikit-learn python library was used for the ML component. Results- We validated our approach against a real clinical case of severe CoA before intervention. The maximum coarctation velocity of our simulations were compared to the measured maximum coarctation velocity obtained from the patient whose geometry is used within the study. Of the 5 ML models used to obtain BCs the top model was within 5\% of the measured maximum coarctation velocity. Conclusion- The framework demonstrated that it was capable of taking variations of the patients heart rate between measurements into account. Thus, enabling the calculation of BCs that were physiologically realistic when the heart rate was scaled across each vessel whilst providing a reasonably accurate solution.

cs.CE↗

arXiv2022

Review of Methods for Handling Class-Imbalanced in Classification Problems

Learning classifiers using skewed or imbalanced datasets can occasionally lead to classification issues; this is a serious issue. In some cases, one class contains the majority of examples while the other, which is frequently the more important class, is nevertheless represented by a smaller proportion of examples. Using this kind of data could make many carefully designed machine-learning systems ineffective. High training fidelity was a term used to describe biases vs. all other instances of the class. The best approach to all possible remedies to this issue is typically to gain from the minority class. The article examines the most widely used methods for addressing the problem of learning with a class imbalance, including data-level, algorithm-level, hybrid, cost-sensitive learning, and deep learning, etc. including their advantages and limitations. The efficiency and performance of the classifier are assessed using a myriad of evaluation metrics.

cs.LG↗

arXiv2022

Sea Ice Concentration Estimation Techniques Using Machine Learning: An End-To-End Workflow for Estimating Concentration Maps from SAR Images

Sea ice concentration is an important metric used to characterize polar sea ice behavior. Understanding this behavior and accurately representing it is of critical importance for climate science research, and also has important uses in the context of maritime navigation. An end-to-end workflow for generating learned concentration estimation models from synthetic aperture radar data, trained on existing passive microwave data, is presented here. A novel objective function was introduced to account for uncertainty in the passive microwave measurements, which can be extended to account for arbitrary sources of error in the training data, and a recent set of in-situ observations was used to evaluate the reliability of the chosen passive microwave concentration estimation model. Google Colaboratory was used as the development platform, and all notebooks, training data, and trained models are available on GitHub. This chapter is an overview of the most interesting aspects of this investigation, and a detailed report is also available on GitHub.

eess.SP↗

arXiv2020

Boron Nitride and Oxide Supported on Dendritic Fibrous Nanosilica for Catalytic Oxidative Dehydrogenation of Propane

In this work, we were able to significantly increase the activity of boron nitride catalysts used for the oxidative dehydrogenation (ODH) of propane by designing and synthesising boron nitride (BN) supported on dendritic fibrous nanosilica (DFNS). DFNS/BN showed a markedly increased catalytic efficiency, accompanied by exceptional stability and selectivity. Textural characterisation together with solid-state NMR and X-ray photoelectron spectroscopic analyses indicate the presence of a combination of unique fibrous morphology of DFNS and various boron sites connected to silica to be the reason for this increase in the catalytic performance. Notably, DFNS/B${}_2$O${}_3$ also showed catalytic activity, although with more moderate selectivity compared to that of DFNS/BN. Solid-state NMR spectra indicates that the higher selectivity of DFNS/BN might stem from a larger amount of hydrogen-bonded hydroxyl groups attached to B atoms. This study indicates that both boron nitride and oxide are active catalysts and by using high surface area support (DFNS), conversion from propane to propene as well as productivity of olefins was significantly increased.

cond-mat.mtrl-sci↗

arXiv2020

Supervised Neural Networks for RFI Flagging

Neural network (NN) based methods are applied to the detection of radio frequency interference (RFI) in post-correlation,post-calibration time/frequency data. While calibration doesaffect RFI for the sake of this work a reduced dataset inpost-calibration is used. Two machine learning approachesfor flagging real measurement data are demonstrated usingthe existing RFI flagging technique AOFlagger as a groundtruth. It is shown that a single layer fully connects networkcan be trained using each time/frequency sample individuallywith the magnitude and phase of each polarization and Stokesvisibilities as features. This method was able to predict aBoolean flag map for each baseline to a high degree of accuracy achieving a Recall of 0.69 and Precision of 0.83 and anF1-Score of 0.75.

astro-ph.IM↗

arXiv2020

Machine Learning Techniques to Detect and Characterise Whistler Radio Waves

Lightning strokes create powerful electromagnetic pulses that routinely cause very low frequency (VLF) waves to propagate across hemispheres along geomagnetic field lines. VLF antenna receivers can be used to detect these whistler waves generated by these lightning strokes. The particular time/frequency dependence of the received whistler wave enables the estimation of electron density in the plasmasphere region of the magnetosphere. Therefore the identification and characterisation of whistlers are important tasks to monitor the plasmasphere in real-time and to build large databases of events to be used for statistical studies. The current state of the art in detecting whistler is the Automatic Whistler Detection (AWD) method developed by Lichtenberger (2009). This method is based on image correlation in 2 dimensions and requires significant computing hardware situated at the VLF receiver antennas (e.g. in Antarctica). The aim of this work is to develop a machine learning-based model capable of automatically detecting whistlers in the data provided by the VLF receivers. The approach is to use a combination of image classification and localisation on the spectrogram data generated by the VLF receivers to identify and localise each whistler. The data at hand has around 2300 events identified by AWD at SANAE and Marion and will be used as training, validation, and testing data. Three detector designs have been proposed. The first one using a similar method to AWD, the second using image classification on regions of interest extracted from a spectrogram, and the last one using YOLO, the current state of the art in object detection. It has been shown that these detectors can achieve a misdetection and false alarm of less than 15% on Marion's dataset.

eess.SP↗