Projects

Unified Scalable 3D SLAM: Designed and built algorithm suite for mapping and localization of mobile robots that is scalable with respect to customer sensing budgets, coupling modes (loose and tight) for different compute budgets, different use case requirements and applications, accuracy and reliability specifications. Created novel modular algorithms supporting sparse, dense, and topological representations with configurable multi-sensor fusion with elaborate system identification and calibration. Built full front-end/back-end pipeline including novel designs for deep-learned feature detection, descriptor hashing, data association, relative pose refinement, motion modeling, local mapping, loop closure detection, relocalization and global graph optimization. Augmented with multi-map representations, global and deformable local map alignment, covariance propagation, and scalable compute modes to support varied sensing budgets and accuracy requirements.

Fleet-Based Incremental Map Updater: Designed centralized fleet-managed map evolution system supporting incremental and periodic environment updates. Developed novel Independent and Parallel Particle (Clone) Evolution Models, coupled/decoupled particle diversity management, trajectory closeout logic, and multi-robot state fusion. Integrated state freezing and re-initialization strategies to stabilize map updates. Developed multi-modal map generation using Hidden Markov Models (HMM) and mixing-time estimation for convergence and stationarity analysis. Enabled continuous deployment updates without disrupting active localization.

2D Lidar Real-Time Mapper: Developed lightweight 2D LiDAR-based mapping and localization framework optimized for compute-constrained platforms and real-time user feedback. Implemented scan matching, motion model prediction, RBPF-based state estimation, occupancy grid generation, map hashing, and loop closure correction.

Distributed & Decentralized Collaborative SLAM: Architected novel peer-to-peer multi-robot SLAM enabling decentralized temporal map sharing, relative pose alignment and daisy-chained joint-localization improvement, and heterogeneous sensor fusion across robots. Crafted multi-robot autonomous exploration for mapping phase and collaborative path planning for deployment phase. Designed multi-master and mesh based communication framework supporting bandwidth-aware state exchange, loop-closure negotiation, and cross-robot re-localization. Supported scalable heterogeneous fleets with differing sensing modalities and compute budgets while maintaining map consistency and collaborative localization accuracy. Integrated with Ignition Gazebo and Nvidia Isaac simulation backends.

Autonomous Mapping & Exploration System: Architected a novel autonomous exploration stack enabled by multi-objective optimization, integrating global and local planning with SLAM-driven uncertainty metrics. Implemented dynamic trajectory generation coupled with local refinement enabled by sampling-based planning (RRT/PRM), frontier prioritization and blacklsting, and collision-aware trajectory tracking using covariance-informed LQG supervisory control with cascaded kinodynamic prioritized velocity–torque loops. Developed Generative Adversarial Network (GAN) based prediction models, dynamic obstacle handling, and adaptive velocity limiting under localization uncertainty. Enabled autonomous maximization of coverage, incremental map refinement, and robust navigation in partially observable and dynamically evolving environments.

Photogrammetry and Camera Pose Estimation Suite: Built algorithm suite for Structure from Motion (SfM – Incremental, Global) based metric accurate drone camera pose estimation and sparse reconstruction, with a metric measurement error of less than 0.1%; adapting and improving classical methods for Camera calibration (April Tags etc.), Color, exposure, vignetting correction; Feature detection (AKAZE, BRISK, AGAST etc.); Feature matching (SSE Optimized Incremental, Exhaustive, Cascade Hashing etc.); Geometric verification and prefiltering (Ransac, Prosac, Arrsac, LMedS, MLE Ransac, SPRT etc.); Relative pose and camera parameters estimation (Perspective-n-Point (PnP), P3P, 5PRP, 4PH, 8PFM, 5PEM, 4PFL, 5PFLRD, 3PRPR, 4PRPR, 2PAPR etc.); Relative pose transformation (Horn, Besl-McKay, Iterative Closest Point (ICP) etc.); Triangulation and Structure estimation (Direct Linear Transform (DLT), MidPoint etc.); Bundle adjustment BA (Multicore - MCBA, Sparse - SBA, SSBA, cvSBA etc.); employing a slew of non-linear least squares optimization techniques (Trust Region: Traditional, Subspace Dogleg, Levenberg Marquardt, Line Search: Steepest Descent, Nonlinear Conjugate Gradient, BFGS, LBFGS etc. and loss functions Robust L1/L2, Nonlinear, Huber, Softlone, Cauchy, Arctan, Tukey etc.); Linear least squares methods, Factorization methods and preconditioners (Dense QR, SPQR, Dense/Sparse Normal Cholesky, Dense/Sparse Schur, Jacobi, Iterative Schur, CGNR, LDL, AMD, COLAMD, Gauss-Seidel relaxation etc. and using numeric, analytic, auto derivatives). Developed novel proprietary algorithms to customize the pipeline for UAV camera pose estimation targeting cell phone towers, wind turbines, building and other free standing structures.

Dense Fixed Baseline Stereo Module: Developed algorithms for generating near real-time sparse feature based and dense stereo depth maps and 3D reconstructions for imagery from UAVs. Module also includes conventional stereo algorithms for calibration and densification (BM, SGBM, SGM etc.).

Ultra Hi-Density Multi-View Dense 3D Stereo Reconstruction Suite: Created novel algorithms for generating high density billion point tower 3D cloud reconstructions, with resolution of 12 voxels/ mm and smallest structure fidelity of 1-2mm, and capable of reading labels. Suite also builds over classical MVS approaches (Clustering (CMVS), Patch MVS (PMVS), Shading aware MVS (BRDF)) and other depth propagation techniques (MRF/ CRF, Belief Propagation, Diffusion, Variational methods, Primal dual solvers etc.).

3D Surface Processing Suite: Implemented, adapted, improved classical algorithms for Point cloud filtering (Spherical PTZ scan filtering, Octree/ kdtree/ BVH outlier filtering, scale, confidence analysis filtering), Surface generation (Laplacian heat diffusion, Poisson, Screened Poisson, Smoothed Signed Distance (SSD), Floating Scale (FSSR), Moving Least Squares (MLS), Grid projection, Marching cubes - Hoppe’s, RBF etc.); Mesh generation (Ear clipping, Greedy projection, Image connectivity meshification etc.); Mesh screening and trimming; Mesh texturing (with global, local seam stitching), recoloring from posed images; Cloud and mesh analysis (Distance measurement); Surface geometry fitting (Primitives, Splines); Rendering pre-processing (Scale-space voxel analysis, Hidden point removal etc.) and developed proprietary energy minimization algorithms for surface processing using contextual priors for cell towers, buildings, wind turbine assets.

3D Point Cloud Sensor Data Fusion: Designed methods for automated Alignment and Refinement of dense 3D data with laser scans, Fused point cloud recoloring.

Novel View Synthesis for Low-Texture Industrial Structures: Designed and implemented a novel view synthesis framework for low-texture, high-reflectance industrial assets (e.g., white wind turbines) using multi-view stereo reconstruction augmented with Neural Radiance Fields (NeRF) and Gaussian Splatting. Addressed degeneracy from texture sparsity via geometry-regularized priors and CAD-model alignment constraints for structural consistency. Integrated photometric consistency losses, sparse feature anchoring, and pose-optimized bundle adjustment to stabilize reconstruction. Enabled high-fidelity view interpolation, structural inspection, and synthetic data generation for downstream perception, inspection, and asset monitoring applications.

HDR Image Processing: Implemented, adapted algorithms for generating High/Low Dynamic Range (HDR/ LDR) images from multiple bracketed exposure images in linear light EXR (supporting Debevec, Robertson, Mertens, and tone-mapping: Drago, Durand, Reinhard, Mantiuk methods) and estimation of Camera Response Function (CRF) to enhance drone imagery.

Ultra Hi-Res HDR Constellation Panorama Generation: Developed algorithms for generating ultra high resolution 30Kx96K, 360ox180o panoramas from 240 constellation poses of imaging PTZ base station. Built a pipeline supporting Constellation projection and remapping, Exposure compensation (method of Gain, Blocks etc.), Seam generation (extending methods of GraphCut, Voronoi, Dynamic programming etc.), and classical Blending (Multiband, Feather etc.). Extended pipeline to generate cubemaps and spheremaps for stitching hi-res imagery from drones, by incorporating Pose estimation, Wave correction, Projection and remapping (Stereographic -Mini Planet, Mercator, Panini etc.) and Compositing.

Parallelization and Cloud/ Cluster Computing: Parallelized Photogrammetry, 3D Dense Stereo, 3D Surface Processing and HDR Panorama Generation suites for deployment on cloud/ cluster computing services towards productization. Developed algorithms to create and execute dependency job graphs and to manage job assets.

UWB Localization for Autonomous Drones: Developed algorithms for localization of autonomous drones within a Ultra-Wideband Sensor Network enabling drone pose estimation through sensor fusion.

Region Proposal Network (RPN)/ Faster R-CNN based Component Detection: Built a tower component object detector and segmentor based on Region Proposal Networks in combination with Faster R-CNN.

Rolling Shutter Artefact Removal: Built image processing algorithms to reduce high vibration rolling shutter artefacts in drone imagery of structures.

Tower Extraction/ Segmentation: Developed a novel segmentation algorithm to extract tower foreground in drone imagery using a combination of depth from blur, defocus, Hough structures and other features.

Image Annotation with SLIC0: Built a region accurate image annotation tool using SLIC0 segmentation and LSH for use with machine learning modules.

Visual Odometry Analysis: Carried out analyses of various Visual Odometry (VO) and Visual SLAM (VSLAM) for improving drone pose estimates and integration with reconstruction suite. Techniques analyzed include SVO, LSD-SLAM, ORB-SLAM, DTAM, PTAM, KinFu etc.

Touch Adaptive Image Segmentation and Object Modeling: Formulated algorithms for touch adaptive segmentation and object modeling for Tegra/ Android tablet devices using C/CUDA for Nvidia.

Feature Based Tracking and Pose Estimation: Contributed to algorithmic improvements for the tracking and pose estimation routines in Qualcomm QCAR/Vuforia SDK framework for Android, iOS environments.

Traffic/Driver Behavior Modeling: Developed novel bio-inspired mirror neural network (MNN) algorithms for traffic event prediction from surveillance video using Markov chain models for EURASIA Pacific Uninet project.

Landmark Detection for Robot Navigation: Designed stereo based object recognition and 3D scene reconstruction algorithms targeted at landmarks such as doorways and room boundaries for indoor robot navigation, room functionality hypothesis generation, as part of Robots@Home IKEA EU project. The core component of the system is a novel Feature Guided Piecewise Depth Diffusion algorithm.

Obstacle Avoidance: Developed and implemented a real-time multi-threaded architecture to support GPS data acquisition, Stereoscopic vision processing, Local path planning and User interface display for Obstacle avoidance as part of a NASA-SBIR on Systems for Autonomous sea vehicles and USVs. Also integrated RS-232 serial port communication based GPS data acquisition for the live system.

Panoramic Video Tracking: Designed and implemented a novel ‘Real-Time Panoramic NTSC Video Human Tracking System based on the Kalman Filter’ on fixed point TMS320C6416 DSP processor, with Pipeline, memory & stack management, Logic and instruction level optimization, Hardware and software trade-offs.

UAV Pose Estimation and Super-Resolution: Designed and implemented super-resolution schemes to aid weak pose estimation for monocular passive ranging (Air-Force SBIR).

Learning for Automated Target Recognition: Researched and built modules for innovative Incremental learning (IncLeDec), Environmental context adaptation, Concept drift and Knowledge assimilation for Decision trees based Naive Bayes Classifier (NBC) and other ensemble learning schemes operating on target image chips for Army SBIR on Perpetual learning and knowledge mining for Automated Target Recognition (ATR).

LLM-Orchestrated Mixed-Methods Analysis of Large-Scale Policy Corpora: Engineered an LLM-driven mixed-methods analysis pipeline to process and structure 189 Indigenous policy documents (6.24M words) into a machine-interpretable, hierarchically coded knowledge base. Designed prompt-engineered extraction workflows with schema-constrained outputs (JSON/ontology-aligned), retrieval-augmented generation (RAG) for cross-document grounding, and multi-pass validation loops to reduce hallucination and semantic drift. Implemented embedding-based clustering, hierarchical topic modeling, statistical co-occurrence matrices, and graph-based thematic network analysis to quantify structural relationships across policies. Integrated human-in-the-loop adjudication for bias mitigation, cultural-linguistic fidelity checks, and ontology refinement. Delivered a scalable, reproducible LLM-assisted analytical framework enabling structured policy synthesis, comparative evaluation, and culture-aware adaptation modeling.

Augmented Virtuality based Telepresence for Mining Robots: Designed an Augmented Virtuality based visualization system for control of mining robots for semi-autonomous operation in critical environments. (UC).

Structural Image Compression: Designed and implemented novel modules for Edge detection & novel edge selection, Progressive encoding, Hybrid (wavelet and edgelet adaptive singularity description) encoding, Laplacian single-grid & Pseudo-full multi-grid anisotropic PDE diffusion using Iterative Back Substitution (IBS) linear equation solver and High fidelity robust unique maritime segmentation & optimization scheme for IR image compression, as a part of Air-Force SBIR on ‘JASSM - An Intelligent and Adaptive Class-Based Compression Technology for Weapon Seekers Suitable for Minimum RF Bandwidth’.

Video Streamer for Immersive Robot Teleoperation: Developed novel algorithms for content driven video compression and streaming over unreliable error-prone tactical ad-hoc wireless networks targeted at immersive robot teleoperation in dynamic urban scenarios, based on Peano-Cesaro tiling, Structural representation, Tree motion compensation and Multiple Description Coding (MDC)/ Layered Coding (LC) - Army SBIR.

Building Footprint Extraction: Built plugins achieving ESRI compatible shape file format conversion for UC’s building footprint extraction GIS tools (DHS SBIR) and benchmark tested UC’s GIS software.

Bio-Inspired Generic Object Recognition: Researched and designed a novel Geon based framework for generic bio-inspired object recognition using Intrinsic image extraction, Curve detection, Laplacian diffusion, Curve space smoothing, Depth segmentation, Part boundary detection, Relaxation labeling - Gradient ascent, Real Coded Genetic Algorithms (RCGA) Superquadrics fitting and DAGSVM learning, intended for Simultaneous Localization and Mapping (SLAM), Human Robot Interaction (HRI) and Autonomous Robot Navigation (Army SBIR).

Kinect RGBD Processing and Object Part Extraction for Scalable Object Grasping: Designed Kinect range sensor/ stereo/ monocular cognitive contour grouping based object part detection, grouping and grasp hypothesis generation algorithms targeted at table-top scenes for scalable grasping of unknown objects and manipulation using a robot arm, as part of GRASP EU project. The core component of the system revolves around a new theory postulated- Grasping by Components (GBC).

Waterfall-Islands Model for Bin Picking and Heap Clearing using Articulated Robots: Depth processing and Superquadrics based grasp primitives generation and planning, and novel physics based motion planning for articulated robots towards deconstructing heaps and removing clutter. 

k-TR Theory to Visual Perception: Formulated a novel evolutionary psychophysics theory to explain human perception and recognition of objects based on evolutionary cognitive algorithmic processes and repeated learning of correlated local features in the object space. The theory, validated using theoretical analyses, psychophysical priming tests (RSVP), neurobiological, linguistic and computer vision models also explains allied aspects of recognition such as Novelty detection, Equivalence classes, Recognition of articulated and natural objects, Attention, Saliency, Memory and Object identity retrieval, Scale of analysis and Choice of features.

Recognition by Component Affordances (RBCA): Created a new theory to object recognition using component affordances. Designed and implemented innovative computer vision algorithms – range processing, object and part semantic segmentation, 3D modeling, affordance ontology mapping, part connectivity calculus, graph matching, view clustering using Non-Negative Matrix Factorization (NNMF), inference mechanisms leading to a Visual Cognitive Engine (VCE) supporting RBCA.

AfNet– The Affordance Network, A-fACToR– Affordance based Perception for ACT-R and Af-kTRAANS- Affordance and k-TR Augmented Abugida based Neuro-Symbolic language: Developed AfNet, an open affordance computing initiative (an analog of Vision-Language-Action/ VLA models), that builds affordance knowledge ontologies in terms of afbits (affordance bits) defined by the user community. The project is hosted at theaffordances.net and provides 68 base affordance features (25 structural, 10 material, 33 grasp), over 200 object category definitions in terms of 4000 afbits. Also created A-fACToR, an AfNet based cognitive architecture for robots with nature, nurture delineation and Af-kTRAANS, a cognitive language for inter-robot and human-robot communication.

AfNet Application for Humanoids and Mobile Manipulators: Employed AfNet to develop numerous cognitive system applications such as Affordance Guided Top-down Search, Bottom-up Semantic Saliency, Deep Learning/Neuromorphic Cognitive Object Category Recognition, Task based Object Manipulation/Interaction, Affordance Sequencing, Functional-Topological Mapping and Navigation etc.

Cognitive Event Recognition: Designed and implemented a novel ‘Bio-inspired Scene Analysis System for Video Indexing and Retrieval’ using the concept of mirror neurons. It uses an Object - Action - Event recognition pipeline employing mirror neuron networks, object state, event state graphs, hierarchy and ontology.

Classical Object Recognition: Designed, implemented and analyzed an object recognition tool in OpenCV, to classify between different kinds of objects, using the Willamowski ‘Bag of Features’ method employing a SIFT-PCA-K means clustering-Feature vector histograms-N nearest neighbor/ SVM classifier pipeline. Conducted error and parameter effect analysis on results.

Other Image Analyses: Implemented and analyzed Lambertian surface shading. Implemented and tested Mean shift & K-means segmenters and analyzed the results of the segmentation with respect to its parameters. Analyzed the performance of Birchfield-Tomasi stereo algorithm based on various parameters such as occlusion penalty, match-reward, reliability etc.

TSA for fMRI imagery: Implemented a tool for Time Series Analysis (TSA) of Functional Magnetic Resonance Images (fMRI) using Wavelets as a part of project Vortex (an fMRI analysis software) for VulcanTech Software, PA, US.

3D Graphics Library: Developed a 3D graphics library using VC++. Components include Z-buffer triangle rendering, Viewpoint transformations, Lighting and shading, Procedural and image texturing, Accumulation-buffer Anti-aliasing, Shadowing etc.

Algorithms for Bio-rendering: Developed couple of novel bio-inspired algorithms for rendering tree growth – Fissured bark geometry and Semi-random cylindrical trunk with astroidal base explosion geometry.

X-ray Fluorescence Spectrometry (XRF) based Analysis of Ancient Egyptian Pigments: This project, carried out at Stanford University, utilizes XRF to identify the elemental composition of pigments used in Ancient Egyptian funerary artifacts such as coffins, mummy masks, funerary portraits, etc., to analyze artistic styles and determine the provenance and authenticity of these artifacts. XRF was used in conjunction with Radiography (X-ray, CT), Microscopy, Lidar Scanning, EO/IR Imaging, Photogrammetry, Iconography, Paleography, and Epigraphy to address key questions related to the construction and preservation methods from specific periods in Ancient Egyptian history.

3D Imaging, Reconstruction and Analysis of Ancient Egyptian Mummies and Funerary Artefacts: The project uses Radiography, X-ray Fluorescence Spectrometry, Microscopy, Lidar Scanning and Photogrammetry and Epigraphy to answer key questions regarding construction/ preservation methods and techniques from specific periods in Ancient Egyptian history. The health and well-being of mummified individuals ante-mortem is studied, as well as, post-mortem changes inferred.

Empirical Construction of Morphology Specific SMBH Mass Functions from Galaxy Demographics: SDSS7-derived stellar mass functions (SMFs), velocity dispersion functions (VDFs), and luminosity functions (LFs), stratified by morphological type, developed to build Monte Carlo-based SMBH mass functions (BHMFs) via established and literature-derived scaling relations. Compared trends across elliptical, spiral, and lenticular galaxies.

Refining Low-Mass BHMF Using AGN Observables: Leveraged the LeMMINGs Palomar AGN sample and dynamic mass measurements of 73 Early-Type Galaxies (ETG) and 28 Late-Type Galaxies (LTG) from 3.6um Spitzer observations, following Sahu, to further refine the BHMF at low-mass end, accounting for selection effects and AGN duty cycles. These models are used to explain discrepancies in PTA observations through morphological population splits and extended mass function models.

Probing SMBH Merger Rates and Stochastic GW Background via Semi-Empirical Cosmological Models: Development of robust data-driven models to trace SMBH growth from high redshift, using abundance matching between dark matter halo accretion and galaxy star formation rates. SMBH accretion is modeled via observed Eddington ratio distributions and integrated over cosmic time. SMBH mergers is incorporated using dark matter halo merger trees to predict merger rates and gravitational wave backgrounds. Investigated implications for LISA and PTA observables.

Observational Analysis of Time Variables using the Liverpool Telescope: Modeling and analysis of time-variable behavior of candidate quadruple-lensed quasar J181730.6+272941, type IIb supernova ZTF22aaotgrc (2022ngb), BY Draconis rotating variable J181746.46+273719.0, and 3 W Ursae Majoris eclipsing variables using Spectroscopy, Aperture/ PSF Photometry and Astrometry. Light curve variability between lensed images, periodicities, cosmological redshift and distance, surface gravity, effective temperature, age and other physical variables were determined. Typing, luminosity class, spectral make-up and chemical composition were ascertained. Theories of Doppler Broadening, Lyman Alpha Reionization and Asteroseismology were validated.

Gravitational Waves (GW) Survey and Analysis: Survey of GW detectors – LIGO, VIRGO, KAGRA and analysis/ discussion of data and results. Validation of General Relativity (GR) hypotheses, Analysis/ discussion of Strong Field Tests, GW/ Graviton Signal Consistency Tests, Parameterized tests of GR using Inspiral, Merger and Ringdown, Tests of GW Propagation, Polarization and Echoes, GW Lensing and Anisotropy. Validation and discussion of hypothesized GW sources – Merger of Black Holes (BH), and Neutron Stars (NS), Supernovae bursts, Pulsars, Boson Dark Matter Clouds, Primordial BH, Continuous GW and Accreting Millisecond X-ray Pulsars (AMXPs), Exotic Objects and Dark Photons.

Quantum Chromodynamics (QCD) Particle Physics Simulations: Designed and conducted Monte Carlo simulation of the probability distribution of clusters of gluon cascade transverse momenta in the hypothesized QCD wave function overlapping saturation regime using equivalent spin-glass overlap functions. The model is consistent with predictions based on the Balitsky Fadin Kuraev and Lipatov (BFKL) and Balitsky Kovechegov (BK) Integro-Differential Evolution equations, evaluated with Runge-Kutta numerical methods & Chebyshev polynomials. 

Parton Distribution Function (PDF) Modeling using Generative Adversarial Networks (GAN): Customized and evaluated GAN networks for fitting experimental gluon and quark data as an alternative to Neural Network PDFs for fast replica generation with equilvalent statistical characteristics especially in the small-x regime relevant to saturation.

Tidal Disruption of a Star Binary by a Massive Black Hole (BH): Estimated and generated plots of trajectories of circular binaries around a black hole using 2nd  order differential equations evaluated by numerical integration employing the Runge Kutta method. Evolution of orbit position and energies was analyzed for various parameterizations of these Hypervelocity stars (HVS).

Barred Galaxy Star Trajectories: Modeling and numerical evaluation of Ordinary Differential Equations (ODE) corresponding to star trajectories in the disc plane of a barred galaxy with Centrifugal and Coriolis forces. Potentials, energy, angular momentum, the combination Jacobi integral were estimated. Fourier transform and spectral analysis of trajectories were carried out.

Monte Carlo Simulations of Parallax Biases: Simulated biases such as Lutz–Kelker (LK) bias, Malmquist bias, as well as, other Statistical and Observational biases associated with absolute magnitude calibrations using parallax measurements, including those measured by GAIA astrometric satellite.

Random Walk and Heat Conduction Simulation: Probabilistic simulations of Random Walk in 2D and 3D were carried out. Numerical evaluation of 3D Heat Conduction Diffusion in spherical coordinates were conducted.

Clinical Management of Alzheimer's Disease with Deep Brain Stimulation (DBS): Designed a deep neural network memory prosthesis device for the management of Alzheimer’s disease and memory enhancement. This was achieved by reinforcement inducing pulse shaping of depth electrodes in the Hippocampal CA3-CA1 loop using Long-Short Term Memory (LSTM) deep learned network.

Participant Information Sheet (PIS) for PTSD: Development of PIS for project on how childhood abuse and trauma alters genetic predisposition, increasing vulnerability to PTSD.

Gene expression and Alzheimer’s Disease: Identification of gene expression changes to GFAP and DCX in the determination of AD. Evaluation using Clinical Dementia Rating (CDR) and Folstein Test/ Mini-Mental State Examination (MMSE).

Obesity and Mental Health: PICO-S and Inclusion-Exclusion criteria development for meta-analyses of literature on obesity and mental disorders during pregnancy and postpartum. Reports with statistically significant odds ratios and study heterogeneity evaluated.

Randomized Control Tests (RCT) design: Research design for pet-assisted therapy in the treatment of chronic Schizophrenia patients using RCT and evaluation using Positive and Negative Symptoms Scale (PANSS), the Living Skills Profile (LSP), the Brief World Health Organization Quality of Life Assessment (WHOQOL-BREF), and the Satisfaction with Treatment Questionnaire (STQ).

Statistical Analysis of Insomnia, Loneliness, Depression Scores: Multiple regression analysis based on R square values, F-statistic, degrees of freedom, ANOVA and correlation/ covariance scores using SPSS.

Multi-modal Study of Ancient Egyptian Mummies: Analysis of mummy of Pharaoh Tutankhamun using Molecular Genetics, DNA, Cranio-Facial Analysis. Critical review of a “pregnant” ancient Egyptian mummy from the 1st century BC. Analysis of PUM II mummy. Mummies of Gebelein and Nekhen – Changing Perceptions on Mummification in the Predynastic Period.

Analysis of Mummies of Extinct Egyptian Species: Mummies of Barbary Lion, Northern Bald Ibis, Crocodylus Suchus etc.

Imaging and Reconstruction Studies of Egyptian Mummies: Virtual autopsy of ancient Egyptian human remains. Radiographic report of mummy of Per-en-bast – chantress of Amun.

Paleopathology of Ancient Egyptian Diseases - Understanding of medical and healing practices in ancient Egypt as evidenced by dental studies. Palaeopathology and treatment in ancient Egypt for 230 diseases. Report on Molecular Paleopathology in Mummy Studies: Past, Present and Future.

Ancient DNA (aDNA) Studies - Critical review of studies of million year old mammoth DNA.

Cultural Egyptology: Critical review of the ‘Opening of the Mouth’ ritual.

Archaeometry: Skeletal inventory recording and physical, chemical and biological analysis.

Game Design: Designed and implemented Intelligent games – ‘Bantumi (Pallanguzhi)’ and ‘Dragon Eclipse’ and implemented using Microsoft Developer Studio – C with OpenGL.

WYHIWYS  Device: Designed, implemented, tested a novel algorithm- a ‘What You Hear Is What You Speak' (WYHIWYS) device, incorporating direct and indirect paths, reverberant fields, effects of voiced segments, vocal tract vibrations, attenuation effect of tissues, cancellous bones and the Eustachian tube.

Speech Processing: Built a simple speech recognizer for distinguishing between numerals using Mel-Frequency Cepstral Coefficients (MFCC) feature extraction, Template description by training and Pattern matching on Dynamic Time Warped (DTW) feature vectors. Developed a tool for Linear Predictive Coding (LPC) analysis by way of computation of autocorrelation matrix and implementation of Levinson Durbin recursive solution.

Room Acoustics & HRTF Analysis: Estimated and analyzed the Impulse response and Reverberation time of an enclosed space. Analyzed the deviant behavior of Head Related Transfer Function (HRTF) with sample selection. Examined role of pinnae in Localization, Front-back and Back-front confusion, Source direction identification using MIT Fred KEMAR data. Designed a multi-stage IIR Reverberator filter to simulate reverberations, with ability to control variation due to different surfaces, relative phase delay and multi-path effects.

Divider for DSP Processors: Developed and implemented a novel Frequency analyzer circuit and a Synchronous constant divider for DSP based real-time image processing (tested using OrCAD).

Analog Devices DSP programming: Implemented and tested JPEG & other Image compression algorithms, Speech echo cancellation algorithms, Gabor filter based recognition on ADSP 2192-12 at IIT DSP Learning Program.

TI DSP programming: Implemented and tested Code Excited Linear Prediction (CELP) Vocoders, Adaptive noise reduction, FIR, IIR filters and various other Real-time image and speech filtering applications on DSK TMS320VC5416, C6713 processors using Code Composer Studio (CCS) C and Linear assembly programming.

Compression and Quantization: Implemented and analyzed Huffman coding (Global statistics and Locally adaptive statistics), Lempel-Ziv coding, Run-length coding, QM Arithmetic coding on different data types. Designed and implemented Lloyd-Max Scalar/Vector quantizers using LBG splitting and GLA, Tree Structured Vector Quantizer (TSVQ). Analyzed rate-control in JPEG, MPEG and designed MC techniques.

Dynamic Topology Design: Designed and implemented ‘A Power Level based Wireless Sensor Network (WSN) Deployment Scheme for Outage Reduction using Irregular Hexagonalization’ by way of modeling the wireless communication channel. The dynamic hex network topology designed takes into account flat fading through Rayleigh, Rician, Log-Normal, Nakagami, Weibull Distributions, reducing the outage probability.

Airborne Networks: Tested UC’s MDP/MARP protocol suite - Mobility aware routing schemes for airborne networks using QualNet emulator (Air-Force SBIR).

Wireless Sensor Signal Processing: Tested and analyzed the performance of various localization techniques in wireless sensor networks, clock skews and error accumulation in TPSN two-way time synchronization. Conducted an experiment and analyzed the Path loss, noise and PRR variations of wireless links in a typical environment using TMOTE SKY motes employing TinyOS programming. Analyzed and optimized network data rates using Linear programming in AMPL and NEOS optimization server.