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Particle filtering is very reliable in modelling non-Gaussian and non-linear elements of physical systems, which makes it ideal for tracking and localization applications. However, a major drawback of particle filters is their computational complexity, which inhibits their use in real-time applications with conventional CPU or DSP based implementation schemes. The re-sampling step in the particle filters creates a computational bottleneck since it is inherently sequential and cannot bedoi:10.1109/access.2021.3094962 fatcat:djylugiq7ncp7b2ymqxaxkeabe
more »... zed. This paper proposes a modification to the existing particle filter algorithm, which enables parallel re-sampling and reduces the effect of the re-sampling bottleneck. We then present a high-speed and dedicated hardware architecture incorporating pipe-lining and parallelization design strategies to supplement the modified algorithm and lower the execution time considerably. From an application standpoint, we propose a novel source localization model to estimate the position of a source in a noisy environment using the particle filter algorithm implemented on hardware. The design has been prototyped using Artix-7 field-programmable gate array (FPGA), and resource utilization for the proposed system is presented. Further, we show the execution time and estimation accuracy of the high-speed architecture and observe a significant reduction in computational time. Our implementation of particle filters on FPGA is scalable and modular, with a low execution time of about 5.62 µs for processing 1024 particles (compared to 64 ms on Intel Core i7-7700 CPU with eight cores clocking at 3.60 GHz) and can be deployed for real-time applications. INDEX TERMS Particle filters, field programmable gate array, bearings-only tracking, Bayesian filtering, unmanned ground vehicle, hardware architectures, real-time processing. 98186 VOLUME 9, 2021
The visually impaired face a plethora of problems. The primary problem they face is navigating from one place to another. The detection of obstacles in the user's proximity is another challenge that needs to be addressed. This paper provides a comparative study of various real-time image recognition and object detection methods that might help develop effective navigation systems for the visually impaired.doi:10.5281/zenodo.4902962 fatcat:iz2vfmypwfb3ppeim5eeixc2be
Krishna significant challenge to the unmanned vehicles to navigate and locate a target in an unknown environment autonomously. ...arXiv:2010.11911v1 fatcat:pgvi2foz3zgntlyifbfarm6q7e
ADITHYA K. KRISHNA is currently pursuing the B.Tech. degree in electronics and communication engineering with the National Institute of Technology at Tiruchirappalli, Tiruchirappalli, India. ...doi:10.1109/access.2020.2979753 fatcat:peo2awwqrzcbjcg3xnnmoem4au
Kodaikanal Observatory is a veritable treasure trove of data, with the data repository covering almost 100 years of observations. For the years 1909-2007, we use calibrated Ca II K spectroheliograms from the Kodaikanal Observatory to detect the plages, fix their heliographic coordinates and also estimate the plage areas. We adopt the following procedure. After ensuring that, for all the years, Kodai calcium images have very negligible ellipcity, a circle is fitted and two central coordinatesarXiv:1909.00406v1 fatcat:zu3sjd2esrfypdiw2a2iohrsiy
more »... radius of calcium images are determined uniquely. For each pixel of the calcium image, we then fix heliographic coordinates and extract plages along with their weighted average coordinates. The heliographic coordinates of these extracted plages are then compared with the heliographic coordinates of photospheric sunspots from the Greenwich sunspot database and chromospheric magnetic plages detected from the SOHO/MDI magnetograms. We find that the heliographic coordinates of calcium plages match very well with the heliographic coordinates of sunspots and magnetic plages authenticating our method of detection of plages and computation of positional coordinates. A code is developed in Python and all the nearly century scale plages data, with accurately estimated heliographic coordinates and areas, is available to the public.
Glaucoma is a leading cause of irreversible vision impairment globally and cases are continuously rising worldwide. Early detection is crucial, allowing timely intervention which can prevent further visual field loss. To detect glaucoma, examination of the optic nerve head via fundus imaging can be performed, at the centre of which is the assessment of the optic cup and disc boundaries. Fundus imaging is non-invasive and low-cost; however, the image examination relies on subjective,arXiv:2204.05591v1 fatcat:4vuterhtpvhgrka42euetn7hii
more »... ng, and costly expert assessments. A timely question to ask is can artificial intelligence mimic glaucoma assessments made by experts. Namely, can artificial intelligence automatically find the boundaries of the optic cup and disc (providing a so-called segmented fundus image) and then use the segmented image to identify glaucoma with high accuracy. We conducted a comprehensive review on artificial intelligence-enabled glaucoma detection frameworks that produce and use segmented fundus images. We found 28 papers and identified two main approaches: 1) logical rule-based frameworks, based on a set of simplistic decision rules; and 2) machine learning/statistical modelling based frameworks. We summarise the state-of-art of the two approaches and highlight the key hurdles to overcome for artificial intelligence-enabled glaucoma detection frameworks to be translated into clinical practice.
Recent reports from our laboratory demonstrated the post-ischaemic expression profile of various matrix metalloproteinases (MMPs) in rats and the detrimental role of MMP-12 in post-stroke brain damage. We hypothesise that the post-stroke dysregulation of MMPs is similar across species and that genetic deletion of MMP-12 would not affect the post-stroke expression of other MMPs. We tested our hypothesis by determining the pre-ischaemic and post-ischaemic expression profile of MMPs in wild-type and MMP-12 knockout mice.doi:10.1136/svn-2018-000142 pmid:30294471 pmcid:PMC6169614 fatcat:i6ns3nl3ffavvgmbswmreswlhq
Filtering of power line interference is very meaningful in the measurement of biomedical events recording, particularly in the case of recording signals as weak as the ECG signal. ECG is a biomedical signal which gives electrical activity of heart. This ECG signal is corrupted by various noises like power line interference, baseline wandering, channel noise, contact noise, muscle artifacts etc. Frequency range of ECG signal is nearly same as the frequency of power line interference. ECG signaldoi:10.17148/ijarcce.2017.64149 fatcat:mla3nx6kfrdufcryxpi47n4veq
more »... as frequency range from 0.5 Hz to 80 Hz and power line interference introduces 50 to 60 Hz frequency component in that signal which is the major cause of corruption of ECG. From various artifacts contaminate electrocardiogram (ECG) recording, the most common are power line interference. Power line interference is not easily recognizable since the interfering voltage in the ECG may have frequency 50 Hz. The most common cause of 50 Hz interference is the disconnected electrode resulting in a very strong disturbing signal, and therefore needs quick action. Electromagnetic interference from the power lines also results in poor quality tracings. Electrical equipment's such as air conditioner, elevators and X-ray units draw heavy power line current, which induce 50 Hz signals in the input circuits of the ECG machine. This project focuses on a technique for de noising of such non stationary ECG signal using adaptive filter algorithms like LMS, NLMS, RLS and compares their performance characteristics using MATLAB. The objective of this report is to separate the valid signal component from the undesired noises so that the accurate interpretation of ECG signals could be done. Recently, Adaptive filtering has become one of the effective and popular methods for the processing and analysis of the ECG signal because of its non-stationary nature. And in this study, Adaptive LMS, NLMS, and RLS algorithms are used to filter power line interference from the ECG signal and compare their performance based on SNR value -  .
Maintaining wild type protein–protein interactions is essential for the normal function of cell and any mutation that alter their characteristics can cause disease. Therefore, the ability to correctly and quickly predict the effect of amino acid mutations is crucial for understanding disease effects and to be able to carry out genome-wide studies. Here, we report a new development of the SAAMBE method, SAAMBE-3D, which is a machine learning-based approach, resulting in accurate predictions anddoi:10.3390/ijms21072563 pmid:32272725 pmcid:PMC7177817 fatcat:maes246fxng2rmjliq5oacwnae
more »... s extremely fast. It achieves the Pearson correlation coefficient ranging from 0.78 to 0.82 depending on the training protocol in benchmarking five-fold validation test against the SKEMPI v2.0 database and outperforms currently existing algorithms on various blind-tests. Furthermore, optimized and tested via five-fold cross-validation on the Cornell University dataset, the SAAMBE-3D achieves AUC of 1.0 and 0.96 on a homo and hereto-dimer test datasets. Another important feature of SAAMBE-3D is that it is very fast, it takes less than a fraction of a second to complete a prediction. SAAMBE-3D is available as a web server and as well as a stand-alone code, the last one being another important feature allowing other researchers to directly download the code and run it on their local computer. Combined all together, SAAMBE-3D is an accurate and fast software applicable for genome-wide studies to assess the effect of amino acid mutations on protein–protein interactions. The webserver and the stand-alone codes (SAAMBE-3D for predicting the change of binding free energy and SAAMBE-3D-DN for predicting if the mutation is disruptive or non-disruptive) are available.
D status may be a significant regulator of cancer progression in cancer patients be-13 Variations of Vitamin D in Various Stages of Breast Carcinoma: A Randomized Case Control Study Citation: Aananda Krishna ...doi:10.31080/ascb.2021.04.0355 fatcat:2nuvgdyvybgqtn3xmfip6mzqyi
Journal of Imaging
Current research in automated disease detection focuses on making algorithms "slimmer" reducing the need for large training datasets and accelerating recalibration for new data while achieving high accuracy. The development of slimmer models has become a hot research topic in medical imaging. In this work, we develop a two-phase model for glaucoma detection, identifying and exploiting a redundancy in fundus image data relating particularly to the geometry. We propose a novel algorithm for thedoi:10.3390/jimaging7060092 fatcat:dtggkwewxnfyrert3zinwmuum4
more »... p and disc segmentation "EffUnet" with an efficient convolution block and combine this with an extended spatial generative approach for geometry modelling and classification, termed "SpaGen" We demonstrate the high accuracy achievable by EffUnet in detecting the optic disc and cup boundaries and show how our algorithm can be quickly trained with new data by recalibrating the EffUnet layer only. Our resulting glaucoma detection algorithm, "EffUnet-SpaGen", is optimized to significantly reduce the computational burden while at the same time surpassing the current state-of-art in glaucoma detection algorithms with AUROC 0.997 and 0.969 in the benchmark online datasets ORIGA and DRISHTI, respectively. Our algorithm also allows deformed areas of the optic rim to be displayed and investigated, providing explainability, which is crucial to successful adoption and implementation in clinical settings.
Over the past decades, developments and scientific breakthroughs in the field of robotics have shown the replacement of wheeled robots with legged robots, which are often inspired by the biological characteristics of legged animals. Many industries and urban-based applications promote quadruped robots because of their dexterous ability to efficiently handle multiple tasks in the working environment. Motivated from the recent works in the field of quadruped robots, this research aims to developdoi:10.3390/app11167458 fatcat:re2kt3r56fbevp3iyf66cx3az4
more »... nd investigate gaits for a 2 DoF mammal-inspired quadruped robot that incorporates 4 hip and 4 knee servo motors as its locomotion element. Forward and inverse kinematic techniques are used to determine the joint angle required for the locomotion and stability calculation are presented to determine the center of mass/center of gravity of the robot. Three types of gaits such as walk, trot, and pace are developed while keeping the center of mass inside the support polygon using a closed-loop control system. To minimize errors and improve the performance of the robot due to its non-linearity, a meta-heuristic algorithm has been developed and addressed in this work. The fitness function is derived based on the Euclidean distance between the target and robot's current position and kinematic equations are used to obtain the relation between joints and coordinates. Based on the literature, particle swarm optimization (PSO) was found to be a promising algorithm for this problem and is developed using Python's 'Pyswarms' package. Experimental studies are carried out quantitatively to determine the convergence characteristics of the control algorithm and to investigate the distance traveled by the robot for different target positions and gaits. Comparison between experimental and theoretical results prove the efficiency of the proposed algorithm and stability of the robot during various gait movements.
Indian Heart Journal
Krishna, M. Adithya ...doi:10.1016/j.ihj.2014.10.226 fatcat:p2upfa4zb5birlgvhrhplfnsra
Krishna Pandu Wicaksono performed DOS under the supervision of Sahat Matondang, and Cosmas Rinaldi Adithya Lesmana performed or supervised the endoscopy procedure. ... All the authors (Krishna Pandu Wicaksono, Sahat Matondang, Christopher Silman, Joedo Prihartono, Cosmas Rinaldi Adithya Lesmana) significantly contributed to this research. ...doi:10.1159/000523757 pmid:35528777 pmcid:PMC9035959 fatcat:7qbmvnl6vfgcdggo2lfozseqoe
2012 ASEE Annual Conference & Exposition Proceedings
An interactive visualization knowledge network platform iKNEER (Interactive Knowledge Network for Engineering Education Research, www.ikneer.org) was designed for researchers in the Engineering Education Research (EER) community. This platform is potentially helpful to first-year PhD students in Engineering Education. The major goal of this study is to investigate the role iKNEER could play in first-year PhD students' decision-making upon their research using a qualitative method. It alsodoi:10.18260/1-2--21456 fatcat:nrayatvxlfhzlgcgrekmcxg4sa
more »... as a qualitative evaluation of the iKNEER platform. Providing a better understanding of how this research tool influences novice researchers' decision-making process, results of this study could inform further development and future design of such tools.
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