1,045 research outputs found
A study of earmould modification effects on the frequency response of body worn hearing aids
No full textA study was made of the influence of different receivers and the effects of variation of ear mould sound bore diameter, length and parallel vents on the frequency response of body worn hearing aids. In order to obtain real ear estimates of the magnitude of the effects, these measurements were made using a modified Zwislocki (IRPIDB-100) ear simulator. The results indicate that while there may be little value in varying the length of the sound bore, diameter variation could be very helpful in the control of the high frequency response of a hearing aid. A wider diameter of the sound bore results in an improved high frequency response, whereas poorer high frequency response is associated with narrower diameters of the sound bore. Parallel venting of ear moulds results in a reduction in the low frequency response of a hearing aid. The length and the diameter of the vent determine the amount of the low frequency cut in the response. The shorter length and the wider diameter of the vent result in a greater amount of low frequency reduction. The effects of parallel venting of ear moulds are independent of the frequency response of the receiver. The study was made using the receivers commonly used with the body worn hearing aids in India. The data of the ear mould modification effects may prove useful in predicting the effects of such work in a clinical situation. However, no subjective assessment of ear mould modification effects were made in this study
Nutritional Composition of Five Soil-Dwelling Scarab Beetles (Coleoptera: Scarabaeidae) of Assam, India
No full textBhattacharyya, Badal, Choudhury, Banajit, Das, Pranati, Dutta, Satyendra Kumar, Bhagawati, Sudhansu, Pathak, Khanin (2018): Nutritional Composition of Five Soil-Dwelling Scarab Beetles (Coleoptera: Scarabaeidae) of Assam, India. The Coleopterists Bulletin 72 (2): 339-346, DOI: 10.1649/0010-065X-72.2.339, URL: http://dx.doi.org/10.1649/0010-065x-72.2.33
Recent Advances in Lossy Mode Resonance-Based Fiber Optic Sensors: A Review
No full textFiber optic sensors (FOSs) based on the lossy mode resonance (LMR) technique have gained substantial attention from the scientific community. The LMR technique displays several important features over the conventional surface plasmon resonance (SPR) phenomenon, for planning extremely sensitive FOSs. Unlike SPR, which mainly utilizes the thin film of metals, a wide range of materials such as conducting metal oxides and polymers support LMR. The past several years have witnessed a remarkable development in the field of LMR-based fiber optic sensors; through this review, we have tried to summarize the overall development of LMR-based fiber optic sensors. This review article not only provides the fundamental understanding and detailed explanation of LMR generation but also sheds light on the setup/configuration required to excite the lossy modes. Several geometries explored in the literature so far have also been addressed. In addition, this review includes a survey of the different materials capable of supporting lossy modes and explores new possible LMR supporting materials and their potential applications in sensing
AxOMaP: Designing FPGA-based Approximate Arithmetic Operators using Mathematical Programming
No full textWith the increasing application of machine learning (ML) algorithms in
embedded systems, there is a rising necessity to design low-cost computer
arithmetic for these resource-constrained systems. As a result, emerging models
of computation, such as approximate and stochastic computing, that leverage the
inherent error-resilience of such algorithms are being actively explored for
implementing ML inference on resource-constrained systems. Approximate
computing (AxC) aims to provide disproportionate gains in the power,
performance, and area (PPA) of an application by allowing some level of
reduction in its behavioral accuracy (BEHAV). Using approximate operators
(AxOs) for computer arithmetic forms one of the more prevalent methods of
implementing AxC. AxOs provide the additional scope for finer granularity of
optimization, compared to only precision scaling of computer arithmetic. To
this end, designing platform-specific and cost-efficient approximate operators
forms an important research goal. Recently, multiple works have reported using
AI/ML-based approaches for synthesizing novel FPGA-based AxOs. However, most of
such works limit usage of AI/ML to designing ML-based surrogate functions used
during iterative optimization processes. To this end, we propose a novel data
analysis-driven mathematical programming-based approach to synthesizing
approximate operators for FPGAs. Specifically, we formulate mixed integer
quadratically constrained programs based on the results of correlation analysis
of the characterization data and use the solutions to enable a more directed
search approach for evolutionary optimization algorithms. Compared to
traditional evolutionary algorithms-based optimization, we report up to 21%
improvement in the hypervolume, for joint optimization of PPA and BEHAV, in the
design of signed 8-bit multipliers.Comment: 23 pages, Under review at ACM TRET
Covid-19 Diagnosis from X-Ray Images using Support Vector Machine
No full textAbstract: Coronavirus disease strike the world in 2019 and commonly called COVID-19 with its update given by the World Health Organization (WHO) on December 31, 2019. It infected more than 100 countries, an infectious disease strike the whole world and people of all age groups became a global health emergency. This disease can transmit from person to person through respiratory droplets and thus is highly contagious. The second wave almost killed billions of persons and lead to several liver problems, pneumonia, respiratory failure, cardiovascular diseases, etc. This can be symptomatic as well as asymptomatic in some patients and thus lead to increased communicability. Machine Learning is a latest trend currently useful in almost all research areas. Using these techniques to diagnose corona makes it highly feasible to cope up with this emergency. Different methods for testing corona virus are present but they require huge delay, are expensive, highly dependent test kits, higher negative false rate and prone to human errors. In this article we provide the state of the art of the covid diagnosis using Chest X ray images and this can guide both clinicians and technologists. A support vector machine is used to train the model and classify images into normal, pneumonia, and covid images. An overall accuracy of 95% is achieved using this method.
Keywords: COVID-19, Corona Virus, Machine Learning, Convolutional Neural Network, Support Vector Machine, X-ray images, Pneumonia.
Title: Covid-19 Diagnosis from X-Ray Images using Support Vector Machine
Author: Satyendra Kumar Sagar
International Journal of Recent Research in Mathematics Computer Science and Information Technology
ISSN 2350-1022
Vol. 9, Issue 2, October 2022 - March 2023
Page No: 24-32
Paper Publications
Website: www.paperpublications.org
Published Date: 16-December-2022
DOI: https://doi.org/10.5281/zenodo.7446436
Paper Download Link (Source)
https://www.paperpublications.org/upload/book/Covid-19%20Diagnosis%20from%20X-Ray-16122022-2.pdfInternational Journal of Recent Research in Mathematics Computer Science and Information Technology, ISSN 2350-1022, Paper Publications, Website: www.paperpublications.or
perators
No full textThis work is supported by the Deutsche Forschungsgemeinschaft (DFG) under the X-ReAp project (Project number 380524764)
AxOCS: Scaling FPGA-based Approximate Operators using Configuration Supersampling
No full textThe rising usage of AI and ML-based processing across application domains has
exacerbated the need for low-cost ML implementation, specifically for
resource-constrained embedded systems. To this end, approximate computing, an
approach that explores the power, performance, area (PPA), and behavioral
accuracy (BEHAV) trade-offs, has emerged as a possible solution for
implementing embedded machine learning. Due to the predominance of MAC
operations in ML, designing platform-specific approximate arithmetic operators
forms one of the major research problems in approximate computing. Recently
there has been a rising usage of AI/ML-based design space exploration
techniques for implementing approximate operators. However, most of these
approaches are limited to using ML-based surrogate functions for predicting the
PPA and BEHAV impact of a set of related design decisions. While this approach
leverages the regression capabilities of ML methods, it does not exploit the
more advanced approaches in ML. To this end, we propose AxOCS, a methodology
for designing approximate arithmetic operators through ML-based supersampling.
Specifically, we present a method to leverage the correlation of PPA and BEHAV
metrics across operators of varying bit-widths for generating larger bit-width
operators. The proposed approach involves traversing the relatively smaller
design space of smaller bit-width operators and employing its associated
Design-PPA-BEHAV relationship to generate initial solutions for
metaheuristics-based optimization for larger operators. The experimental
evaluation of AxOCS for FPGA-optimized approximate operators shows that the
proposed approach significantly improves the quality-resulting hypervolume for
multi-objective optimization-of 8x8 signed approximate multipliers.Comment: 11 pages, under review with IEEE TCAS-
A Compact Wearable Textile Antenna for NB-IoT and ISM Band Patient Tracking Applications
No full textThis paper proposes a novel multi-band textile monopole antenna for patient tracking applications. The designed antenna has compact footprints (0.13 lambda 02) and works in the narrow band-internet of things (NB-IoT) 1.8 GHz, radio frequency identification (RFID), and industrial, scientific, and medical (ISM) 2.45 GHz and 5.8 GHz bands. The impedance bandwidths and gain of the antenna at 1.8 GHz, 2.45 GHz, and 5.8 GHz are 310 MHz, 960 MHz, and 1140 MHz; 3.7 dBi, 5.3 dBi, and 9.6 dBi, respectively. Also, the antenna's behavior is checked on different body parts of the human body in various bending scenarios. As per the evaluated link budget, the designed antenna can easily communicate up to 100 m of distance. The specific absorption rate values of the designed antenna are also within acceptable limits as per the (FCC/ICNIRP) standards at the reported frequency bands. Unlike traditional rigid antennas, the proposed textile antenna is non-intrusive, enhancing user safety and comfort. The denim material makes it comfortable for extended wear, reducing the risk of skin irritation. It can also withstand regular wear and tear, including stretching and bending. The presented denim-based antenna can be seamlessly integrated into clothing and accessories, making it less obtrusive and more aesthetically pleasing
Emergent design challenges for embedded systems and paths forward: Mixed-criticality, energy, reliability and security perspectives: Special Session Paper
No full textModern embedded systems need to cater for several needs depending upon the application domain in which they are deployed. For example, mixed-critically needs to be considered for real-time and safety-critical systems and energy for battery-operated systems. At the same time, many of these systems demand for their reliability and security as well. With electronic systems being used for increasingly varying type of applications, novel challenges have emerged. For example, with the use of embedded systems in increasingly complex applications that execute tasks with varying priorities, mixed-criticality systems present unique challenges to designing reliable systems. The large design space involved in implementing cross-layer reliability in heterogeneous systems, particularly for mixed-critical systems, poses new research problems. Further, malicious security attacks on these systems pose additional extraordinary challenges in the system design. In this paper, we cover both the industry and academia perspectives of the challenges posed by these emergent aspects of system design towards designing high-performance, energy-efficient, reliable and/or secure embedded systems. We also provide our views on paths forward
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