11 research outputs found
Intelligent Circuits and Systems
ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society. This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering
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ReSCon '12, Research Student Conference: Book of Abstracts
The fifth SED Research Student Conference (ReSCon2012) was hosted over three days, 18-20 June 2012, in the Hamilton Centre at Brunel University. The conference consisted of 130 oral and 70 poster presentations, based on the high quality and diverse research being conducted within the School of Engineering and Design by postgraduate research students. The conference is held annually, and ReSCon plays a key role in contributing to research and innovations within the School
Conference on Intelligent Robotics in Field, Factory, Service, and Space (CIRFFSS 1994), volume 1
The AIAA/NASA Conference on Intelligent Robotics in Field, Factory, Service, and Space (CIRFFSS '94) was originally proposed because of the strong belief that America's problems of global economic competitiveness and job creation and preservation can partly be solved by the use of intelligent robotics, which are also required for human space exploration missions. Individual sessions addressed nuclear industry, agile manufacturing, security/building monitoring, on-orbit applications, vision and sensing technologies, situated control and low-level control, robotic systems architecture, environmental restoration and waste management, robotic remanufacturing, and healthcare applications
Local tone mapping operator for detail preserving reproduction of high dynamic range images.
Opseg osvetljaja koji se javlja u prirodnim scenama uveliko prevazilazi mogućnosti
standardnih uređaja za snimanje i reprodukciju slike. Ljudski vizuelni sistem je
evoluirao, tako da omogući efikasno funkcionisanje i percepciju detalja u uslovima
velike promene osvetljaja. Kako bi se omogućila što realnija reprodukcija slika i video
sadržaja, potrebno je obezbediti mogućnost snimanja i reprodukcije što šireg
dinamičkog opsega osvetljaja. Razvoj tehnika za snimanje je napredovao i danas postoji
mogućnost snimanja celokupnog dinamičkog opsega osvetljaja scene korišćenjem
standardnih senzora. Razvoj displeja je međutim napredovao sporije i većina displeja
koji su danas u upotrebi ima skroman dinamički opseg osvetljaja. Operator za redukciju
dinamičkog opsega predstavlja ključnu komponentu sistema za reprodukciju scena
širokog dinamičkog opsega (HDR), na standardnim displejima nižeg dinamičkog
opsega (LDR)...Light intensity variations in natural scenes greatly exceed the capabillities of standard
imaging and display devices. The human visual system has evolved to deal with these
lightning conditions and enable efficient perception of details. In order to enable
realistic reproduction of natural images and video, it is necessary to develop techniques
and devices for capturing and reproduction of the high dynamic range content.
Capturing techniques have evolved and now it is possible to capture entire dynamic
range of the scene using standard sensors. The development of displays, however, has
progressed more slowly and most of the displays that are used today exhibits modest
dynamic range capabilities. Tone mapping operator is a key component that enables
reproduction of the high dynamic range (HDR) images on the low dynamic range
(LDR) displays..
Neural Radiance Fields: Past, Present, and Future
The various aspects like modeling and interpreting 3D environments and
surroundings have enticed humans to progress their research in 3D Computer
Vision, Computer Graphics, and Machine Learning. An attempt made by Mildenhall
et al in their paper about NeRFs (Neural Radiance Fields) led to a boom in
Computer Graphics, Robotics, Computer Vision, and the possible scope of
High-Resolution Low Storage Augmented Reality and Virtual Reality-based 3D
models have gained traction from res with more than 1000 preprints related to
NeRFs published. This paper serves as a bridge for people starting to study
these fields by building on the basics of Mathematics, Geometry, Computer
Vision, and Computer Graphics to the difficulties encountered in Implicit
Representations at the intersection of all these disciplines. This survey
provides the history of rendering, Implicit Learning, and NeRFs, the
progression of research on NeRFs, and the potential applications and
implications of NeRFs in today's world. In doing so, this survey categorizes
all the NeRF-related research in terms of the datasets used, objective
functions, applications solved, and evaluation criteria for these applications.Comment: 413 pages, 9 figures, 277 citation
The 2nd International Electronic Conference on Applied Sciences
This book is focused on the works presented at the 2nd International Electronic Conference on Applied Sciences, organized by Applied Sciences from 15 to 31 October 2021 on the MDPI Sciforum platform. Two decades have passed since the start of the 21st century. The development of sciences and technologies is growing ever faster today than in the previous century. The field of science is expanding, and the structure of science is becoming ever richer. Because of this expansion and fine structure growth, researchers may lose themselves in the deep forest of the ever-increasing frontiers and sub-fields being created. This international conference on the Applied Sciences was started to help scientists conduct their own research into the growth of these frontiers by breaking down barriers and connecting the many sub-fields to cut through this vast forest. These functions will allow researchers to see these frontiers and their surrounding (or quite distant) fields and sub-fields, and give them the opportunity to incubate and develop their knowledge even further with the aid of this multi-dimensional network
Proceedings of the 19th Sound and Music Computing Conference
Proceedings of the 19th Sound and Music Computing Conference - June 5-12, 2022 - Saint-Étienne (France).
https://smc22.grame.f
Optimizing Common Spatial Pattern for a Motor Imagerybased BCI by Eigenvector Filteration
One of the fundamental criterion for the successful application of a brain-computer interface (BCI) system is to extract significant features that confine invariant characteristics specific to each brain state. Distinct features play an important role in enabling a computer to associate different electroencephalogram (EEG) signals to different brain states. To ease the workload on the feature extractor and enhance separability between different brain states, the data is often transformed or filtered to maximize separability before feature extraction. The common spatial patterns (CSP) approach can achieve this by linearly projecting the multichannel EEG data into a surrogate data space by the weighted summation of the appropriate channels. However, choosing the optimal spatial filters is very significant in the projection of the data and this has a direct impact on classification. This paper presents an optimized pattern selection method from the CSP filter for improved classification accuracy. Based on the hypothesis that values closer to zero in the CSP filter introduce noise rather than useful information, the CSP filter is modified by analyzing the CSP filter and removing/filtering the degradative or insignificant values from the filter. This hypothesis is tested by comparing the BCI results of eight subjects using the conventional CSP filters and the optimized CSP filter. In majority of the cases the latter produces better performance in terms of the overall classification accuracy
Optimizing Common Spatial Pattern for a Motor Imagerybased BCI by Eigenvector Filteration
One of the fundamental criterion for the successful application of a brain-computer interface (BCI) system is to extract significant features that confine invariant characteristics specific to each brain state. Distinct features play an important role in enabling a computer to associate different electroencephalogram (EEG) signals to different brain states. To ease the workload on the feature extractor and enhance separability between different brain states, the data is often transformed or filtered to maximize separability before feature extraction. The common spatial patterns (CSP) approach can achieve this by linearly projecting the multichannel EEG data into a surrogate data space by the weighted summation of the appropriate channels. However, choosing the optimal spatial filters is very significant in the projection of the data and this has a direct impact on classification. This paper presents an optimized pattern selection method from the CSP filter for improved classification accuracy. Based on the hypothesis that values closer to zero in the CSP filter introduce noise rather than useful information, the CSP filter is modified by analyzing the CSP filter and removing/filtering the degradative or insignificant values from the filter. This hypothesis is tested by comparing the BCI results of eight subjects using the conventional CSP filters and the optimized CSP filter. In majority of the cases the latter produces better performance in terms of the overall classification accuracy