863 research outputs found

    Spatial dissection of a soundfield using spherical harmonic decomposition

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    A real-world soundfield is often contributed by multiple desired and undesired sound sources. The performance of many acoustic systems such as automatic speech recognition, audio surveillance, and teleconference relies on its ability to extract the desired sound components in such a mixed environment. The existing solutions to the above problem are constrained by various fundamental limitations and require to enforce different priors depending on the acoustic condition such as reverberation and spatial distribution of sound sources. With the growing emphasis and integration of audio applications in diverse technologies such as smart home and virtual reality appliances, it is imperative to advance the source separation technology in order to overcome the limitations of the traditional approaches. To that end, we exploit the harmonic decomposition model to dissect a mixed soundfield into its underlying desired and undesired components based on source and signal characteristics. By analysing the spatial projection of a soundfield, we achieve multiple outcomes such as (i) soundfield separation with respect to distinct source regions, (ii) source separation in a mixed soundfield using modal coherence model, and (iii) direction of arrival (DOA) estimation of multiple overlapping sound sources through pattern recognition of the modal coherence of a soundfield. We first employ an array of higher order microphones for soundfield separation in order to reduce hardware requirement and implementation complexity. Subsequently, we develop novel mathematical models for modal coherence of noisy and reverberant soundfields that facilitate convenient ways for estimating DOA and power spectral densities leading to robust source separation algorithms. The modal domain approach to the soundfield/source separation allows us to circumvent several practical limitations of the existing techniques and enhance the performance and robustness of the system. The proposed methods are presented with several practical applications and performance evaluations using simulated and real-life dataset

    Multimodal attention in a simulated driving environment - Novel approaches to the quantification of attention based on brain activity

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    The concept of attention is an established focus of study in neurosciences. The quantification of attention during driving can help identify situations in which the driver is not completely aware of the situation. This work deals with the implementation of a setup to simulate a driving environment that enables audiovisual tasks to be embedded into the driving task while acquiring biosignals such as electroencephalography. The main goal of this dissertation was to find a correlation between attention and brain activity as seen on the electroencephalographic activity while driving. By using the principle of phase-amplitude coupling in electroencephalographic signals, it was hypothesized that Theta-Gamma phase-amplitude coupling might correlate to multimodal attention and thus might be eligible as a biomarker of attention in tasks such as driving. Surface electroencephalography was measured simultaneously in drivers and copilots while participating in simulated driving scenarios with varying multimodal attentional demands. The phase-amplitude coupling between Theta-band phase and Gamma-band amplitude from the electroencephalograpic signal was obtained and evaluated. Results showed significant phase-amplitude coupling differences between drivers and copilots in areas related to multimodal attention (prefrontal cortex, frontal eye fields, primary motor cortex, and visual cortex). The results were confirmed by behavioral data acquired during the test (detection task). We conclude that phase-amplitude coupling does function as a biomarker for attentional demand by detecting cortical areas being activated through specific multimodal (in this case, driving) tasks. Additionally, the data acquired in the main work of this thesis was used to test an auditory stimulus reconstruction algorithm previously tested by our work group. The stimulus reconstruction allowed to obtain post-hoc additional information regarding attentional effort during driving (success of the stimulus reconstruction was significantly correlated to auditory effort) and serves as a compliment to the main results. This dissertation thus offers an insight on attentional systems in multimodal situations and the neurophysiological systems underlying attention. It develops methods to measure attention in a driving environment, both as seen using phase-amplitude coupling and by being able to single out auditory effort by reconstructing the auditory stimuli. Finally, these methods can be translated to other activities since they are both based on non-invasive electroencephalography

    Proceedings of the Second International Mobile Satellite Conference (IMSC 1990)

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    Presented here are the proceedings of the Second International Mobile Satellite Conference (IMSC), held June 17-20, 1990 in Ottawa, Canada. Topics covered include future mobile satellite communications concepts, aeronautical applications, modulation and coding, propagation and experimental systems, mobile terminal equipment, network architecture and control, regulatory and policy considerations, vehicle antennas, and speech compression

    Advances in Robotics, Automation and Control

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    The book presents an excellent overview of the recent developments in the different areas of Robotics, Automation and Control. Through its 24 chapters, this book presents topics related to control and robot design; it also introduces new mathematical tools and techniques devoted to improve the system modeling and control. An important point is the use of rational agents and heuristic techniques to cope with the computational complexity required for controlling complex systems. Through this book, we also find navigation and vision algorithms, automatic handwritten comprehension and speech recognition systems that will be included in the next generation of productive systems developed by man

    Articulatory features for robust visual speech recognition

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    On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator

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    Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise

    Three-dimensional point-cloud room model in room acoustics simulations

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    ์ž๋™์ฐจ ์‚ฌ์–‘ ๋ณ€๊ฒฝ์„ ์‹ค์‹œ๊ฐ„ ๋ฐ˜์˜ํ•˜๋Š” ๋ฐ์ดํ„ฐ ๊ธฐ๋ฐ˜ ๋””์ž์ธ ์ ‘๊ทผ ๋ฐฉ๋ฒ•

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์œตํ•ฉ๊ณผํ•™๊ธฐ์ˆ ๋Œ€ํ•™์› ์œตํ•ฉ๊ณผํ•™๋ถ€(์ง€๋Šฅํ˜•์œตํ•ฉ์‹œ์Šคํ…œ์ „๊ณต), 2020. 8. ๊ณฝ๋…ธ์ค€.The automotive industry is entering a new phase in response to changes in the external environment through the expansion of eco-friendly electric/hydrogen vehicles and the simplification of modules during the manufacturing process. However, in the existing automotive industry, conflicts between structured production guidelines and various stake-holders, who are aligned with periodic production plans, can be problematic. For example, if there is a sudden need to change either production parts or situation-specific designs, it is often difficult for designers to reflect those requirements within the preexisting guidelines. Automotive design includes comprehensive processes that represent the philosophy and ideology of a vehicle, and seeks to derive maximum value from the vehicle specifications. In this study, a system that displays information on parts/module components necessary for real-time design was proposed. Designers will be able to use this system in automotive design processes, based on data from various sources. By applying the system, three channels of information provision were established. These channels will aid in the replacement of specific component parts if an unexpected external problem occurs during the design process, and will help in understanding and using the components in advance. The first approach is to visualize real-time data aggregation in automobile factories using Google Analytics, and to reflect these in self-growing characters to be provided to designers. Through this, it is possible to check production and quality status data in real time without the use of complicated labor resources such as command centers. The second approach is to configure the data flow to be able to recognize and analyze the surrounding situation. This is done by applying the vehicles camera to the CCTV in the inventory and distribution center, as well as the direction inside the vehicle. Therefore, it is possible to identify and record the parts resources and real-time delivery status from the internal camera function without hesitation from existing stakeholders. The final approach is to supply real-time databases of vehicle parts at the site of an accident for on-site repair, using a public API and sensor-based IoT. This allows the designer to obtain information on the behavior of parts to be replaced after accidents involving light contact, so that it can be reflected in the design of the vehicle. The advantage of using these three information channels is that designers can accurately understand and reflect the modules and components that are brought in during the automotive design process. In order to easily compose the interface for the purpose of providing information, the information coming from the three channels is displayed in their respective, case-specific color in the CAD software that designers use in the automobile development process. Its eye tracking usability evaluation makes it easy for business designers to use as well. The improved evaluation process including usability test is also included in this study. The impact of the research is both dashboard application and CAD system as well as data systems from case studies are currently reflected to the design ecosystem of the motors group.์ž๋™์ฐจ ์‚ฐ์—…์€ ์นœํ™˜๊ฒฝ ์ „๊ธฐ/์ˆ˜์†Œ ์ž๋™์ฐจ์˜ ํ™•๋Œ€์™€ ์ œ์กฐ ๊ณต์ •์—์„œ์˜ ๋ชจ๋“ˆ ๋‹จ์ˆœํ™”๋ฅผ ํ†ตํ•ด์„œ ์™ธ๋ถ€ ํ™˜๊ฒฝ์˜ ๋ณ€ํ™”์— ๋”ฐ๋ฅธ ์ƒˆ๋กœ์šด ๊ตญ๋ฉด์„ ๋งž์ดํ•˜๊ณ  ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ๊ธฐ์กด์˜ ์ž๋™์ฐจ ์‚ฐ์—…์—์„œ ๊ตฌ์กฐํ™”๋œ ์ƒ์‚ฐ ๊ฐ€์ด๋“œ๋ผ์ธ๊ณผ ๊ธฐ๊ฐ„ ๋‹จ์œ„ ์ƒ์‚ฐ ๊ณ„ํš์— ๋งž์ถฐ์ง„ ์—ฌ๋Ÿฌ ์ดํ•ด๊ด€๊ณ„์ž๋“ค๊ณผ์˜ ๊ฐˆ๋“ฑ์€ ๋ณ€ํ™”์— ๋Œ€์‘ํ•˜๋Š” ๋ฐฉ์•ˆ์ด ๊ด€์„ฑ๊ณผ ๋ถ€๋”ชํžˆ๋Š” ๋ฌธ์ œ๋กœ ๋‚˜ํƒ€๋‚  ์ˆ˜ ์žˆ๋‹ค. ์˜ˆ๋ฅผ ๋“ค์–ด, ๊ฐ‘์ž‘์Šค๋Ÿฝ๊ฒŒ ์ƒ์‚ฐ์— ํ•„์š”ํ•œ ๋ถ€ํ’ˆ์„ ๋ณ€๊ฒฝํ•ด์•ผ ํ•˜๊ฑฐ๋‚˜ ํŠน์ • ์ƒํ™ฉ์— ์ ์šฉ๋˜๋Š” ๋””์ž์ธ์„ ๋ณ€๊ฒฝํ•  ๊ฒฝ์šฐ, ์ฃผ์–ด์ง„ ๊ฐ€์ด๋“œ๋ผ์ธ์— ๋”ฐ๋ผ ๋””์ž์ด๋„ˆ๊ฐ€ ์ง์ ‘ ์˜๊ฒฌ์„ ๋ฐ˜์˜ํ•˜๊ธฐ ์–ด๋ ค์šด ๊ฒฝ์šฐ๊ฐ€ ๋งŽ๋‹ค. ์ž๋™์ฐจ ๋””์ž์ธ์€ ์ฐจ์ข…์˜ ์ฒ ํ•™๊ณผ ์ด๋…์„ ๋‚˜ํƒ€๋‚ด๊ณ  ํ•ด๋‹น ์ฐจ๋Ÿ‰์ œ์›์œผ๋กœ ์ตœ๋Œ€์˜ ๊ฐ€์น˜๋ฅผ ๋Œ์–ด๋‚ด๊ณ ์ž ํ•˜๋Š” ์ข…ํ•ฉ์ ์ธ ๊ณผ์ •์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์—ฌ๋Ÿฌ ์›์ฒœ์˜ ๋ฐ์ดํ„ฐ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ์ž๋™์ฐจ ๋””์ž์ธ ๊ณผ์ •์—์„œ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋„๋ก ๋””์ž์ธ์— ํ•„์š”ํ•œ ๋ถ€ํ’ˆ/๋ชจ๋“ˆ ๊ตฌ์„ฑ์š”์†Œ๋“ค์— ๋Œ€ํ•œ ์ •๋ณด๋ฅผ ์‹ค์‹œ๊ฐ„์œผ๋กœ ํ‘œ์‹œํ•ด์ฃผ๋Š” ์‹œ์Šคํ…œ์„ ๊ณ ์•ˆํ•˜์˜€๋‹ค. ์ด๋ฅผ ์ ์šฉํ•˜์—ฌ ์ž๋™์ฐจ ๋””์ž์ธ ๊ณผ์ •์—์„œ ์˜ˆ์ƒ ๋ชปํ•œ ์™ธ๋ถ€ ๋ฌธ์ œ๊ฐ€ ๋ฐœ์ƒํ–ˆ์„ ๋•Œ ์„ ํƒํ•  ๊ตฌ์„ฑ ๋ถ€ํ’ˆ์„ ๋Œ€์ฒดํ•˜๊ฑฐ๋‚˜ ์‚ฌ์ „์— ํ•ด๋‹น ๋ถ€ํ’ˆ์„ ์ดํ•ดํ•˜๊ณ  ๋””์ž์ธ์— ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋„๋ก ์„ธ ๊ฐ€์ง€ ์ •๋ณด ์ œ๊ณต ์ฑ„๋„์„ ๊ตฌ์„ฑํ•˜์˜€๋‹ค. ์ฒซ ๋ฒˆ์งธ๋Š” ์ž๋™์ฐจ ๊ณต์žฅ ๋‚ด ์‹ค์‹œ๊ฐ„ ๋ฐ์ดํ„ฐ ์ง‘๊ณ„๋ฅผ Google Analytics๋ฅผ ํ™œ์šฉํ•˜์—ฌ ์‹œ๊ฐํ™”ํ•˜๊ณ , ์ด๋ฅผ ๊ณต์žฅ ์ž์ฒด์˜ ์ž๊ฐ€ ์„ฑ์žฅ ์บ๋ฆญํ„ฐ์— ๋ฐ˜์˜ํ•˜์—ฌ ๋””์ž์ด๋„ˆ์—๊ฒŒ ์ œ๊ณตํ•˜๋Š” ๋ฐฉ์‹์ด๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ์ข…ํ•ฉ์ƒํ™ฉ์‹ค ๋“ฑ์˜ ๋ณต์žกํ•œ ์ธ๋ ฅ ์ฒด๊ณ„ ์—†์ด๋„ ์ƒ์‚ฐ ๋ฐ ํ’ˆ์งˆ ํ˜„ํ™ฉ ๋ฐ์ดํ„ฐ๋ฅผ ์‹ค์‹œ๊ฐ„์œผ๋กœ ํ™•์ธ ๊ฐ€๋Šฅํ•˜๋„๋ก ํ•˜์˜€๋‹ค. ๋‘ ๋ฒˆ์งธ๋Š” ์ฐจ๋Ÿ‰์šฉ ์ฃผ์ฐจ๋ณด์กฐ ์„ผ์„œ ์นด๋ฉ”๋ผ๋ฅผ ์ฐจ๋Ÿ‰ ๋ถ€์ฐฉ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์ธ๋ฒคํ† ๋ฆฌ์™€ ๋ฌผ๋ฅ˜์„ผํ„ฐ์˜ CCTV์—๋„ ์ ์šฉํ•˜์—ฌ ์ฃผ๋ณ€์ƒํ™ฉ์„ ์ธ์‹ํ•˜๊ณ  ๋ถ„์„ํ•  ์ˆ˜ ์žˆ๋„๋ก ๊ตฌ์„ฑํ•˜์˜€๋‹ค. ์ฐจ๋Ÿ‰์˜ ์กฐ๋ฆฝ ์ƒ์‚ฐ ๋‹จ๊ณ„์—์„œ ๋ถ€ํ’ˆ ๋‹จ์œ„์˜ ์ด๋™, ์šด์†ก, ์ถœํ•˜๋ฅผ ๊ฑฐ์ณ ์™„์„ฑ์ฐจ์˜ ์ฃผํ–‰ ๋‹จ๊ณ„์— ์ด๋ฅด๊ธฐ๊นŒ์ง€ ๋ฐ์ดํ„ฐ ํ๋ฆ„์„ ํŒŒ์•…ํ•˜๋Š” ๊ฒƒ์ด ๋””์ž์ธ ๋ถ€๋ฌธ์— ํ•„์š”ํ•œ ์ •๋ณด๋ฅผ ์ œ๊ณตํ•  ์ˆ˜ ์žˆ๋Š” ๋ฐฉ๋ฒ•์œผ๋กœ ํ™œ์šฉ๋˜์—ˆ๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ๊ธฐ์กด ์ดํ•ด๊ด€๊ณ„์ž๋“ค์˜ ํฐ ๋ฐ˜๋ฐœ ์—†์ด ๋‚ด๋ถ€์˜ ์นด๋ฉ”๋ผ ๊ธฐ๋Šฅ์œผ๋กœ๋ถ€ํ„ฐ ๋ถ€ํ’ˆ ๋ฆฌ์†Œ์Šค์™€ ์šด์†ก ์ƒํƒœ๋ฅผ ์‹ค์‹œ๊ฐ„ ํŒŒ์•… ๋ฐ ๊ธฐ๋ก ๊ฐ€๋Šฅํ•˜๋„๋ก ํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ๊ณต๊ณต API์™€ ์„ผ์„œ ๊ธฐ๋ฐ˜์˜ ์‚ฌ๋ฌผ์ธํ„ฐ๋„ท์„ ํ™œ์šฉํ•ด์„œ ๋„๋กœ ์œ„ ์ฐจ๋Ÿ‰ ์‚ฌ๊ณ ๊ฐ€ ๋ฐœ์ƒํ•œ ์œ„์น˜์—์„œ์˜ ํ˜„์žฅ ์ˆ˜๋ฆฌ๋ฅผ ์œ„ํ•œ ์ฐจ๋Ÿ‰ ๋ถ€ํ’ˆ ์ฆ‰์‹œ ์ˆ˜๊ธ‰ ๋ฐ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šคํ™” ๋ฐฉ๋ฒ•๋„ ๊ฐœ๋ฐœ ๋˜์—ˆ๋‹ค. ์ด๋Š” ๋””์ž์ด๋„ˆ๋กœ ํ•˜์—ฌ๊ธˆ ๊ฐ€๋ฒผ์šด ์ ‘์ด‰ ์‚ฌ๊ณ ์—์„œ์˜ ๋ถ€ํ’ˆ ๊ต์ฒด ํ–‰ํƒœ์— ๋Œ€ํ•œ ์ •๋ณด๋ฅผ ์–ป๊ฒŒ ํ•˜์—ฌ ์ฐจ๋Ÿ‰์˜ ๋””์ž์ธ์— ๋ฐ˜์˜ ๊ฐ€๋Šฅํ•˜๋„๋ก ํ•˜์˜€๋‹ค. ์‹œ๋‚˜๋ฆฌ์˜ค๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ด ์„ธ ๊ฐ€์ง€ ์ •๋ณด ์ œ๊ณต ์ฑ„๋„์„ ํ™œ์šฉํ•  ๊ฒฝ์šฐ, ์ž๋™์ฐจ ๋””์ž์ธ ๊ณผ์ •์—์„œ ๋ถˆ๋Ÿฌ๋“ค์—ฌ์˜ค๋Š” ๋ถ€ํ’ˆ ๋ฐ ๋ชจ๋“ˆ์˜ ๊ตฌ์„ฑ ์š”์†Œ๋“ค์„ ๋””์ž์ด๋„ˆ๊ฐ€ ์ •ํ™•ํžˆ ์•Œ๊ณ  ๋ฐ˜์˜ํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ์žฅ์ ์ด ๋ถ€๊ฐ๋˜์—ˆ๋‹ค. ์ •๋ณด ์ œ๊ณต์˜ ์ธํ„ฐํŽ˜์ด์Šค๋ฅผ ์‰ฝ๊ฒŒ ๊ตฌ์„ฑํ•˜๊ธฐ ์œ„ํ•ด์„œ, ์‹ค์ œ๋กœ ๋””์ž์ด๋„ˆ๋“ค์ด ์ž๋™์ฐจ ๊ฐœ๋ฐœ ๊ณผ์ •์—์„œ ๋””์ž์ธ ํ”„๋กœ์„ธ์Šค ์ƒ์—์„œ ํ™œ์šฉํ•˜๋Š” CAD software์— ์„ธ ๊ฐ€์ง€ ์ฑ„๋„๋“ค๋กœ๋ถ€ํ„ฐ ๋“ค์–ด์˜ค๋Š” ์ •๋ณด๋ฅผ ์‚ฌ๋ก€๋ณ„ ์ปฌ๋Ÿฌ๋กœ ํ‘œ์‹œํ•˜๊ณ , ์ด๋ฅผ ์‹œ์„ ์ถ”์  ์‚ฌ์šฉ์„ฑ ํ‰๊ฐ€๋ฅผ ํ†ตํ•ด ํ˜„์—… ๋””์ž์ด๋„ˆ๋“ค์ด ์‚ฌ์šฉํ•˜๊ธฐ ์‰ฝ๊ฒŒ ๊ฐœ์„ ํ•œ ๊ณผ์ •๋„ ๋ณธ ์—ฐ๊ตฌ์— ํฌํ•จ์‹œ์ผœ ์„ค๋ช…ํ•˜์˜€๋‹ค.1 Introduction 1 1.1 Research Background 1 1.2 Objective and Scope 2 1.3 Environmental Changes 3 1.4 Research Method 3 1.4.1 Causal Inference with Graphical Model 3 1.4.2 Design Thinking Methodology with Co-Evolution 4 1.4.3 Required Resources 4 1.5 Research Flow 4 2 Data-driven Design 7 2.1 Big Data and Data Management 6 2.1.1 Artificial Intelligence and Data Economy 6 2.1.2 API (Application Programming Interface) 7 2.1.3 AI driven Data Management for Designer 7 2.2 Datatype from Automotive Industry 8 2.2.1 Data-driven Management in Automotive Industry 8 2.2.2 Automotive Parts Case Studies 8 2.2.3 Parameter for Generative Design 9 2.3 Examples of Data-driven Design 9 2.3.1 Responsive-reactive 9 2.3.2 Dynamic Document Design 9 2.3.3 Insignts from Data-driven Design 10 3 Benchmark of Data-driven Automotive Design 12 3.1 Method of Global Benchmarking 11 3.2 Automotive Design 11 3.2.1 HMI Design and UI/UX 11 3.2.2 Hardware Design 12 3.2.3 Software Design 12 3.2.4 Convergence Design Process Model 13 3.3 Component Design Management 14 4 Vehicle Specification Design in Mobility Industry 16 4.1 Definition of Vehicle Specification 16 4.2 Field Study 17 4.3 Hypothesis 18 5 Three Preliminary Practical Case Studies for Vehicle Specification to Datadriven 21 5.1 Production Level 31 5.1.1 Background and Input 31 5.1.2 Data Process from Inventory to Designer 41 5.1.3 Output to Designer 51 5.2 Delivery Level 61 5.2.1 Background and Input 61 5.2.2 Data Process from Inventory to Designer 71 5.2.3 Output to Designer 81 5.3 Consumer Level 91 5.3.1 Background and Input 91 5.3.2 Data Process from Inventory to Designer 101 5.3.3 Output to Designer 111 6 Two Applications for Vehicle Designer 86 6.1 Real-time Dashboard DB for Decision Making 123 6.1.1 Searchable Infographic as a Designer's Tool 123 6.1.2 Scope and Method 123 6.1.3 Implementation 123 6.1.4 Result 124 6.1.5 Evaluation 124 6.1.6 Summary 124 6.2 Application to CAD for vehicle designer 124 6.2.1 CAD as a Designer's Tool 124 6.2.2 Scope and Method 125 6.2.3 Implementation and the Display of the CAD Software 125 6.2.4 Result 125 6.2.5 Evaluation: Usability Test with Eyetracking 126 6.2.6 Summary 128 7 Conclusion 96 7.1 Summary of Case Studies and Application Release 129 7.2 Impact of the Research 130 7.3 Further Study 131Docto

    CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

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    Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research
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