Пьезоэлектричекие широкополосные микро-наноактюаторы

Рассмотрены особенности расчета и стабильность параметров нового класса широкополосных пьезоэлектрических преобразователей для актюаторов широкого спектра использования. Показана перспективность использования широкополосных пьезоактюаторов

Traffic sign recognition based on human visual perception.

This thesis presents a new approach, based on human visual perception, for detecting and recognising traffic signs under different viewing conditions. Traffic sign recognition is an important issue within any driver support system as it is fundamental to traffic safety and increases the drivers' awareness of situations and possible decisions that are ahead. All traffic signs possess similar visual characteristics, they are often the same size, shape and colour. However shapes may be distorted when viewed from different viewing angles and colours are affected by overall luminosity and the presence of shadows. Human vision can identify traffic signs correctly by ignoring this variance of colours and shapes. Consequently traffic sign recognition based on human visual perception has been researched during this project. In this approach two human vision models are adopted to solve the problems above: Colour Appearance Model (CIECAM97s) and Behavioural Model of Vision (BMV). Colour Appearance Model (CIECAM97s) is used to segment potential traffic signs from the image background under different weather conditions. Behavioural Model of Vision (BMV) is used to recognize the potential traffic signs. Results show that segmentation based on CIECAM97s performs better than, or comparable to, other perceptual colour spaces in terms of accuracy. In addition, results illustrate that recognition based on BMV can be used in this project effectively to detect a certain range of shape transformations. Furthermore, a fast method of distinguishing and recognizing the different weather conditions within images has been developed. The results show that 84% recognition rate can be achieved under three weather and different viewing conditions

Traffic sign recognition based on human visual perception

This thesis presents a new approach, based on human visual perception, for detecting and recognising traffic signs under different viewing conditions. Traffic sign recognition is an important issue within any driver support system as it is fundamental to traffic safety and increases the drivers' awareness of situations and possible decisions that are ahead. All traffic signs possess similar visual characteristics, they are often the same size, shape and colour. However shapes may be distorted when viewed from different viewing angles and colours are affected by overall luminosity and the presence of shadows. Human vision can identify traffic signs correctly by ignoring this variance of colours and shapes. Consequently traffic sign recognition based on human visual perception has been researched during this project. In this approach two human vision models are adopted to solve the problems above: Colour Appearance Model (CIECAM97s) and Behavioural Model of Vision (BMV). Colour Appearance Model (CIECAM97s) is used to segment potential traffic signs from the image background under different weather conditions. Behavioural Model of Vision (BMV) is used to recognize the potential traffic signs. Results show that segmentation based on CIECAM97s performs better than, or comparable to, other perceptual colour spaces in terms of accuracy. In addition, results illustrate that recognition based on BMV can be used in this project effectively to detect a certain range of shape transformations. Furthermore, a fast method of distinguishing and recognizing the different weather conditions within images has been developed. The results show that 84% recognition rate can be achieved under three weather and different viewing conditions.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

В 2 томах

В сборнике представлены результаты исследований различных процессов механической обработки деталей и технологии их изготовления. Изложены новые принципы проектирования некоторых инструментов, станков и другого технологического оборудования. Приведены результаты работ по электрофизическим и электрохимическим способам обработки материалов. Представлены некоторые направления развития механики структур и материалов. Рассмотрены проблемы динамики и прочности машин. Изложены актуальные вопросы экономики машиностроительного производства, инженерной педагогики и психологии

Vision models-based identification of traffic signs.

During the last 10 years,computer hardware technology has been improved rapidly.Large memory,storage is no longer a problem.Therefore some trade-off (dirty and quick algorithms)for traffic sign recognition between accuracy and speed should be improved.In this study,a new approach has been developed for accurate and fast recognition of traffic signs based on human vision models.It applies colour appearance model CIECAM97s to segment traffic signs from the rest of scenes.A Behavioural Model of Vision (BMV)is then utilised to identify the signs after segmented images are converted into grey-level representation.Two standard traffic sign databases are established.One is British traffic signs and the other is Russian traffic signs.Preliminary results show that around 90%signs taken from the British road with various viewing conditions have been correctly identified