Image Restoration

This book represents a sample of recent contributions of researchers all around the world in the field of image restoration. The book consists of 15 chapters organized in three main sections (Theory, Applications, Interdisciplinarity). Topics cover some different aspects of the theory of image restoration, but this book is also an occasion to highlight some new topics of research related to the emergence of some original imaging devices. From this arise some real challenging problems related to image reconstruction/restoration that open the way to some new fundamental scientific questions closely related with the world we interact with

Mobile Biometric Challenge Recording Interface

Autentimine arvutivõrkudes on protsess, mis üritab kontrollida digitaalset identiteeti, kasutades paroole. Parooli olemasolu aitab tagada kasutaja autentsuse. Kasutajaid saab biomeetiriliselt kindlaks teha ka nende sõrmejälje, hääle või näo järgi kasutades kindlat riistvara ja parooli toimingu kinnitamiseks. Samas, juhul kui paljud kasutajad on seotud sedatüüpi autentimisega, võib selline lahendus muutuda liiga aeglaseks ja suhteliselt kulukaks. Seetõttu vajab edukas biomeetriline autentimise süsteem põhjalikku läbimõeldust paljude tegurite osas (PBworks, 2007). Minu magistritöö eesmärk on uurida erinevaid biomeetrilise autentimise meetodeid, luues kirjeldused mobiilseks kasutamiseks ning töötada välja ja realiseerida vastav prototüüp. Kui biomeetrilise testi andmed on kogutud, näiteks video või hale kaudu, siis saadud andmed läbivad töötluse, peale mida kasutatakse ainult unikaalset osa andmetest. See osa andmetest salvestatakse andmebaasi. Kuid, enne andmete kasutamist, tuleb telefonis rakendada mitmeid meetmeid. Biomeetria puhul kasutatakse kahte peamist stsenaariumit: kontrollimine ja isikuandmete tuvastamine. Kontrollimise puhul võrreldakse ainult ühte andmebaasi, mis on kirjeldatav kui üks-ühele võrdulus. Isikuandmete tuvastamisel kasutatakse üks-mitmele andmebaaside võrdlust. Mõlemal juhul peavad salvestatud andembaasid toetama vastavaid stsenaariume. Magistritöös valmis vastav kasutajaliides nõutud omadustega.Mobile Biometric Challenge Recording Interface In computer network, authentication is the process of attempting to verify the digital identity using login passwords. Knowledge of the password is presumed to guarantee that the user is authentic. Likewise, Users may biometrically authenticate via their fingerprint, voice, face, or using provided hardware and password to confirm the operation. However, when many of users are involved this type of authentication is not suitable because this solution may become too slow and relatively expensive. In brief, successful distribution of a biometric authentication system needs careful deliberation of many factors (PBworks, 2007). My thesis core will be to study different biometric authentication techniques, derive requirements from these for a mobile interface, and implement and refine an interface prototype. When a biometric sample or trait is acquired, such as a video or a voice sample, the captured data goes through some amount of processing to prepare it for the extraction of a relatively small set of numbers, which represent the unique aspects of the data. This extracted set of data in record called a recording template. However, to prepare this recording optimally for biometric algorithms several measures already have to be taken on the phone. There are two basic usage scenarios for biometrics, known as verification and identification. Verification is the scenario where a newly created template is compared with only one other template in a database, which is described as an one-to-one comparison. The second biometric usage scenario is called identification, which is used when it is necessary to compare a newly created template too many enrolled templates, which is described as an one-to-many comparison

Bar code standardization by creating new symbology

Ovaj članak predlaže novu tipizaciju bar koda koja pojednostavljuje korištenje bilo kojeg dvodimenzionalnog matričnog bar koda na tržištu iz perspektive korisnika, istovremeno poštujući pravila svakog od njih. Predlaganjem nove tipizacije postojeći funkcijski uzorci su prebačeni u digitalni prostor i dodatni su dijelovi ugrađeni. Dodavanjem dijela koji se zove obrub (the rim), bilo koji 2D matrični bar kod može imati iste funkcijske uzorke i biti prepoznatljiv korisniku, što omogućuje standardizaciju bar koda. Prateći korake u ovom članku, za svaki 2D matrični bar kod, obrub se može precizno izračunati. Nova tipizacija također može imati dva različita bar koda u isto vrijeme implementiranjem maski koje mogu transformirati bitove kako bi se formirao željeni grafiči uzorak. Ovim se istraživanjem pridonosi cijelokupnoj standardizaciji 2D matričnih bar kodova što će omogućiti korisnicima identificiranje bilo kojeg tipa 2D matričnog bar koda na isti način kao i druge 2D matrične bar kod tipizacije. Ovom metodom moguće je stvoriti jednostavnije bar kodove za korisnika.This paper proposes a new symbology which eases the use of any two-dimensional matrix bar code on the market from a user perspective, respecting the rules of each 2D matrix bar code. By proposing a new symbology, the exisiting function patterns are transfered into a digital space and additional parts are added. By adding a part called the rim, any 2D matrix bar code can have the same function patterns and can be recognizable to the user, which enables the bar code standardization. For each 2D matrix bar code the rim can be precisely calculated. It can also carry two different 2D bar codes by implementing masks which can rearrange bits to form the desired graphic pattern. This research will contribute overall 2D matrix bar code standardization which will enable users to identify 2D matrix bar codes in the same way as other 2D matrix bar code symbologies. With this method a more user friendly bar code can be achieved

Embedded Machine Learning: Emphasis on Hardware Accelerators and Approximate Computing for Tactile Data Processing

Machine Learning (ML) a subset of Artificial Intelligence (AI) is driving the industrial and technological revolution of the present and future. We envision a world with smart devices that are able to mimic human behavior (sense, process, and act) and perform tasks that at one time we thought could only be carried out by humans. The vision is to achieve such a level of intelligence with affordable, power-efficient, and fast hardware platforms. However, embedding machine learning algorithms in many application domains such as the internet of things (IoT), prostheses, robotics, and wearable devices is an ongoing challenge. A challenge that is controlled by the computational complexity of ML algorithms, the performance/availability of hardware platforms, and the application\u2019s budget (power constraint, real-time operation, etc.). In this dissertation, we focus on the design and implementation of efficient ML algorithms to handle the aforementioned challenges. First, we apply Approximate Computing Techniques (ACTs) to reduce the computational complexity of ML algorithms. Then, we design custom Hardware Accelerators to improve the performance of the implementation within a specified budget. Finally, a tactile data processing application is adopted for the validation of the proposed exact and approximate embedded machine learning accelerators. The dissertation starts with the introduction of the various ML algorithms used for tactile data processing. These algorithms are assessed in terms of their computational complexity and the available hardware platforms which could be used for implementation. Afterward, a survey on the existing approximate computing techniques and hardware accelerators design methodologies is presented. Based on the findings of the survey, an approach for applying algorithmic-level ACTs on machine learning algorithms is provided. Then three novel hardware accelerators are proposed: (1) k-Nearest Neighbor (kNN) based on a selection-based sorter, (2) Tensorial Support Vector Machine (TSVM) based on Shallow Neural Networks, and (3) Hybrid Precision Binary Convolution Neural Network (BCNN). The three accelerators offer a real-time classification with monumental reductions in the hardware resources and power consumption compared to existing implementations targeting the same tactile data processing application on FPGA. Moreover, the approximate accelerators maintain a high classification accuracy with a loss of at most 5%

Flexible structure control laboratory development and technology demonstration

An experimental structure is described which was constructed to demonstrate and validate recent emerging technologies in the active control and identification of large flexible space structures. The configuration consists of a large, 20 foot diameter antenna-like flexible structure in the horizontal plane with a gimballed central hub, a flexible feed-boom assembly hanging from the hub, and 12 flexible ribs radiating outward. Fourteen electrodynamic force actuators mounted to the hub and to the individual ribs provide the means to excite the structure and exert control forces. Thirty permanently mounted sensors, including optical encoders and analog induction devices provide measurements of structural response at widely distributed points. An experimental remote optical sensor provides sixteen additional sensing channels. A computer samples the sensors, computes the control updates and sends commands to the actuators in real time, while simultaneously displaying selected outputs on a graphics terminal and saving them in memory. Several control experiments were conducted thus far and are documented. These include implementation of distributed parameter system control, model reference adaptive control, and static shape control. These experiments have demonstrated the successful implementation of state-of-the-art control approaches using actual hardware

Técnicas de compresión de imágenes hiperespectrales sobre hardware reconfigurable

Tesis de la Universidad Complutense de Madrid, Facultad de Informática, leída el 18-12-2020Sensors are nowadays in all aspects of human life. When possible, sensors are used remotely. This is less intrusive, avoids interferces in the measuring process, and more convenient for the scientist. One of the most recurrent concerns in the last decades has been sustainability of the planet, and how the changes it is facing can be monitored. Remote sensing of the earth has seen an explosion in activity, with satellites now being launched on a weekly basis to perform remote analysis of the earth, and planes surveying vast areas for closer analysis...Los sensores aparecen hoy en día en todos los aspectos de nuestra vida. Cuando es posible, de manera remota. Esto es menos intrusivo, evita interferencias en el proceso de medida, y además facilita el trabajo científico. Una de las preocupaciones recurrentes en las últimas décadas ha sido la sotenibilidad del planeta, y cómo menitoirzar los cambios a los que se enfrenta. Los estudios remotos de la tierra han visto un gran crecimiento, con satélites lanzados semanalmente para analizar la superficie, y aviones sobrevolando grades áreas para análisis más precisos...Fac. de InformáticaTRUEunpu