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1.
Software based encoder/decoder generation for data exchange optimization in the internet of things : master's thesis
Tjaž Vračko, 2022, master's thesis

Abstract: Efficient encoding of data is an important part of projects in the Internet of Things space. Communication packets must be kept as small as possible in order to minimize the power consumption of devices. In this thesis, an automatic code generation tool, irpack, is proposed that will unify the way packets are defined across all future projects at Institute IRNAS. Using a schema, this tool generates source code of encoders and decoders in target programming languages. A schema evolution system is also defined, by which changes to packets can be compatible across multiple versions. The tool is then applied to a selection of past projects to gauge its usefulness. It is determined that irpack is able to encode the same data into a similar or smaller size packet, while also providing additional versioning information.
Keywords: encoding/decoding, schema, schema evolution, bit packing, code generation
Published in DKUM: 31.01.2022; Views: 348; Downloads: 50
.pdf Full text (2,58 MB)

2.
Razpoznavanje kovancev v digitalnih slikah s pomočjo računalniškega vida in strojnega učenja
Tjaž Vračko, 2018, undergraduate thesis

Abstract: V diplomskem delu smo raziskali metode računalniškega vida za detekcijo in razpoznavanje evrskih kovancev v digitalnih slikah. Preučili in analizirali smo obstoječe metode za detekcijo kovancev ter predstavili njihove prednosti in slabosti. V delu predstavimo lasten algoritem za detekcijo in razpoznavo evrskih kovancev, ki temelji na Houghovi transformaciji, barvnih in teksturnih značilnicah, postopku na osnovi vreče besed in strojih podpornih vektorjev; za popravljanje rezultatov razpoznave uporablja informacije o velikostnih razmerjih kovancev. Algoritem smo implementirali in testirali na štirih testnih množicah slik. Ugotovili smo, da naš algoritem deluje najbolje na slikah z belim ozadjem, na katerih se nahaja veliko različnih tipov kovancev. Na takšnih slikah smo v povprečju dosegli 81,53-odstotno uspešnost pravilnega razpoznavanja kovancev. Izkazalo se je tudi, da je 20 cm tista oddaljenost kamere od kovancev, pri kateri dosežemo najvišjo uspešnost razpoznave in ustreza razdalji, ki bi jo tudi sicer izbrali za slikanje od 10 do 30 kovancev, položenih na mizo.
Keywords: razpoznavanje kovancev, HOG, SIFT, Houghova transformacija, stroji podpornih vektorjev
Published in DKUM: 03.05.2018; Views: 1144; Downloads: 133
.pdf Full text (3,01 MB)

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