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Dodat grafički apstrakt u uputstvo #12

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Dodat grafički apstrakt u uputstvo #12

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Dodat grafički apstrakt u uputstvo
djokjulapfe Oct 16, 2022
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KockaAdmiralac Oct 16, 2022
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KockaAdmiralac committed Oct 16, 2022
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Expand Up @@ -27,44 +27,29 @@ Dakle, bar 20x15 cm u 300dpi, odnosno nekih 2400x1800 pixela.

Ovde su predstavljeni primeri grafičkih apstrakata. Cilj je bilo prikazati raznovrsnost, nisu svi navdeni apstrakti nužno “najbolji”. Neki od navedenih slika nemaju prateći tekst - to je loša praksa, treba pisati prateći tekst - te slike su ovde navedene zato što su okej vizuelni primeri.

Jedna varijanta koja predstavlja vizualizovanu blok šemu sistema.
Jedna varijanta iz *[Keyframe-based recognition and localization during video-rate parallel tracking and mapping](https://www.sciencedirect.com/science/article/pii/S0262885611000436)* koja predstavlja vizualizovanu blok šemu sistema.

Izvor: http://www.sciencedirect.com/science/article/pii/S0262885611000436
![Grafički apstrakt rada "Keyframe-based recognition and localization during video-rate parallel tracking and mapping" koji prikazuje njihov metod rada.](/images/uputstvo/gap1.png)

![gap1](/images/uputstvo/gap1.png)
Primer gde je tema rada konstrukcija izvesnog senzora pH vrednosti. Autori su se smisleno odlučili za grafički apstrakt iz dva dela: prikaz šeme senzora i prikaz rezultata (karakteristika senzora). Izvor: *[Well-established materials in microelectronic devices systems for differential-mode extended-gate field effect transistor chemical sensors](https://www.sciencedirect.com/science/article/pii/S0167931716301484)*.

Primer gde je tema rada konstrukcija izvesnog senzora pH vrednosti. Autori su se smisleno odlučili za grafički apstrakt iz dva dela: prikaz šeme senzora i prikaz rezultata (karakteristika senzora)
![Grafički apstrakt rada "Well-established materials in microelectronic devices systems for differential-mode extended-gate field effect transistor chemical sensors" koji prikazuje šemu senzora sa leve strane i rezultate sa desne.](/images/uputstvo/gap2.png)

Izvor: http://www.sciencedirect.com/science/article/pii/S0167931716301484
Grafički apstrakt koji se sastoji iz dva dela. Autor je želeo da istakne sam algoritam koji je koristio (gornji deo) ali i šta taj njegov pristup omogućava kao rezultat (donji deo). Obratite pažnju na dobro korišćenje opisa uz sliku. Izvor: *[Keyframe-Based Visual-Inertial Online SLAM with Relocalization](https://arxiv.org/pdf/1702.02175.pdf)*.

![gap2](/images/uputstvo/gap2.png)
![Grafički apstrakt rada "Keyframe-Based Visual-Inertial Online SLAM with Relocalization" ispod koga se nalazi tekst "Fig. 1: Our visual-inertial SLAM system performs visualinertial camera pose tracking and keyframe-based pose graph optimization in parallel. Top: Overview of the individual input and processing steps of our SLAM approach and their timing. Bottom: Our system allows for relocalization in a previous keyframe map and continued SLAM operation. Keyframes (dots) along the previous (red) and new trajectory (blue) are continously merged into a combined SLAM map (purple) through loop closure constraints (green lines)."](/images/uputstvo/gap3.png)

Grafički apstrakt koji se sastoji iz dva dela. Autor je želeo da istakne sam algoritam koji je koristio (gornji deo) ali i šta taj njegov pristup omogućava kao rezultat (donji deo). Obratite pažnju na dobro korišćenje opisa uz sliku.
Vrlo kratka varijanta koja objašnjava suštinu. Obratite pažnju na korišćenje pratećeg opisa slike. Izvor: *[Learning from Simulated and Unsupervised Images through Adversarial Training](https://arxiv.org/pdf/1612.07828.pdf)*.

Izvor: https://arxiv.org/pdf/1702.02175.pdf

![gap3](/images/uputstvo/gap3.png)

Vrlo kratka varijanta koja objašnjava suštinu. Obratite pažnju na korišćenje pratećeg opisa slike.

Izvor:
https://arxiv.org/pdf/1612.07828.pdf

![gap4](/images/uputstvo/gap4.png)
![Grafički apstrakt rada "Learning from Simulated and Unsupervised Images through Adversarial Training" ispod koga se nalazi tekst "Figure 1. Simulated+Unsupervised (S+U) learning. The task is to learn a model that improves the realism of synthetic images from a simulator using unlabeled real data, while preserving the annotation information."](/images/uputstvo/gap4.png)

Isti izvor, ali varijanta koja u prvi plan ističe metod.

![gap5](/images/uputstvo/gap5.png)

Jednostavna blok šema algoritma sa pratećim formulama. Ako je u samom algoritmu suština ne mora ništa više fensi.

![gap6](/images/uputstvo/gap6.png)

Jednostavna upravljačka petlja sa prikazom aparature i algoritma u jednom.
![Grafički apstrakt rada "Learning from Simulated and Unsupervised Images through Adversarial Training" ispod koga se nalazi tekst "Figure 2. Overview of SimGAN. We refine the output of the simulator with a refiner neural network, R, that minimizes the combination of a local adversarial loss and a ‘selfregularization’ term. The adversarial loss ‘fools’ a discriminator network, D, that classifies an image as real or refined. The self-regularization term minimizes the image difference between the synthetic and the refined images. The refiner network and the discriminator network are updated alternately."](/images/uputstvo/gap5.png)

Izvor: http://www.sciencedirect.com/science/article/pii/S0957415817301423
Jednostavna upravljačka petlja sa prikazom aparature i algoritma u jednom. Izvor: *[Dynamics modeling and deviation control of the composites winding system](http://www.sciencedirect.com/science/article/pii/S0957415817301423)*.

![gap7](/images/uputstvo/gap7.png)
![Grafički apstrakt rada "Dynamics modeling and deviation control of the composites winding system" koji prikazuje način funkcionisanja sistema.](/images/uputstvo/gap7.png)

### Uvod

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