LogiSketch: A Free-Sketch Digital Circuit Design and Simulation SystemLogiSketch

Alvarado, Christine; Kearney, Andy; Keizur, Alexa; Loncaric, Calvin; Parker, Miranda; Peck, Jessica L.; Sobel, Kiley; Tay, Fiona

doi:10.1007/978-3-319-15594-4_8

Cited by 7 publications

(6 citation statements)

References 11 publications

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“…We have found computer-based learning environments that use the handwriting or sketch-based methods as the data input to improve the learning of the students for a variety of domains, such as music theory (Barreto et al, 2016), physics (Cheema and LaViola, 2012;Lee et al, 2012;Cheema and LaViola Jr., 2018), Japanese kanji writing , drawing techniques (Dixon et al, 2010;Cummmings et al, 2012), mathematics (Laviola, 2007;Anthony et al, 2007a;Vuong et al, 2010;Anthony et al, 2012;Pacheco-Venegas et al, 2015;Phon-Amnuaisuk et al, 2015;Wang et al, 2016), biology , essentials of writing (Thompson et al, 2016), engineering statics concepts (Valentine et al, 2015), freehand engineering sketching (Hilton et al, 2019), digital circuit design (Alvarado et al, 2015), and geometry (Kang et al, 2016(Kang et al, , 2017. From this set of works, we have selected only the papers that are more similar to the research goal of our work and the domain of equations solving.…”

Section: Literature Reviewmentioning

confidence: 99%

Does handwriting impact learning on math tutoring systems?

Morais¹,

Jaques²

2021

Informatics in Education

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Intelligent Tutoring Systems (ITSs) for Math still use traditional data input methods: computers' keyboard and mouse. However, students usually solve math tasks using paper and pen. Therefore, the gap between the manner the students work and the requirements imposed by these typing-based systems expose students to an extraneous cognitive load, impairing their learning. Our study investigates the impact of the data input method on students' learning and fluency in solving equations using step-based math ITSs. More specifically, we have considered the standard typing and handwriting input methods. We hypothesized that the students would be more fluent using their handwriting with online recognition to solve math equations than using the typing input method. This fluency indicates a reduction in cognitive load, freeing working memory for logical reasoning instead of interface preconditions, leading to improved learning. We have conducted an experiment with 55 seventh-grade students from a private school to validate the hypothesis, randomly assigned to control and experimental groups. Each group used one of the input methods on two different devices (desktop computers and tablets). Although students using handwriting solved more equations and were faster than students who typed their equations, we could not find statistically significant differences in the learning between students that used typing or handwriting. Additionally, we have found that the input method used in a not ideal device (e.g., handwriting with a computer's mouse instead of using a touch screen device) can negatively affect the students' performance.

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Section: Literature Reviewmentioning

confidence: 99%

Does handwriting impact learning on math tutoring systems?

Morais¹,

Jaques²

2021

Informatics in Education

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“…Multiple research papers have focused on recognizing digital circuits sketches based on the pen strokes while drawing the circuit draft [3,[5][6][7]12]. The SketchRead [5] system uses shape description language to describe shapes.…”

Section: Online Recognitionmentioning

confidence: 99%

“…As soon as the user releases the pen, the drawn part is sent to the recognition engine, which relies on the strokes' patterns and the recorded list of events to interpret the drawn gate. LogiSketch [6] uses algorithms like Rubine, which relies on the sequence of strokes to recognize the drawn shape. Most recently, DCSR [12] uses the $P algorithm to record the strokes needed in the recognition step.…”

Section: Online Recognitionmentioning

confidence: 99%

“…The Sketic technique proposed in the present paper is an offline recognition platform for digital circuits; it makes the recognition more efficient and conformable because it does not rely on the sequence of strokes as in previous studies [3,[5][6][7]12]. Instead, the input to Sketic is an image of the complete drafted circuit that can be simply taken using a mobile camera.…”

Section: Online Recognitionmentioning

confidence: 99%

“…While most of the above-mentioned steps are automated using design automation tools, drafting the digital circuit and then converting it to a valid format are not automated efficiently. Several works have been proposed to automate this step [3][4][5][6][7]. The work proposed by Zamora and Eyjólfsdóttir [7] relies on the pen strokes on the computer or tablet screen to recognize the drawn digital circuit.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sketic: a machine learning-based digital circuit recognition platform

Abdel-Majeed¹,

Almousa²,

Alsalman³

et al. 2020

Turk J Elec Eng & Comp Sci

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In digital system design, digital logic circuit diagrams are built using interconnects and symbolic representations of the basic logic gates. Constructing such diagrams using free sketches is the first step in the design process.After that the circuit schematic or code has to be generated before being able to simulate the design. While most of the mentioned steps are automated using design automation tools, drafting the schematic circuit and then converting it into a valid format that can be simulated are still done manually due to the lack of robust tools that can recognize the free sketches and incorporate them into end user simulators. Hence, the goal of this paper is to construct and deploy computer simulation tools capable of understanding free sketches and incorporate them into useful simulation tools.Such a tool will be useful at both the educational and the industrial levels. Moreover, while this tool is designed to deal with sketched logic circuits, it can be generalized and applied to many other fields to convert the sketched design into a digital format. To implement this tool, we relied on the emerging machine learning and image processing concepts to make sure that the designed system is robust and accurate. Our results show that our system is able to recognize all the gates in the digital circuit with more than 95% accuracy.

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A Public Ground-Truth Dataset for Handwritten Circuit Diagram Images

Thoma

Bayer

et al. 2021

Lecture Notes in Computer Science

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LogiSketch: A Free-Sketch Digital Circuit Design and Simulation SystemLogiSketch

Cited by 7 publications

References 11 publications

Does handwriting impact learning on math tutoring systems?

Does handwriting impact learning on math tutoring systems?

Sketic: a machine learning-based digital circuit recognition platform

A Public Ground-Truth Dataset for Handwritten Circuit Diagram Images

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