Do you have space for dessert? a verified space cost semantics for CakeML programs

It is a difficult task to binarize image under uneven illumination, and this problem is always met in the image recognition system, such as two-dimensional barcode scanning terminal. In this paper, an efficient approach is proposed to binarize image which can tolerant uneven illumination and different light intensity. The method initializes thresholds with local average gray level and adjusts thresholds by calculating light density ratio. Due to characteristic of our approach, it can even obtain a sound result by limiting number of iterations which will seriously reduce computations and space cost. According to experiments, we can find that our method can achieve a good performance and meet the real-time requirement and quality demand for barcode scanning terminal.

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Sequence Pattern Mining for Web Logs

Pattern Discovery Using Sequence Data Mining ◽

10.4018/978-1-61350-056-9.ch014 ◽

2012 ◽

pp. 237-243

Author(s):

Pradeep Kumar ◽

Raju S. Bapi ◽

P. Radha Krishna

Keyword(s):

Pattern Mining ◽

Time And Space ◽

Sequence Pattern ◽

Interestingness Measures ◽

Web Logs ◽

Space Cost ◽

Reduced Time

Interestingness measures play an important role in finding frequently occurring patterns, regardless of the kind of patterns being mined. In this work, we propose variation to the AprioriALL Algorithm, which is commonly used for the sequence pattern mining. The proposed variation adds up the measure interest during every step of candidate generation to reduce the number of candidates thus resulting in reduced time and space cost. The proposed algorithm derives the patterns which are qualified and more of interest to the user. The algorithm, by using the interest, measure limits the size the candidates set whenever it is produced by giving the user more importance to get the desired patterns.

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Wide-Speed Range Sensorless Control of an IPM Motor for Multi-Purpose Applications

Inventions ◽

10.3390/inventions5030024 ◽

2020 ◽

Vol 5 (3) ◽

pp. 24

Author(s):

Maria Laura Bacci ◽

Ferdinando Luigi Mapelli ◽

Stefano Mossina ◽

Davide Tarsitano ◽

Michele Vignati

Keyword(s):

Permanent Magnet Synchronous Machine ◽

Mechanical Reliability ◽

High Frequency Signal ◽

Constant Torque ◽

High Speeds ◽

Speed Range ◽

Interior Permanent Magnet ◽

Signal Injection ◽

Space Cost ◽

Wide Speed Range

In a growing number of battery-driven applications the need of removing any position and speed transducer is taking over due to space, cost and mechanical reliability constraints, further than making the installation easier as requiring less wiring. This paper presents the development of a sensorless algorithm capable of running an Interior Permanent Magnet Synchronous Machine (IPMSM), assuring constant torque production in the whole speed range, form standstill to high speeds. This is achieved with an hybrid method: at standstill and very low speeds the saliency of the IPM is exploited through an High Frequency Signal Injection (HFSI), which assures a robust estimation of the rotor position. At medium to high speeds an advanced V-I estimator is adopted in order to enhance the motor performances. The developed algorithm comes out of being highly scalable as it requires very little tuning, resulting in a multi-purpose application which can be employed with any motor size.

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A survey of space cost models

10.1063/1.54899 ◽

1998 ◽

Cited By ~ 1

Author(s):

W. Thomas Harwick

Keyword(s):

Cost Models ◽

Space Cost

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ML for ML: Learning Cost Semantics by Experiment

Tools and Algorithms for the Construction and Analysis of Systems - Lecture Notes in Computer Science ◽

10.1007/978-3-662-54577-5_11 ◽

2017 ◽

pp. 190-207 ◽

Cited By ~ 1

Author(s):

Ankush Das ◽

Jan Hoffmann

Keyword(s):

Cost Semantics

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Implicit self-adjusting computation for purely functional programs

Journal of Functional Programming ◽

10.1017/s0956796814000033 ◽

2014 ◽

Vol 24 (1) ◽

pp. 56-112 ◽

Cited By ~ 9

Author(s):

YAN CHEN ◽

JOSHUA DUNFIELD ◽

MATTHEW A. HAMMER ◽

UMUT A. ACAR

Keyword(s):

Programming Languages ◽

Type System ◽

Type Systems ◽

Type Inference ◽

Source Program ◽

Target Program ◽

Implicit Approach ◽

Asymptotic Complexity ◽

Cost Semantics ◽

Output Behavior

AbstractComputational problems that involve dynamic data, such as physics simulations and program development environments, have been an important subject of study in programming languages. Building on this work, recent advances in self-adjusting computation have developed techniques that enable programs to respond automatically and efficiently to dynamic changes in their inputs. Self-adjusting programs have been shown to be efficient for a reasonably broad range of problems, but the approach still requires an explicit programming style, where the programmer must use specific monadic types and primitives to identify, create, and operate on data that can change over time. We describe techniques for automatically translating purely functional programs into self-adjusting programs. In this implicit approach, the programmer need only annotate the (top-level) input types of the programs to be translated. Type inference finds all other types, and a type-directed translation rewrites the source program into an explicitly self-adjusting target program. The type system is related to information-flow type systems and enjoys decidable type inference via constraint solving. We prove that the translation outputs well- typed self-adjusting programs and preserves the source program's input–output behavior, guaranteeing that translated programs respond correctly to all changes to their data. Using a cost semantics, we also prove that the translation preserves the asymptotic complexity of the source program.

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