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In the paper, we show that matter waves can be derived from discreteness and causality. Namely we show that matter waves can naturally be ascribed to finite discrete causal systems, the Mealy automata having binary input/output which are bit sequences. If assign real numerical values (?measured quantities?) to bit sequences, the waves arise as a correspondence between the numerical values of input sequences (?impacts?) and output sequences (?system-evoked responses?). We show that among all discrete causal systems with arbitrary (not necessarily binary) inputs/outputs, only the ones with binary input/output can be ascribed to matter waves ?(x,t) = ei(kx??t).
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PÉREZ ARCILA, MAURICIO, and MARTIN ALONSO TAMAYO VELEZ. "IMPLEMENTATION OF LEBESGUE SAMPLING METHOD AND DIGITAL SENSORS FOR CONTROLLING THE LEVEL VARIABLE IN A CONTINUOUS SYSTEM." DYNA NEW TECHNOLOGIES 8, no. 1 (November 11, 2021): [14 P.]. http://dx.doi.org/10.6036/nt10248.
This study aims to show that the continuous control from a level system can be efficiently measured and controlled using capacitive digital binary sensors, which in this case, replace the measurement signal from an analog differential pressure transmitter in a level control system. The binary sensors low cost and the digital output they process allow the reproduction of a correct signal and the estimation of a variable for controlling the water level inside the process tank through a proportional pneumatic level control valve, which receives the control signal from the Lebesgue sampling estimation algorithm applied herein for processing digital measurements. In this particular case, the Lebesgue algorithm is applied to reproduce the estimation of values obtained from the continuous signal in the real level process for the measurement and control. Also, are compared both, simulated and real outputs obtained using the Lebesgue algorithm and digital sensors, which were applied to a state observer controller that relates digital signals for controlling the real level system output. The application of the Lebesgue algorithm in the real level process concludes that the analog level signal can be efficiently reproduced using this method. In addition, the controller enables the system to smoothly conduct digital output processing using digital sensors to control the system output correctly, validating that not only analog sensors should be applied for controlling the output of proportional actuators, because it is shown that digital binary signals can be used for controlling and emulating continuous signals, which were processed and applied to the pneumatic valve. Keywords: Lebesgue sampling, estimation, binary sensor, observer controller, finite state machine, continuous system, control, LTI systems, identification, state variable, estimated output, proportional actuator
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Gurevich, Yuri, and Saharon Shelah. "Time polynomial in input or output." Journal of Symbolic Logic 54, no. 3 (September 1989): 1083–88. http://dx.doi.org/10.2307/2274767.
AbstractWe introduce the class PIO of functions computable in time that is polynomial in max {the length of input, the length of output}, observe that there is no notation system for total PIO functions but there are notation systems for partial PIO functions, and give an algebra of partial PIO functions from binary strings to binary strings.
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Fateh, Rachid, Hicham Oualla, Es-said Azougaghe, Anouar Darif, Ahmed Boumezzough, Said Safi, Mathieu Pouliquen, and Miloud Frikel. "Machine Learning Based System Identification with Binary Output Data Using Kernel Methods." Journal of Telecommunications and Information Technology, no. 1 (February 12, 2024): 17–25. http://dx.doi.org/10.26636/jtit.2024.1.1430.
Within the realm of machine learning, kernel methods stand out as a prominent class of algorithms with widespread applications, including but not limited to classification, regression, and identification tasks. Our paper addresses the challenging problem of identifying the finite impulse response (FIR) of single-input single-output nonlinear systems under the influence of perturbations and binary-valued measurements. To overcome this challenge, we exploit two algorithms that leverage the framework of reproducing kernel Hilbert spaces (RKHS) to accurately identify the impulse response of the Proakis C channel. Additionally, we introduce the application of these kernel methods for estimating binary output data of nonlinear systems. We showcase the effectiveness of kernel adaptive filters in identifying nonlinear systems with binary output measurements, as demonstrated through the experimental results presented in this study.
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Oualla, Hicham, Mathieu Pouliquen, Miloud Frikel, and Said Safi. "Recursive identification of IIR system using binary input/output measurements." International Journal of Modelling, Identification and Control 40, no. 4 (2022): 327. http://dx.doi.org/10.1504/ijmic.2022.125549.
Safi, Said, Miloud Frikel, Hicham Oualla, and Mathieu Pouliquen. "Recursive identification of IIR system using binary input/output measurements." International Journal of Modelling, Identification and Control 40, no. 4 (2022): 327. http://dx.doi.org/10.1504/ijmic.2022.10050542.
Zlateva, P. "Output stabilization of fermentation processes: A binary control system approach." Bioprocess Engineering 23, no. 1 (July 27, 2000): 81–87. http://dx.doi.org/10.1007/s004499900127.
Luo, Chao, Jun Zhao, Yulie Gong, Yongzhen Wang, and Weibin Ma. "Energy efficiency comparison between geothermal power systems." Thermal Science 21, no. 6 Part A (2017): 2633–42. http://dx.doi.org/10.2298/tsci151225074l.
The geothermal water which can be considered for generating electricity with the temperature ranging from 80? to 150? in China because of shortage of electricity and fossil energy. There are four basic types of geothermal power systems: single flash, double flash, binary cycle, and flash-binary system, which can be adapted to geothermal energy utilization in China. The paper discussed the performance indices and applicable conditions of different power system. Based on physical and mathematical models, simulation result shows that, when geofluid temperature ranges from 100? to 130?, the net power output of double flash power is bigger than flash-binary system. When the geothermal resource temperature is between 130? and 150?, the net power output of flash-binary geothermal power system is higher than double flash system by the maximum value 5.5%. However, the sum water steam amount of double flash power system is 2 to 3 times larger than flash-binary power system, which will cause the bigger volume of equipment of power system. Based on the economy and power capacity, it is better to use flash-binary power system when the geofluid temperature is between 100? and 150?.
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Collins, R., J. Pemberton, S. J. D. Phoenix, and J. A. D. Matthew. "Output velocity distribution of a Langevin system with random binary input." European Journal of Physics 9, no. 4 (October 1, 1988): 312–18. http://dx.doi.org/10.1088/0143-0807/9/4/013.
Singh, Gajendra Pratap, and Sujit Kumar Singh. "Petri Net Recommender System for Generating of Perfect Binary Tree." International Journal of Knowledge and Systems Science 10, no. 2 (April 2019): 1–12. http://dx.doi.org/10.4018/ijkss.2019040101.
In modeling a discrete event driven system, Petri net recommender systems can play a very important role in describing the structural and behavioral properties of complex and tricky networks. The finite and infinite perfect binary tree forms a predictive model which can map the input information to output information based on the inputs' attributes. A perfect binary tree can be used for three types of recommender systems such as: collaborative filtering, a content-based approach, and a hybrid approach. In this article, the authors show the existence of a Petri net whose reachability tree is a Perfect Infinite Binary Tree (PIBT).
Dissertations / Theses on the topic "Binary output system":
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Oualla, Hicham. "Contributions à l'identification en boucle ouverte/fermée des systèmes à base de données binaires." Electronic Thesis or Diss., Normandie, 2022. http://www.theses.fr/2022NORMC229.
Cette thèse est consacrée à l'identification des systèmes à base de données binaires. Dans un premier temps, une présentation succincte de l'ensemble des méthodes d'identification des systèmes basé sur l'utilisation des données binaires existant dans la littérature est donnée. Dans la suite nous nous sommes intéressé au problème d'identification en boucle ouverte des systèmes à sortie et entrée binaires. Nous proposons des méthodes permettant l'identification des systèmes RIF et plus complexes les RII ayant une entrée et sortie binaires. Ces méthodes sont analysées et testées par des exemples numériques. Dans le reste de ce travail, nous proposons des premières solutions aux problèmes d'identification en boucle fermée des systèmes à base de données binaires. Les premières solutions sont dédiées aux systèmes à sortie binaire, l'excitation de la boucle fermée est supposée être à haute résolution. Enfin, deux méthodes sont proposées pour les systèmes en boucle fermée à sortie et entrée binaires. Ces solutions sont testées sur des exemples numériques pour mesurer leurs performances This thesis is devoted to the identification of systems based on binary data. First, a brief presentation of all the methods of identification of systems based on the use of binary data existing in the literature is given. In the following, we are interested in the problem of open loop identification of systems with binary output and input. We propose methods for the identification of FIR systems and more complex IIR systems with binary input and output. These methods are analyzed and tested by numerical examples. In the rest of this work, we propose first solutions to the problems of closed-loop identification of systems based on binary data. The first solutions are dedicated to binary output systems, the closed loop excitation is assumed to be high resolution. Finally, two methods are proposed for closed loop systems with binary output and input. These solutions are tested on numerical examples to quantify their performances
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Mestrah, Ali. "Identification de modèles sous forme de représentation d'état pour les systèmes à sortie binaire." Electronic Thesis or Diss., Normandie, 2023. http://www.theses.fr/2023NORMC255.
Cette thèse porte sur la modélisation paramétrique des systèmes linéaires invariants à partir de mesures binaires de la sortie. Ce problème demodélisation est abordée via l’usage des méthodes des sous-espaces. Ces méthodes permettent l’estimation de modèles sous forme de représentation d’état,un des avantages de ces méthodes étant que leur mise en œuvre ne nécessite pas la connaissance préalable de l’ordre du système. Ces méthodes ne sontinitialement pas adaptées au traitement de données binaires, l’objectif de cette thèse est ainsi leur adaptation à ce contexte d’identification. Dans cette thèse nousproposons trois méthodes des sous-espaces. Les propriétés de convergence de deux d’entre elles sont établies. Des résultats de simulations de Monte-Carlo sontprésentés afin de montrer les bonnes performances, mais aussi les limites, de ces méthodes This thesis focuses on parametric modeling of invariant linear systems from binary output measurements. This identification problem is addressed via the use ofsubspace methods. These methods allow the estimation of state-space models, an added benefit of these methods being the fact that their implementation doesnot require the prior knowledge of the order of the system. These methods are initially adapted to high resolution data processing, the objective of this thesis istherefore their adaptation to the identification using binary measurements. In this thesis we propose three subspace methods. Convergence properties of two ofthem are established. Monte Carlo simulation results are presented to show the good performance, but also limits, of these methods
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(11184744), Vinayak Suresh. "BINARY FEEDBACK IN COMMUNICATION SYSTEMS: BEAM ALIGNMENT, ADVERSARIES AND ENCODING." Thesis, 2021.
The availability of feedback from the receiver to the transmitter in a communication system can play a significant role. In this dissertation, our focus is specifically on binary or one-bit feedback. First, we study the problem of successive beam alignment for millimeter-wave channels where the receiver sends back only one-bit of information per beam sounding. The sparse nature of the channel allows us to interpret channel sounding as a form of questioning. By posing the alignment problem as a questioning strategy, we describe adaptive (closed-loop) and non-adaptive (open-loop) channel sounding techniques which are robust to erroneous feedback signals caused by noisy quantization. In the second part, we tightly characterize the capacity for two binary stochastic-adversarial mixed noise channels. Specifically, the transmitter (Alice) intends to convey a message to the receiver (Bob) over a binary symmetric channel (BSC) or a binary erasure channel (BEC) in the presence of an adversary (Calvin) who injects additional noise at the channel's input subject to a budget constraint. Calvin is online or causal in that at any point during the transmission, he can infer the bits being sent by Alice and those being received by Bob via a feedback link. Finally in the third part, we study the applicability of binary feedback for encoding and propose the framework of linearly adapting block feedback codes. We also prove a new result for Reed-Muller (RM) codes to demonstrate how an uncoded system can mimic a RM code under this framework, against remarkably large feedback delays.
Books on the topic "Binary output system":
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Hoste, Eric A. J., John A. Kellum, and Norbert Lameire. Definitions, classification, epidemiology, and risk factors of acute kidney injury. Edited by Norbert Lameire. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780199592548.003.0220_update_001.
The lack of a precise biochemical definition of acute kidney injury (AKI) resulted in at least 35 definitions in the medical literature, which gave rise to a wide variation in reported incidence and clinical significance of AKI, impeded a meaningful comparison of studies.The first part of this chapter describes and discusses different definitions and classification systems of AKI. Patient outcome and the need for renal replacement therapy are directly related to the severity of AKI, an observation that supports the use of a categorical staging system rather than a simple binary descriptor. The severity of AKI is commonly characterized using the relative changes in serum creatinine and urine output. Recently introduced staging systems including the RIFLE classification and the Acute Kidney Injury Network (AKIN) use these relatively simple and readily available parameters allowing the assignment of individual patients to different AKI stages. More recently, a Kidney Disease: Improving Global Outcomes (KDIGO) workgroup developed a consensus-based AKI staging system drawing elements of both RIFLE and AKIN. The potential pitfalls and limitations of the proposed definitions and classifications are briefly described.The second part of the chapter describes the epidemiology of AKI in different clinical settings; the intensive care unit (ICU), the hospitalized population, and the community. The different spectrum of AKI in the emerging countries is discussed and the most important causes and aetiologies of the major clinical types of AKI, prerenal, renal, and post-renal are summarized in table form. Finally the patient survival and renal functional outcome of AKI are briefly discussed
Book chapters on the topic "Binary output system":
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Schirmer, Pascal A., and Iosif Mporas. "Binary versus Multiclass Deep Learning Modelling in Energy Disaggregation." In Springer Proceedings in Energy, 45–51. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63916-7_6.
AbstractThis paper compares two different deep-learning architectures for the use in energy disaggregation and Non-Intrusive Load Monitoring. Non-Intrusive Load Monitoring breaks down the aggregated energy consumption into individual appliance consumptions, thus detecting device operation. In detail, the “One versus All” approach, where one deep neural network per appliance is trained, and the “Multi-Output” approach, where the number of output nodes is equal to the number of appliances, are compared to each other. Evaluation is done on a state-of-the-art baseline system using standard performance measures and a set of publicly available datasets out of the REDD database.
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Lin, Yi, Lucas M. Tabajara, and Moshe Y. Vardi. "ZDD Boolean Synthesis." In Tools and Algorithms for the Construction and Analysis of Systems, 64–83. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99524-9_4.
AbstractMotivated by applications in boolean-circuit design, boolean synthesis is the process of synthesizing a boolean function with multiple outputs, given a relation between its inputs and outputs. Previous work has attempted to solve boolean functional synthesis by converting a specification formula into a Binary Decision Diagram (BDD) and quantifying existentially the output variables. We make use of the fact that the specification is usually given in the form of a Conjunctive Normal Form (CNF) formula, and we can perform resolution on a symbolic representation of a CNF formula in the form of a Zero-suppressed Binary Decision Diagram (ZDD). We adapt the realizability test to the context of CNF and ZDD, and show that the Cross operation defined in earlier work can be used for witness construction. Experiments show that our approach is complementary to BDD-based Boolean synthesis.
Jacobs, Swen, and Mouhammad Sakr. "AIGEN: Random Generation of Symbolic Transition Systems." In Computer Aided Verification, 435–46. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81688-9_20.
AbstractAIGEN is an open source tool for the generation of transition systems in a symbolic representation. To ensure diversity, it employs a uniform random sampling over the space of all Boolean functions with a given number of variables. AIGEN relies on reduced ordered binary decision diagrams (ROBDDs) and canonical disjunctive normal form (CDNF) as canonical representations that allow us to enumerate Boolean functions, in the former case with an encoding that is inspired by data structures used to implement ROBDDs. Several parameters allow the user to restrict generation to Boolean functions or transition systems with certain properties, which are then output in AIGER format. We report on the use of AIGEN to generate random benchmark problems for the reactive synthesis competition SYNTCOMP 2019, and present a comparison of the two encodings with respect to time and memory efficiency in practice.
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Khellat-Kihel, Souad, Zhenan Sun, and Massimo Tistarelli. "An Hybrid Attention-Based System for the Prediction of Facial Attributes." In Lecture Notes in Computer Science, 116–27. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82427-3_9.
AbstractRecent research on face analysis has demonstrated the richness of information embedded in feature vectors extracted from a deep convolutional neural network. Even though deep learning achieved a very high performance on several challenging visual tasks, such as determining the identity, age, gender and race, it still lacks a well grounded theory which allows to properly understand the processes taking place inside the network layers. Therefore, most of the underlying processes are unknown and not easy to control. On the other hand, the human visual system follows a well understood process in analyzing a scene or an object, such as a face. The direction of the eye gaze is repeatedly directed, through purposively planned saccadic movements, towards salient regions to capture several details. In this paper we propose to capitalize on the knowledge of the saccadic human visual processes to design a system to predict facial attributes embedding a biologically-inspired network architecture, the HMAX. The architecture is tailored to predict attributes with different textural information and conveying different semantic meaning, such as attributes related and unrelated to the subject’s identity. Salient points on the face are extracted from the outputs of the S2 layer of the HMAX architecture and fed to a local texture characterization module based on LBP (Local Binary Pattern). The resulting feature vector is used to perform a binary classification on a set of pre-defined visual attributes. The devised system allows to distill a very informative, yet robust, representation of the imaged faces, allowing to obtain high performance but with a much simpler architecture as compared to a deep convolutional neural network. Several experiments performed on publicly available, challenging, large datasets demonstrate the validity of the proposed approach.
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Bromberger, Martin, Irina Dragoste, Rasha Faqeh, Christof Fetzer, Larry González, Markus Krötzsch, Maximilian Marx, Harish K. Murali, and Christoph Weidenbach. "A Sorted Datalog Hammer for Supervisor Verification Conditions Modulo Simple Linear Arithmetic." In Tools and Algorithms for the Construction and Analysis of Systems, 480–501. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99524-9_27.
AbstractIn a previous paper, we have shown that clause sets belonging to the Horn Bernays-Schönfinkel fragment over simple linear real arithmetic (HBS(SLR)) can be translated into HBS clause sets over a finite set of first-order constants. The translation preserves validity and satisfiability and it is still applicable if we extend our input with positive universally or existentially quantified verification conditions (conjectures). We call this translation a Datalog hammer. The combination of its implementation in SPASS-SPL with the Datalog reasoner VLog establishes an effective way of deciding verification conditions in the Horn fragment. We verify supervisor code for two examples: a lane change assistant in a car and an electronic control unit of a supercharged combustion engine.In this paper, we improve our Datalog hammer in several ways: we generalize it to mixed real-integer arithmetic and finite first-order sorts; we extend the class of acceptable inequalities beyond variable bounds and positively grounded inequalities; and we significantly reduce the size of the hammer output by a soft typing discipline. We call the result the sorted Datalog hammer. It not only allows us to handle more complex supervisor code and to model already considered supervisor code more concisely, but it also improves our performance on real world benchmark examples. Finally, we replace the before file-based interface between SPASS-SPL and VLog by a close coupling resulting in a single executable binary.
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Pete, Biggs. "Interfacing computers to experiments." In Computers in Chemistry. Oxford University Press, 2000. http://dx.doi.org/10.1093/hesc/9780198504467.003.0003.
This chapter introduces techniques on getting experimental information into a computer and causing a computer to control the experiment. It explains that an analogue to digital converter (ADC) is a device that is used to measure voltage, while the digital to analogue converter (DAC) is a device used to generate a specific voltage. It also describes the output of an ADC in a computer system. This is designed to reflect one or more memory locations on the microprocessor bus. The chapter discusses the parallel input and output (PIO) device, which allows a single bit or group of bits to be output or input. It also senses whether a signal is on or off. It highlights the principle of DAC, wherein the device produces a voltage proportional to binary data.
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Zoppi, Tommaso, Andrea Ceccarelli, and Andrea Bondavalli. "Ensembling Uncertainty Measures to Improve Safety of Black-Box Classifiers." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2023. http://dx.doi.org/10.3233/faia230635.
Machine Learning (ML) algorithms that perform classification may predict the wrong class, experiencing misclassifications. It is well-known that misclassifications may have cascading effects on the encompassing system, possibly resulting in critical failures. This paper proposes SPROUT, a Safety wraPper thROugh ensembles of UncertainTy measures, which suspects misclassifications by computing uncertainty measures on the inputs and outputs of a black-box classifier. If a misclassification is detected, SPROUT blocks the propagation of the output of the classifier to the encompassing system. The resulting impact on safety is that SPROUT transforms erratic outputs (misclassifications) into data omission failures, which can be easily managed at the system level. SPROUT has a broad range of applications as it fits binary and multi-class classification, comprising image and tabular datasets. We experimentally show that SPROUT always identifies a huge fraction of the misclassifications of supervised classifiers, and it is able to detect all misclassifications in specific cases. SPROUT implementation contains pre-trained wrappers, it is publicly available and ready to be deployed with minimal effort.
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Coolen, A. C. C., R. Kühn, and P. Sollich. "Dynamics of online gradient descent learning." In Theory of Neural Information Processing Systems, 385–96. Oxford University PressOxford, 2005. http://dx.doi.org/10.1093/oso/9780198530237.003.0018.
Abstract In this chapter we consider learning in systems with continuous real-valued outputs, rather than those with binary outputs as in the previous chapter. A natural way of constructing learning algorithms for such systems is by gradient descent on some appropriate error measure. This error measure should tell us by how much student and teacher output differ. A popular choice is the squared deviation between actual and desired outputs; carrying out gradient descent on this measure for multilayer networks yields the well-known error backpropagation algorithm. The results of our analysis are therefore of relevance to practical neural network learning.
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Konar, Debanjan, and Suman Kalyan Kar. "An Efficient Handwritten Character Recognition Using Quantum Multilayer Neural Network (QMLNN) Architecture." In Quantum-Inspired Intelligent Systems for Multimedia Data Analysis, 262–76. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5219-2.ch008.
This chapter proposes a quantum multi-layer neural network (QMLNN) architecture suitable for handwritten character recognition in real time, assisted by quantum backpropagation of errors calculated from the quantum-inspired fuzziness measure of network output states. It is composed of three second-order neighborhood-topology-based inter-connected layers of neurons represented by qubits known as input, hidden, and output layers. The QMLNN architecture is a feed forward network with standard quantum backpropagation algorithm for the adjustment of its weighted interconnection. QMLNN self-organizes the quantum fuzzy input image information by means of the quantum backpropagating errors at the intermediate and output layers of the architecture. The interconnection weights are described using rotation gates. After the network is stabilized, a quantum observation at the output layer destroys the superposition of quantum states in order to obtain true binary outputs.
Conference papers on the topic "Binary output system":
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Oualla, Hicham, Mathieu Pouliquen, Miloud Frikel, Said Safi, and Tristan Bonargent. "Spectral Analysis for System Identification from Input/Output Binary Measurements." In 2020 European Control Conference (ECC). IEEE, 2020. http://dx.doi.org/10.23919/ecc51009.2020.9143871.
Kim, Myung Soo, and Clark C. Guest. "Opto-neural system for pattern classification." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1990. http://dx.doi.org/10.1364/oam.1990.mjj3.
An optoneural system, which is a combination of an optical processor and a neural network, is developed for pattern classification. The system takes advantages of the two-dimensional processing capability of optics and the mapping capability of neural networks. The optical processor consists of a binary phase-only filter, and the neural network consists of three nonlinear mapping layers: the input layer, a hidden layer, and the output layer. Correlation outputs of the optical processor are used as inputs to the neural network. Binary phase in the binary phase-only filter and weights of the neural network are simultaneously trained with a simulated annealing algorithm. Gray-tone texture patterns that are not easily classified with annealed binary phase-only filters are well classified with the trained optoneural system.
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You, Keyou, Erik Weyer, and Girish Nair. "Identification of a gain system with binary input and output measurements." In 2015 54th IEEE Conference on Decision and Control (CDC). IEEE, 2015. http://dx.doi.org/10.1109/cdc.2015.7402576.
Lohmann, Adolf W., and Jorge Ojeda Castaneda. "Trading one dimension for binary simplicity." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1993. http://dx.doi.org/10.1364/oam.1993.wqq.1.
In this paper we propose a programmable wavefront generation system using two binary phase spatial light modulators (BPSLMs). The output of the binary phase hologram has inversion symmetry and is restricted to be space–invariant, if the output of the hologram is viewed in the Fourier plane of a lens. Inversion symmetry may be broken by introducing a fixed binary phase hologram [1,2] but in this case diffraction efficiency ηd is not increased. Alternatively, an asymmetric output may be generated by increasing the number of phase levels [3]. Results obtained in this way increase ηd but the output remains space-invariant.
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Patkar, Niteen A., Thomas F. Krile, and John F. Walkup. "Digital Optical Multiplication by Color Multiplexing." In Optical Computing. Washington, D.C.: Optica Publishing Group, 1985. http://dx.doi.org/10.1364/optcomp.1985.tuf1.
A system is described which performs the multiplication of two binary numbers in a highly parallel fashion. The number of output levels is kept to a maximum of three. The required logical AND operation is performed using a 2-bits-at-a-time approach. A scheme is also presented for performing the conversion of mixed binary numbers to binary numbers. As the maximum number of output levels is restricted to three, the system's noise immunity is high.
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Oualla, Hicham, Mathieu Pouliquen, Miloud Frikel, Said Safi, and Ali Mestrah. "Closed-loop system parametric identification based on binary measurement on the input and the output." In 2022 8th International Conference on Control, Decision and Information Technologies (CoDIT). IEEE, 2022. http://dx.doi.org/10.1109/codit55151.2022.9803966.
Neuhäuser, Karl, and Rudibert King. "Robust Active Flow Control of a Stator Cascade With Integer Control Functions and Sum-Up Rounding." In ASME Turbo Expo 2019: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/gt2019-91249.
Abstract This work is part of a research initiative that aims at increasing the overall gas turbine efficiency by means of constant volume combustion (CVC). For that purpose, flow control in the compressor becomes important, since unsteady combustion effects that may occur in a CVC are very likely to affect stability and efficiency of the compressor negatively due to flow disturbances. Active Flow Control (AFC) often has to deal with uncertain flow conditions, e.g., due to turbulence, varying operating ranges, or simply environmental effects. By that, system parameters such as gain or time constants of the system model also become uncertain, making it difficult for control algorithms to ensure optimality or even stable behavior. Robust control in the sense of ℋ∞ control tackles these problems using an uncertainty description and a nominal model of the system. In this contribution, robust control applied to a linear stator cascade is addressed when only a binary control output from solenoid valves is available. Moreover, a surrogate control variable is proposed, describing the extent of the velocity deficit. By means of a principal component analysis, this control variable is reconstructed from a single measurement input. AFC is realized via trailing edge blowing. In comparison to proportional valves, solenoid valves are cheaper and offer faster switching times with the drawback of a restricted range of the control output to integer or even binary values. Since the ℋ∞ controller, as well as most other control algorithms, results in a real-valued signal u(t) ∈ ℝ, a sum-up rounding strategy is applied to the controller output, forming a binary control output ub (t) ∈ {0, 1}. Although it is impossible for the two outputs to completely match, unless both are integer-valued, there is proof that the difference of real-valued to binary output is bounded in its integral value. The investigations show that a switching frequency of the valves of 100 Hz is sufficient to ensure that the control error via binary control matches its expected equivalent via real-valued control for the presented system.
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Karim, M. A., A. A. S. Awwal, and U. Dayton. "Programmable logic array design using polarization-encoded optical shadow-casting." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1987. http://dx.doi.org/10.1364/oam.1987.thi6.
An efficient optical computing system may involve the use of a programmable logic array (PLA) where only on outputs are stored in locations addressable by the inputs. The lensless optical shadow-casting (OSC) system1 using LEDs provides an easier and reliable means to generate logic operations. This scheme has been recently extended2,3 to include polarized sources, masks, and encoding codes, thus increasing the design flexibility. In an OSC, each LED produces a shadow of the input overlap pattern, which along with the other similarly produced shadows give an output overlap pattern which is subsequently decoded by means of decoding masks. The original1 and polarization-encoded2,3 optical shadow-casting systems explored so far are designed to generate only one output at a time. We report the design of a polarization-encoded PLA device where multiple outputs are generated simultaneously for all the input pixels without switching either LEDs or mask. The proposed algorithm is thereafter employed to design an optimal multiple-output binary multiplication unit.
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Javidi, B., and S. F. Odeh. "Real-time Object Recognition and Classification by Code Division Multiplexed Phase-only Encoded Filters." In Spatial Light Modulators and Applications. Washington, D.C.: Optica Publishing Group, 1988. http://dx.doi.org/10.1364/slma.1988.the13.
A multiobject shift invariant pattern recognition system that uses Code division multiplexed binary phase-only filters is presented. The system computes the binary correlation between an input pattern with a generalized set of pattern functions. This technique uses a filter which consists of a set of binary phase-only code division multiplexed reference pattern functions. The functions in the reference set may correspond to either different objects or different variations of the object under study. In the proposed architecture, the position of the output binary correlation peaks represent the locations of the objects in the input plane. This is different from phase coded pattern recognition systems in which the location of the output peak determines the class of the input object+. Here, the system is shift invariant and multiple objects can be present at the input plane. The pattern recognition is performed without increasing the space-bandwidth product requirements of the system. Computer simulations of the architecture are also used to study the performance of the pattern recognition architecture. The signal-to-noise ratio (SNR), and the peak to the maximum correlation sidelobe ratio (P/SL) are evaluated as the criteria for the system performance.
Reports on the topic "Binary output system":
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Farhi, Edward, and Hartmut Neven. Classification with Quantum Neural Networks on Near Term Processors. Web of Open Science, December 2020. http://dx.doi.org/10.37686/qrl.v1i2.80.
We introduce a quantum neural network, QNN, that can represent labeled data, classical or quantum, and be trained by supervised learning. The quantum circuit consists of a sequence of parameter dependent unitary transformations which acts on an input quantum state. For binary classification a single Pauli operator is measured on a designated readout qubit. The measured output is the quantum neural network’s predictor of the binary label of the input state. We show through classical simulation that parameters can be found that allow the QNN to learn to correctly distinguish the two data sets. We then discuss presenting the data as quantum superpositions of computational basis states corresponding to different label values. Here we show through simulation that learning is possible. We consider using our QNN to learn the label of a general quantum state. By example we show that this can be done. Our work is exploratory and relies on the classical simulation of small quantum systems. The QNN proposed here was designed with near-term quantum processors in mind. Therefore it will be possible to run this QNN on a near term gate model quantum computer where its power can be explored beyond what can be explored with simulation.
