Holographic photonic neuron

Neuromorphic Computing and Engineering ◽

10.1088/2634-4386/ac3ba5 ◽

2021 ◽

Author(s):

Vincent Ricardo Daria

Keyword(s):

Artificial Intelligence ◽

Pattern Recognition ◽

Quantum Computing ◽

Angular Momentum ◽

Single Phase ◽

Transmission Protocol ◽

Matched Filters ◽

Recent Developments ◽

Computing Platforms ◽

The Brain

Abstract The promise of artificial intelligence (AI) to process complex datasets has brought about innovative computing paradigms. While recent developments in quantum-photonic computing have reached significant feats, mimicking our brain’s ability to recognize images are poorly integrated in these ventures. Here, I incorporate orbital angular momentum (OAM) states in a classical Vander Lugt optical correlator to create the holographic photonic neuron (HoloPheuron). The HoloPheuron can memorize an array of matched filters in a single phase-hologram, which is derived by linking OAM states with elements in the array. Successful correlation is independent of intensity and yields photons with OAM states of lℏ, which can be used as a transmission protocol or qudits for quantum computing. The unique OAM identifier establishes the HoloPheuron as a fundamental AI device for pattern recognition that can be scaled and integrated with other computing platforms to build-up a neuromorphic quantum-photonic processor that mimics the brain

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Software pattern recognition applied to nanodiffraction patterns from a STEM instrument

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s042482010014484x ◽

1986 ◽

Vol 44 ◽

pp. 694-695

Author(s):

G.Y. Fan ◽

J.M. Cowley

Keyword(s):

Image Processing ◽

Pattern Recognition ◽

Computer Software ◽

Processing System ◽

Light Level ◽

Host Computer ◽

Low Light ◽

Image Processing System ◽

Recent Developments ◽

On Line

In recent developments, the ASU HB5 has been modified so that the timing, positioning, and scanning of the finely focused electron probe can be entirely controlled by a host computer. This made the asynchronized handshake possible between the HB5 STEM and the image processing system which consists of host computer (PDP 11/34), DeAnza image processor (IP 5000) which is interfaced with a low-light level TV camera, array processor (AP 400) and various peripheral devices. This greatly facilitates the pattern recognition technique initiated by Monosmith and Cowley. Software called NANHB5 is under development which, instead of employing a set of photo-diodes to detect strong spots on a TV screen, uses various software techniques including on-line fast Fourier transform (FFT) to recognize patterns of greater complexity, taking advantage of the sophistication of our image processing system and the flexibility of computer software.

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Virtual Screening Meets Deep Learning

Current Computer - Aided Drug Design ◽

10.2174/1573409914666181018141602 ◽

2018 ◽

Vol 15 (1) ◽

pp. 6-28 ◽

Cited By ~ 6

Author(s):

Javier Pérez-Sianes ◽

Horacio Pérez-Sánchez ◽

Fernando Díaz

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Deep Learning ◽

Virtual Screening ◽

Great Increase ◽

New Drugs ◽

Learning Approach ◽

Screening Strategies ◽

Computer Aided ◽

Recent Developments

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

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Recent Developments in Detection of Central Serous Retinopathy through Imaging and Artificial Intelligence Techniques – A Review

IEEE Access ◽

10.1109/access.2021.3108395 ◽

2021 ◽

pp. 1-1

Author(s):

Syed Ale Hassan ◽

Shahzad Akbar ◽

Amjad Rehman ◽

Tanzila Saba ◽

Hoshang Kolivand ◽

...

Keyword(s):

Artificial Intelligence ◽

Central Serous Retinopathy ◽

Artificial Intelligence Techniques ◽

Recent Developments

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Principles of induction and approaches to attribute based induction

The Knowledge Engineering Review ◽

10.1017/s0269888900005920 ◽

1991 ◽

Vol 6 (4) ◽

pp. 307-333 ◽

Cited By ~ 3

Author(s):

G. Kalkanis ◽

G. V. Conroy

Keyword(s):

Artificial Intelligence ◽

Pattern Recognition ◽

Cognitive Psychology ◽

Expert Systems ◽

Basic Principles ◽

Inductive Approach ◽

Induction Process

AbstractThis paper presents a survey of machine induction, studied mainly from the field of artificial intelligence, but also from the fields of pattern recognition and cognitive psychology. The paper consists of two parts: Part I discusses the basic principles and features of the machine induction process; Part II uses these principles and features to review and criticize the major supervised attribute-based induction methods. Attribute-based induction has been chosen because it is the most commonly used inductive approach in the development of expert systems and pattern recognition models.

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A review on phase prediction in high entropy alloys

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/09544062211008935 ◽

2021 ◽

pp. 095440622110089

Author(s):

Vinay Kumar Soni ◽

S Sanyal ◽

K Raja Rao ◽

Sudip K Sinha

Keyword(s):

Solid Solution ◽

Phase Formation ◽

Single Phase ◽

High Entropy Alloy ◽

High Entropy Alloys ◽

Modeling Tools ◽

High Entropy ◽

Recent Developments ◽

Phase Prediction ◽

The Stability

The formation of single phase solid solution in High Entropy Alloys (HEAs) is essential for the properties of the alloys therefore, numerous approach were proposed by many researchers to predict the stability of single phase solid solution in High Entropy Alloy. The present review examines some of the recent developments while using computational intelligence techniques such as parametric approach, CALPHAD, Machine Learning etc. for prediction of various phase formation in multicomponent high entropy alloys. A detail study of this data-driven approaches pertaining to the understanding of structural and phase formation behaviour of a new class of compositionally complex alloys is done in the present investigation. The advantages and drawbacks of the various computational are also discussed. Finally, this review aims at understanding several computational modeling tools complying the thermodynamic criteria for phase formation of novel HEAs which could possibly deliver superior mechanical properties keeping an aim at advanced engineering applications.

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Pattern Recognition in Musculoskeletal Imaging Using Artificial Intelligence

Seminars in Musculoskeletal Radiology ◽

10.1055/s-0039-3400266 ◽

2020 ◽

Vol 24 (01) ◽

pp. 38-49 ◽

Cited By ~ 2

Author(s):

Natalia Gorelik ◽

Jaron Chong ◽

Dana J. Lin

Keyword(s):

Artificial Intelligence ◽

Pattern Recognition ◽

Bone Tumors ◽

Image Interpretation ◽

Bone Age ◽

Musculoskeletal Imaging ◽

Radiology Workflow ◽

Disease Response ◽

Osseous Metastases ◽

Applications Of Ai

AbstractArtificial intelligence (AI) has the potential to affect every step of the radiology workflow, but the AI application that has received the most press in recent years is image interpretation, with numerous articles describing how AI can help detect and characterize abnormalities as well as monitor disease response. Many AI-based image interpretation tasks for musculoskeletal (MSK) pathologies have been studied, including the diagnosis of bone tumors, detection of osseous metastases, assessment of bone age, identification of fractures, and detection and grading of osteoarthritis. This article explores the applications of AI for image interpretation of MSK pathologies.

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Mesencephalic GABA neuronal development: no more on the other side of oblivion

BioMolecular Concepts ◽

10.1515/bmc-2014-0023 ◽

2014 ◽

Vol 5 (5) ◽

pp. 371-382 ◽

Cited By ~ 1

Author(s):

Suyan Li ◽

Sampada Joshee ◽

Anju Vasudevan

Keyword(s):

Transcriptional Control ◽

Neuronal Development ◽

Developmental Regulation ◽

Molecular Characteristics ◽

Psychiatric Illnesses ◽

Neuronal Populations ◽

Gaba Neurons ◽

Recent Developments ◽

And Function ◽

The Brain

AbstractMidbrain GABA neurons, endowed with multiple morphological, physiological and molecular characteristics as well as projection patterns are key players interacting with diverse regions of the brain and capable of modulating several aspects of behavior. The diversity of these GABA neuronal populations based on their location and function in the dorsal, medial or ventral midbrain has challenged efforts to rapidly uncover their developmental regulation. Here we review recent developments that are beginning to illuminate transcriptional control of GABA neurons in the embryonic midbrain (mesencephalon) and discuss its implications for understanding and treatment of neurological and psychiatric illnesses.

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