Towards artificial intelligence based automatic adaptive response analyzer for high frequency analog BIST

2008 ◽  
Author(s):  
E. Petlenkov ◽  
A. Jutman ◽  
S. Nomm ◽  
R. Ubar
Keyword(s):  
Artificial Intelligence ◽  
High Frequency ◽  
Adaptive Response ◽  
Analog Bist

Ethical and Political-Economic Dimensions and Potential Reforms of the Hybrid Leveraged, High Frequency, Artificial Intelligence Trading Model

2021 ◽  
Vol 40 (2) ◽  
pp. 189-222
Author(s):  
Richard P. Nielsen ◽  
Keyword(s):  
Artificial Intelligence ◽  
High Frequency ◽  
Ethical Issues ◽  
Hedge Fund ◽  
Capital Allocation ◽  
Market Liquidity ◽  
Shared Responsibility ◽  
Trading Model ◽  
Political Economic ◽  
Finance Capitalism

The average annual profits before fees of the $10 billion plus Renaissance Technologies’ hybrid Medallion “Leveraged, High Frequency, Artificial Intelligence (LHFAI)” trading hedge fund between 1988 and 2019 were about 66 percent. Total trading profits during this period were over $100 billion. The fund has never had a losing year. The fund is not open to the general public. First, distinctions among, in more or less historical order, the traditional market-maker trading model, the hedge fund trading model, the artificial intelligence trading model, and the hybrid LHFAI trading model are discussed. Second, the micro components of the LHFAI trading model are explained in the context of Renaissance Technologies’ Medallion Fund. Third, key positive contributions of the model with respect to profitability, low annual volatility, market liquidity, and intellectual property development; negative ethical issues concerning exclusive access, tax fairness, financial transparency, shared responsibility for losses and systemic risk, and short vs. long-term capital allocation are discussed. Potential reforms that retain the positives, reduce the negatives, and that could positively transform the model are discussed. Fourth, potential impacts that the potential reforms might have on the macro LHFAI form of finance capitalism and the larger finance capitalism political-economic system are considered. Fifth, conclusions are offered and discussed.

Long-term adaptive response to high-frequency light signals in the unicellular photosynthetic eukaryoteDunaliella salina

2015 ◽  
Vol 112 (6) ◽  
pp. 1111-1121 ◽  
Author(s):  
Charlotte Combe ◽  
Philipp Hartmann ◽  
Sophie Rabouille ◽  
Amelie Talec ◽  
Olivier Bernard ◽  
...  
Keyword(s):  
High Frequency ◽  
Adaptive Response ◽  
Light Signals

scholarly journals Predicting Stock Market: An Approach with Artificial Intelligence

2018 ◽  
Vol 13 (02) ◽  
Author(s):  
Abhishek Srivastava ◽  
Indrani Sengupta
Keyword(s):  
Artificial Intelligence ◽  
Stock Market ◽  
High Frequency ◽  
Stock Exchange ◽  
Computational Power ◽  
Exchange Market ◽  
Stock Trading ◽  
Long Run ◽  
Trading Pattern

Artificial Intelligence (AI) technology has advanced impressively since inventors began tampering with its potential. Many believe that the next great use for AI technology will be in the field of financial market speculation. Technology can be used either to make our lives better or make money. The stock exchange market is the most volatile and most dynamic of all. Special care has to be exercised in buying and selling of stocks from different companies or businesses. The probability of losing the stocks and acquiring benefits through the stocks are fifty-fifty. Volatility of the stock market jumbles up a trader’s nervous system making it difficult to understand or thin rationally. Artificial Intelligence is supposed to be a predictive model that looks at more than technical patterns of trading. It has the ability to identify financial features of companies (e.g. price to earnings ratio, long term (business loans) that will make money in the long run. This requires capabilities from different areas of study and massive computational power which is why it is only prevalent in recent years. This paper tries to attempt of coming up with a basis and prediction using Artificial Intelligence in identifying trading pattern relations which appropriately inter relates with High Frequency Stock Trading based on pre-set criteria

Understanding artificial intelligence: insights on China

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Eleonora Veglianti ◽  
Yaya Li ◽  
Elisabetta Magnaghi ◽  
Marco De Marco
Keyword(s):  
Artificial Intelligence ◽  
High Frequency ◽  
Qualitative Data ◽  
Low Cost ◽  
Ecological Constraints ◽  
Content Type ◽  
Different Types ◽  
The Government ◽  
The Impact ◽  
Broad Level

Purpose The high frequency of disruption and dislocation of many industries, the migration to low-cost countries of different assets and activities, the increase in systemic risk, the birth of social and ecological constraints, as well as the new worldwide competitors require businesses and the overall society to change. In a so-called Industry 4.0. era, understanding the impact of artificial intelligence (AI) in developed as well as in underdeveloped economies has become increasingly crucial. The purpose of this study is to shed the light on the peculiarities of Chinese AI assessing the state of art of AI in this unique and valuable context. Design/methodology/approach Through a research based on a qualitative data analysis, the present paper suggests a new way to analyse AI and to support a better understanding of the local Chinese aspects influencing its development and implementation. Findings The development and implementation of AI in China required tailor solutions which account for the following three main dimensions: the location (i.e. territorial extension, the administrative boundaries); the government approach; and the human capital. Originality/value The analysis presents a broad level activity. In addition, the paper focused on Chinese scientific literature and different types of data (i.e. institutional documents, professional reports, websites and speeches in Chinese). The paper used a multi-faceted approach, including also the tacit knowledge of the authors about the context under investigation.

scholarly journals MODEL BASED-TESTING OF SPATIAL AND TIME DOMAIN ARTIFICIAL INTELLIGENCE SMART ANTENNA FOR ULTRA-HIGH FREQUENCY ELECTRIC DISCHARGE DETECTION IN DIGITAL POWER SUBSTATIONS

2021 ◽  
Vol 99 ◽  
pp. 91-101
Author(s):  
Lorothy Morrison Singkang ◽  
Kismet Anak Hong Ping ◽  
Herman Kunsei ◽  
Kumarasamy Somasundaram Senthilkumar ◽  
Kandasamy Pirapaharan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Electric Discharge ◽  
Time Domain ◽  
High Frequency ◽  
Smart Antenna ◽  
Ultra High Frequency ◽  
Model Based ◽  
Model Based Testing ◽  
Discharge Detection ◽  
Power Substations

The Microstructure of Steatite (Protoenstatite)

1985 ◽  
Vol 43 ◽  
pp. 236-237
Author(s):  
W. E. Lee ◽  
A. H. Heuer
Keyword(s):  
High Frequency ◽  
Glass Ceramics ◽  
Magnesium Silicate ◽  
Beam Current ◽  
Glassy Matrix ◽  
Angle Of Incidence ◽  
X Ray ◽  
Mechanical And Electrical Properties ◽  
Argon Ions ◽  
High Temperature Polymorph

IntroductionTraditional steatite ceramics, made by firing (vitrifying) hydrous magnesium silicate, have long been used as insulators for high frequency applications due to their excellent mechanical and electrical properties. Early x-ray and optical analysis of steatites showed that they were composed largely of protoenstatite (MgSiO3) in a glassy matrix. Recent studies of enstatite-containing glass ceramics have revived interest in the polymorphism of enstatite. Three polymorphs exist, two with orthorhombic and one with monoclinic symmetry (ortho, proto and clino enstatite, respectively). Steatite ceramics are of particular interest a they contain the normally unstable high-temperature polymorph, protoenstatite.Experimental3mm diameter discs cut from steatite rods (∼10” long and 0.5” dia.) were ground, polished, dimpled, and ion-thinned to electron transparency using 6KV Argon ions at a beam current of 1 x 10-3 A and a 12° angle of incidence. The discs were coated with carbon prior to TEM examination to minimize charging effects.

Simulations of high-resolution images of amorphous silicon films

1986 ◽  
Vol 44 ◽  
pp. 544-545
Author(s):  
G. Y. Fan ◽  
J. M. Cowley
Keyword(s):  
Electron Microscope ◽  
High Frequency ◽  
Fourier Transformation ◽  
Amorphous Materials ◽  
Low Frequency ◽  
Chromatic Aberration ◽  
Structure Information ◽  
Frequency Components ◽  
High Resolution Images ◽  
Spatial Frequency Spectrum

It is well known that the structure information on the specimen is not always faithfully transferred through the electron microscope. Firstly, the spatial frequency spectrum is modulated by the transfer function (TF) at the focal plane. Secondly, the spectrum suffers high frequency cut-off by the aperture (or effectively damping terms such as chromatic aberration). While these do not have essential effect on imaging crystal periodicity as long as the low order Bragg spots are inside the aperture, although the contrast may be reversed, they may change the appearance of images of amorphous materials completely. Because the spectrum of amorphous materials is continuous, modulation of it emphasizes some components while weakening others. Especially the cut-off of high frequency components, which contribute to amorphous image just as strongly as low frequency components can have a fundamental effect. This can be illustrated through computer simulation. Imaging of a whitenoise object with an electron microscope without TF limitation gives Fig. 1a, which is obtained by Fourier transformation of a constant amplitude combined with random phases generated by computer.

SEM image sharpness analysis

1996 ◽  
Vol 54 ◽  
pp. 142-143
Author(s):  
M. T. Postek ◽  
A. E. Vladar
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Quantitative Information ◽  
Primary Electron ◽  
Image Sharpness ◽  
Critical Dimensions ◽  
Sem Image ◽  
Frequency Changes ◽  
Electron Microscopes ◽  
Scanning Electron

Fully automated or semi-automated scanning electron microscopes (SEM) are now commonly used in semiconductor production and other forms of manufacturing. The industry requires that an automated instrument must be routinely capable of 5 nm resolution (or better) at 1.0 kV accelerating voltage for the measurement of nominal 0.25-0.35 micrometer semiconductor critical dimensions. Testing and proving that the instrument is performing at this level on a day-by-day basis is an industry need and concern which has been the object of a study at NIST and the fundamentals and results are discussed in this paper.In scanning electron microscopy, two of the most important instrument parameters are the size and shape of the primary electron beam and any image taken in a scanning electron microscope is the result of the sample and electron probe interaction. The low frequency changes in the video signal, collected from the sample, contains information about the larger features and the high frequency changes carry information of finer details. The sharper the image, the larger the number of high frequency components making up that image. Fast Fourier Transform (FFT) analysis of an SEM image can be employed to provide qualitiative and ultimately quantitative information regarding the SEM image quality.

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