The field of Affective Computing AC expects to narrow the communicative gap between the highly emotional human and the emotionally challenged computer by developing computational systems that recognize and respond to the affective states of the user.

CopyrightAssociation for Computing Machinery, Inc. Abstract Floating-point arithmetic is considered an esoteric subject by many people.

This is rather surprising because floating-point is ubiquitous in computer systems. Almost every language has a floating-point datatype; computers from PCs to supercomputers have floating-point accelerators; most compilers will be called upon to compile floating-point algorithms from time to time; and virtually every operating system must respond to floating-point exceptions such as overflow.

This paper presents a tutorial on those aspects of floating-point that have a direct impact on designers of computer systems. It begins with background on floating-point representation and rounding error, continues with a discussion of the IEEE floating-point standard, and concludes with numerous examples of how computer builders can better support floating-point.

Categories and Subject Descriptors: General -- instruction set design; D.

Processors -- compilers, optimization; G. General -- computer arithmetic, error analysis, numerical algorithms Secondary D.

Formal Definitions and Theory -- semantics; D. Process Management -- synchronization. Denormalized number, exception, floating-point, floating-point standard, gradual underflow, guard digit, NaN, overflow, relative error, rounding error, rounding mode, ulp, underflow.

Introduction Builders of computer systems often need information about floating-point arithmetic. There are, however, remarkably few sources of detailed information about it. One of the few books on the subject, Floating-Point Computation by Pat Sterbenz, is long out of print. This paper is a tutorial on those aspects of floating-point arithmetic floating-point hereafter that have a direct connection to systems building.

It consists of three loosely connected parts.

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The first section, Rounding Errordiscusses the implications of using different rounding strategies for the basic operations of addition, subtraction, multiplication and division. It also contains background information on the two methods of measuring rounding error, ulps and relative error.

The second part discusses the IEEE floating-point standard, which is becoming rapidly accepted by commercial hardware manufacturers. Included in the IEEE standard is the rounding method for basic operations. The discussion of the standard draws on the material in the section Rounding Error. The third part discusses the connections between floating-point and the design of various aspects of computer systems.VHDL implementation of CORDIC algorithm for wireless LAN Master thesis in Electronics Systems at Linköping Institute of Technology by Anastasia Lashko.

The Vision of the Department of Electronics and Communication Engineering, National Institute of Technology Silchar is to be a model of excellence for undergraduate and post graduate education and research in the country. STM32FF6 - Mainstream ARM Cortex-M0 Access line MCU with 32 Kbytes Flash, 48 MHz CPU, motor control, STM32FF6P7, STM32FF6P6, STM32FF6P6TR, STM32FF6P7TR, STMicroelectronics.

CORDIC (for COordinate Rotation DIgital Computer), also known as Volder's algorithm, is a simple and efficient algorithm to calculate hyperbolic and trigonometric functions, typically converging with one digit (or bit) per iteration..

CORDIC is therefore also an example of digit-by-digit algorithms..

CORDIC and closely related methods known as pseudo-multiplication and pseudo-division or. The IEEE standard only specifies a lower bound on how many extra bits extended precision provides. The minimum allowable double-extended format is sometimes referred to as bit format, even though the table shows it using 79 alphabetnyc.com reason is that hardware implementations of extended precision normally do not use a hidden bit, and so would use 80 rather than 79 bits.

An artificial neural network is a network of simple elements called artificial neurons, which receive input, change their internal state (activation) according to that input, and produce output depending on the input and activation..

An artificial neuron mimics the working of a biophysical neuron with inputs and outputs, but is not a biological neuron model.

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CORDIC - Wikipedia