Digital technology works so well because at the heart of digital representation, there are only a few basic components. Nowadays, these basic components usually are binary digits, bits for short, called binary because they are designed to stand for only two different values, conveniently called zero and one. A stored or transmitted bit's state can deteriorate quite badly before a processing device will be mistaken in deciding which of the two possible values is the original.

Binary Arithmetic

It is well known that if each coefficient value of a digital filter is a sum of signed power-of-two (SPT) terms, the filter can be implemented without using multipliers. In the past decade, several methods have been developed for the design of filters whose coefficient values are sums of SPT terms. Most of these methods are for the design of filters where all the coefficient values have the same number of SPT terms. It has also been demonstrated recently that significant advantage can be achieved if the coefficient values are allocated with different number of SPT terms while keeping the total number of SPT terms for the filter fixed. In this paper, we present a new method for allocating the number of SPT terms to each coefficient value. In our method, the number of SPT terms allocated to a coefficient is determined by the statistical quantization step-size of that coefficient and the sensitivity of the frequency response of the filter to that coefficient. After the assignment of the SPT terms, an integer-programming algorithm is used to optimize the coefficient values. Our technique yields excellent results but does not guarantee optimum assignment of SPT terms. Nevertheless, for any particular assignment of SPT terms, the result obtained is optimum with respect to that assignment.

Signed Power-of-Two Term Allocation Scheme for the Design of Digital Filters

This paper studies the reliability of three different majority gates full adder (FA) designs, and compares them with that of a standard XOR-based FA. The analysis provides insights into different parameters that affect the reliability of FAs. The probability transfer matrix method is used to exactly calculate the reliability of the FAs under investigation. All simulation results show that majority gates FAs are more robust than a standard XOR-based FA. They also show how different gates affect the FAs' reliabilities and are extrapolated to give reliability estimates from the device level. Such reliability analyses should be used for a better characterization of FA designs for future nanoelectronic technologies, in addition to the well-known speed and power consumption (which have long been used for selecting and ranking FA designs).

On the Reliability of Majority Gates Full Adders

Potential of RT, bagging and RS ensemble learning algorithms for reference evapotranspiration prediction using climatic data-limited humid region in Bangladesh

Difficulties are faced when formulating hydrological processes, including that of evapotranspiration (ET). Conventional empirical methods for formulating these possess some shortcomings. The artificial intelligence approach emerges as the best possible solution to map the relationships between climatic parameters and ET, even with limited knowledge of the interactions between variables. This review presents the state-of-the-art application of artificial intelligence models in ET estimation, along with different types and sources of data. This paper discovers the most significant climatic parameters for different climate patterns. The characteristics of the basic artificial intelligence models are also explored in this review. To overcome the pitfalls of the individual models, hybrid models which use techniques such as data fusion and ensemble modeling, data decomposition as well as remote sensing-based hybridization, are introduced. In particular, the principles and applications of the hybridization techniques, as well as their combinations with basic models, are explained. The review covers most of the related and excellent papers published from 2011 to 2019 to keep its relevancy in terms of time frame and field of study. Guidelines for the future prospects of ET estimation in research are advocated. It is anticipated that such work could contribute to the development of agriculture-based economy.

https://www.mdpi.com/2073-4395/10/1/101/pdf

Recent Advances in Evapotranspiration Estimation Using Artificial Intelligence Approaches with a Focus on Hybridization Techniques—A Review

Appropriate irrigation planning with reference evapotranspiration (ETo) plays an important role in managing the water resources for agriculture. This paper presents machine learning models for predicting ETo and thereby aiding in irrigation planning. Daily weather data which include minimum and maximum temperature, relative humidity, solar radiation, soil temperature and wind speed were used to forecast ETo. The datasets were trained, validated and tested using multiple linear regression, random forest and gradient boost regression (GBR) algorithms. The results of the machine learning algorithms with and without the proposed preprocess techniques were compared. Performance evaluations of these models were done by comparing the root mean square error, mean absolute error and coefficient of determination. It was found that the preprocessed GBR model edges past the other two models in predicting the ETo. The paper also discusses the mechanism of irrigation planning with the help of ETo, crop evapotranspiration (ETc) and irrigation system efficiency.

Daily evapotranspiration prediction using gradient boost regression model for irrigation planning

Full adders are important components in applications such as digital signal processors (DSP) architectures and microprocessors. In addition to its main task, which is adding two numbers, it participates in many other useful operations such as subtraction, multiplication, division,, address calculation,..etc. In most of these systems the adder lies in the critical path that determines the overall speed of the system. So enhancing the performance of the 1-bit full adder cell (the building block of the adder) is a significant goal.Demands for the low power VLSI have been pushing the development of aggressive design methodologies to reduce the power consumption drastically. To meet the growing demand, we propose a new low power adder cell by sacrificing the MOS Transistor count that reduces the serious threshold loss problem, considerably increases the speed and decreases the power when compared to the static energy recovery full (SERF) adder. So a new improved 14T CMOS l-bit full adder cell is presented in this paper. Results show 50% improvement in threshold loss problem, 45% improvement in speed and considerable power consumption over the SERF adder and other different types of adders with comparable performance.

A Novel Low Power, High Speed 14 Transistor CMOS Full Adder Cell with 50% Improvement in Threshold Loss Problem

A Novel Low Power and High Performance 14 Transistor CMOS Full Adder Cell

Background/Objectives: The objective of the paper is to analyze the General Purpose Operation System (GPOS) performance with Real Time Operating System(RTOS) using a mobile robot as a real time application. The mobile robot module is implemented on a single board computer having ARM11 as its core. Methods/Statistical Analysis: The method used to calculate response is realfeel method which uses dedicated timer and interrupt to calculate the response. Findings: The response of the mobile robot is calculated by interrupting the mobile robot with some obstacles. Findings:In addition to this, other parameters such as speed, rotations time and precision resulted by the sensor are also calculated. RTOS have an average of 20μs where as GPOS have an average of 102μs of response time in real time environment.Conclusion:The results show that RTOS has better response time than GPOS. The minimum distance required to stop the mobile robot in RTOS is more accurate than GPOS. Compare to GPOS, the RTOS is able to avoid the obstacle collision even for a shorter distance.

Performance Analysis of Real Time Operating System with General Purpose Operating System for Mobile Robotic System

Image compression techniques find an extensive role in the field of biomedical image processing. Transform based image compression algorithms efficiency is mainly depending on the decoding methods, adopted. In this work, wavelet Transform based compression algorithms are developed for computer tomography image. Symlet based transformation of the CT images of lung are proposed in this work for the decomposition of the CT medical image. The decomposed images are encoded using the various encoding techniques such as Embedded Zero wavelet, (EZW), Set Partitioning in Hierarchical Trees (SPIHT).The developed compression algorithms are evaluated in terms of PSNR, Compression ratio, Means square error and bits per pixel. The optimum compression algorithm is also found based on the results obtained, so as to characterize the CT image the features are extracted and it is proven that after compression, the CT images show its ability for identifying types of defects. The results are an indicator to the promising application of this for medical image compression schemes. This paper provides the approach and analysis methodologies and the results obtained.

T. Vigneswaran

Papers

Daily evapotranspiration prediction using gradient boost regression model for irrigation planning

A Novel Low Power, High Speed 14 Transistor CMOS Full Adder Cell with 50% Improvement in Threshold Loss Problem

A Novel Low Power and High Performance 14 Transistor CMOS Full Adder Cell

Performance Analysis of Real Time Operating System with General Purpose Operating System for Mobile Robotic System

Characterization of CT Cancer Lung Image Using Image Compression Algorithms and Feature Extraction