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Utilizing ICT in Sustainning National Development Using Digital Economy and IOT
Ismail Abdulkarim Adamu,
Mustapha Umar,
Joshua Umaru,
Haruna Ahmed Dokoro
Issue:
Volume 8, Issue 2, December 2020
Pages:
18-21
Received:
4 August 2020
Accepted:
15 December 2020
Published:
22 December 2020
Abstract: The pervasive utilization of information Technology devices is in crescendo and taking dynamic shift as the day goes by. This started from using computer system for storing of data, communication and transfer of data and now to the use of computer for online marketing and transaction and remote monitoring of device. With computer and mobile devices now people and sell and promote their services at ease on just a click over the internet. And in recent times the availability of strong wired and wireless network connectivity has led to the idea of internet of things. This idea makes possible for human to machine interaction and machine to machine interaction. This development has promised better and easy life even though with some consequences. It provides smart education allowing student learn from anywhere at any time, smart city, smart homes and e-health. This research focuses on exploring the diverse opportunity provided by the IOT and Digital economy for national development. Considering the present outright and outbreak of Covid-19 pandemic, the effective utilization of these techniques can lead to a boost in economy and un- interruptive and smooth running of government, industries educational institution in the face of natural disaster, compared to the gross damaged in economy, industry and educational institution caused by the Covid-19 pandemic.
Abstract: The pervasive utilization of information Technology devices is in crescendo and taking dynamic shift as the day goes by. This started from using computer system for storing of data, communication and transfer of data and now to the use of computer for online marketing and transaction and remote monitoring of device. With computer and mobile devices...
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Asymptotically Optimal Low-Power Digital Filtering Using Adaptive Approximate Processing
Issue:
Volume 8, Issue 2, December 2020
Pages:
22-38
Received:
28 May 2021
Accepted:
15 June 2021
Published:
13 July 2021
Abstract: Techniques for reducing power consumption in digital circuits have become increasingly important because of the growing demand for portable multimedia devices. Digital filters, being ubiquitous in such devices, are a prime candidate for low-power design. We present a new algorithmic approach to low-power frequency-selective digital filtering which is based on the concepts of adaptive approximate processing. This approach is formalized by introducing the class of approximate filtering algorithms in which the order of a digital filter is dynamically varied to provide time-varying stopband attenuation in proportion to the time-varying signal-to-noise ratio (SNR) of the input signal, while maintaining a fixed SNR at the filter output. Since power consumption in digital filter implementations is proportional to the order of the filter, dynamically varying the filter order is a strategy which may be used to conserve power. From this practical technique we abstract a theoretical problem which involves the determination of an optimal filter order based on observations of the input data and a set of concrete assumptions on the statistics of the input signal. Two solutions to this theoretical problem are presented, and the key results are used to interpret the solution to the practical low-power filtering problem. We construct a framework to explore the statistical properties of approximate filtering algorithms and show that under certain assumptions, the performance of approximate filtering algorithms is asymptotically optimal.
Abstract: Techniques for reducing power consumption in digital circuits have become increasingly important because of the growing demand for portable multimedia devices. Digital filters, being ubiquitous in such devices, are a prime candidate for low-power design. We present a new algorithmic approach to low-power frequency-selective digital filtering which ...
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Analysis of Clustering Algorithms for Mall
Issue:
Volume 8, Issue 2, December 2020
Pages:
39-47
Received:
31 January 2021
Accepted:
17 March 2021
Published:
4 August 2021
Abstract: Clustering is a technique that use to finding similar information within the cluster. The data has same things in the dataset cluster use to together base on the most and the minimum of the data. Clustering is procedures in which matter that clustered and divided group are together, based the rule to maximize the in the group resemblance and minimizing the inter-group resemblance. In other words, it is a combination of links, associations and whole patterns contained in massive databases however hidden or unknown. So as to perform the analysis, we'd like software system and tools. Set of tool, that are permit to user analyze information for various perspectives and angles, in order to find meaningful relationships. Cluster if similar information in the information set is the data is separate in the file. Clustering if similar data in the dataset is the data is separate in the file. In this paper, we study and compare the varying algorithms and technique used the group analysis that is used for RAPIDMINER. The best working on datasets for these type of cluster. Different clustering algorithms have been developed different results. In the paper we analysis two type of clustering for Algorithm: x-Mean &k-Mean cluster algorithm that compute the work in two type of cluster algorithm that work on correct classes. In the test of one field of Mall Customers data set working on RAPID MINER tools to find correct cluster.
Abstract: Clustering is a technique that use to finding similar information within the cluster. The data has same things in the dataset cluster use to together base on the most and the minimum of the data. Clustering is procedures in which matter that clustered and divided group are together, based the rule to maximize the in the group resemblance and minimi...
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