108 research outputs found
Investigating the impact of image content on the energy efficiency of hardware-accelerated digital spatial filters
Battery-operated low-power portable computing devices are becoming an inseparable part of human daily life. One of the major goals is to achieve the longest battery life in such a device. Additionally, the need for performance in processing multimedia content is ever increasing. Processing image and video content consume more power than other applications. A widely used approach to improving energy efficiency is to implement the computationally intensive functions as digital hardware accelerators. Spatial filtering is one of the most commonly used methods of digital image processing. As per the Fourier theory, an image can be considered as a two-dimensional signal that is composed of spatially extended two-dimensional sinusoidal patterns called gratings. Spatial frequency theory states that sinusoidal gratings can be characterised by its spatial frequency, phase, amplitude, and orientation. This article presents results from our investigation into assessing the impact of these characteristics of a digital image on the energy efficiency of hardware-accelerated spatial filters employed to process the same image. Two greyscale images each of size 128 Ć 128 pixels comprising two-dimensional sinusoidal gratings at maximum spatial frequency of 64 cycles per image orientated at 0Ā° and 90Ā°, respectively, were processed in a hardware implemented Gaussian smoothing filter. The energy efficiency of the filter was compared with the baseline energy efficiency of processing a featureless plain black image. The results show that energy efficiency of the filter drops to 12.5% when the gratings are orientated at 0Ā° whilst rises to 72.38% at 90Ā°
Challenging the Assumptions of Unconstrained Electronic Trade across the Internet Space
We examine the prevailing factors influencing the uptake, scope and modality of internet-worked trade amongst Small and Medium Enterprises (SMEs) and the extent to which this effectively re-defines our notions of what constitutes viable and attractive local, regional or global trading zones as viewed by SMEs for the purpose of Electronic Commerce (EC). It is noted that such de-facto re-definitions, for some potential internet transactors, may arise through their preference to operate within the virtual sub-space confined to those zones or modes of electronic trade which are perceived by them as relatively more familiar and secure. The factors responsible for the paradox between this and the modern market metaphors of global village and virtual borderless world are examined in the context of evolving notions of virtual network enterprises or Net-conurbations with the development of intranets and extranets
State of the art of learning styles-based adaptive educational hypermedia systems (Ls-Baehss)
The notion that learning can be enhanced when a teaching approach matches a learnerās learning style has been widely accepted in classroom settings since the latter represents a predictor of studentās attitude and preferences. As such, the traditional approach of āone-size-fits-allā as may be applied to teaching delivery in Educational Hypermedia Systems (EHSs) has to be changed with an approach that responds to usersā needs by exploiting their individual differences. However, establishing and implementing reliable approaches for matching the teaching delivery and modalities to learning styles still represents an innovation challenge which has to be tackled. In this paper, seventy six studies are objectively analysed for several goals. In order to reveal the value of integrating learning styles in EHSs, different perspectives in this context are discussed. Identifying the most effective learning style models as incorporated within AEHSs. Investigating the effectiveness of different approaches for modelling studentsā individual learning traits is another goal of this study. Thus, the paper highlights a number of theoretical and technical issues of LS-BAEHSs to serve as a comprehensive guidance for researchers who interest in this area
Model-Driven Quantum Federated Learning (QFL)
Recently, several studies have proposed frameworks for Quantum Federated
Learning (QFL). For instance, the Google TensorFlow Quantum (TFQ) and
TensorFlow Federated (TFF) libraries have been deployed for realizing QFL.
However, developers, in the main, are not as yet familiar with Quantum
Computing (QC) libraries and frameworks. A Domain-Specific Modeling Language
(DSML) that provides an abstraction layer over the underlying QC and Federated
Learning (FL) libraries would be beneficial. This could enable practitioners to
carry out software development and data science tasks efficiently while
deploying the state of the art in Quantum Machine Learning (QML). In this
position paper, we propose extending existing domain-specific Model-Driven
Engineering (MDE) tools for Machine Learning (ML) enabled systems, such as
MontiAnna, ML-Quadrat, and GreyCat, to support QFL.Comment: Quantum Programming (QP) 2023 Workshop, Programming 2023, Tokyo,
Japa
Real-time feature selection technique with concept drift detection using adaptive micro-clusters for data stream mining
Data streams are unbounded, sequential data instances that are generated with high Velocity. Classifying sequential data instances is a very challenging problem in machine learning with applications in network intrusion detection, financial markets and applications requiring real-time sensor-networks-based situation assessment. Data stream classification is concerned with the automatic labelling of unseen instances from the stream in real-time. For this the classifier needs to adapt to concept drifts and can only have a single pass through the data if the stream is fast moving. This research paper presents work on a real-time pre-processing technique, in particular feature tracking. The feature tracking technique is designed to improve Data Stream Mining (DSM) classification algorithms by enabling and optimising real-time feature selection. The technique is based on tracking adaptive statistical summaries of the data and class label distributions, known as Micro-Clusters. Currently the technique is able to detect concept drifts and identify which features have been influential in the drift
Design, implementation and evaluation of broadband law noise amplifier (LNA) for radiometer.
The two major applications of microwave remote sensors are radiometer and radar. Because of its importance and the nature of the application, much research has been made on the various aspects of the radar. This paper will focus on the various aspects of the radiometer from a design point of view and the Low Noise Amplifier will be designed and implemented. The paper is based on a study in radio Frequency Communications engineering and understanding of electronic and RF circuits. Some research study about the radiometer and practical implementation of Low Noise Amplifier for Radiometer will be the main focus of this paper. Basically the paper is divided into two parts. In the first part some background study about the radiometer will be carried out and commonly used types of radiometer will be discussed. In the second part LNA for the radiometer will be designed
Enabling Un-/Semi-Supervised Machine Learning for MDSE of the Real-World CPS/IoT Applications
In this paper, we propose a novel approach to support domain-specific
Model-Driven Software Engineering (MDSE) for the real-world use-case scenarios
of smart Cyber-Physical Systems (CPS) and the Internet of Things (IoT). We
argue that the majority of available data in the nature for Artificial
Intelligence (AI), specifically Machine Learning (ML) are unlabeled. Hence,
unsupervised and/or semi-supervised ML approaches are the practical choices.
However, prior work in the literature of MDSE has considered supervised ML
approaches, which only work with labeled training data. Our proposed approach
is fully implemented and integrated with an existing state-of-the-art MDSE tool
to serve the CPS/IoT domain. Moreover, we validate the proposed approach using
a portion of the open data of the REFIT reference dataset for the smart energy
systems domain. Our model-to-code transformations (code generators) provide the
full source code of the desired IoT services out of the model instances in an
automated manner. Currently, we generate the source code in Java and Python.
The Python code is responsible for the ML functionalities and uses the APIs of
several ML libraries and frameworks, namely Scikit-Learn, Keras and TensorFlow.
For unsupervised and semi-supervised learning, the APIs of Scikit-Learn are
deployed. In addition to the pure MDSE approach, where certain ML methods,
e.g., K-Means, Mini-Batch K-Means, DB-SCAN, Spectral Clustering, Gaussian
Mixture Model, Self-Training, Label Propagation and Label Spreading are
supported, a more flexible, hybrid approach is also enabled to support the
practitioner in deploying a pre-trained ML model with any arbitrary
architecture and learning algorithm.Comment: Preliminary versio
Building adaptive data mining models on streaming data in real-time
Advances in hardware and software, over the past two decades have enabled the capturing, recording and processing of potentially large and infinite streaming data. The field of research in Data Stream Mining (DSM) has emerged to respond to the challenges and opportunities of developing the required analytics to unlock valuable knowledge. Thus DSM is focused on building Data Mining models, workflows and algorithms enabling the efficient and effective analysis of such streaming data at a large scale- the so-called āBig Dataā. Examples of application areas of Data Stream Mining techniques include real-time telecommunication data, telemetric data from large industrial plants, credit card transactions, cyber security threat modelling, social media data, etc. For some applications it is acceptable to provide data processing, modelling and analysis in batch mode using the traditional Data Mining approaches. However, for other application, particularly where continuous monitoring and contingent response are required, the model building and analytics have to take place in real-time as soon as new data becomes available i.e. to accommodate infinite streams and fast changing concepts in the data. This article highlights some of the key concepts and emergent techniques in DSM as presented in the authorsā recent publications as also outlined in a talk given at the UK Symposium on Knowledge Discovery from Data in London on May 24th, 2019 discussing the challenges, opportunities and innovative solutions in Data Stream Mining
Towards real-time feature tracking technique using adaptive micro-clusters
Data streams are unbounded, sequential data instances that are generated with high velocity. Classifying sequential data instances is a very challenging problem in machine learning with applications in network intrusion detection, ļ¬nancial markets and sensor networks. Data stream classiļ¬cation is concerned with the automatic labelling of unseen instances from the stream in real-time. For this the classiļ¬er needs to adapt to concept drifts and can only have a single pass through the data if the stream is fast. This research paper presents our work on a real-time pre-processing technique, in particular a feature tracking technique that takes concept drift into consideration. The feature tracking technique is designed to improve Data Stream Mining (DSM) classiļ¬cation algorithms by enabling real-time feature selection. The technique is based on adaptive summaries of the data and class distributions, known as Micro-Clusters. Currently the technique is able to detect concept drift and identiļ¬es which features have been involved
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