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All Journal International Journal of Electrical and Computer Engineering TELKOMNIKA (Telecommunication Computing Electronics and Control)
Kevin Curran
Ulster University
Computer Science & IT Electrical & Electronics Engineering

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Forecasting Short-term Wholesale Prices on the Irish Single Electricity Market Francesco Arci; Jane Reilly; Pengfei Li; Kevin Curran; Ammar Belatreche
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 6: December 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1123.558 KB) | DOI: 10.11591/ijece.v8i6.pp4060-4078

Abstract

Electricity markets are different from other markets as electricity generation cannot be easily stored in substantial amounts and to avoid blackouts, the generation of electricity must be balanced with customer demand for it on a second-by-second basis. Customers tend to rely on electricity for day-to-day living and cannot replace it easily so when electricity prices increase, customer demand generally does not reduce significantly in the short-term. As electricity generation and customer demand must be matched perfectly second-by-second, and because generation cannot be stored to a considerable extent, cost bids from generators must be balanced with demand estimates in advance of real-time. This paper outlines a a forecasting algorithm built on artificial neural networks to predict short-term wholesale prices on the Irish Single Electricity Market so that market participants can make more informed trading decisions. Research studies have demonstrated that an adaptive or self-adaptive approach to forecasting would appear more suited to the task of predicting energy demands in territory such as Ireland. We have identified the features that such a model demands and outline it here.
Streaming Audio Using MPEG–7 Audio Spectrum Envelope to Enable Self-similarity within Polyphonic Audio Jonathan Doherty; Kevin Curran; Paul McKevitt
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 1: March 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v15i1.4581

Abstract

One method overlooked to date, which can work alongside existing audio compression schemes, is that which takes account of the semantics and natural repetition of music through meta-data tagging. Similarity detection within polyphonic audio has presented problematic challenges within the field of Music Information Retrieval.  This paper presents a method (SoFI) for improving the quality of stored audio being broadcast over any wireless medium through meta-data which has a number of market applications all with market value. Our system works at the content level thus rendering it applicable in existing streaming services. Using the MPEG-7 Audio Spectrum Envelope (ASE) gives features for extraction and combined with k-means clustering enables self-similarity to be performed within polyphonic audio. SoFI uses string matching to identify similarity between large sections of clustered audio. Objective evaluations of SoFI give positive results which show that SoFI is shown to detect high levels of similarity on varying lengths of time within an audio file. In a scale between 0 and 1 with 0 the best, a clear correlation between similarly identified sections of 0.2491 shows successful identification.
Using SVD and DWT Based Steganography to Enhance the Security of Watermarked Fingerprint Images Mandy Douglas; Karen Bailey; Mark Leeney; Kevin Curran
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 3: September 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v15i3.6437

Abstract

Watermarking is the process of embedding information into a carrier file for the protection of ownership/copyright of digital media, whilst steganography is the art of hiding information. This paper presents, a hybrid steganographic watermarking algorithm based on Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) transforms in order to enhance the security of digital fingerprint images. A facial watermark is embedded into fingerprint image using a method of singular value replacement. First, the DWT is used to decompose the fingerprint image from the spatial domain to the frequency domain and then the facial watermark is embedded in singular values (SV’s) obtained by application of SVD. In addition, the original fingerprint image is not required to extract the watermark. Experimental results provided demonstrate the methods robustness to image degradation and common signal processing attacks, such as histogram and filtering, noise addition, JPEG and JPEG2000 compression with various levels of quality.
The Locator Framework for Detecting Movement Indoors Kevin Curran
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 1: February 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i1.7393

Abstract

There are many advantages to being able to track in real-time the movement of things or humans. This is especially important in tracking goods in the supply chain, in security and also in health and safety. The Global Positioning Satellite (GPS) system works well in outdoor environments but it cannot track items indoors. There is also the problem of power hungry sensor chips inherent in some GPS trackers. Mobile Cellular triangulation also works well for many outdoor solutions but problems with cost, accuracy and reliability make it difficult to deploy for indoor tracking scenarions. The levels of accuracy can vary by up to 50 meters which hinder its ability for adoption in many use case scenarios. There are also problems with poor cellular coverage in rural areas. Solutions built on WiFi–the IEEE 802.11 standard overcome many of these issues. WiFi location tracking works via sampling of the received signal strength (RSS) which along with triangulation and prior mapping allows systems to locate items or humans with fine-granularity. This WiFi fingerprinting is a viable cost-effective approach to determining movement within indoor enviroments. This paper presents an overview of popular techniques and off-the-shelf solutions which can be used to determine movement of people and objects indoors. We outline the Locator frameworks which is built on both active and passive indoor localisation techniques for tracking movement within indoor environments.
Improving compliance with bluetooth device detection Martin Davies; Eoghan Furey; Kevin Curran
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 5: October 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i5.12929

Abstract

The number of devices containing Bluetooth chipsets is continuing to rise and there exists a need to stem the tidal wave of vulnerabilities brought by the Bring Your Own Device (BYOD) and Internet of Things (IoT) phenomena. With Bluetooth enabled but discovery mode turned off, auditing for Bluetooth devices, or creating an accurate Bluetooth device hardware log is limited. The software tools and hardware devices to monitor WiFi networking signals have long been a part of the security auditor’s arsenal, but similar tools for Bluetooth are bespoke, expensive, and not adopted by most security pentesters. However, this has changed with the introduction of the Ubertooth One, a low-cost and open-source platform for monitoring Bluetooth Classic signals. Using a combination of the Ubertooth One, and other high-power Bluetooth devices, an auditor should now be able to actively scan for rogue devices that may otherwise have been missed. This research examines various hardware combinations that can be used to achieve this functionality, and the possible implications from a compliance point of view, with a focus on the standards used by the Payment Card Industry Data Security Standard (PCI-DSS), and the guidelines offered by the National Institute of Standards and Technology (NIST). We compare the results of scanning with traditional Bluetooth devices as opposed to an Ubertooth/Bluetooth combination. We show how the ability to monitor a larger portion of Bluetooth traffic can highlight serious implications in the compliance landscape of many organisations and companies. We demonstrate that identifying non-discoverable devices with Bluetooth enabled is a crucial element in holistic security monitoring of threats.
Co-Authors Ammar Belatreche Eoghan Furey Francesco Arci Jane Reilly Jonathan Doherty Karen Bailey Mandy Douglas Mark Leeney Martin Davies Paul McKevitt Pengfei Li