Description:
In passive localization, sensors try to locate an event without any knowledge of event's emitted power. So, this is a more challenging problem compared to active localization. Existing passive localization schemes use expensive and noise-vulnerable range-based techniques. In this paper, we propose, to the best of our knowledge for the first time, a cost-effective range-free passive localization scheme exploiting hybrid sensor network model where mobile sensors are deployed on demand once an event is sensed by a static sensor. Efficient use of mobile sensors leads to two concomitant optimization problems: (1) positioning the mobile sensors so that the expected possible event location area is minimized; and (2) minimizing their overall traversed distance. To solve the first problem, we have developed a novel arc-coding based range-free localization technique that can accurately define the area of possible event location from the feedback of arbitrarily placed sensors without relying on expensive hardware to estimate range of signals. We have achieved significantly high localization accuracy with a low number of mobile sensors even after considering significant environmental noise. To solve the second problem, three alternative deployment strategies for the mobile sensors were simulated to recommend the best.
Description:
Blockchain-based peer-to-peer (P2P) ecosystem is well suited for distributed energy trading as it is inherently decentralised. In a distributed energy trading, an auctioneer passes unspent reservations to the next auctioneer, as dictated by the passing mechanism. However, traditional P2P energy trading systems used passing mechanisms that only partially consider the auction capability of the next auctioneer. We propose iPass, which incorporates price information when passing unspent auction reservations in P2P energy trading environment. The three performance metrics applied to measure the trading efficiency are (a) auction convergence time, (b) the number of auction settlements, and (c) the economic surplus of buyers and sellers. We simulated the proposed mechanism in Hyperledger Fabric, a permissioned blockchain framework. Hyperledger Fabric manages the data storage and smart contracts. Experiments show iPass is more efficient compared to existing passing mechanisms.
Description:
A novel Memetic Algorithm (MA) is proposed for investigating the complex ab initio protein structure prediction problem. The proposed MA has a new fitness function incorporating domain knowledge in the form of two new measures (H-compliance and P-compliance) to indicate hydrophobic and hydrophilic nature of a residue. It also includes two novel techniques for dynamically preserving best fit schema and for providing a guided search. The algorithm performance is investigated with the aid of commonly studied 2D lattice hydrophobic polar (HP) model for the benchmark as well as non-benchmark sequences. Comparative studies with other search algorithms reveal superior performance of the proposed technique
Description:
A novel Memetic Algorithm (MA) is proposed for investigating the complex ab initio protein structure prediction problem. The proposed MA has a new fitness function incorporating domain knowledge in the form of two new measures (H-compliance and P-compliance) to indicate hydrophobic and hydrophilic nature of a residue. It also includes two novel techniques for dynamically preserving best fit schema and for providing a guided search. The algorithm performance is investigated with the aid of commonly studied 2D lattice hydrophobic polar (HP) model for the benchmark as well as non-benchmark sequences. Comparative studies with other search algorithms reveal superior performance of the proposed technique
Description:
In this paper, we present a new stochastic model based approach for enhanced image segmentation in biometric identification systems. Biometric features such as fingerprint, face, iris, hand geometry and more recently dental features are being used for human identification. Image analysis of each of these biometric features has various challenges to overcome. To address such contemporary problems of image segmentation, we provide a novel approach based on maximum a posteriori (MAP) fitting Gaussian mixture model using Expectation-Minimization (EM) algorithm within the Bayesian framework. Our new algorithm captures the pixel intensity by the likelihood term in Bayesian Networks, and a priori biasing term of the spatial location information with the help of Markov Random Fields (MRF) model. We have employed a novel approach of using Daubechies wavelet transform for texture feature extraction that uses MRF model and a robust technique of determining the number of pixel classes based on Cluster Ensembles for a reliable segmentation of dental X-ray images. We present how our approach could be applied in dental biometrics to achieve very fast and reliable human identification. Experiments show that our new unsupervised image segmentation technique provides accurate feature extraction and teeth segmentation for effective biometric identification.
Description:
The process of meaningful image object identification is the critical first step in the extraction of image information for computer vision and image understanding. The disjoint regions correspond to visually distinct objects in a scene. In this particular work, we investigate and propose a novel stochastic model based approach to implement a robust unsupervised color image content understanding technique that segments a color textured image into its constituent parts automatically and meaningfully. The aim of this work is to produce precise segmentation of different objects in a color image using color information, texture information and neighborhood relationships among neighboring image pixels in terms of their features using Markov Random Field (MRF) Model to get the maximum accuracy in segmentation. The evaluation of the results is done through comparison of the segmentation quality and accuracy with another similar existing method which demonstrates that the proposed approach outperforms the existing method by achieving better segmentation accuracy with faithful segmentation results.
Description:
Internet of Things (IoT) image sensors for surveillance and monitoring, digital cameras, smart phones and social media generate huge volume of digital images every day. Image splicing and copy-move attacks are the most common types of image forgery that can be done very easily using modern photo editing software. Recently, digital forensics has drawn much attention to detect such tampering on images. In this paper, we introduce a novel feature extraction technique, namely Sum of Relevant Inter-Cell Values (SRIV) using which we propose a passive (blind) image forgery detection method based on Discrete Cosine Transformation (DCT) and Local Binary Pattern (LBP). First, the input image is divided into non-overlapping blocks and 2D block DCT is applied to capture the changes of a tampered image in the frequency domain. Then LBP operator is applied to enhance the local changes among the neighbouring DCT coefficients, magnifying the changes in high frequency components resulting from splicing and copy-move attacks. The resulting LBP image is again divided into non-overlapping blocks. Finally, SRIV is applied on the LBP image blocks to extract features which are then fed into a Support Vector Machine (SVM) classifier to identify forged images from authentic ones. Extensive experiment on four well-known benchmark datasets of tampered images reveal the superiority of our method over recent state-of-the-art methods.
Description:
Internet of Things (IoT) image sensors for surveillance and monitoring, digital cameras, smart phones and social media generate huge volume of digital images every day. Image splicing and copy-move attacks are the most common types of image forgery that can be done very easily using modern photo editing software. Recently, digital forensics has drawn much attention to detect such tampering on images. In this paper, we introduce a novel feature extraction technique, namely Sum of Relevant Inter-Cell Values (SRIV) using which we propose a passive (blind) image forgery detection method based on Discrete Cosine Transformation (DCT) and Local Binary Pattern (LBP). First, the input image is divided into non-overlapping blocks and 2D block DCT is applied to capture the changes of a tampered image in the frequency domain. Then LBP operator is applied to enhance the local changes among the neighbouring DCT coefficients, magnifying the changes in high frequency components resulting from splicing and copy-move attacks. The resulting LBP image is again divided into non-overlapping blocks. Finally, SRIV is applied on the LBP image blocks to extract features which are then fed into a Support Vector Machine (SVM) classifier to identify forged images from authentic ones. Extensive experiment on four well-known benchmark datasets of tampered images reveal the superiority of our method over recent state-of-the-art methods.
Description:
Image sensors are generating limitless digital images every day. Image forgery like splicing and copy-move are very common type of attacks that are easy to execute using sophisticated photo editing tools. As a result, digital forensics has attracted much attention to identify such tampering on digital images. In this paper, a passive (blind) image tampering identification method based on Discrete Cosine Transformation (DCT) and Local Binary Pattern (LBP) has been proposed. First, the chroma components of an image is divided into fixed sized non-overlapping blocks and 2D block DCT is applied to identify the changes due to forgery in local frequency distribution of the image. Then a texture descriptor, LBP is applied on the magnitude component of the 2D-DCT array to enhance the artifacts introduced by the tampering operation. The resulting LBP image is again divided into non-overlapping blocks. Finally, summations of corresponding inter-cell values of all the LBP blocks are computed and arranged as a feature vector. These features are fed into a Support Vector Machine (SVM) with Radial Basis Function (RBF) as kernel to distinguish forged images from authentic ones. The proposed method has been experimented extensively on three publicly available well-known image splicing and copy-move detection benchmark datasets of color images. Results demonstrate the superiority of the proposed method over recently proposed state-of-the-art approaches in terms of well accepted performance metrics such as accuracy, area under ROC curve and others.
Description:
2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018
Description:
Image sensors are generating limitless digital images every day. Image forgery like splicing and copy-move are very common type of attacks that are easy to execute using sophisticated photo editing tools. As a result, digital forensics has attracted much attention to identify such tampering on digital images. In this paper, a passive (blind) image tampering identification method based on Discrete Cosine Transformation (DCT) and Local Binary Pattern (LBP) has been proposed. First, the chroma components of an image is divided into fixed sized non-overlapping blocks and 2D block DCT is applied to identify the changes due to forgery in local frequency distribution of the image. Then a texture descriptor, LBP is applied on the magnitude component of the 2D-DCT array to enhance the artifacts introduced by the tampering operation. The resulting LBP image is again divided into non-overlapping blocks. Finally, summations of corresponding inter-cell values of all the LBP blocks are computed and arranged as a feature vector. These features are fed into a Support Vector Machine (SVM) with Radial Basis Function (RBF) as kernel to distinguish forged images from authentic ones. The proposed method has been experimented extensively on three publicly available well-known image splicing and copy-move detection benchmark datasets of color images. Results demonstrate the superiority of the proposed method over recently proposed state-of-the-art approaches in terms of well accepted performance metrics such as accuracy, area under ROC curve and others.
Description:
2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018
Description:
Energy efficiency is a key factor in the next generation wireless communication systems. Sleep mode implementation in multi-tier 5G networks has proven to be a very good approach for improving the energy efficiency. In this paper, we propose a novel reinforcement learning based decision making algorithm to implement sleep mode in the base stations (BSs) used in multi-tier 5G networks. We propose a Markovian Decision process (MDP) based algorithm to switch between three different power consumption modes of a BS for improving the energy efficiency of the 5G network. The MDP based approach intelligently switches between the states of the BS based on the offered traffic whilst maintaining a prescribed minimum channel rate per user. Our results show that there is a significant gain in the energy efficiency when using our proposed MDP algorithm together with the three-state BSs. We have also shown the energy-delay tradeoff in order to design a delay aware network.
Description:
Energy efficiency is a key factor in the next generation wireless communication systems. Sleep mode implementation in multi-tier 5G networks has proven to be a very good approach for improving the energy efficiency. In this paper, we propose a novel reinforcement learning based decision making algorithm to implement sleep mode in the base stations (BSs) used in multi-tier 5G networks. We propose a Markovian Decision process (MDP) based algorithm to switch between three different power consumption modes of a BS for improving the energy efficiency of the 5G network. The MDP based approach intelligently switches between the states of the BS based on the offered traffic whilst maintaining a prescribed minimum channel rate per user. Our results show that there is a significant gain in the energy efficiency when using our proposed MDP algorithm together with the three-state BSs. We have also shown the energy-delay tradeoff in order to design a delay aware network.
Description:
Thermographic scanning of electrical installations is sometimes conducted as part of electrical safety audits. This scanning is mostly considered based on the present temperature identified on a particular device or location and according to a pre-specified temperature category. But in most of the cases the recommended temperature of the particular devices cannot be considered during temperature categorization. There are many electrical audits using Thermographic scanning and recommend solution based on normal category of temperature rather than manufacturer's suggestion or specified standard for particular electrical device. Now a days, thermographic scanning is being used in Readymade Garments (RMG) industries in Bangladesh in such simple way where details analysis are not in practice and hence exact recommendation is absent in reports. The importance of this topic is focused on the 4.2 million people working in more than 5,000 factories in Bangladesh, where 80% of the fire incident are caused by inappropriate electrical installation or short circuits (as per Fire Service and Civil Defense statistics of Bangladesh). Recently many violent industrial accidents caused death of more than 1,500 people in the last 3 years. Among these, majority of the incidents were due to unsafe electrical system and inappropriate maintenance. The paper will further explore the importance of thermal scanning on electrical devices and compare the temperature considering manufacturer's recommendation for identifying system errors. The paper is written based on previously published recommendations and based on conducting an experimental sample audit aimed to develop a thermography based auditing standard. Based on the statistical and systematic analysis of the obtained data (e.g. temperature values, visual abnormalities, comparative differences) the paper will further discuss the possible area(s) that can be improved by introducing the thermal reports for the safety audits. This paper is aimed to develop an auditing structure proposed to implement identified areas within a standard electrical safety audit process.
Description:
Multiple unit of PFC converters can be utilized for better power quality in the residential applications. It is important to set proper amount of reactive power contribution from each unit of PFC converter to reduce power loss and increase stability. To set the referenced amount of reactive power contribution for each PFC converter, a dynamic supervisory controller is necessary. In this paper, a wireless based supervisory controller is introduced to mitigate the problem. Multiple unit of unidirectional bridgeless ac-dc boost PFC converters were used as reactive power resources in a smart home application. The supervisory controller is comprised of a discrete Proportional-Integral controller and distributor which sets the reactive power references for each PFC converter. The controller was designed based on the systems identification method. The controller was designed and simulated considering different communication delays. In the ideal condition, the system had no communication delay. However, actual practical system had some reasonable delay. The effect of the use of supervisory controller was observed for dynamic load changing conditions. The simulation was done by using SIMULINK. Reactive power consumption from grid becomes 0VAR within 0.25 second for ideal condition. However, for the actual condition this become 9.35 seconds.
Description:
A smart grid is an electricity transmission and distribution network that embeds digital and other advanced ICT technologies to sense, monitor, communicate and manage the energy flows, real time electricity asset management, and take online decision from all generation sources to meet the varying electricity demands of end-users. Smart Grids oversees the real time capabilities of the generating, transmitting, distribution assets and allows the power system operators manage the balance between generation and load requirements at most efficient manner. Smart Grid is aimed at maintaining system resiliency, stability, reliability to regulatory standards and at the same time minimises the environmental impact by allowing maximum renewable power generation connected to the grid. The role of modern digital devices and IT technologies are critical to achieve the above objectives. In this paper, some of the digital technology applications in Demand Side Management, Automated Tariff Structure, and Protection of modern substation are presented.
Description:
Wind power generation and Solar PV installations continues to grow and are the two key contributors to the large scale variable power generation mix. Utility transmission system operators impose stringent grid codes internationally. Grid code regulations are defined by system operators to outline the rights and responsibilities of all the generators and loads that are connected to the transmission/distribution system. In the past, grid codes did not include any regulations for Wind Power Plants (WPPs) and PV plants as the penetration levels were small. However, the situation has now radically changed as many countries have installed many GW worth of variable power generations. The increased reliance on renewable power generation has raised some serious concerns regarding the negative impacts of large WPPs and PV on the stability of power networks. Modern grid codes require WPPS and PVs not only withstand various grid disturbances but also to contribute to network stability support and ancillary services provision, as do conventional generating units. Solar shading can cause PV output to drop by as much as 70% in a space of few seconds. This requires strategies for solar smoothing. In this plenary paper, status review, research challenges and opportunities in developing enabling technologies in grid code compliances, solar smoothing, storage and congestion management to maximise renewable energy penetration into the grid are presented.
Description:
Conventional power converters control pulse width modulation (PWM) signals based on the directly sensed feedback signals or estimated state signals. Recently, information and communication technologies are beginning to integrate power converters into smart grid applications. This paper presents a Hardware-In-The-Loop Testbed using a WirelessHART network for smart home applications in a case study. The main components of the testbed are OP4510 RT-LAB-RCP/HIL systems, Texas Instruments Digital Signal Controllers and AwiaTech wireless modules. In this testbed, wireless based controller is integrated with conventional digital controller. OP4510 is used to simulate power stages of the power converters in real time. Texas Instruments Digital Signal Controllers provide PWM signals from the control loop and communication loop. AwiaTech wireless modules are used to build a WirelessHART network to communicate with power converter from a supervisory program. The case study is to support reactive power in smart home applications using 3 bridgeless unidirectional power factor correction (PFC) converters. For this scenario, supervisor collects reactive power measurement data from the input power node and wirelessly transfers reactive power command values to the home appliances driven by PFC converters. Thus, reactive power demand can be minimized. The experimental result demonstrates the performance of the testbed in different communication delays.
Description:
Obstructive Sleep Apnoea (OSA), a sleeping disorder, is a serious health issue with significant public health implications. Due to the interrupted sleep, OSA patients suffer with excessive day-time sleepiness, fatigue and other health complexities that lead to on-road and work-related accidents and incur billions of dollars per year. Traditionally, treatment of OSA begins with nasal continuous positive airway pressure (CPAP). Alternatively, Mandibular Repositioning Appliances or surgical interventions can be used. Although Maxillomandibular Advancement (MMA) surgery is often advised as the last line of treatment due to the expense and significant changes in the facial appearance, it is the only permanent solution to OSA with a definitive outcome especially for the patients with significant facial deformation or anomalies. In this article, three dimensional (3D) image-guided predictive algorithms are proposed to improve the treatment planning and overall outcome of the MMA surgery. 3D analysis of the facial surface data and Computational Biomechanics-based 3D modelling of airway segmented from Cone Beam Computed Tomography (CBCT) data are proposed to predict the required physiological changes to ensure optimal air-flow through the airway. Moreover, 3D Computer Graphics-based techniques are proposed to visualise and demonstrate the expected facial outcomes to inform patients and surgeons prior to this non-reversible surgery.
Description:
A nonlinear differential equation with delay serving as a mathematical model of several applied problmes is considered. Sufficient conditions for the global asymptotic stability and for the existence of periodic solutions are given. Two particular applications are treated in detail. The first one is a blood cell production model by Mackey, for which new periodicity criteria are derived. The second application is a modified economic model with delay due to Ramsay. An optimization problem for a maximal consumption is stated and solved for the latter.
Description:
A nonlinear differential equation with delay serving as a mathematical model of several applied problmes is considered. Sufficient conditions for the global asymptotic stability and for the existence of periodic solutions are given. Two particular applications are treated in detail. The first one is a blood cell production model by Mackey, for which new periodicity criteria are derived. The second application is a modified economic model with delay due to Ramsay. An optimization problem for a maximal consumption is stated and solved for the latter.
Description:
Contemporary healthcare has witnessed a wide deployment of Implantable Cardioverter Defibrillators (ICDs), which have the capability to be controlled remotely, making them equally accessible from both home and hospitals. The therapeutic benefits of ICDs seem to outweigh potential security concerns, yet overlooking the presence of malicious attacks cannot be justified. This study investigates the scenario where an adversary falsifies a controller command and sends instructions to issue high electric shocks in succession. We propose a novel security hardening mechanism to protect data communications between ICD and controller from malicious data manipulations. Our proposed method verifies the correctness of an external command with respect to the history of heart rhythms. The proposed method is evaluated using real data. Multi-aspect analyses show the effectiveness of the proposed scheme.
Description:
Proceedings of the IEEE International Conference on Industrial Technology
Description:
Contemporary healthcare has witnessed a wide deployment of Implantable Cardioverter Defibrillators (ICDs), which have the capability to be controlled remotely, making them equally accessible from both home and hospitals. The therapeutic benefits of ICDs seem to outweigh potential security concerns, yet overlooking the presence of malicious attacks cannot be justified. This study investigates the scenario where an adversary falsifies a controller command and sends instructions to issue high electric shocks in succession. We propose a novel security hardening mechanism to protect data communications between ICD and controller from malicious data manipulations. Our proposed method verifies the correctness of an external command with respect to the history of heart rhythms. The proposed method is evaluated using real data. Multi-aspect analyses show the effectiveness of the proposed scheme.
Description:
Proceedings of the IEEE International Conference on Industrial Technology
Description:
This paper presents a hardware design for a Travelling Wave (TW) Detector and data recorder for a three phase Medium Voltage (MV) distribution network. The proposed pole mounted platform consists of a capacitively coupled receiver system, a GPS receiver and a Texas Instruments Delfino 28377 processor based travelling wave detection unit. The data recording system uses an Intel Atom base single board computer, a four channel 10Ms/s analogue to digital converter card along with Wi-Fi and GORS communications links. The proposed system is capable of recording three phase voltages simultaneously with the ability to trigger remotely. The platform is mounted in an IP56 enclosure and can be mounted on the MV distribution poles. The paper provides a brief review of hardware and software developed for the TW detector.