Cultural heritage sites are exposed to several factors that cause their deterioration and degradation, namely moisture content. There are several destructive and non-destructive methods available to monitor moisture. However, destructive methods are avoided in cultural heritage to prevent surface damage, while non-destructive methods are limited to penetration capabilities. This study proposed geo-positioning of non-destructive moisture assessment based on microwave spectroscopy and close-range photogrammetry. The experimental data were collected at the Museum of King Jan III's Palace at Wilan ' ow, Poland. The data was analysed using clustering algorithms (t-SNE, PCA, K-Means and Hierarchical), which demonstrated clear clusters. However, the microwave results did not fully align with the pin-type moisture data as the proposed microwave system was able to penetrate through the material, whereas the pin-type meter only measured the surface. Therefore, the microwave sensing approach provided more detailed moisture information of the measured material.

This paper proposes an algorithm that enhances horizontal handover (HO) in dense wireless local area networks (WLANs), which is implemented in a software-defined wireless networking (SDWN)-based architecture. The algorithm considers the concept of user prioritisation, classifying the WLAN stations (STAs) into two categories representing high and low priorities respectively, and always attempts to guarantee the best quality of experience (QoE) to the high priority users. The architecture that implements the algorithm leverages the flexibility, programmability, and centralised nature of SDWN to efficiently manage the HO process. Moreover, the paper presents a performance evaluation campaign that demonstrates significant achievements against a state-of-the-art solution in terms of the provided QoE, throughput and delay. Finally, we discuss the importance of considering user prioritisation in a HO algorithm for dense WLAN s.

A smart grid ecosystem requires intelligent Home Energy Management Systems (HEMSs) that allow the adequate monitoring and control of appliance-level energy consumption in a given household. They should be able to: i) profile highly non-stationary and non-linear measurements and ii) conduct correlations of such measurements with diverse inputs (e.g. environmental factors) in order to improve the end-user experience, as well as to aid the overall demand-response optimisation process. However, traditional approaches in HEMS lack the ability to capture diverse variations in appliance-level energy consumption due to unpredictable human behavior and also require high computation to process large datasets. In this article, we go beyond current profiling schemes by proposing Deep COLA; a novel Deep COmpetitive Learning Algorithm that addresses the limitations of existing work in terms of high dimensional data and enables more efficient and accurate clustering of appliance-level energy consumption. The proposed approach reduces human intervention by automatically selecting load profiles and models variations and uncertainty in human behavior during appliance usage. We demonstrate that our proposed scheme is far more computationally efficient and scalable data-wise than three popular conventional clustering approaches namely, K-Means, DBSCAN and SOM, using real household datasets. Moreover, we exhibit that Deep COLA identifies per-household behavioral associations that could aid future HEMSs.

Today, there is a lot of enthusiasm to fulfil global energy needs from alternative energy resources. Due to the increasing demand for electricity, the traditional electricity market relies on decisions to plan electricity systems, and to generate and distribute electricity to their consumers to balance demand and supply. The peak demands of electricity highly affect these decisions and often cause system failure and shortage of electricity. By predicting energy requirements, these peak demands and the uncertainties in human behaviour in households are optimised to balance the load through various demand response programmes. A smart grid ecosystem requires intelligent Home Energy Management Systems (HEMSs) to profile highly non-stationary and non-linear measurements and conduct correlations of such measurements with diverse inputs (e.g. environmental factors) in order to improve the end-user experience, as well as to aid the overall demand-response optimisation process. The huge amount of energy consumption information collected from the individual appliances opens up lots of opportunities to mine the hidden patterns in the data in order to understand the human behaviour related to energy usage. However, processing huge amounts of information for analysis purposes demands lots of resources e.g. time and computational power. Parallelisation techniques allow the processing of large amounts of data while requiring less computational time. However, neural networks widely used in data processing are highly complex to parallelise during the model parallelisation due to their sequential nature. A key challenge here is to exploit the parallelisation capabilities of hardware as well as software in terms of multicore/multithreaded CPUs and GPUs (Graphical Processing Units). To overcome these challenges, in this research work, we propose a new unified approach to predict day, week and month-wide energy consumption by reducing the computation resources and model human behaviour in households in order to save scarce energy resources and improve demand response programmes. We go beyond current profiling schemes by proposing Deep COLA; a Deep Competitive Learning Algorithm that addresses limitations of high dimensional data and enables accurate modelling of appliance-level energy consumption. We show that our proposed scheme is far more computationally efficient and scalable data-wise than three conventional clustering approaches namely, K-Means, DBSCAN and SOM, using real household datasets. This research work includes a number of contributions. The first is a dominant feature selection algorithm from the pool of features to increase the performance of the forecasting model. The second is a prediction model based on deep learning algorithms to improve the forecasting accuracy and processing time. The third is a clustering algorithm based on competitive learning to profile day, week and month-wide energy consumption patterns, using appliance-level data for a given household. The current methods are based on K-Means, DBSCAN and rule mining which require expert knowledge to get improved clustering. However, the proposed concept of competitive learning allows to extract compact and well separated clusters. This approach automatically extracts the optimal number of clusters without using Elbow, Silhouette or Bootstrap methods commonly seen in the existing work. The fourth is the profiling of appliance-level energy consumption in synergy with environmental factors in order to reveal per-household behavioural characteristics under three associations: appliance-to-appliance, appliance-to-time and appliance-to-environment. The last one is a parallelisation approach in the form of data parallelisation to forecast energy consumption by utilizing large amounts of data.

In this paper, we have evaluated the performance of heuristic algorithms: Genetic Algorithm (GA) and Artificial Fish Swarm Algorithm (AFSA) for Demand Side Management. Our prime focus in this paper, is to optimally schedule appliances in a smart home in such a way that the Peak to Average Ratio (PAR) and the electricity cost can be reduced. The pricing scheme used in this paper is real time pricing. Our Simulation results validate that the two nature inspired schemes successfully reduce PAR and electricity cost by transferring load of on peak hours to off peak hours. Our results also depict a trade off between electricity cost and comfort of a user.

Accurate load forecasting greatly influences the planning processes undertaken in operation centres of energy providers that relate to the actual electricity generation, distribution, system maintenance as well as electricity pricing. This paper exploits the applicability of and compares the performance of the Feed-forward Deep Neural Network (FF-DNN) and Recurrent Deep Neural Network (R-DNN) models on the basis of accuracy and computational performance in the context of time-wise short term forecast of electricity load. The herein proposed method is evaluated over real datasets gathered in a period of 4 years and provides forecasts on the basis of days and weeks ahead. The contribution behind this work lies with the utilisation of a time-frequency (TF) feature selection procedure from the actual "raw" dataset that aids the regression procedure initiated by the aforementioned DNNs. We show that the introduced scheme may adequately learn hidden patterns and accurately determine the short-term load consumption forecast by utilising a range of heterogeneous sources of input that relate not necessarily with the measurement of load itself but also with other parameters such as the effects of weather, time, holidays, lagged electricity load and its distribution over the period. Overall, our generated outcomes reveal that the synergistic use of TF feature analysis with DNNs enables to obtain higher accuracy by capturing dominant factors that affect electricity consumption patterns and can surely contribute significantly in next generation power systems and the recently introduced SmartGrid.

in today's modern world, a simple malware attack can result catastrophically and can cause havoc. In spite of numerous types of antiviruses available in the market, there is a dearth in detection techniques of these antiviruses. This paper proposes a complete system, which is a combination of conventional and new techniques for detecting malware. We first evaluate the antiviruses against 10,000+ malware samples to highlight their weaknesses and then propose, implement, and benchmark the cloud-based system against some defined parameters. We have tested the effectiveness and efficiency of the proposed system by monitoring the detection rate and processing power it consumes in order to operate in a host machine.