Modelling and Analysis of Smart Grids for Critical Data Communication

Practical models for the subnetworks of smart grid are presented and analyzed. Critical packet-delay bounds for these subnetworks are determined, with the overall objective of identifying parameters that would help in the design of smart grid with least end-to-end delay. A single-server non-preemptive queueing model with prioritized critical packets is presented for Home Area Network (HAN). Closed-form expressions for critical packet delay are derived and illustrated as a function of: i) critical packet arrival rate, ii) service rate, iii) utilization factor, and iv) rate of arrival of non-critical packets. Next, wireless HANs using FDMA and TDMA are presented. Upper and lower bounds on critical packet delay are derived in closed-form as functions of: i) average of signal-to interference-plus-noise ratio, ii) random channel scale, iii) transmitted power strength, iv) received power strength, v) number of EDs, vi) critical packet size, vii) number of channels, viii) path loss component, ix) distances between electrical devices and mesh client, x) channel interference range, xi) channel capacity, xii) bandwidth of the channel, and xiii) number of time/frequency slots. Analytical and simulation results show that critical packet delay is smaller for TDMA compared to FDMA. Lastly, an Intelligent Distributed Channel-Aware Medium Access Control (IDCA-MAC) protocol for wireless HAN using Distributed Coordination Function (DCF) is presented. The protocol eliminates collision and employs Multiple Input Multiple Output (MIMO) system to enhance system performance. Simulation results show that critical packet delay can be reduced by nearly 20% using MA-Aware protocol compared to IDCA-MAC protocol. However, the latter is superior in terms throughput. A wireless mesh backbone network model for Neighbourhood Area Network (NAN) is presented for forwarding critical packets received from HAN to an identified gateway. The routing suggested is based on selected shortest path using Voronoi tessellation. CSMA/CA and CDMA protocols are considered and closed{form upper and lower bounds on critical packet delay are derived and examined as functions of i) signal-to-noise ratio, ii) signal interference, iii) critical packet size, iv) number of channels, v) channel interference range, vi) path loss components, vii) channel bandwidth, and viii) distance between MRs. The results show that critical packet delay to gateway using CDMA is lower compared to CSMA/CA protocol. A fiber optic Wide Area Network (WAN) is presented for transporting critical packets received from NAN to a control station. A Dynamic Fastest Routing Strategy (DFRS) algorithm is used for routing critical packets to control station. Closed-form expression for mean critical packet delay is derived and is examined as a function of: i) traffic intensity, ii) capacity of fiber links, iii) number of links, iv) variance of inter-arrival time, v) variance of service time, and vi) the latency of links. It is shown that delay of critical packets to control station meets acceptable standards set for smart grid

Perceptions of Existing Wearable Robotic Devices for Upper Extremity and Suggestions for Their Development: Findings From Therapists and People With Stroke

Background: Advances in wearable robotic technologies have increased the potential of these devices for rehabilitation and as assistive devices. However, the utilization of these devices is still limited and there are questions regarding how well these devices address users’ (therapists and patients) needs. Objective: The aims of this study were to (1) describe users’ perceptions about existing wearable robotic devices for the upper extremity; (2) identify if there is a need to develop new devices for the upper extremity and the desired features; and (3) explore obstacles that would influence the utilization of these new devices. Methods: Focus groups were held to collect data. Data were analyzed thematically. Results: A total of 16 participants took part in the focus group discussions. Our analysis identified three main themes: (1) “They exist, but...” described participants’ perceptions about existing devices for upper extremity; (2) “Indeed, we need more, can we have it all?” reflected participants’ desire to have new devices for the upper extremity and revealed heterogeneity among different participants; and (3) “Bumps on the road” identified challenges that the participants felt needed to be taken into consideration during the development of these devices. Conclusions: This study resonates with previous research that has highlighted the importance of involving end users in the design process. The study suggests that having a single solution for stroke rehabilitation or assistance could be challenging or even impossible, and thus, engineers should clearly identify the targeted stroke population needs before the design of any device for the upper extremity

Energy Disaggregation using Two-Stage Fusion of Binary Device Detectors

A data-driven methodology to improve the energy disaggregation accuracy during Non-Intrusive Load Monitoring is proposed. In detail, the method is using a two-stage classification scheme, with the first stage consisting of classification models processing the aggregated signal in parallel and each of them producing a binary device detection score, and the second stage consisting of fusion regression models for estimating the power consumption for each of the electrical appliances. The accuracy of the proposed approach was tested on three datasets (ECO, REDD and iAWE), which are available online, using four different classifiers. The presented approach improves the estimation accuracy by up to 4.1% with respect to a basic energy disaggregation architecture, while the improvement on device level was up to 10.1%. Analysis on device level showed significant improvement of power consumption estimation accuracy especially for continuous and non-linear appliances across all evaluated datasets

Digital Filter Design Using Improved Artificial Bee Colony Algorithms

Digital filters are often used in digital signal processing applications. The design objective of a digital filter is to find the optimal set of filter coefficients, which satisfies the desired specifications of magnitude and group delay responses. Evolutionary algorithms are population-based meta-heuristic algorithms inspired by the biological behaviors of species. Compared to gradient-based optimization algorithms such as steepest descent and Newton’s like methods, these bio-inspired algorithms have the advantages of not getting stuck at local optima and being independent of the starting point in the solution space. The limitations of evolutionary algorithms include the presence of control parameters, problem specific tuning procedure, premature convergence and slower convergence rate. The artificial bee colony (ABC) algorithm is a swarm-based search meta-heuristic algorithm inspired by the foraging behaviors of honey bee colonies, with the benefit of a relatively fewer control parameters. In its original form, the ABC algorithm has certain limitations such as low convergence rate, and insufficient balance between exploration and exploitation in the search equations. In this dissertation, an ABC-AMR algorithm is proposed by incorporating an adaptive modification rate (AMR) into the original ABC algorithm to increase convergence rate by adjusting the balance between exploration and exploitation in the search equations through an adaptive determination of the number of parameters to be updated in every iteration. A constrained ABC-AMR algorithm is also developed for solving constrained optimization problems.There are many real-world problems requiring simultaneous optimizations of more than one conflicting objectives. Multiobjective (MO) optimization produces a set of feasible solutions called the Pareto front instead of a single optimum solution. For multiobjective optimization, if a decision maker’s preferences can be incorporated during the optimization process, the search process can be confined to the region of interest instead of searching the entire region. In this dissertation, two algorithms are developed for such incorporation. The first one is a reference-point-based MOABC algorithm in which a decision maker’s preferences are included in the optimization process as the reference point. The second one is a physical-programming-based MOABC algorithm in which physical programming is used for setting the region of interest of a decision maker. In this dissertation, the four developed algorithms are applied to solve digital filter design problems. The ABC-AMR algorithm is used to design Types 3 and 4 linear phase FIR differentiators, and the results are compared to those obtained by the original ABC algorithm, three improved ABC algorithms, and the Parks-McClellan algorithm. The constrained ABC-AMR algorithm is applied to the design of sparse Type 1 linear phase FIR filters of filter orders 60, 70 and 80, and the results are compared to three state-of-the-art design methods. The reference-point-based multiobjective ABC algorithm is used to design of asymmetric lowpass, highpass, bandpass and bandstop FIR filters, and the results are compared to those obtained by the preference-based multiobjective differential evolution algorithm. The physical-programming-based multiobjective ABC algorithm is used to design IIR lowpass, highpass and bandpass filters, and the results are compared to three state-of-the-art design methods. Based on the obtained design results, the four design algorithms are shown to be competitive as compared to the state-of-the-art design methods

Open source SCADA systems for small renewable power generation

Low cost monitoring and control is essential for small renewable power systems. While large renewable power systems can use existing commercial technology for monitoring and control, that is not cost-effective for small renewable generation. Such small assets require cost-effective, flexible, secure, and reliable real-time coordinated data monitoring and control systems. Supervisory control and data acquisition (SCADA) is the perfect technology for this task. The available commercial SCADA solutions are mostly pricey and economically unjustifiable for smaller applications. They also pose interoperability issues with the existing components which are often from multiple vendors. Therefore, an open source SCADA system represents the most flexible and the most cost-effective SCADA solution. This thesis has been done in two phases. The first phase demonstrates the design and dynamic simulation of a small hybrid power system with a renewable power generation system as a case study. In the second phase, after an extensive study of the proven commercial SCADA solutions and some open source SCADA packages, three different secure, reliable, low-cost open source SCADA options are developed using the most recent SCADA architecture, the Internet of Things. The implemented prototypes of the three open source SCADA systems were tested extensively with a small renewable power system (a solar PV system). The results show that the developed open source SCADA systems perform optimally and accurately, and could serve as viable options for smaller applications such as renewable generation that cannot afford commercial SCADA solutions

Printed Ridge Gap Waveguide 3-dB Coupler: Analysis and Design Procedure

Communication systems are witnessing an outstanding revolution that has a clear impact on all aspects of life. The world technology is drifting towards high frequency and data rate solutions to accommodate the future expansion in applications such as 5G communications. The 5G technology will offer advanced features in the mm-Wave frequency band which requires intelligent subsystems such as beam switching. Therefore, the microwave components, especially couplers, still need a significant improvement to follow the rapid variations in future technologies. One of the most recent and promising guiding technologies for mm-Wave applications is the printed ridge gap waveguide (PRGW). In this paper, a design of 3-dB planar quadrature hybrid coupler based on PRGW is presented. The proposed design has superior characteristics such as compactness, low loss, and low dispersion device. The prototype of the proposed coupler is fabricated and tested, where the measured and simulated results show an excellent agreement

Digital Filter Design Using Improved Teaching-Learning-Based Optimization

Digital filters are an important part of digital signal processing systems. Digital filters are divided into finite impulse response (FIR) digital filters and infinite impulse response (IIR) digital filters according to the length of their impulse responses. An FIR digital filter is easier to implement than an IIR digital filter because of its linear phase and stability properties. In terms of the stability of an IIR digital filter, the poles generated in the denominator are subject to stability constraints. In addition, a digital filter can be categorized as one-dimensional or multi-dimensional digital filters according to the dimensions of the signal to be processed. However, for the design of IIR digital filters, traditional design methods have the disadvantages of easy to fall into a local optimum and slow convergence. The Teaching-Learning-Based optimization (TLBO) algorithm has been proven beneficial in a wide range of engineering applications. To this end, this dissertation focusses on using TLBO and its improved algorithms to design five types of digital filters, which include linear phase FIR digital filters, multiobjective general FIR digital filters, multiobjective IIR digital filters, two-dimensional (2-D) linear phase FIR digital filters, and 2-D nonlinear phase FIR digital filters. Among them, linear phase FIR digital filters, 2-D linear phase FIR digital filters, and 2-D nonlinear phase FIR digital filters use single-objective type of TLBO algorithms to optimize; multiobjective general FIR digital filters use multiobjective non-dominated TLBO (MOTLBO) algorithm to optimize; and multiobjective IIR digital filters use MOTLBO with Euclidean distance to optimize. The design results of the five types of filter designs are compared to those obtained by other state-of-the-art design methods. In this dissertation, two major improvements are proposed to enhance the performance of the standard TLBO algorithm. The first improvement is to apply a gradient-based learning to replace the TLBO learner phase to reduce approximation error(s) and CPU time without sacrificing design accuracy for linear phase FIR digital filter design. The second improvement is to incorporate Manhattan distance to simplify the procedure of the multiobjective non-dominated TLBO (MOTLBO) algorithm for general FIR digital filter design. The design results obtained by the two improvements have demonstrated their efficiency and effectiveness

A Review on Detection of Medical Plant Images

Both human and non-human life on Earth depends heavily on plants. The natural cycle is most significantly influenced by plants. Because of the sophistication of recent plant discoveries and the computerization of plants, plant identification is particularly challenging in biology and agriculture. There are a variety of reasons why automatic plant classification systems must be put into place, including instruction, resource evaluation, and environmental protection. It is thought that the leaves of medicinal plants are what distinguishes them. It is an interesting goal to identify the species of plant automatically using the photo identity of their leaves because taxonomists are undertrained and biodiversity is quickly vanishing in the current environment. Due to the need for mass production, these plants must be identified immediately. The physical and emotional health of people must be taken into consideration when developing drugs. To important processing of medical herbs is to identify and classify. Since there aren't many specialists in this field, it might be difficult to correctly identify and categorize medicinal plants. Therefore, a fully automated approach is optimal for identifying medicinal plants. The numerous means for categorizing medicinal plants that take into interpretation based on the silhouette and roughness of a plant's leaf are briefly précised in this article

Harvesting-aware energy management for environmental monitoring WSN

Wireless sensor networks can be used to collect data in remote locations, especially when energy harvesting is used to extend the lifetime of individual nodes. However, in order to use the collected energy most effectively, its consumption must be managed. In this work, forecasts of diurnal solar energies were made based on measurements of atmospheric pressure. These forecasts were used as part of an adaptive duty cycling scheme for node level energy management. This management was realized with a fuzzy logic controller that has been tuned using differential evolution. Controllers were created using one and two days of energy forecasts, then simulated in software. These controllers outperformed a human-created reference controller by taking more measurements while using less reserve energy during the simulated period. The energy forecasts were comparable to other available methods, while the method of tuning the fuzzy controller improved overall node performance. The combination of the two is a promising method of energy management.Web of Science105art. no. 60