This article describes a framework for load shedding techniques using dynamic pricing and multi-agent system. The islanded microgrid uses solar panels and battery energy management system as a source of energy to serve remote communities who have no access to the grid with a randomized type of power in terms of individual load. The generated framework includes modeling of solar panels, battery storage and loads to optimize the energy usage and reduce the electricity bills. In this work, the loads are classified as critical and non-critical. The agents are designed in a decentralized manner, which includes solar agent, storage agent and load agent. The load shedding experiment of the framework is mapped with the manual operation done at Kisiju village, Pwani, Tanzania. Experiment results show that the use of pricing factor as a demand response makes the microgrid sustainable as it manages to control and monitor its supply and demand, hence, the load being capable of shedding its own appliances when the power supplied is not enough. 

In this paper a novel, compact 10-bit chipless radio frequency identification (RFID) tag with stable readable characteristics is proposed. The tag is composed of several concentric novel kite-shaped loop resonators. The desired data sequence is obtained by the insertion or removal of nested elements, offering a 1:1 data bit to resonance correspondence. Each data sequence is encoded in the spectral domain of the tag. The proposed tag shows operability at different polarizations and incident angles of the impinging electromagnetic waves. Operating within the ultra-wideband (UWB), the tag is realized on the Roger RT/duroid^® 5880 substrate having physical dimensions of 13.55 × 13.55 mm². The measurement results of the design reveal a high code density and spectral efficiency of 5.44 bits/cm² and 2 bits/GHz, respectively. The compact and robust passive chipless RFID tag having scalable data storage potential can be deployed in numerous low-profile applications such as those of wearable and healthcare.

Newly emerged dynamic vision sensors (DVS) offer a great potential over traditional sensors (e.g. CMOS) since they have a high temporal resolution in the order of μs, ultra-low power consumption and high dynamic range up to 140 dB compared to 60 dB in frame cameras. Unlike traditional cameras, the output of DVS cameras is a stream of events that encodes the location of the pixel, time, and polarity of the brightness change. An event is triggered when the change of brightness, i.e. log intensity, of a pixel exceeds a certain threshold. The output of event cameras often contains a significant amount of noise (outlier events) alongside the signal (inlier events). The main cause of that is transistor switch leakage and noise. This paper presents a dynamic background activity filtering, called DBA-filter, for event cameras based on an adaptation of the K-nearest neighbor (KNN) algorithm and the optical flow. Results show that the proposed algorithm is able to achieve a high signal to noise ratio up to 13.64 dB. 

The real-time recognition of pain level is required to perform an accurate pain assessment of patients in the intensive care unit, infants, and other subjects who may not be able to communicate verbally or even express the sensation of pain. Facial expression is a key pain-related behavior that may unlock the answer to an objective pain measurement tool. In this work, a machine learning-based pain level classification system using data collected from facial electromyograms (EMG) is presented. The dataset was acquired from part of the BioVid Heat Pain database to evaluate facial expression from an EMG corrugator and EMG zygomaticus and an EMG signal processing and data analysis flow is adapted for continuous pain estimation. The extracted pain-associated facial electromyography (fEMG) features classification is performed by K-nearest neighbor (KNN) by choosing the value of k which depends on the nonlinear models. The presentation of the accuracy estimation is performed, and considerable growth in classification accuracy is noticed when the subject matter from the features is omitted from the analysis. The ML algorithm for the classification of the amount of pain experienced by patients could deliver valuable evidence for health care providers and aid treatment assessment. The proposed classification algorithm has achieved a 99.4% accuracy for classifying the pain tolerance level from the baseline (P0 versus P4) without the influence of a subject bias. Moreover, the result on the classification accuracy clearly shows the relevance of the proposed approac

The availability of secure, efficient, and reliable communication systems is critical for the successful deployment and operations of new power systems such as microgrids. These systems provide a platform for implementing intelligent and autonomous algorithms that improve the power control process. However, building a secure communication system for microgrid purposes that is also efficient and reliable remains a challenge. Conventional security mechanisms introduce extra processing steps that affect performance by increasing the latency of microgrid communication beyond acceptable limits. They also do not scale well and can impact the reliability of power operations as the size of a microgrid grows. This paper proposes a low latency secure communication architecture for control operations in an islanded IoT-based microgrid that solves these problems. The architecture provides a secure platform that optimises the standard CoAP/DTLS implementation to reduce communication latency. It also introduces a traffic scheduler component that uses a fixed priority preemptive algorithm to ensure reliability as the microgrid scales up. The architecture is implemented on a lab-scale IoT-based microgrid prototype to test for performance and security. Results show that the proposed architecture can mitigate the main security threats and provide security services necessary for power control operations with minimal latency performance. Compared to other implementations using existing secure IoT protocols, our secure architecture was the only one to satisfy and maintain the recommended latency requirements for power control operations, i.e., 100 ms under all conditions.

While the effectiveness of fog computing in Internet of Things (IoT) applications has been widely investigated in various studies, there is still a lack of techniques to efficiently utilize the computing resources in a fog platform to maximize Quality of Service (QoS) and Quality of Experience (QoE). This paper presents a resource management model for service placement of distributed multitasking applications in fog computing through mathematical modeling of such a platform. Our main design goal is to reduce communication between the candidate nodes hosting different task modules of an application by selecting a group of nodes near each other and as close to the source of the data as possible. We propose a method based on a greedy principle that demonstrates a highly scalable and near-optimal performance for resource mapping problems for multitasking applications in fog computing networks. Compared with the commercial Gurobi optimizer, our proposed algorithm provides a mapping solution that obtains 93% of the performance, attributed to a higher communication cost, while outperforming the reference method in terms of the computing speed, cutting the mapping execution time to less than 1% of that of the Gurobi optimizer.

An electronically beam-steerable antenna (BSA) is envisioned. The presented BSA is a possible solution to overthrow the limitations inherent to phased antenna arrays. The design consists of a gap coupling inset feed rectangular patch (driven element) and 3 x 1 passive parasitic patches deployed on both sides of the driven patch. Prototype having 20 x 20 mm dimensions is printed on Rogers(R) RT/duroid(R)5870. Four switches are used to load the reactive impedance on parasitic patches, which in turn, change the phases of surface current on parasitic elements and the driven element. Based on the different ON and OFF configuration of switches in parasitic array elements, the main beam is steered along with different directions. The simulated results show that the design can operate between 26.8 and 30.3 GHz a wide impedance bandwidth |S-11|< -10 dB (12.5%) with a peak gain of 8.9 dBi and wide 3-dB scanning angle that is, -37 degrees to 156 degrees in the azimuth plane. The exhibited performance of BSA with favourable characteristics, such as wideband, adequate gain, wide-angle beam switching, and low profile renders the BSA a good candidate for 5G millimetre wave handheld devices. Moreover, to corroborate the performance, the design is fabricated, and experimental measurements were performed. Congruence is observed between the experimentally measured and computationally simulated results. The simulated results of spherical coverage analysis of BSA with the integration of smartphone form factor are also presented.

A nested octagon passive chipless radio frequency identification (RFID) tag with an encoding capacity of 12 bits has polarization diversity and 1:1 slot-to-bit correspondence. Its compact 23 x 23 mm footprint yields a bit density of 2.26 bits/cm(2). The tag is fabricated on a flexible Rogers RT/duroid 5880 laminate.

This work focuses on the formation reshaping in an optimized manner in autonomous swarm of drones. Here, the two main problems are: 1) how to break and reshape the initial formation in an optimal manner, and 2) how to do such reformation while minimizing the overall deviation of the drones and the overall time, i.e. without slowing down. To address the first problem, we introduce a set of routines for the drones/agents to follow while reshaping to a secondary formation shape. And the second problem is resolved by utilizing the temperature function reduction technique, originally used in the point set registration process. The goal is to be able to dynamically reform the shape of multi-agent based swarm in near-optimal manner while going through narrow openings between, for instance obstacles, and then bringing the agents back to their original shape after passing through the narrow passage using point set registration technique.

The energy sector is experiencing a revolution that is fuelled by a multitude of factors. Among them are the aging grid system, the need for cleaner energy and the increasing demands on energy sector. The demand-response program is an advanced feature in smart grid that strives to match suppliers to their demands using price-based and incentive programs. The objective of the work is to analyse the performance of the load shedding technique using dynamic pricing algorithm. The system was designed using multi-agent system (MAS) for a DC microgrid capable of real-time monitoring and controlling of power using price-based demand-response program. As a proof of concept, the system was implemented using intelligent physical agents, Java Agent Development Framework (JADE), and agent simulation platform (REPAST) with two residential houses (non-critical loads) and one hospital (critical load). The architecture has been implemented using embedded devices, relays, and sensors to control the operations of load shedding and energy trading in residential areas that have no access to electricity. The measured results show that the system can shed the load with the latency of less than 600 ms, and energy cost saving with an individual houses by 80% of the total cost with 2USD per day. The outcome of the studies demonstrates the effectiveness of the proposed multi-agent approach for real-time operation of a microgrid and the implementation of demand-response program.