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Topic Review
Reinforcement Learning in Home Energy Management System
Artificial Intelligence, more specifically machine learning, is one of the key contributing factors that have helped realize Home Energy Management Systems today. Reinforcement Learning is a class of machine learning algorithms that is making deep inroads in various applications in energy management in smart homes. This machine learning paradigm allows an algorithmic entity, called an agent, to make sequences of decisions and implement actions from experience in the same manner as a human being. Reinforcement learning is increasingly being used in various home energy management applications today. This article surveys the application of this learning paradigm in smart home energy management. Applications have been divided into five broad categories, and the reinforcement learning approach applied in each case have been investigated separately, in terms of building types, objective functions, and algorithm classes.
  • 967
  • 14 Sep 2022
Topic Review
Smart Materials
The introduction of smart materials (SMs) will become increasingly relevant as biomedical technologies progress. Smart materials sense and respond to external stimuli (e.g., chemical, electrical, mechanical, or magnetic signals) or environmental circumstances (e.g., temperature, illuminance, acidity, or humidity), and provide versatile platforms for studying various biological processes because of the numerous analogies between smart materials and biological systems. 
  • 967
  • 07 Jul 2023
Topic Review
Peer-to-Peer Energy Trading
Peer-to-peer energy trading (P2P-ET) provides an online marketplace where direct energy exchange can occur between its participants, namely, prosumers and consumers. P2P-ET offers techno-economical benefits to its participants and brings savings to them.
  • 967
  • 08 Dec 2022
Topic Review
Takagi–Sugeno Fuzzy-PI Controller Hardware
The intelligent system Field Programmable Gate Array (FPGA) is represented as Takagi--Sugeno Fuzzy-PI controller. The implementation uses a fully parallel strategy associated with a hybrid bit format scheme (fixed-point and floating-point). Two hardware designs are proposed; the first one uses a single clock cycle processing architecture, and the other uses a pipeline scheme. The bit accuracy was tested by simulation with a nonlinear control system of a robotic manipulator. The area, throughput, and dynamic power consumption of the implemented hardware are used to validate and compare the results of this proposal. The results achieved allow the use of the proposed hardware in applications with high-throughput, low-power, and ultra-low-latency requirements such as teleoperation of robot manipulators, tactile internet, or industry 4.0 automation, among others.
  • 966
  • 29 Oct 2020
Topic Review
Visibility Enhancement and Fog Detection
In mobile systems, fog, rain, snow, haze, and sun glare are natural phenomena that can be very dangerous for drivers. In addition to the visibility problem, the driver must face also the choice of speed while driving. The main effects of fog are a decrease in contrast and a fade of color. Rain and snow cause also high perturbation for the driver while glare caused by the sun or by other traffic participants can be very dangerous even for a short period. In the field of autonomous vehicles, visibility is of the utmost importance. To solve this problem, different researchers have approached and offered varied solutions and methods. It is useful to focus on what has been presented in the scientific literature over the past ten years relative to these concerns. 
  • 966
  • 28 May 2021
Topic Review
Mobile Robot Path Planning
One of the most significant processes in the autonomous navigation is path planning. Path planning involves the determination of a possible path for a mobile robot to move from a start to a target location in a particular environment while considering optimization parameters like path distance, time and path smoothness. Mobile robot path planning is a subcategory of trajectory planning.
  • 966
  • 21 Nov 2022
Topic Review
Deep Learning Applications for Optical Coherence Tomography
With non-invasive and high-resolution properties, optical coherence tomography (OCT) has been widely used as a retinal imaging modality for the effective diagnosis of ophthalmic diseases. The retinal fluid is often segmented by medical experts as a pivotal biomarker to assist in the clinical diagnosis of age-related macular diseases, diabetic macular edema, and retinal vein occlusion. In recent years, the advanced machine learning methods, such as deep learning paradigms, have attracted more and more attention from academia in the retinal fluid segmentation applications. The automatic retinal fluid segmentation based on deep learning can improve the semantic segmentation accuracy and efficiency of macular change analysis, which has potential clinical implications for ophthalmic pathology detection.
  • 966
  • 07 May 2022
Topic Review
Ultra-Reliable Low-Latency V2X Communications
Vehicular communication is a promising technology that has been announced as a main use-case of the fifth-generation cellular system (5G). Vehicle-to-everything (V2X) is the vehicular communication paradigm that enables the communications and interactions between vehicles and other network entities, e.g., road-side units (RSUs). This promising technology faces many challenges related to reliability, availability and security of the exchanged data. 
  • 965
  • 07 Jan 2022
Topic Review
Steganography and Steganalysis in VoIP
The rapid advance and popularization of VoIP (Voice over IP) has also brought security issues. VoIP-based secure voice communication has two sides: first, for legitimate users, the secret voice can be embedded in the carrier and transmitted safely in the channel to prevent privacy leakage and ensure data security; second, for illegal users, the use of VoIP Voice communication hides and transmits illegal information, leading to security incidents. Therefore, in recent years, steganography and steganography analysis based on VoIP have gradually become research hotspots in the field of information security. Steganography and steganalysis based on VoIP can be divided into two categories, depending on where the secret information is embedded: steganography and steganalysis based on voice payload or protocol. The former mainly regards voice payload as the carrier, and steganography or steganalysis is performed with respect to the payload. It can be subdivided into steganography and steganalysis based on FBC (fixed codebook), LPC (linear prediction coefficient), and ACB (adaptive codebook). The latter uses various protocols as the carrier and performs steganography or steganalysis with respect to some fields of the protocol header and the timing of the voice packet. It can be divided into steganography and steganalysis based on the network layer, the transport layer, and the application layer. Recent research results of steganography and steganalysis based on protocol and voice payload are classified in this paper, and the paper also summarizes their characteristics, advantages, and disadvantages. The development direction of future research is analyzed. Therefore, this research can provide good help and guidance for researchers in related fields. 
  • 965
  • 23 Feb 2021
Topic Review
Microtransfer Printing Methods
In recent years, with the rapid development of the flexible electronics industry, there is an urgent need for a large-area, multilayer, and high-production integrated manufacturing technology for scalable and flexible electronic products. To solve this technical demand, researchers have proposed and developed microtransfer printing technology, which picks up and prints inks in various material forms from the donor substrate to the target substrate, successfully realizing the integrated manufacturing of flexible electronic products.
  • 965
  • 18 Nov 2021
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