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Topic Review
HAR in Smart Homes
Human Activity Recognition (HAR) consists in monitoring and analyzing the behavior of one or more persons in order to deduce their activity. In a smart home context, the HAR consists in monitoring daily activities of the residents, based on a network of IoT devices. Owing to this monitoring, a smart home can offer personalized home assistance services to improve quality of life, autonomy and health of their residents, especially for elderly and dependent people.
  • 1.0K
  • 19 Nov 2021
Topic Review Peer Reviewed
Tokenization in the Theory of Knowledge
Tokenization is a procedure for recovering the elements of interest in a sequence of data. This term is commonly used to describe an initial step in the processing of programming languages, and also for the preparation of input data in the case of artificial neural networks; however, it is a generalizable concept that applies to reducing a complex form to its basic elements, whether in the context of computer science or in natural processes. In this entry, the general concept of a token and its attributes are defined, along with its role in different contexts, such as deep learning methods. Included here are suggestions for further theoretical and empirical analysis of tokenization, particularly regarding its use in deep learning, as it is a rate-limiting step and a possible bottleneck when the results do not meet expectations.
  • 1.0K
  • 11 Apr 2023
Topic Review
Facial Information for Healthcare Applications
The document is not limited to global face analysis but it also concentrates on methods related to local cues (e.g. the eyes). A research taxonomy is introduced by dividing the face in its main features: eyes, mouth, muscles, skin, and shape. For each facial feature, the computer vision-based tasks aiming at analyzing it and the related healthcare goals that could be pursued are detailed.
  • 1.0K
  • 28 Oct 2020
Topic Review
Artificial Intelligence Surgery
Most surgeons are skeptical as to the feasibility of autonomous actions in surgery. Interestingly, many examples of autonomous actions already exist and have been around for years. Since the beginning of this millennium, the field of artificial intelligence (AI) has grown exponentially with the development of machine learning (ML), deep learning (DL), computer vision (CV) and natural language processing (NLP). This entry will highlight the most recent issues regarding how AI will get us to more autonomous actions in surgery by discussing the different degrees of surgical autonomy, recent advances with reinforcement learning and the ethical roadblocks that lie ahead.
  • 1.0K
  • 25 Aug 2021
Topic Review
Computer Vision in Self-Steering Tractors
Agricultural machinery, such as tractors, is meant to operate for many hours in large areas and perform repetitive tasks. The automatic navigation of agricultural vehicles can ensure the high intensity of automation of cultivation tasks, the enhanced precision of navigation between crop structures, an increase in operation safety and a decrease in human labor and operation costs.
  • 1.0K
  • 24 Feb 2022
Topic Review
Path-Planning Approaches for Multiple Mobile Robots
Numerous path-planning studies have been conducted due to the challenges of obtaining optimal solutions. The multi-robot path-planning approaches have been classified as classical approaches, heuristic algorithms, bio-inspired techniques, and artificial intelligence approaches. Bio-inspired techniques are the most employed approaches, and artificial intelligence approaches have gained more attention. 
  • 1.0K
  • 15 Sep 2022
Topic Review
Universal Intelligence for Sustainability
Artificial intelligence (AI), as a product of biological intelligence, is a technological tool based on data and the information-processing power of discrete machines that carry out a series of interdependent operations to generate and store discrete data and information, using discrete, finite, and closed algorithms. In turn, the concept of sustainability is increasingly considered an almost essential component of discourses designed to support and justify decision-making at all levels of human activities.  The strong and functional couplings among ecological, economic, social, and technological processes explain the complexification of human-made systems, and phenomena such as globalization, climate change, the increased urbanization and inequality of human societies, and the power of information, and the COVID-19 syndemic. Sustainability for complex systems implies enough efficiency to explore and exploit their dynamic phase spaces and enough flexibility to coevolve with their environments. This means solving intractable nonlinear semi-structured dynamic multi-objective optimization problems, with conflicting, incommensurable, non-cooperative objectives and purposes, under dynamic uncertainty, restricted access to materials, energy, and information, and a given time horizon. Given the high stakes; the need for effective, efficient, diverse solutions; their local and global, and present and future effects; and their unforeseen short-, medium-, and long-term impacts; achieving sustainable complex systems implies the need for Sustainability-designed Universal Intelligent Agents (SUIAs). The proposed philosophical and technological SUIAs will be heuristic devices for harnessing the strong functional coupling between human, artificial, and nonhuman biological intelligence in a non-zero-sum game to achieve sustainability.
  • 1.0K
  • 21 Jun 2022
Topic Review
Robotic Platform for Horticulture
The modern level of development of infocommunication and computer technologies, microprocessor technology and equipment, communication and positioning makes possible the development and practical application of automated and robotic technologies and technical means to improve the efficiency of agricultural production. Currently, intensive horticulture is becoming increasingly widespread due to rapid fruiting and high yield rates. At the same time, the process of harvesting apples in intensive horticulture is the most time-consuming, and harvesting is carried out mainly by a team of pickers. In the production process of cultivating fruit crops, this is an important final stage which requires the development of automated devices and robotic platforms with a control system capable of offline harvesting.
  • 1.0K
  • 02 Dec 2022
Topic Review
Abstractive vs. Extractive Summarization
Due to the huge and continuously growing size of the textual corpora existing on the Internet, important information may go unnoticed or become lost. At the same time, the task of summarizing these resources by human experts is tedious and time consuming. This necessitates the automation of the task. Natural language processing (NLP) is a multidisciplinary research field, merging aspects and approaches from computer science, artificial intelligence and linguistics; it deals with the development of processes that semantically and efficiently analyze vast amounts of textual data. Text summarization (TS) is a fundamental NLP subtask, which has been defined as the process of the automatic creation of a concise and fluent summary that captures the main ideas and topics of one or multiple documents.
  • 1.0K
  • 07 Jul 2023
Topic Review
Fall Detection and Prevention
A fall can be described as an unpredicted event leading the participants to rest on the lower level (ground or floor). As a result, it causes injuries that can often be fatal. Psychological grievances are also considered as the consequence of falls. People may suffer from anxiety, depression, activity restriction, and fear of falling. The primary physiological issue in older adults is fear of falling, restricting their Activities of Daily Life (ADL). This fear leads to activity restriction, which may lead to inadequate gait balance and weakened muscle that affects the mobility and independence of older adults. Therefore, remote/wearable technologies are required to track, detect, and prevent falls for improving the overall quality of life (QoL). For this purpose, understanding of falls can be classified as fall prevention and fall detection. Fall detection refers to the detection of a fall using sensors/cameras to summon help. In contrast, fall prevention aims to avert falls by observing human locomotion. Numerous systems have been developed using different sensors and algorithms to detect and prevent the fall.
  • 1.0K
  • 09 Aug 2021
Topic Review
Intelligent Energy Management Systems for Electric Vehicle Transportation
Electric Vehicles (EVs) have been gaining interest as a result of their ability to reduce vehicle emissions. Developing an intelligent system to manage EVs charging demands is one of the fundamental aspects of this technology to better adapt for all-purpose transportation utilization. It is necessary for EVs to be connected to the Smart Grid (SG) to communicate with charging stations and other energy resources in order to control charging schedules, while Artificial Intelligent (AI) techniques can be beneficial for improving the system, they can also raise security and privacy threats. Privacy preservation methodologies have been introduced to ensure data security. Federated Learning (FL) and blockchain technology are two emerging strategies to address information protection concerns. 
  • 1.0K
  • 22 Nov 2022
Topic Review
Augmented Reality Mobile App to Learn Writing
Augmented reality (AR) has been widely used in education, particularly for child education. This entry presents the design and implementation of a novel mobile app, Learn2Write, using machine learning techniques and augmented reality to teach alphabet writing.
  • 1.0K
  • 10 Jan 2022
Topic Review
Computer Vision and Artificial Intelligence for Fish Recognition
Computer vision has been applied to fish recognition. With the inception of deep learning techniques in the early 2010s, the use of digital images grew strongly, and this trend is likely to continue. As the number of articles published grows, it becomes harder to keep track of the state of the art and to determine the best course of action for new studies.
  • 1.0K
  • 24 Nov 2022
Topic Review
Learning for Unmanned Ground Vehicles
The problem of autonomous navigation of a ground vehicle in unstructured environments is both challenging and crucial for the deployment of this type of vehicle in real-world applications. We present a review on the recent contributions in the roboticsliterature adopting learning-based methods to solve the problem of environment perception andinterpretation with the final aim of the autonomous context-aware navigation of ground vehicles inunstructured environments.
  • 1.0K
  • 29 Apr 2021
Topic Review
Artificial Neural Networks and Energy Forecasting
Load prediction with higher accuracy and less computing power has become an important problem in the smart grids domain in general and especially in demand-side management (DSM), as it can serve to minimize global warming and better integrate renewable energies. Indeed, artificial neural networks (ANN) are the most used methods in forecasting electrical load. They are widely employed in this field for their numerous advantages. In fact, the complexity of this task is considerable due to several factors/parameters, such as weather and holidays (linear and non-linear relationships), which is a well-suited problem for ANNs and their capacity to deal with non-linear relationships.
  • 1.0K
  • 21 Jun 2022
Topic Review
IoT
This entry presents the problems of powering wireless sensor networks operating in the structures of the Internet of Things (IoT). This issue was discussed on the example of a universal end node in IoT technology containing RFID (Radio Frequency Identification) tags. The basic methods of signal transmission in these types of networks are discussed and their impact on the basic requirements such as range, transmission speed, low energy consumption, and the maximum number of devices that can simultaneously operate in the network. The issue of low power consumption of devices used in IoT solutions is one of the main research objects. The analysis of possible communication protocols has shown that there is a possibility of effective optimization in this area. The wide range of power sources available on the market, used in nodes of wireless sensor networks, was compared. The alternative possibilities of powering the network nodes from Energy Harvesting (EH) generators are presented.
  • 1.0K
  • 25 May 2021
Topic Review
Lamport Timestamps
The algorithm of Lamport timestamps is a simple algorithm used to determine the order of events in a distributed computer system. As different nodes or processes will typically not be perfectly synchronized, this algorithm is used to provide a partial ordering of events with minimal overhead, and conceptually provide a starting point for the more advanced vector clock method. They are named after their creator, Leslie Lamport. Distributed algorithms such as resource synchronization often depend on some method of ordering events to function. For example, consider a system with two processes and a disk. The processes send messages to each other, and also send messages to the disk requesting access. The disk grants access in the order the messages were sent. For example process [math]\displaystyle{ A }[/math] sends a message to the disk requesting write access, and then sends a read instruction message to process [math]\displaystyle{ B }[/math]. Process [math]\displaystyle{ B }[/math] receives the message, and as a result sends its own read request message to the disk. If there is a timing delay causing the disk to receive both messages at the same time, it can determine which message happened-before the other: [math]\displaystyle{ A }[/math] happens-before [math]\displaystyle{ B }[/math] if one can get from [math]\displaystyle{ A }[/math] to [math]\displaystyle{ B }[/math] by a sequence of moves of two types: moving forward while remaining in the same process, and following a message from its sending to its reception. A logical clock algorithm provides a mechanism to determine facts about the order of such events. Lamport invented a simple mechanism by which the happened-before ordering can be captured numerically. A Lamport logical clock is a numerical software counter value maintained in each process. Conceptually, this logical clock can be thought of as a clock that only has meaning in relation to messages moving between processes. When a process receives a message, it re-synchronizes its logical clock with that sender. The above-mentioned vector clock is a generalization of the idea into the context of an arbitrary number of parallel, independent processes.
  • 1.0K
  • 24 Oct 2022
Topic Review
Generative Adversarial Network in Amodal Completion
The generative adversarial network (GAN) is a structured probabilistic model that consists of two networks, a generator that captures the data distributions and a discriminator that decides whether the produced data come from the actual data distribution or from the generator. The two networks train in a two-player minimax game fashion until the generator can generate samples that are similar to the true samples, and the discriminator can no longer distinguish between the real and the fake samples. Although current computer vision systems are closer to the human intelligence when it comes to comprehending the visible world than previously, their performance is hindered when objects are partially occluded. Since we live in a dynamic and complex environment, we encounter more occluded objects than fully visible ones. Therefore, instilling the capability of amodal perception into those vision systems is crucial. However, overcoming occlusion is difficult and comes with its own challenges. GAN, on the other hand, is renowned for its generative power in producing data from a random noise distribution that approaches the samples that come from real data distributions.
  • 1.0K
  • 24 Apr 2023
Topic Review
Biologically inspired metaheuristics
A metaheuristic is a high level, problem-independent framework that provides a series of steps and guidelines used to develop heuristic optimizers. Nowadays, the tendency is to use the term for both the general framework and for the algorithms built based on its rules. In the latest years, the literature has shown an increase in the number of proposals of new optimization metaheuristics and their improvements through step alterations, local search procedures or hybridizations.
  • 994
  • 09 Oct 2021
Topic Review
Fine-Grained Change Detection
Fine-grained change detection in sensor data is very challenging for artificial intelligence though it is critically important in practice. It is the process of identifying differences in the state of an object or phenomenon where the differences are class-specific and are difficult to generalise. As a result, many recent technologies that leverage big data and deep learning struggle with this task.
  • 993
  • 12 Jul 2021
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