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Topic Review
Monocular 3D Object Detection Methods
Owing to recent advancements in deep learning methods and relevant databases, it is becoming increasingly easier to recognize 3D objects using only RGB images from single viewpoints. 
  • 2.5K
  • 07 Apr 2021
Topic Review
Shunting-Yard Algorithm
In computer science, the shunting-yard algorithm is a method for parsing arithmetical or logical expressions, or a combination of both, specified in infix notation. It can produce either a postfix notation string, also known as Reverse Polish notation (RPN), or an abstract syntax tree (AST). The algorithm was invented by Edsger Dijkstra and named the "shunting yard" algorithm because its operation resembles that of a railroad shunting yard. Dijkstra first described the Shunting Yard Algorithm in the Mathematisch Centrum report MR 34/61. Like the evaluation of RPN, the shunting yard algorithm is stack-based. Infix expressions are the form of mathematical notation most people are used to, for instance "3 + 4" or "3 + 4 × (2 − 1)". For the conversion there are two text variables (strings), the input and the output. There is also a stack that holds operators not yet added to the output queue. To convert, the program reads each symbol in order and does something based on that symbol. The result for the above examples would be (in Reverse Polish notation) "3 4 +" and "3 4 2 1 − × +", respectively. The shunting yard algorithm will correctly parse all valid infix expressions, but does not reject all invalid expressions. For example, "1 2 +" is not a valid infix expression, but would be parsed as "1 + 2". The algorithm can however reject expressions with mismatched parentheses. The shunting-yard algorithm was later generalized into operator-precedence parsing.
  • 2.5K
  • 28 Nov 2022
Topic Review
Food-Waste-Reduction Based on IoT and Big Data
IoT technology through ICT infrastructure and smart devices combines to gather huge amounts of data in real-time, which is commonly known as big data. The big data generated by IoT devices will be stored in the big data storage system and will be used for analysis. The importance of Food Wastage Reduction (FWR) is related to the loss of all the natural resources in the supply chain, including expenditures related to the use of land, water supply, and energy consumption. The application of IoT to FWR systems is also examined where use RFID sensors as a key tool to monitor food waste for each individual in accordance with the proposed model, while describe the application of IoT-based technologies to agricultural supply chain management in developing countries.
  • 2.5K
  • 08 Dec 2023
Topic Review
Sensor Fusion for Radar Detection
Sensor fusion can be considered as the mapping of different modalities into a common latent space where different features of the same object can be associated together. Sensor fusion frameworks are classified into four categories: input fusion, ROI fusion, feature map fusion, and decision fusion.
  • 2.5K
  • 08 Jun 2022
Topic Review
Wireless Sensors for Brain Activity
Over the last decade, the area of electroencephalography (EEG) witnessed a progressive move from high-end large measurement devices, relying on accurate construction and providing high sensitivity, to miniature hardware, more specifically wireless wearable EEG devices. While accurate, traditional EEG systems need a complex structure and long periods of application time, unwittingly causing discomfort and distress on the users. Given their size and price, aside from their lower sensitivity and narrower spectrum band(s), wearable EEG devices may be used regularly by individuals for continuous collection of user data from non-medical environments. This allows their usage for diverse, nontraditional, non-medical applications, including cognition, BCI, education, and gaming. Given the reduced need for standardization or accuracy, the area remains a rather incipient one, mostly driven by the emergence of new devices that represent the critical link of the innovation chain.
  • 2.5K
  • 26 Jan 2021
Topic Review
Computer Vision and Convolutional Neural Networks
Computer vision (CV) combined with a deep convolutional neural network (CNN) has emerged as a reliable analytical method to effectively characterize and quantify high-throughput phenotyping of different grain crops, including rice, wheat, corn, and soybean. In addition to the ability to rapidly obtain information on plant organs and abiotic stresses, and the ability to segment crops from weeds, such techniques have been used to detect pests and plant diseases and to identify grain varieties. The development of corresponding imaging systems to assess the phenotypic parameters, yield, and quality of crop plants will increase the confidence of stakeholders in grain crop cultivation, thereby bringing technical and economic benefits to advanced agriculture.
  • 2.5K
  • 14 Nov 2022
Topic Review
Smart Healthcare Using ML and Cognitive Radio Technologies
The rapid technological advancements in the modern world bring the attention of researchers to fast and real-time healthcare and monitoring systems. Smart healthcare is one of the best choices for this purpose, in which different on-body and off-body sensors and devices monitor and share patient data with healthcare personnel and hospitals for quick and real-time decisions about patients’ health. Cognitive radio (CR) can be very useful for effective and smart healthcare systems to send and receive patient’s health data by exploiting the primary user’s (PU) spectrum.
  • 2.4K
  • 21 Sep 2023
Topic Review
IoT Intrusion Detection Taxonomy
The taxonomy includes (1) IoT security attacks, (2) IoT architecture layers, (3) intrusion-detection systems for IoT, (3) DL techniques used in the IoT IDSs, (4) common datasets used in the evaluation of the DL systems, and (5) their classification strategies. The different areas included in the taxonomy are in various ways interconnected as root causes of IoT security vulnerabilities in IoT and/or solutions to counter such causes.
  • 2.4K
  • 29 Oct 2021
Topic Review
Crop Yield Prediction Approaches
Crop yield prediction is becoming more important because of the growing concern about food security. Early crop yield prediction plays an important role in reducing famine by estimating the food availability for the growing world population. Deep learning has emerged as a potential tool for crop yield prediction, allowing the model to automatically extract features and learn from the datasets. Meanwhile, smart farming technology enables the farmers to achieve maximum crop yield by extracting essential parameters of crop growth.
  • 2.4K
  • 12 May 2022
Topic Review
Deepfake Detection
Deepfakes can compromise the privacy and societal security of both individuals and governments. In addition, deepfakes are a threat to national security, and democracies are progressively being harmed. To overcome/mitigate the impact of deepfakes, different methods and approaches have been introduced that can identify deepfakes and appropriate corrective actions can be taken. 
  • 2.4K
  • 20 Jul 2022
Topic Review
AI and General movements (GMs)
       General movements (GMs) are spontaneous movements of infants up to five months post-term involving the whole body varying in sequence, speed, and amplitude. The assessment of GMs has shown its importance for identifying infants at risk for neuromotor deficits, especially for the detection of cerebral palsy. As the assessment is based on videos of the infant that are rated by trained professionals, the method is time-consuming and expensive. Therefore, approaches based on Artificial Intelligence have gained significantly increased attention in the last years.
  • 2.4K
  • 18 Feb 2021
Topic Review Peer Reviewed
Generative AI and Large Language Models in Industry 5.0: Shaping Smarter Sustainable Cities
This review paper examines how Generative AI (GAI) and Large Language Models (LLMs) can transform smart cities in the Industry 5.0 era. Through selected case studies and portions of the literature, we analyze these technologies’ impact on industrial processes and urban management. The paper targets GAI as an enabler for industrial optimization and predictive maintenance, underlining how domain experts can work with LLMs to improve municipal services and citizen communication, while addressing the practical and ethical challenges in deploying these technologies. We also highlight promising trends, as reflected in real-world case studies ranging from factories to city-wide test-beds and identify pitfalls to avoid. Widespread adoption of GAI still faces challenges that include infrastructure and lack of specialized knowledge as a limitation of proper implementation. While LLMs enable new services for citizens in smart cities, they also expose certain privacy issues, which we aim to investigate in this study. Finally, as a way forward, the paper suggests future research directions covering new ethical AI frameworks and long-term studies on societal impacts. Our paper is a starting point for industrial pioneers and urban developers to navigate the complexity of GAI and LLM integration, balancing the demands of technological innovation on one hand and ethical responsibility on the other.
  • 2.4K
  • 06 Mar 2025
Topic Review
Deep Learning-Based Crack Detection Approaches
The application of deep architectures inspired by the fields of artificial intelligence and computer vision has made a significant impact on the task of crack detection. As the number of studies being published in this field is growing fast, it is important to categorize the studies at deeper levels.
  • 2.4K
  • 24 Feb 2022
Topic Review
Artificial Intelligence Applied to Photovoltaic Systems
Solar energy is one of the most important renewable energies, and the investment of businesses and governments is increasing every year. Artificial intelligence (AI) is used to solve the most important problems found in photovoltaic (PV) systems, such as the tracking of the Max Power Point of the PV modules, the forecasting of the energy produced by the PV system, the estimation of the parameters of the equivalent model of PV modules or the detection of faults found in PV modules or cells. AI techniques perform better than classical approaches, even though they have some limitations such as the amount of data and the high computation times needed for performing the training.
  • 2.4K
  • 24 Oct 2022
Topic Review
Social Distancing
Social Distancing is a new terminology that became a popular term since mid-2020, after a global hit and pandemic by the new generation of the coronavirus (COVID-19). Social distancing is the act of maintaining a safe distance (equal to 6 feet or 2 meters) between individuals as a recommended solution by the World Health Organisation (WHO) to minimise the spread of COVID-19 in public places.  By the end of 2020, the majority of governments and national health authorities have set the 2-meter physical distancing as a mandatory measure in shopping centres, schools, pubs, restaurants, and public places.   
  • 2.4K
  • 06 Mar 2021
Topic Review
Coverage Path Planning Methods Focusing on Energy Efficient
The coverage path planning (CPP) algorithms aim to cover the total area of interest with minimum overlapping. The goal of the CPP algorithms is to minimize the total covering path and execution time. Significant research has been done in robotics, particularly for multi-unmanned unmanned aerial vehicles (UAVs) cooperation and energy efficiency in CPP problems.
  • 2.4K
  • 10 Feb 2022
Topic Review
Remote Sensing and Deep Learning
The advances in remote sensing technologies, hence the fast-growing volume of timely data available at the global scale, offer new opportunities for a variety of applications. Deep learning being significantly successful in dealing with Big Data, is a great candidate for exploiting the potentials of such complex massive data. However, with remote sensing, there are some challenges related to the ground-truth, resolution, and the nature of data that require further efforts and adaptions of deep learning techniques.
  • 2.3K
  • 26 Jan 2021
Topic Review
Accelerometer-Based Human Fall Detection
Human falls are a global public health issue resulting in over 37.3 million severe injuries and 646,000 deaths yearly. Falls result in direct financial cost to health systems and indirectly to society productivity. Unsurprisingly, human fall detection and prevention is a major focus of health research. In this article, we consider deep learning for fall detection in an IoT and fog computing environment. We propose a Convolutional Neural Network composed of three convolutional layers, two maxpool, and three fully-connected layers as our deep learning model. We evaluate its performance using three open data sets and against extant research. Our approach for resolving dimensionality and modelling simplicity issues is outlined. Accuracy, precision, sensitivity, specificity, and the Matthews Correlation Coefficient are used to evaluate performance. The best results are achieved when using data augmentation during the training process. 
  • 2.3K
  • 20 Jan 2021
Topic Review
Communication Architectures
Communication architecture plays an important role in the intelligent control and autonomous collaboration of UAV (Unmanned Air Vehicle) swarms. And we know that UAV swarm communication architecture technology has already made great progress. When faced with different mission scenarios, there are different communication architectures to choose from. Centralized communication architecture is suitable for scenarios where the UAV swarm is small, and the task is relatively simple. Each individual UAV requires a long-range communication link with the infrastructure. The decentralized communication architecture expands communication coverage through a multi-hop network. The dedicated gateway UAV is responsible for U-T-I (UAV to Infrastructure) communication. The “single-group swarm Ad hoc network” architecture is appropriate for a swarm of the same type UAVs, while “multi-group swarm Ad hoc network” and “multi-layer swarm Ad hoc network” architectures can be deployed using different types of UAVs. In a “multi-group swarm Ad hoc network”, communication between two different groups can also suffer from delays. In addition, in terms of robustness, "multi-layer swarm Ad hoc network" architecture is a relatively reliable system because it overcomes SPOF (Single Point of Failure).
  • 2.3K
  • 12 Apr 2021
Topic Review
LogNNet Neural Network
LogNNet - neural network which uses filters based on logistic mapping. LogNNet has a feedforward network structure, but possesses the properties of reservoir neural networks. The input weight matrix, set by a recurrent logistic mapping, forms the kernels that transform the input space to the higher-dimensional feature space. The most effective recognition of a handwritten digit from MNIST-10 occurs under chaotic behavior of the logistic map. The correlation of classification accuracy with the value of the Lyapunov exponent was obtained. An advantage of LogNNet implementation on IoT devices is the significant savings in memory used. At the same time, LogNNet has a simple algorithm and performance indicators comparable to those of the best resource-efficient algorithms available at the moment. The presented network architecture uses an array of weights with a total memory size from 1 to 29 kB and achieves a classification accuracy of 80.3–96.3%. Memory is saved due to the processor, which sequentially calculates the required weight coefficients during the network operation using the analytical equation of the logistic mapping. The proposed neural network can be used in implementations of artificial intelligence based on constrained devices with limited memory, which are integral blocks for creating ambient intelligence in modern IoT environments. From a research perspective, LogNNet can contribute to the understanding of the fundamental issues of the influence of chaos on the behavior of reservoir-type neural networks.
  • 2.3K
  • 11 Nov 2020
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