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Topic Review
Technological Breakthroughs in Sport
We are currently witnessing an unprecedented era of digital transformation in sports, driven by the revolutions in Artificial Intelligence (AI), Virtual Reality (VR), Augmented Reality (AR), and Data Visualization (DV). These technologies hold the promise of redefining sports performance analysis, automating data collection, creating immersive training environments, and enhancing decision-making processes. Traditionally, performance analysis in sports relied on manual data collection, subjective observations, and standard statistical models. These methods, while effective, had limitations in terms of time and subjectivity.
  • 254
  • 29 Feb 2024
Topic Review
Data Lake, Spark and Hive
Big data are a large number of datasets that are difficult to store and process using existing database management tools. Big data have some characteristics, denoted by 5Vs: volume, velocity, veracity, variety, and value. Volume refers to the size of the data, velocity refers to the speed of the data from the sources to the destination (data flow), variety refers to different format types of the data, veracity refers to the quality of the data, and value refers to the importance of the data collected without analysis and insight. Lastly, the characteristics have become more than ten, like volatility and visualization value. 
  • 137
  • 29 Feb 2024
Topic Review
Deep Learning for IDSs in Time Series Data
Classification-based intrusion detection systems (IDSs) use machine learning algorithms to classify incoming data into different categories based on a set of features. Even though classification-based IDSs are effective in detecting known attacks, they can be less effective in identifying new and unknown attacks that have a small correlation with the training dataset. On the other hand, anomaly detection-based approaches use statistical models and machine learning algorithms to establish a baseline of normal behavior and identify deviations from that baseline.
  • 111
  • 29 Feb 2024
Topic Review
Remote Sensing Applications in Almond Orchards
Almond cultivation is of great socio-economic importance worldwide. With the demand for almonds steadily increasing due to their nutritional value and versatility, optimizing the management of almond orchards becomes crucial to promote sustainable agriculture and ensure food security.
  • 132
  • 29 Feb 2024
Topic Review
Deepfake Detection Datasets
Deepfakes are notorious for their unethical and malicious applications to achieve economic, political, and social reputation goals. Although deepfakes were initially associated with entertainment such as movie visual effects, camera filters, and digital avatars, they are defined as “believable generated media by Deep Neural Network” and have evolved into a mainstream tool for facial forgery. With the development of multiple forgery methods, deepfake data are increasing at a annual rate of ~300%. However, the data published online have different forgery qualities. 
  • 276
  • 28 Feb 2024
Topic Review
Data Placement Using a Classifier for Hybrid SSDs
Modern Solid-State Drives (SSDs) are increasingly adopting Quad-Level Cell (QLC) flash memory, a technology that allows for the storage of four bits of data in a single cell, as their primary storage medium to significantly enhance storage capacity.
  • 156
  • 28 Feb 2024
Topic Review
Far and Near Field Communication
In an RFID system, for communication to exist between a reader and a tag, energy and information must be transferred between them. There are two ways to transfer energy and information in passive tags. The first mode is Near Field, which involves coupling the tag inductively to an approximately circular magnetic field around the reader. And the second mode is Far Field, which uses a reflection technique called backscatter.
  • 88
  • 28 Feb 2024
Topic Review
RFID Technology and Main Components
RFID Technology is an automatic identification technology, which works through radio signals, retrieving and storing data remotely. It is used in hundreds, or perhaps even thousands, of applications with the aim of collecting data about objects in order to identify ways to solve everyday problems.
  • 126
  • 28 Feb 2024
Topic Review
Weakly Supervised Crowd-Counting Models
Crowd-counting networks have become the mainstream method to deploy crowd-counting techniques on resource-constrained devices. Significant progress has been made in this field, with many outstanding lightweight models being proposed successively.  However, challenges like scare variation, global feature extraction, and fine-grained head annotation requirements still exist in relevant tasks, necessitating further improvement. In this research, the researchers propose a weakly-supervised hybrid lightweight crowd-counting network that integrates the initial layers of GhostNet as the backbone to efficiently extract local features and enrich intermediate features. The experimental results for accuracy and inference speed evaluation on some mainstream datasets validate the effective design principle of the model.
  • 75
  • 28 Feb 2024
Topic Review
Feature Extraction Methods in Autonomous Driving
The upsurge of autonomous vehicles in the automobile industry will lead to better driving experiences while also enabling the users to solve challenging navigation problems. Reaching such capabilities will require significant technological attention and the flawless execution of various complex tasks, one of which is ensuring robust localization and mapping. Herein, a discussion of the contemporary methods of extracting relevant features from equipped sensors and their categorization as semantic, non-semantic, and deep learning methods is presented. Representativeness, low cost, and accessibility are crucial constraints in the choice of the methods to be adopted for localization and mapping tasks. 
  • 227
  • 28 Feb 2024
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