Remote sensing image semantic segmentation plays a crucial role in various fields, such as environmental monitoring, urban planning, and agricultural land classification. However, most current research primarily focuses on utilizing the spatial and s...
Misinformation propagates more rapidly than factual content on social media, presenting significant challenges for automated misinformation detection. Existing approaches often focus solely on textual features without incorporating temporal informati...
Phased-array radar networks require real-time collaborative scheduling of search, tracking, and identification tasks under coupled resource, beam, time-window, and power constraints. Current decisions affect future residual power, task refresh interv...
Earth observation (EO) sensors deliver data at daily or weekly intervals. Most land use and land cover classification (LULC) approaches, however, are designed for cloud-free and mono-temporal observations. The increasing temporal capabilities of toda...
This study introduces a novel, practical approach for designing a hierarchical online anomaly detection system for industrial cyber-physical systems. The proposed method utilizes the Hierarchical Temporal Memory (HTM) unsupervised learning algorithm,...
Three-dimensional (3D) pose estimation has been widely used in many three-dimensional human motion analysis applications, where inertia-based path estimation is gradually being adopted. Systems based on commercial inertial measurement units (IMUs) us...
Satellite image time series (SITS) classification is a challenging application concurrently driven by long-term, large-scale, and high spatial-resolution observations acquired by remote sensing satellites. The focus of current SITS classification res...
Traditional imitation learning is prone to distribution shift and trajectory divergence in highly dynamic, strongly time-varying flight tasks. To address this, we propose a temporally adaptive action-chunking framework built on temporal-derivative en...
15 July 2024
In the rapidly evolving domain of finance, quantitative stock selection strategies have gained prominence, driven by the pursuit of maximizing returns while mitigating risks through sophisticated data analysis and algorithmic models. Yet, prevailing...
Crop classification in agriculture is one of important applications for polarimetric synthetic aperture radar (PolSAR) data. For agricultural crop discrimination, compared with single-temporal data, multi-temporal data can dramatically increase crop...
Trophallaxis, a fundamental social behavior observed among honeybees, involves the redistribution of food and chemical signals. The automation of its detection under field-realistic conditions poses a significant challenge due to the presence of crow...
Despite advanced machine learning methods, the implementation of emotion recognition systems based on real-world video content remains challenging. Videos may contain data such as images, audio, and text. However, the application of multimodal models...
In recent times, many studies concerning surgical video analysis are being conducted due to its growing importance in many medical applications. In particular, it is very important to be able to recognize the current surgical phase because the phase...
Neuromorphic computing seeks to mimic structure and function of biological neural systems to enable energy-efficient, adaptive information processing. A critical component of this paradigm is neural encoding—the translation of analog or digital...
Remote photoplethysmography (rPPG) enables non-contact physiological measurement for emotion recognition, yet the temporally sparse nature of emotional cardiovascular responses, intrinsic measurement noise, weak session-level labels, and subtle corre...
Electroencephalography (EEG)-based emotion recognition is an important biosensing technique for affective brain-computer interfaces (BCIs), mental-state assessment, and physiological monitoring. Existing methods often rely on a single spectral descri...
Communicating information at the few photon level typically requires some complexity in the transmitter or receiver in order to operate in the presence of noise. This in turn incurs expense in the necessary spatial volume and power consumption of the...
Viewport prediction is a key component in tile-based 360° video streaming. Existing viewport prediction models based on Long Short-term Memory Networks (LSTM) or Transformer typically output a single deterministic future trajectory through determ...
In laser powder bed fusion (LPBF), melt pool instability can lead to the development of pores in printed parts, reducing the part’s structural strength. While camera-based monitoring systems have been introduced to improve melt pool stability,...
Auditory temporal processing is a vital component of auditory stream segregation, or the process in which complex sounds are separated and organized into perceptually meaningful objects. Temporal processing can degrade prior to hearing loss, and is s...
Multivariate time series prediction models perform the required operation on a specific window length of a given input. However, capturing complex and nonlinear interdependencies in each temporal window remains challenging. The typical attention mech...
With the advancement of IoT technology, a vast amount of floating-point time series data has emerged, posing significant challenges for data storage and transmission. To address this issue, the efficient compression of floating-point time series data...
In this study, we propose a one-branch IncepTAE network to extract local and global hybrid temporal attention simultaneously and congruously for fine-grained satellite image time series (SITS) classification. Transformer and the temporal self-attenti...
Infrared dim and small target detection aims to accurately localize targets within complex backgrounds or clutter. However, under extremely low signal-to-noise ratio (SNR) conditions, single-frame detection methods often fail to effectively detect su...
In response to the shortcomings of current spatiotemporal prediction models, which frequently encounter difficulties in temporal feature extraction and the forecasting of medium to high echo intensity regions over extended sequences, this study prese...
Automatic video anomaly detection remains challenging because abnormal events are infrequent, visually heterogeneous, and weakly bounded in time. This study proposes an unsupervised framework trained only with normal video segments. The framework int...
A vertical ITO/SnO2/ITO optoelectronic synaptic device was fabricated by magnetron sputtering for optical sensing and temporal-state encoding. The ITO/SnO2 stack exhibited 88.3% average transmittance over 400–800 nm. The device responded to 380...
Crop maps are key inputs for crop inventory production and yield estimation and can inform the implementation of effective farm management practices. Producing these maps at detailed scales requires exhaustive field surveys that can be laborious, tim...
Arabic automatic speech recognition (ASR) faces distinct challenges due to its complex morphology, dialectal variations, and the presence of diacritical marks that strongly influence pronunciation and meaning. This study introduces a lightweight appr...
Accurate runoff forecasting plays a critical role in sustainable flood risk management and climate-resilient water resources planning, particularly in plain river-network basins, where runoff processes are influenced by strong temporal variability an...
Extracting geoscientific dataset names from the literature is crucial for building a literature–data association network, which can help readers access the data quickly through the Internet. However, the existing named-entity extraction methods...
Video anomaly detection plays a vital role in intelligent video monitoring systems. It has found extensive applications in the fields of public safety and social security. Nevertheless, the area of video anomaly detection continues to be a formidable...
Trajectory prediction is a critical component of autonomous driving, intelligent transportation systems, and human–robot interactions, particularly in complex environments like intersections, where diverse road constraints and multi-agent inter...
Wind speed forecasting is an essential part of weather prediction, with significant value in economics, business, and management. Utilizing multiple meteorological variables can improve prediction accuracy, but existing methods face challenges such a...
In the era of big data, prediction has become a fundamental capability. Current prediction methods primarily focus on sequence elements; however, in multivariate time series forecasting, time is a critical factor that must not be overlooked. While so...
Pseudolite positioning systems offer precise localization when GPS signals are unavailable, advancing the development of intelligent transportation systems. However, in confined indoor environments such as kilometer-long tunnels, where vehicles move...
Integrated crop–livestock systems (ICLS) are among the main viable strategies for sustainable agricultural production. Mapping these systems is crucial for monitoring land use changes in Brazil, playing a significant role in promoting sustainab...
High-speed maneuvering flight vehicles operating in the subsonic-to-transonic regime (250–500 m/s) pose severe challenges to defense interception systems due to their rapid and unpredictable maneuvering behaviors. Accurate short-term trajectory...
Short-term forecasting of solar radiation is essential for the efficient operation of solar energy systems. This study presents a neural network-based approach for short-term solar radiation forecasting using a hybrid framework that integrates tempor...
Speech emotion recognition (SER) is a challenging task in human–computer interaction (HCI) systems. One of the key challenges in speech emotion recognition is to extract the emotional features effectively from a speech utterance. Despite the pr...
Existing 3D human pose estimation methods use fixed computation strategies processing diverse action sequences, leading to computational redundancy for simple actions, insufficient high-frequency information capture for complex actions, and low long-...
Neurofeedback (NF) shows promise in enhancing memory, but its application to the medial temporal lobe (MTL) still needs to be studied. Therefore, we aimed to develop an NF system for the memory function of the MTL and examine neural activity changes...
Accurate estimation of battery health is an effective means of improving the safety and reliability of electrical equipment. However, developing data-driven models to estimate battery state of health (SOH) is challenging when the amount of data is re...
As a link connecting the environmental perception system and the decision-making system, accurate obstacle trajectory prediction provides a reliable guarantee of correct decision-making by autonomous vehicles. Oriented toward a mixed human-driven and...
Short-term electrical load forecasting plays an important role in the safety, stability, and sustainability of the power production and scheduling process. An accurate prediction of power load can provide a reliable decision for power system manageme...
Accurate dairy cow behavior recognition is important for health monitoring, welfare assessment, and early warning in smart livestock farming. However, recognizing fine-grained behaviors such as feeding, drinking, and rumination remains difficult in r...
We propose the Hybrid Spatio-Temporal Transformer with Multi-Branch CNN/RNN and Extreme-Event Head (HST–MB–CREH), a hybrid spatio-temporal deep learning architecture for joint short-term photovoltaic (PV) power forecasting and the detecti...
DoS and DDoS attacks remain a major threat to service availability in modern IP and IoT networks, yet many learning-based detectors depend on dataset-specific flow exports, feature tables, or preprocessing conventions. This article presents a unified...
With the development of smart cities and smart transportation, cities can gradually provide people with more information to facilitate their life and travel, and parking is also inseparable from both of them. Accurate on-street parking demand predict...
Pedestrian-future-trajectory prediction is a critical task in intelligent driving, traffic-scene understanding, and active safety decision-making. In real-world road environments, pedestrians’ historical trajectories are frequently affected by...
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