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Wednesday, February 4, 2026

Solving the Master Equation on river networks#popularengineerawards #Riv...

CNN in Deep Learning: Algorithm and Machine Learning Uses

Convolutional Neural Networks (CNNs) are a core component of modern deep learning and have revolutionized the field of machine learning. Designed to process structured data such as images and videos, CNNs enable computers to understand visual information with remarkable accuracy. Today, CNNs are widely used in artificial intelligence applications ranging from healthcare to autonomous systems.

The CNN algorithm works by automatically learning features from input data through multiple layered operations. It begins with convolution layers, where filters slide over the input data to capture important patterns like edges, corners, and textures. These features are then refined using activation functions such as ReLU, which introduce non-linearity and improve learning efficiency.

Next, pooling layers reduce the spatial size of feature maps, lowering computational cost while preserving essential information. Finally, fully connected layers interpret the extracted features and generate predictions or classifications. CNNs are trained using backpropagation and optimization techniques like gradient descent to minimize prediction errors.

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Honor Excellence – Acknowledge remarkable achievements in data-driven research and innovation.

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Tuesday, February 3, 2026

Computer Network Big Data Detection Based on Internet of Things Technolo...

Anomaly Detection Using Spatial and Temporal Information in Multivariate Time Series

In today’s data-driven world, systems across industries generate massive volumes of multivariate time series data ranging from industrial sensors and smart grids to healthcare monitoring and financial markets. Detecting anomalies within this complex data is critical, as anomalies often signal system faults, cyber-attacks, operational risks, or abnormal behavior. Traditional anomaly detection methods, however, struggle to capture the intricate spatial and temporal dependencies inherent in multivariate time series.

Recent advances in anomaly detection focus on jointly modeling temporal dynamics (how data evolves over time) and spatial correlations (relationships among multiple variables or sensors). By integrating both dimensions, modern approaches can identify subtle and early-stage anomalies that would otherwise go unnoticed.

Deep learning techniques such as Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Temporal Convolutional Networks (TCNs), and Graph Neural Networks (GNNs) are increasingly used to learn complex temporal patterns and spatial dependencies simultaneously. These models can capture interactions between variables while preserving time-based trends, seasonality, and sudden changes. Hybrid frameworks combining attention mechanisms and autoencoders further enhance anomaly localization and interpretability.

The applications of spatial-temporal anomaly detection are vast. In smart manufacturing, it enables early fault detection and predictive maintenance. In energy systems, it improves grid stability by identifying abnormal consumption or failures. In healthcare, it supports real-time patient monitoring by detecting physiological irregularities. Financial systems also benefit from improved fraud detection and risk management.

As data complexity continues to grow, anomaly detection methods that leverage both spatial and temporal information are becoming essential. These intelligent systems not only improve accuracy but also provide actionable insights, making them a cornerstone of next-generation monitoring and decision-support solutions.

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Monday, February 2, 2026

Behavior tree generation and social robot control with LLMs#popularengin...

Individual Differences Shape Young Children’s Engagement With Social Robots

Social robots interactive machines designed to engage with humans in a lifelike way are moving beyond science fiction and into real learning environments for young children. Researchers around the world are now uncovering how children’s individual traits influence their interactions, engagement, and even learning outcomes when they partner with robots.

Recent studies in human-robot interaction show that children between 5 and 8 years old often prefer interacting with a robot tutor over a human instructor during learning tasks like puzzles and problem-solving. In experiments where children worked on tasks with both robot and human instructors, researchers found that kids gazed longer at the robot and engaged in more social referencing that is, looking to adults nearby for cues on how to interpret the robot’s behavior.

This suggests that robots capture attention in a unique way, partly due to their novelty and physical presence characteristics different from tablets or non-interactive media.

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Sunday, February 1, 2026

Internal data validation in computer vision#popularengineerawards#Comput...

RoboBees: Autonomous Flying Microrobots

Small flying robots inspired by insects especially RoboBees continue to make headlines as researchers around the world push the boundaries of autonomous flight, maneuverability, and practical applications. These cutting-edge microrobots could one day transform fields from agriculture and environmental monitoring to disaster response and beyon

Just days ago, European researchers unveiled bee-inspired navigation chips designed to power fleets of insect-sized robots using extremely low energy. These chips could one day help swarms of micro-robots navigate complex environments autonomously a major step toward real-world deployment.

In April 2025, the Harvard RoboBee project introduced crane fly-inspired legs that allow microrobots to land softly and reliably on a range of surfaces. Landing has been one of the biggest challenges for tiny flying robots, and this innovation lays important groundwork for operating outside controlled labs.

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Friday, January 30, 2026

Spiking Neural networks with artificial astrocytes#popularengineerawards...

Binocular stereo vision-based relative positioning algorithm for drone swarm

Drone swarms are transforming applications such as search and rescue, environmental monitoring, smart agriculture, and defense operations. A key challenge in swarm intelligence is accurate relative positioning among drones without relying heavily on GPS. To address this, researchers are increasingly turning to binocular stereo vision-based relative positioning algorithms.

Binocular stereo vision mimics human depth perception by using two synchronized cameras mounted on a drone. By capturing left and right images of neighboring drones, the system calculates depth through stereo matching and disparity estimation. This allows each drone to estimate the relative distance, orientation, and motion of others in real time.

The proposed relative positioning algorithm typically integrates feature extraction, epipolar geometry, triangulation, and pose estimation. Advanced methods combine stereo vision with Kalman filtering or visual–inertial fusion to improve robustness under dynamic flight conditions. Unlike GPS-based solutions, stereo vision works reliably in indoor, urban canyon, or GPS-denied environments.

One major advantage of this approach is its low communication overhead. Each drone performs onboard visual processing, enabling decentralized coordination and scalable swarm deployment. The algorithm also supports collision avoidance, formation control, and cooperative navigation, which are essential for autonomous swarm behavior.

Despite challenges such as lighting variations, motion blur, and computational cost, recent advances in deep learning-based stereo matching and lightweight vision processors are making binocular stereo vision a practical solution for real-world drone swarms.

Overall, binocular stereo vision-based relative positioning represents a promising step toward fully autonomous, GPS-independent drone swarms, opening new possibilities for intelligent multi-robot systems.

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Thursday, January 29, 2026

ABO for Anomaly Detection and Computer Network Optimization#popularengin...

New Research Makes AI Better at ‘Seeing’ Rotated Images - Inspired by Human Mental Rotation

A recent advance in deep learning promises to make visual AI systems much more robust to rotated images a problem that has long challenged convolutional neural networks (CNNs) and vision transformers (ViTs). Traditional models struggle when an object appears at a novel angle, often misclassifying it unless the training data includes similar rotations.

Researchers from Switzerland and Saudi Arabia introduced Artificial Mental Rotation (AMR) a clever new method inspired by the way humans mentally rotate objects in their minds to recognize them from different viewpoints. Instead of relying solely on data augmentation or bespoke rotation-equivariant architectures, AMR first predicts the rotation angle of a given image and then realigns it to a canonical orientation before feeding it into a standard deep model.

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Honor Excellence – Acknowledge remarkable achievements in data-driven research and innovation.

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