Spatial-Temporal Graph Enhanced DETR Towards Multi-Frame 3D Object Detection.

Human trajectory prediction is a crucial yet challenging problem, which is of fundamental importance to robotics and autonomous driving vehicles. The core challenge lies in effectively modeling the socially aware spatial interaction and complex temporal dependencies among crowds. However, previous methods usually model spatial and temporal information separately and only use individual features, while ignoring group-level motion characteristics. We propose a novel trajectory prediction framework termed GA-STT, a group aware spatial-temporal transformer network to address these issues. Specifically, we first get the individual representations supervised by group-based annotations. Then, we model the complex spatial-temporal interactions with spatial and temporal transformers separately and fuse spatial-temporal embedding through the cross-attention mechanism. Results on the publicly available ETH/UCY datasets show that our model outperforms the state-of-the-art method by 19.4% in average displacement error(ADE) and 16.9% in final displacement error(FDE) and successfully predicts complex spatial-temporal interactions.

Human Activity Recognition (HAR) is essential for monitoring and analyzing human behavior, particularly in health applications such as fall detection and chronic disease management. Traditional methods, even those incorporating attention mechanisms, often oversimplify the complex temporal and spatial dependencies in sensor data by processing features uniformly, leading to inadequate modeling of high-dimensional interactions. To address these limitations, we propose a novel framework: the Temporal-Spatial Feature Decoupling Unit with Layer-wise Training Convolutional Neural Network (CNN-TSFDU-LW). Our model enhances HAR accuracy by decoupling temporal and spatial dependencies, facilitating more precise feature extraction and reducing computational overhead. The TSFDU mechanism enables parallel processing of temporal and spatial features, thereby enriching the learned representations. Furthermore, layer-wise training with a local error function allows for independent updates of each CNN layer, reducing the number of parameters and improving memory efficiency without compromising performance. Experiments on four benchmark datasets (UCI-HAR, PAMAP2, UNIMIB-SHAR, and USC-HAD) demonstrate accuracy improvements ranging from 0.9% to 4.19% over state-of-the-art methods while simultaneously reducing computational complexity. Specifically, our framework achieves accuracy rates of 97.90% on UCI-HAR, 94.34% on PAMAP2, 78.90% on UNIMIB-SHAR, and 94.71% on USC-HAD, underscoring its effectiveness in complex HAR tasks. In conclusion, the CNN-TSFDU-LW framework represents a significant advancement in sensor-based HAR, delivering both improved accuracy and computational efficiency, with promising potential for enhancing health monitoring applications.

Fire detection and alarm systems are recognized as key features of a building’s fire prevention and protection strategy. This chapter presents a systematic technique to be used by fire protection engineers in the design and analysis of detection and alarm systems. The majority of discussion is directed toward systems used in buildings. However, many of the techniques and procedures also apply to systems used to protect planes, ships, outside storage yards, and other nonbuilding environments.

Traffic forecasting is a challenging task due to complex spatial and temporal dependencies across sensor locations and time. Interest in solving this task has increased, but current research focuses on manually constructing neural network architectures without the aid of neural architecture search (NAS). In our work, we explore evolutionary neural architecture search (ENAS) by deploying a genetic algorithm (GA) to find optimal neural network architectures for predicting traffic conditions. The search space for the GA consists of arbitrary combinations of dilated convolutions and graph convolutions for modelling temporal and spatial dependencies respectively, limited in complexity only by technical constraints. Experimental results show that model architectures obtained via GA are able to match the current state-of-the-art on traffic prediction benchmarks.

— In today's era, the challenge of object detection within laboratory spaces is paramount. The need for automated monitoring systems supporting object detection within laboratory settings, particularly for service robots, is crucial for scientific research and safety. Utilizing convolutional artificial neural networks has shown promising capabilities in object recognition. This paper proposes a novel method for detecting and localizing specific objects within laboratory environments using deep learning techniques. Employing a lightweight primary feature extractor allows for the identification of crucial object features without imposing excessive computational demands. Additionally, a depthwise separable convolution module is introduced, designed to capture essential components within multi-level convolutional patches specific to laboratory conditions. To address the challenges encountered within laboratory spaces, a new dataset is proposed, encompassing issues such as lighting variations, blurriness, occlusions, and complex backgrounds. Evaluation results indicate that the proposed model surpasses other lightweight object detection models in laboratory settings, offering high accuracy and efficiency. This model can be efficiently implemented on low-tier devices, ensuring real-time object detection and processing within laboratory environments. Keywords — object detection, service robot, convolutional neural networks, You Only Look Once version 5. Abstrak — Di era sekarang ini, tantangan deteksi objek di dalam ruang laboratorium menjadi sangat penting. Kebutuhan akan sistem pemantauan otomatis yang mendukung deteksi objek di dalam laboratorium, khususnya untuk robot servis, sangat penting untuk penelitian ilmiah dan keselamatan. Memanfaatkan jaringan saraf tiruan convolutional telah menunjukkan kemampuan yang menjanjikan dalam pengenalan objek. Makalah ini mengusulkan metode baru untuk mendeteksi dan melokalisasi objek tertentu di dalam lingkungan laboratorium menggunakan teknik pembelajaran mendalam. Menggunakan ekstraktor fitur utama yang ringan memungkinkan identifikasi fitur objek yang penting tanpa membebani komputasi yang berlebihan. Selain itu, modul konvolusi yang dapat dipisahkan secara mendalam juga diperkenalkan, yang dirancang untuk menangkap komponen penting dalam patch konvolusi multi-level yang spesifik untuk kondisi laboratorium. Untuk mengatasi tantangan yang dihadapi di dalam ruang laboratorium, dataset baru diusulkan, yang mencakup masalah seperti variasi pencahayaan, kekaburan, oklusi, dan latar belakang yang kompleks. Hasil evaluasi menunjukkan bahwa model yang diusulkan melampaui model pendeteksian objek ringan lainnya di lingkungan laboratorium, menawarkan akurasi dan efisiensi yang tinggi. Model ini dapat diimplementasikan secara efisien pada perangkat tingkat rendah, memastikan pendeteksian dan pemrosesan objek secara real-time di lingkungan laboratorium. Kata kunci — deteksi objek, robot layanan, jaringan saraf konvolusi, Anda Hanya Melihat Sekali versi 5.

Total Internal Reflection Fluorescence Microscopy (TIRFM) allows us to image fluorescenttagged proteins near the plasma membrane of living cells with high spatial-temporal resolution. Using TIRFM imaging of GFP-tagged clathrin endocytic proteins, areas of fluorescence are observed as overlapping spots of different sizes and durations. Standard procedures to measure protein-protein colocalization of dual labeled samples threshold the original graylevel images to segment areas covered by different proteins. This binary logic is not appropriate as it leaves a free tuning parameter which can influence the conclusions. Moreover, these procedures rely on simple statistical analysis based on correlation coefficients or visual inspection. We propose a probabilistic model to examine spatial-temporal dependencies. Image sequences of two proteins are modeled as a realization of a bivariate fuzzy temporal random set. Spatial-temporal dependencies are described by means of the pair-correlation function and the K-function and are tested using a Monte Carlo test. Five simulated image sequences were used to validate the performance of the procedure. Spatial and spatial-temporal dependencies were generated using a linked pairs model and a Poisson cluster model for the germs. To demonstrate the applicability in addressing current biological questions, we applied the procedure to fluorescent-tagged proteins involved in endocytosis (Clathrin, Hip1R, Epsin, and Caveolin). Results show that this procedure allows biologists to automatically quantify dependencies between molecules in a more formal and robust way. Image sequences and a Matlab toolbox for simulation and testing are available at http://www.uv.es/tracs/.

This paper presents a robust foreground detection method capable of adapting to different motion speeds in scenes. A key contribution of this paper is the background estimation using a proposed novel algorithm, neighbor-based intensity correction (NIC), that identifies and modifies the motion pixels from the difference of the background and the current frame. Concretely, the first frame is considered as an initial background that is updated with the pixel intensity from each new frame based on the examination of neighborhood pixels. These pixels are formed into windows generated from the background and the current frame to identify whether a pixel belongs to the background or the current frame. The intensity modification procedure is based on the comparison of the standard deviation values calculated from two pixel windows. The robustness of the current background is further measured using pixel steadiness as an additional condition for the updating process. Finally, the foreground is detected by the background subtraction scheme with an optimal threshold calculated by the Otsu method. This method is benchmarked on several well-known data sets in the object detection and tracking domain, such as CAVIAR 2004, AVSS 2007, PETS 2009, PETS 2014, and CDNET 2014. We also compare the accuracy of the proposed method with other state-of-the-art methods via standard quantitative metrics under different parameter configurations. In the experiments, NIC approach outperforms several advanced methods on depressing the detected foreground confusions due to light artifact, illumination change, and camera jitter in dynamic scenes.

Dynamic brain networks are more effective than static networks in characterizing the evolving patterns of brain functional connectivity, making them a more promising tool for diagnosing neurodegenerative diseases. However, existing classification methods for dynamic brain networks often rely on sliding windows to extract multi-window features, leading to suboptimal performance due to the spatio-temporal coupling on these windows and limited ability to effectively integrate complex topological features. To address these limitations, we propose a novel method called Confidence-Driven Dynamic Spatio-Temporal Convolutional Network (CD-DSTCN). First, our proposed method employs a spatio-temporal convolutional network integrated with a temporal attention mechanism to extract spatio-temporal features within each window. By propagating information across temporal windows during spatial convolution, the method effectively captures and integrates complex temporal and spatial dependencies. Second, each window generates an output probability, which quantifies prediction confidence based on the true class probability (TCP). This confidence score serves as a weight to assess the relative importance of different time windows. Finally, the confidence-weighted fused features are passed through a multilayer perceptron (MLP) for final classification. Extensive experiments on Alzheimer's and Parkinson's datasets show that the proposed method out-performs the state-of-the-art algorithms and can provide valuable biomarkers for brain disease diagnosis. Our code is publicly available at: https://github.com/YNingCode/CDDSTCN.

Transportation infrastructure is vital for sustaining communities and fostering economic development. Urbanization and climate change have led to the rapid deterioration of road transport systems, posing significant challenges for future sustainability. Current transportation infrastructure maintenance planning often prioritizes immediate needs and short-term deterioration indicators, which can overlook long-term changes and future funding constraints. Long-term road maintenance planning is challenged by the large number of decision variables and the complex temporal and spatial dependencies that govern pavement deterioration. Most existing optimization models overlook spatial relationships among road segments, resulting in low computational efficiency, especially for large-scale networks. To address this gap, this study proposes a Spatiotemporal Particle Swarm Optimization for Cost Allocation (SPOCA) model that integrates spatial clustering and heuristic optimization for large-scale decision-making. An age-filtered spatial clustering process first groups roads with similar ages and proximity to preserve spatial structure and reduce problem dimensionality, while a spatial relationship term embedded in the optimization captures correlations among neighboring clusters to improve coordinated decision-making. A case study of Western Australia demonstrates that the SPOCA model reduces computational time by 38% compared with the non-spatial model, while maintaining comparable accuracy and significantly improving network-level pavement quality. The SPOCA model provides a scalable and practical tool to support policymakers in developing efficient and sustainable infrastructure maintenance strategies.

As one of the most widely used applications of the Internet of Things (IoT), intelligent transportation system (ITS) is of great significance for urban traffic planning, traffic control, and traffic guidance. However, widespread traffic congestion occurs with the increased number of vehicles. The traffic flow prediction is a good idea for traffic congestion. Therefore, many schemes have been proposed for accurate and real-time traffic flow prediction, but there still exist many issues, including low accuracy, weak adaptability and inferior real-time. Meanwhile, the complex spatial and temporal dependencies in traffic flow are still challenging. To address the above issues, we propose a novel spatial-temporal Chebyshev graph neural network model (ST-ChebNet) for traffic flow prediction to capture the spatial-temporal features, which can ensure accurate traffic flow prediction. Concretely, we first add a fully connected layer to fuse the features of traffic data into a new feature to generate a matrix, and then the long short-term memory (LSTM) model is adopted to learn traffic state changes for capturing the temporal dependencies. Then, we use the Chebyshev graph neural network (ChebNet) to learn the complex topological structures in the traffic network for capturing the spatial dependencies. Eventually, the spatial features and the temporal features are fused to guarantee the traffic flow prediction. The experiments show that ST-ChebNet can make accurate and real-time traffic flow prediction compared with other eight baseline methods on real-world traffic data sets PeMS.

CPNet: Conditionally parameterized graph convolutional network for traffic forecasting

IML-UNet: A brain-inspired spatiotemporal collaborative encoding method for medical image sequence registration.

Multilevel analysis in rural cancer control: A conceptual framework and methodological implications

The design and development of an advanced object detection system are presented in this work, which was guided by a thorough literature review and feasibility assessment. The literature review emphasises how object detection techniques have evolved, highlighting the shift from conventional techniques to deep learning approaches. Important developments are highlighted, such as feature pyramid networks, anchor-based localization, and region-based and single-stage detectors. Furthermore, offered are insights about assessment metrics, transfer learning, and data augmentation. A feasibility study assesses the suggested systems operational, technological, and financial viability and finds that it is highly feasible in each of these areas. The architecture of the system is modular and scalable, including backend services, data management, an object detection engine, and a user interface among its constituent parts. Specifications for both functional and non-functional needs are provided, which direct the development of the system. The development phases, resource allocation, development process, and quality assurance procedures are all outlined in the implementation plan. Through the integration of deep learning techniques, the suggested system seeks to achieve high-performance object identification capabilities that are appropriate for a variety of applications while being scalable, reliable, and user-friendly.

The design and performance of distributed signal detection systems with processor failures is presented. Using a general processor fault model, which also models an imperfect channel, the optimal design given prior failure probabilities is considered. The optimal design provides a performance bound for each system when failures may occur. It is shown that for general distributed systems, a likelihood ratio test is the optimal design for each local processor, provided failures are independent of the received observation vectors. The optimal design performs significantly better than the design that assumes no failures. It is shown that the fusion network performs better than a single centralized processor for many channels. This illustrates that, for the fusion network, the performance gained by decreasing the effect of failures outweighs the performance lost by distributing the processing. The serial network always performs worse than centralized; its performance is bounded as the number of channels increases.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>