Attention deficit hyperactivity disorder (ADHD) is one of the most common mental health disorders. As a neuro development disorder, neuroimaging technologies, such as magnetic resonance imaging (MRI), coupled with machine learning algorithms, are being increasingly explored biomarkers in ADHD. Among various methods, deep has demonstrated excellent performance on many tasks. With availability publically-available, large data sets for training purposes, learning-based automatic diagnosis...

Article Free Access Share on Load balancing for multi-projector rendering systems Authors: Rudrajit Samanta Princeton University UniversityView Profile , Jiannan Zheng Thomas Funkhouser Kai Li Jaswinder Pal Singh Authors Info & Claims HWWS '99: Proceedings of the ACM SIGGRAPH/EUROGRAPHICS workshop Graphics hardwareJuly 1999 Pages 107–116https://doi.org/10.1145/311534.311584Online:01 July 1999Publication History 90citation656DownloadsMetricsTotal Citations90Total Downloads656Last 12...

Accurate two-dimensional to three-dimensional (2-D/3-D) registration of preoperative 3-D data and intraoperative 2-D x-ray images is a key enabler for image-guided therapy. Recent advances in 2-D/3-D formulate the problem as learning-based approach exploit modeling power convolutional neural networks (CNN) significantly improve accuracy efficiency registration. However, surgery-related applications, collecting large clinical dataset with accurate annotations training can be very challenging...

Convolutional neural networks have been highly successful in hyperspectral image classification owing to their unique feature expression ability. However, the traditional data partitioning strategy tandem with patch-wise may lead information leakage and result overoptimistic experimental insights. In this paper, we propose a novel scheme triple-attention parallel network (TAP-Net) enhance performance of HSI without leakage. The dataset is simple yet effective avoid overfitting, allows fair...

Congestive heart failure (CHF) is a progressive and complex syndrome resulted from ventricular dysfunction, which difficult to detect at early stages. Heart rate variability (HRV) has been identified as prognostic indicator for CHF. The traditional diagnosis methods based on analyzing the electrocardiogram (ECG) are time-consuming laborious, interpretation of results subjective. Inspired by outstanding performance U-shaped networks in medical image segmentation, this article, we propose...

There has been a growing interest in food image recognition for wide range of applications. Among existing methods, mid‐level part‐based approaches show promising performances due to their suitability modelling deformable parts (FPs). However, the achievable accuracy is limited by FP representations based on low‐level features. Benefiting from capacity learn powerful features with labelled data, deep learning achieved state‐of‐the‐art several problems. Both mid‐level‐based and convolutional...

In this paper, we aim to develop a deep learning based automatic Attention Deficit Hyperactive Disorder (ADHD) diagnosis algorithm using resting state functional magnetic resonance imaging (rs-fMRI) scans. However, relative millions of parameters in neural networks (DNN), the number fMRI samples is still limited learn discriminative features from raw data. light this, first encode our prior knowledge on 3D voxel-wisely, including Regional Homogeneity (ReHo), fractional Amplitude Low...

Food image recognition is a key enabler for many smart home applications such as kitchen and personal nutrition log. In order to improve living experience life quality, systems collect valuable insights of users’ preferences, intake health conditions via accurate robust food recognition. addition, efficiency also major concern since are deployed on mobile devices where high-end GPUs not available. this paper, we investigate compact efficient methods, namely low-level mid-level approaches....

Food is an inseparable part of people's lives. image recognition has been attracting increasing attention due to the advances Internet, imaging techniques and social media. Approaches for food are mainly focused on two main directions: low-level approaches mid-level approaches. Low-level extract local features, such as SIFT or SURF, following feature encoding techniques. Mid-level higher-level parts have shown promising results in many problems. Compared with other problems, images highly...

This paper introduces the AltumView Sentinare smart activity sensor for senior care and patient monitoring. The uses an AI chip deep learning algorithms to monitor of people, collect statistics, notify caregivers when emergencies such as falls are detected. To protect privacy, only skeleton (stick figure) animations transmitted instead videos. is highly affordable, accessible, versatile. It was a CES 2021 Innovation Award Honoree, has been selected by Amazon one three fall detection devices...

At present, the mixing oil will be produced in sequential transportation of various refined inescapable. The is not qualified product which needed to dealt with. Volume less better. How reduce volume key cost and improve pipeline efficiency. This paper discusses cause mechanism mixed products characteristics mountain pipelines. Then, it providing a calculation method quantity, detecting section, treatment after receiving oil.

The modeling of channel and temporal information is crucial importance for action recognition tasks. To build a high-performance network by effectively capturing information, we propose CLS-Net: an algorithm based on channel-temporal modeling. proposed CLS-Net characterizes inserting multiple modules to end-to-end backbone network, including attention module (CA module) long-term (LT short-term (ST information. Specifically, the CA extracts correlation between feature channels so can learn...

Liver cancer is a part of the common causes death worldwide, and accurate diagnosis hepatic malignancy important for effective next treatment. In this paper, we propose convolutional neural network (CNN) based on spatiotemporal excitation (STE) module identification in four-phase computed tomography (CT) images. To enhance display detail lesion, expand single-channel CT images into three channels by using channel expansion method. Our proposed STE consists spatial (SE) temporal interaction...

To explore the potential targets and mechanism of action Sishen Decoction in treatment rheumatoid arthritis (RA) using a network pharmacology approach.Firstly, we analyzed differentially expressed genes Gene Expression Omnibus (GEO) database constructed protein-protein interaction (PPI) core target proteins determined by STRING platform Cytoscape software. We also performed gene ontology functional enrichment analysis, Kyoto Encyclopedia Genes Genomes (KEGG) signaling pathway set analysis...

Wagon text extraction mainly depends on manual identification of relevant information, which is laborious, time consuming, monotonous and error-prone. To address this concern, we develop a two-stage wagon system based the combination transfer learning defect-restore generative adversarial network (GAN). Considering limited number images vast computer resource required, texts are first detected via refined connectionist proposal network. In study, focus recognition generic strategy comprising...

Combined vacuum and surcharge preloading has gradually been widely used because of its advantages low cost, green environmental protection, good treatment effect. The conventional prefabricated vertical drain presents obvious defects in treatment, such as silting, serious bending the drainage board, large attenuation degree board along depth, long construction period, so on, which affect final reinforcement In this paper, MIDAS finite element simulation combined drains (PVDs) horizontal...

The traditional teaching approach in visual communication design courses is encountering significant challenges, as the conventional methods are too rigid to accommodate learning needs of contemporary students. In context rapid development artificial intelligence (AI) technology, this study revealed limitations delivering personalized content and practical experiences. It proposed a new AI-based collaborative developed task allocation model tailored for courses, along with knowledge-skill...

Systemic Lupus Erythematosus (SLE) is a multifactorial and complex immune disease; however, the relevance of COVID-19 infection in SLE patients remains uncertain.

The traditional paradigm of visual communication design education is encountering significant challenges in aligning with the dynamic learning preferences contemporary students. This paper delves into limitations conventional educational approaches, particularly their inadequacy delivering personalized content and hands-on experiences. In response, we propose a groundbreaking collaborative teaching model, seamlessly integrated Artificial Intelligence (AI) technologies. model emphasizes...