Exploring the Performance of Deep Neural Networks on Embedded Many-Core Processors

Phonon thermal transport is a key feature for the operation of thermoelectric materials, but it is challenging to accurately calculate the thermal conductivity of materials with strong anharmonicity or large cells. In this work, a deep neural network potential (NNP) is developed using a dataset based on density functional theory (DFT) and applied to describe the lattice dynamics of Sb2Te3 and Bi2Te3/Sb2Te3 superlattices. The lattice thermal conductivities of Sb2Te3 are first predicted using equilibrium molecular dynamics (EMD) simulations combined with an NNP and the results match well with experimental values. Then, through further exploration of weighted phase spaces and the Grüneisen parameter, we find that there is a stronger anharmonicity in the out-of-plane direction in Sb2Te3, which is the reason why the thermal conductivities are overestimated more in the out-of-plane direction than in the in-plane direction by solving the phonon Boltzmann transport equation (BTE) with only three-phonon scattering processes being considered. More importantly, the lattice thermal conductivities of Bi2Te3/Sb2Te3 superlattices with different periods are accurately predicted using non-equilibrium molecular dynamics (NEMD) simulations together with an NNP, which serves as a good example to explore the thermal transport physics of superlattices using a deep neural network potential.

• A high-accuracy Deep Neural Network Potential (DNNP) for FeCrV-based Refractory Medium Entropy Alloys (RMEAs) is developed. • The DNNP successfully predicts phase separation, showing good agreement with experimental data. • The important role of Short-Range Ordering (SRO) in influencing dislocation density, yield strength, and mechanical properties of the RMEA is revealed. Refractory medium entropy alloys (RMEAs) have attracted significant attention in recent years due to their exceptional mechanical properties and high-temperature stability, making them suitable for a number of advanced applications. While computational modelling such as density functional theory (DFT) and molecular dynamics (MD) are powerful for investigating RMEAs, these traditional methods are often constrained by high computational cost and limited accuracy. In this work, a deep neural network potential (DNNP) was developed to address the complex compositional nature of FeCr 2 V-based RMEAs with varying levels of tungsten doping. The DNNP demonstrated high accuracy, comparable to that of DFT calculations. Utilizing the DNNP, high-accuracy MD simulations were conducted to examine large-scale effects, including short-range ordering (SRO), twining, and dislocation behaviour, on the mechanical properties of the RMEAs. The results indicate that in the SRO structure, the covalency of V-V, Cr-Cr, and V-W ordered atomic pairs enhances local bonding strength and increases the elastic modulus of the RMEA. As the simulation temperature increases, dislocation mobility improves while dislocation density decreases, thereby enhancing the ductility of the material. Above 823 K, the SRO structure demonstrates superior mechanical performance, which is attributed to the increased length of 1/2<111>, dislocations facilitated by the formation of Cr-Fe and Cr-Cr ordered twins. This work underscores the potential of DNNP and MD simulations in predicting and analyzing the mechanical properties of RMEAs, advancing their development for various applications.

At present, many cities have established sophisticated metro transportation systems, and metro passenger traffic accounts for about 50 to 80 percent of the total passenger traffic. Unfortunately, there are hundreds of incidents of train delays and even casualties caused by foreign objects in metros of various cities every year. The detection of foreign objects between metro doors and Platform Screen Doors (PSDs) has become an urgent problem to be solved. The main contribution of this paper is that this is the first attempt to use deep neural networks to solve the problem of foreign object detection between the PSDs and metro doors. In this paper, we first introduce the object detection framework called You Only Look Once (YOLO series) and Single Shot MultiBox Detector (SSD). We further explore the performance of YOLO v3 and SSD by implementing their models to do the foreign object detecting task in our dataset (from real-world data). The experimental results demonstrate that deep neural networks achieve good detection accuracy in the task of foreign object detection between the PSDs and metro doors. In addition, the deep neural network can also give the category information of foreign objects, which greatly reducing the time of artificial judgment and relieving traffic pressure. We hope our paper will serve as a solid baseline and help ease future research in foreign object detection between the PSDs and metro doors.

BackgroundCognitive impairment and dementia pose a significant challenge to the aging population, impacting the well-being, quality of life, and autonomy of affected individuals. As the population ages, this will place enormous strain on health care and economic systems. While computerized cognitive training programs have demonstrated some promise in addressing cognitive decline, adherence to these interventions can be challenging.ObjectiveThe objective of this study is to improve the accuracy of predicting adherence lapses to ultimately develop tailored adherence support systems to promote engagement with cognitive training among older adults.MethodsData from 2 previously conducted cognitive training intervention studies were used to forecast adherence levels among older participants. Deep convolutional neural networks were used to leverage their feature learning capabilities and predict adherence patterns based on past behavior. Domain adaptation (DA) was used to address the challenge of limited training data for each participant, by using data from other participants with similar playing patterns. Time series data were converted into image format using Gramian angular fields, to facilitate clustering of participants during DA. To the best of our knowledge, this is the first effort to use DA techniques to predict older adults’ daily adherence to cognitive training programs.ResultsOur results demonstrated the promise and potential of deep neural networks and DA for predicting adherence lapses. In all 3 studies, using 2 independent datasets, DA consistently produced the best accuracy values.ConclusionsOur findings highlight that deep learning and DA techniques can aid in the development of adherence support systems for computerized cognitive training, as well as for other interventions aimed at improving health, cognition, and well-being. These techniques can improve engagement and maximize the benefits of such interventions, ultimately enhancing the quality of life of individuals at risk for cognitive impairments. This research informs the development of more effective interventions, benefiting individuals and society by improving conditions associated with aging.

Exploiting potential of deep neural networks by layer-wise fine-grained parallelism

Multiscale brain MRI super-resolution using deep 3D convolutional networks

Object detection is challenging in the computer vision area and is crucial in autonomous driving systems. The largest number of traditional techniques or the use of deep learning are evaluated in the urban area, but in rural areas, there is little research carried out. The goal of this work is object detection in rural roads, this paper presents the use of deep learning frameworks used as You Only Look Once (YOLO) and another that belongs to the same category of one-stage is the well-known Single Shot Multi-Box Detector (SSD), in the state of the literature, produces excellent results in detecting objects in real-time. These models behave differently in network architecture, for this reason, we use images of rural roads with different environments to achieve an optimal balance between precision and precision in the detection of objects. Furthermore, as there is no dataset in these environments, we created our own data set to perform the experiments due to the difficulty of this problem. The result of both detectors has produced acceptable results under certain conditions like lighting conditions, viewing perspectives, partial occlusion of the object.

This paper proposes a data augmentation strategy for improving on-road object detection based on a deep neural network. The method uses a single camera and detects objects based on an optimized deep neural network for a driving environment. The strategy also uses a single-shot multi-box detector (SSD) for object detection, which is a state-of-the-art deep-learning algorithm. The performance is improved by using data augmentation for an advanced driver assist system (ADAS) specific to on-road object recognition. The problem of object detection is first analyzed based on a deep neural network in the ADAS domain, and then representative object detection methods that use deep neural networks are surveyed. A restricted random crop process is suggested for detecting small objects in an image, and then a patch resampling strategy is proposed for solving the long tail property in an on-road dataset. The proposed ADAS domain-specific data augmentation method is adjusted for the original object detection method based on a deep neural network. The object detection results were evaluated using an embedded board on the KITTI benchmark dataset, and the suggested data augmentation method improves the average precision by 30%.

Object detection plays a crucial role in computer vision applications, ranging from autonomous vehicles to surveillance systems. One-stage object detectors have emerged as a powerful solution by simplifying detection pipelines, enabling real-time performance while maintaining competitive accuracy. This article provides a comprehensive survey of one-stage obj ect detection models, including you only look once (YOLO), single shot multibox detector (SSD), and PaddlePaddle YOLO (PP-YOLO) variants. A central question guides this exploration: How can incremental architectural refinements and training strategies improve the trade-off between speed, accuracy, and computational efficiency in object detection models for real-time applications? Through a detailed analysis of key innovations, performance metrics, and practical applications, this survey highlights the evolution of these models and their contributions to bridging the gap between research and deployment. Additionally, the paper identifies current challenges, such as small-object detection and resource efficiency, and proposes future research directions to advance the field further.

The objective of this study is to examine two powerful components: the YOLO (You Only Look Once) framework for object recognition on edge devices and the MobileNet architecture. With the rising demand for real-time, efficient, and accurate object detection in constrained environments, these models offer distinct advantages suited to different applications. YOLO is renowned for its high accuracy and speed, making it ideal for complex detection tasks, whereas MobileNet is optimized for efficiency, enabling deployment on devices with limited computational resources. The paper discusses the evolution of these models, their architectural differences, and their respective strengths and limitations when implemented on edge devices. Furthermore, we explore the feasibility of integrating these frameworks into a hybrid model that leverages the lightweight feature extraction of MobileNet and the robust detection capabilities of YOLO. Such a hybrid approach could significantly enhance real-time object detection performance across a wide range of hardware platforms.&nbsp; Researchers and practitioners seeking to deploy advanced object detection models in real-world applications, particularly where computational efficiency and accuracy are paramount may find this review as a valuable resource.

This paper describes the use of two different deep-learning algorithms for object detection to recognize different badgers. We use recordings of four different badgers under varying background illuminations. In total four different object detection algorithms based on deep neural networks are compared: The single shot multi-box detector (SSD) with the Inception-V2 or MobileNet as a backbone, and the faster region-based convolutional neural network (Faster R-CNN) combined with Inception-V2 or residual networks. Furthermore, two different activation functions are compared to compute probabilities that some badger is in the detected region: the softmax and sigmoid functions. The results of all eight models show that SSD obtains higher recognition accuracies (97.8%–98.6%) than Faster R-CNN (84.8%–91.7%). However, the training time of Faster R-CNN is much shorter than that of SSD. The use of different output activation functions seems not to matter much.

In industry, it is becoming common to detect and recognize industrial workpieces using deep learning methods. In this field, the lack of datasets is a big problem, and collecting and annotating datasets in this field is very labor intensive. The researchers need to perform dataset annotation if a dataset is generated by themselves. It is also one of the restrictive factors that the current method based on deep learning cannot expand well. At present, there are very few workpiece datasets for industrial fields, and the existing datasets are generated from ideal workpiece computer aided design (CAD) models, for which few actual workpiece images were collected and utilized. We propose an automatic industrial workpiece dataset generation method and an automatic ground truth annotation method. Included in our methods are three algorithms that we proposed: a point cloud based spatial plane segmentation algorithm to segment the workpieces in the real scene and to obtain the annotation information of the workpieces in the images captured in the real scene; a random multiple workpiece generation algorithm to generate abundant composition datasets with random rotation workpiece angles and positions; and a tangent vector based contour tracking and completion algorithm to get improved contour images. With our procedures, annotation information can be obtained using the algorithms proposed in this paper. Upon completion of the annotation process, a json format file is generated. Faster R-CNN (Faster R-convolutional neural network), SSD (single shot multibox detector) and YOLO (you only look once: unified, real-time object detection) are trained using the datasets proposed in this paper. The experimental results show the effectiveness and integrity of this dataset generation and annotation method.

X-ray baggage inspection is an essential task to detect threat objects at important controlled access places, which can guard personal safety and prevent crime. Generally, it is carried out by screeners to visually determine whether or not a bag contains threat objects. Whereas, manual detection exhibits distinct shortcomings, from high detection errors to different detection results produced by screeners. These limitations can be addressed by introducing automated detection model of threat objects for X-ray baggage inspection. However, existing automated detection methods cannot realize end-to-end detection and the detection results include only classification without location. In this paper, we propose an automated detection model of threat objects based on depthwise separable convolution. Our model is able to not only categorize the threat object but also locate it simultaneously. The network model has the advantage of high detection accuracy, fast computational speed, and a few parameters. Meanwhile, the precision of threat object regions is enhanced with the help of multi-scale prediction. A deformation layer is added in our model, which can provide invariance to affine warping. The experiments on the GDXray database (Mery et al. in J Nondestr Eval 34(4):42, 2015) demonstrate that the overall performance of our proposed model is superior to YOLOv3 (Redmon J and Farhadi A in YOLOv3: an incremental improvement, 2018) model, SSD (Liu et al. in SSD: single shot multibox detector. In: European Conference on Computer Vision (ECCV), pp. 21–37, 2016) model, and Tiny_YOLO (Redmon et al. in You only look once: unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788, 2015) model.

Hand gesture recognition has great applications in human-computer interaction (HCI), human-robot interaction (HRI), and supporting the deaf and mute. To build a hand gesture recognition model using deep learning (DL) with high results then needs to be trained on many data and in many different conditions and contexts. In this paper, we publish the TQU-HG dataset of large RGB images with low resolution (640×480) pixels, low light conditions, and fast speed (16 fps). TQU-HG dataset includes 60,000 images collected from 20 people (10 male, 10 female) with 15 gestures of both left and right hands. A comparative study with two branches: i) based on Mediapipe TML and ii) Based on convolutional neural networks (CNNs) (you only look once (YOLO); YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLO-Nas, single shot multiBox detector (SSD) VGG16, residual network (ResNet)18, ResNext50, ResNet152, ResNext50, MobileNet V3 small, and MobileNet V3 large), the architecture and operation of CNNs models are also introduced in detail. We especially fine-tune the model and evaluate it on TQU-HG and HaGRID datasets. The quantitative results of the training and testing are presented (F1-score of YOLOv8, YOLO-Nas, MobileNet V3 small, ResNet50 is 98.99%, 98.98%, 99.27%, 99.36%, respectively on the TQU-HG dataset and is 99.21%, 99.37%, 99.36%, 86.4%, 98.3%, respectively on the HaGRID dataset). The computation time of YOLOv8 is 6.19 fps on the CPU and 18.28 fps on the GPU.

With the continuous development of technology, the application of computer vision technology in cost monitoring of construction projects is becoming increasingly important. By utilizing computer vision technology, construction companies can monitor the progress of construction sites, material usage, and allocation of human resources in real-time, thereby better controlling project costs. The application of this technology can not only improve the efficiency of construction projects, but also reduce human errors and waste, which is of great significance for the successful completion of projects. Therefore, construction companies should actively adopt computer vision technology to improve the efficiency and accuracy of cost monitoring. In recent years, China's economy has continued to grow, continuously driving the development of infrastructure and manufacturing industries. However, at the same time, the production safety situation in China is becoming increasingly severe, and safety accidents continue to occur. One of the main causes of safety accidents is human misconduct, which includes workers wearing uniforms and safety helmets improperly during construction. Workers engage in some dangerous behaviors during construction, such as making phone calls, falling, and squatting for long periods of time. In response to the above issues, this article studies how the SSD (Single Shot MultiBox Detector) algorithm, CNN (Convolutional Neural Networks) algorithm, and YOLO (You only look once) algorithm can be applied to the behavior detection of construction workers, and compares them with existing object detection algorithms through experiments. The experimental data shows that the AP (Average precision), AP50, and AP75 values of the SSD-CNN algorithm in this article are 51.29%, 69.85%, and 54.81%, respectively. Among all the algorithms, the values of the three indicators rank in the top few.