Level Of Pyramid Research Articles

SCADA Systems With Focus on Continuous Manufacturing and Steel Industry: A Survey on Architectures, Standards, Challenges and Industry 5.0

Recent technological advances encompassed by the smart factory concept have fundamentally changed industrial control systems in the way they are structured and how they operate. Majority of these changes affect Supervisory Control And Data Acquisition (SCADA) systems, shifting them to a higher level of interoperability, heterogeneous networks, big data and toward internet technologies and services in general. However, this transformation does not affect all SCADA systems equally. The immediate industrial environment and controlled processes have a significant impact as well. This paper presents a holistic approach to SCADA systems implemented in continuous flow production control within the steel industry production environment. We outline the multi-layer architecture of the SCADA control framework and the aspects of interoperability and interconnection within the architecture reference models, together with the research challenges and opportunities arising from the recent rapid increasement of the industrial control systems complexity and digital transformation under the Industry 4.0 paradigm, resulting in disrupting levels of the traditional automation pyramid based on Purdue model toward a higher level of integration and interoperability enabling cross-level data exchange empowered by the Industrial Internet of Things. Furthermore, the paper addresses the problem of proprietary SCADA systems and elaborates the causal correlation between SCADA quality requirements and adoption of new technology in relation to the specific industrial environment of the steel manufacturing process.

Progressive Motion Context Refine Network for Efficient Video Frame Interpolation

Recently, flow-based frame interpolation methods have achieved great success by first modeling optical flow between target and input frames, and then building synthesis network for target frame generation. However, above cascaded architecture can lead to large model size and inference delay, hindering them from mobile and real-time applications. To solve this problem, we propose a novel Progressive Motion Context Refine Network (PMCRNet) to predict motion fields and image context jointly for higher efficiency. Different from others that directly synthesize target frame from deep feature, we explore to simplify frame interpolation task by borrowing existing texture from adjacent input frames, which means that decoder in each pyramid level of our PMCRNet only needs to update easier intermediate optical flow, occlusion merge mask and image residual. Moreover, we introduce a new annealed multi-scale reconstruction loss to better guide the learning process of this efficient PMCRNet. Experiments on multiple benchmarks show that proposed approaches not only achieve favorable quantitative and qualitative results but also reduces current model size and running time significantly.

Extended Feature Pyramid Network for Small Object Detection

Small object detection remains an unsolved challenge because it is hard to extract the information of small objects with only a few pixels. While scale-level corresponding detection in feature pyramid network alleviates this problem, we find feature coupling of various scales still impairs the performance of small objects. In this paper, we propose an extended feature pyramid network (EFPN) with an extra high-resolution pyramid level specialized for small object detection. Specifically, we design a novel module, named feature texture transfer (FTT), which is used to super-resolve features and extract credible regional details simultaneously. Moreover, we introduce a cross resolution distillation mechanism to transfer the ability of perceiving details across the scales of the network, where a foreground-background-balanced loss function is designed to alleviate area imbalance of foreground and background. In our experiments, the proposed EFPN is efficient on both computation and memory, and yields state-of-the-art results on small traffic-sign dataset Tsinghua-Tencent 100 K and small category of general object detection dataset MS COCO.

Entrepreneurship and After Life : How Does the Job Market Respond to Entrepreneurs Seeking Opportunities After the Death of Their Ventures?

Purpose : The paper aims to explore corporate response to job seekers having entrepreneurial experience. 9 out of 10 entrepreneurial ventures fail within five years of their ignition in India. This pushes many entrepreneurs to switch their roles from job providers to job seekers. When entrepreneurs shut their shops and join job market as job seekers they possibly have to face some unique challenges or opportunities on account of their entrepreneurial experience. Corporate may respond to them in different ways and treat their entrepreneurial experience, mindset and skill set with some incredulity. This research paper aims to improve our understanding of how does job market respond to former entrepreneurs seeking job opportunities? Also what could be potential sources of unique challenges faced during this pursuit of a job? Major Findings : There are inconsistencies in the way candidates with entrepreneurial experience are treated by the job market across Tier I, II and III cities. Candidates with entrepreneurial experience are more welcomed at the top level of the organizational pyramid if they have experience of significant number of years. Some degree of prior corporate work experience in addition to the entrepreneurial experience is more preferred by corporates. Freelance economy also provides an opportunity for former entrepreneurs to provide their skills to employers. Practical Implications : This paper recommends five Action Points to the stakeholders of entrepreneurial ecosystem including incubation centers, educational institutions, corporates and startup mentors to ensure effective support system for safer landing (or crashing) of entrepreneurs after the death of their ventures. Social implications : This paper has the potential to ignite interaction and integration among various stakeholders of entrepreneurial ecosystem to ensure smoother transition of a former startup founder from being a job provider to a job seeker. Originality/value : The use of qualitative research methodology for the aforementioned purpose is not very widely discussed and researched especially in the context of Indian startup ecosystem.

Visual experience recognition using adaptive support vector machine

Video has more information than the isolated images. Processing, analyzing and understanding of contents present in videos are becoming very important. Consumer videos are generally captured by amateurs using handheld cameras of events and it contains considerable camera motion, occlusion, cluttered background, and large intraclass variations within the same type of events, making their visual cues highly variable and less discriminant. So visual event recognition is an extremely challenging task in computer vision. A visual event recognition framework for consumer videos is framed by leveraging a large amount of loosely labeled web videos. The videos are divided into training and testing sets manually. A simple method called the Aligned Space-Time Pyramid Matching method was proposed to effectively measure the distances between two video clips from different domains. Each video is divided into space-time volumes over multiple levels. A new transfer learning method is referred to as Adaptive Multiple Kernel Learning fuse the information from multiple pyramid levels, features, and copes with the considerable variation in feature distributions between videos from two domains web video domain and consumer video domain.With the help of MATLAB Simulink videos are divided and compared with web domain videos. The inputs are taken from the Kodak data set and the results are given in the form of MATLAB simulation.

Image-Based 3D Ultrasound Reconstruction with Optical Flow via Pyramid Warping Network.

3D Ultrasound (US) contains rich spatial information which is helpful for medical diagnosis. However, current reconstruction methods with tracking devices are not suitable for clinical application. The sensorless freehand methods reconstruct based on US images which is less accuracy. In this paper, we proposed a network which reconstructs the US volume based on US images features and optical flow features. We proposed the pyramid warping layer which merges the image features and optical flow features with warping operation. To fuse the warped features of different scales in different pyramid levels, we adopted the fusion module using the attention mechanism. Meanwhile, we adopted the channel attention and spatial attention to our network. Our method was evaluated in 100 freehand US sweeps of human forearms which exhibits the efficient performance on volume reconstruction compared with other methods.

Low-light image enhancement by diffusion pyramid with residuals

With the advancement of the camera-related technology in mobile devices, the vast amount of photos have been taken and shared in our daily life. However, many users still have unsatisfactory experiences with low-visible photos, which are frequently acquired under complicated real-world environments. In this paper, a novel yet simple method for low-light image enhancement has been proposed without any learning procedure. The key idea of the proposed method is to estimate properties of the scene illumination both in global and local manner by exploiting the diffusion pyramid with residuals. Specifically, the residual of each scale level in the diffusion pyramid is combined with the corresponding input. This restored result efficiently highlights local details across different scale spaces, thus it is helpful for preserving the boundary of illuminations. By conducting max-pooling with restored results from different levels of the diffusion pyramid, which are resized to the original resolution, the illumination component is accurately inferred from a given image. Compared to recent learning-based approaches, one important advantage of the proposed method is to effectively avoid the overfitting problem to the specific training dataset. Experimental results on various benchmark datasets demonstrate the efficiency and robustness of the proposed method for low-light image enhancement in real-world scenarios.

Location of key points in infrared palm image based on MDFPF-ResNet

As an auxiliary diagnostic method that can infer the health status of human organs, hand diagnosis plays an important role in the development of traditional Chinese medicine and intelligent traditional Chinese medicine. In order to solve the problem of inaccurate positioning of key points in infrared image, a residual network based on multi-scale hole convolution feature pyramid fusion is proposed to locate key points in infrared palm image. In each feature pyramid fusion module, the parallel structure of hole convolution pyramid level and improved bottleneck module is adopted, which increases the receptive field and improves the generalization ability of residual network; multiple multi-scale hole convolution feature pyramid fusion modules are cascaded to gradually obtain high-level semantic features, and full connection layer and dropout layer are added at the output end to obtain high-level semantic features Key point positioning coordinates. The experimental results show that the proposed method has better positioning performance and stronger robustness in infrared palm image.

Improvement of Optical Flow Estimation by Using the Hampel Filter for Low-End Embedded Systems

Owing to the recent advances in the field of deep-learning-based approaches, state-of-the-art performance has been achieved for optical flow estimation. However, non-deep-learning-based improvement in the optical flow estimation performance is still required because many platforms, such as small UGVs and drones, involve constraints that make mounting GPUs difficult. Thus, in this study, we do not apply deep learning methods; rather, we improve the accuracy of the optical flow pipeline by optimizing only its fundamental parameters. The optical flow estimation performance is influenced by the number of coarse-to-fine estimation pyramids, the filter applied to each pyramid level, the window size, and the graduated nonconvexity (GNC) step number. No significant differences from the previous research were achieved by optimizing the parameters heuristically because they have already been optimized. Therefore, we decided to change the filter applied in each pyramid, which is the most important factor in determining the optical flow estimation performance. As a result of verification, the optical flow with the median filter did not show good optical flow estimation performance due to oversmoothing of the image boundary, and the optical flow with the weighted median filter proposed to overcome this drawback could not well address the complexity of the equation and deal with the large computation cost. The proposed Hampel filter shows better performance by minimizing the loss of the original image in the image smoothing process and reduced computational complexity compared to the weighted median filter. The principle of the Hampel filter is similar to that of the median filter, except that if the reference pixel is statistically close to the pixel median in the window, it works by preserving the original pixel value. This is usually used for filtering outliers in a 1D signal but has never been applied for 2D image filters. This study expanded the application of the existing Hampel filter to include 2D images. Compared to the conventional median filter, this filter demonstrates relatively good performance, as it can considerably reduce the loss in the image data and improve the excessive edge smoothing effect such that it can better identify motion. The performance-related improvements achieved by this research were verified using the KITTI Vision Benchmark Suite, and the simulation results demonstrate that the Hampel filter can replace the existing filter and that it has strengths related to multi-object detection, background recognition, and the identification of moving objects at the edges of images.

Haar Wavelet Pyramid Based Melanoma Skin Cancer Identification with Ensemble of Machine Learning Algorithms

Melanoma is a mortal type of skin cancer. Early detection of melanoma significantly improves the patient’s chances of survival. Detection of melanoma at an early juncture demands expert doctors. The scarcity of such expert doctors is a major issue with healthcare systems globally. Computer-assisted diagnostics may prove helpful in this case. This paper proposes a health informatics system for melanoma identification using machine learning with dermoscopy skin images. In the proposed method, the features of dermoscopy skin images are extracted using the Haar wavelet pyramid various levels. These features are employed to train machine learning algorithms and ensembles for melanoma identification. The consideration of higher levels of Haar Wavelet Pyramid helps speed up the identification process. It is observed that the performance gradually improves from the Haar wavelet pyramid level 4x4 to 16x16, and shows marginal improvement further. The ensembles of machine learning algorithms have shown a boost in performance metrics compared to the use of individual machine learning algorithms.

Haar Wavelet Pyramid Based Melanoma Skin Cancer Identification with Ensemble of Machine Learning Algorithms

Melanoma is a mortal type of skin cancer. Early detection of melanoma significantly improves the patient’s chances of survival. Detection of melanoma at an early juncture demands expert doctors. The scarcity of such expert doctors is a major issue with healthcare systems globally. Computer-assisted diagnostics may prove helpful in this case. This paper proposes a health informatics system for melanoma identification using machine learning with dermoscopy skin images. In the proposed method, the features of dermoscopy skin images are extracted using the Haar wavelet pyramid various levels. These features are employed to train machine learning algorithms and ensembles for melanoma identification. The consideration of higher levels of Haar Wavelet Pyramid helps speed up the identification process. It is observed that the performance gradually improves from the Haar wavelet pyramid level 4x4 to 16x16, and shows marginal improvement further. The ensembles of machine learning algorithms have shown a boost in performance metrics compared to the use of individual machine learning algorithms.

Laplacian pyramid networks: A new approach for multispectral pansharpening

Pansharpening is about fusing a high spatial resolution panchromatic image with a simultaneously acquired multispectral image with lower spatial resolution. In this paper, we propose a Laplacian pyramid pansharpening network architecture for accurately fusing a high spatial resolution panchromatic image and a low spatial resolution multispectral image, aiming at getting a higher spatial resolution multispectral image. The proposed architecture considers three aspects. First, we use the Laplacian pyramid method whose blur kernels are designed according to the sensors’ modulation transfer functions to separate the images into multiple scales for fully exploiting the crucial spatial information at different spatial scales. Second, we develop a fusion convolutional neural network (FCNN) for each scale, combining them to form the final multi-scale network architecture. Specifically, we use recursive layers for the FCNN to share parameters across and within pyramid levels, thus significantly reducing the network parameters. Third, a total loss consisting of multiple across-scale loss functions is employed for training, yielding higher accuracy. Extensive experimental results based on quantitative and qualitative assessments exploiting benchmarking datasets demonstrate that the proposed architecture outperforms state-of-the-art pansharpening methods. Code is available at https://github.com/ChengJin-git/LPPN.

Mixed-methods evaluation of acceptability of the District Health Information Software (DHIS2) for neglected tropical diseases program data in Cameroon

Background The District Health Information Software (DHIS2), adopted as national health information system by the ministry of public health in Cameroon, did not integrate neglected tropical diseases (NTD) program data. Integrating NTD program data into the national DHIS2 might require more than technical skills. Our study aimed to explore the factors that affect acceptability and use of DHIS2 by NTD stakeholders for successful integration of NTD program data into the national DHIS2. Methods For purposes of this mixed-methods study, the data were collected through a self-administered questionnaire targeting NTD stakeholders at different levels of the health pyramid from all the ten Regions in Cameroon. The questionnaire was based on a modified Unified Theory of Acceptance and Use of Technology (UTAUT) model, supplemented by a qualitative analysis to assess the acceptability, and use of the DHIS2 as a platform for NTD program data in Cameroon. Results We found 81.9% (95% confidence interval, CI=0.784-0.859; standard error=0.019) of intention to use DHIS2 for NTDs program data and 18.4% (95% CI=0.130-0.289; standard error=0.041) of actual use among survey participants. Social influence (β=0.269, P=0.000), voluntariness of use (β=0.243, P=0.000), performance expectancy (β=0.186, P=0.010), and training adequacy (β=0.199, P=0.000) would positively influence intention to use DHIS2. Computer anxiety (β=-0.230, P=0.000) and technology experience (β=0.374, P=0.000) would have a significant negative and positive effect on actual use, respectively. The most critical challenges in using DHIS2 referred to facilitating conditions (conditions of the work environment), specifically electricity and internet connection, impeding actual use of DHIS2. Conclusions Our study revealed that NTD stakeholders in Cameroon are ready to accept DHIS2 for NTD program in Cameroon. However, to ensure its successful implementation. For example, we recommend that NTD program managers plan adequate support in providing proper training, non-vendor specific 2G-3G-4G internet modems with data bundle and smartphones/laptops to ease the use of DHIS2 by NTD stakeholders. We showed that acceptability of DHIS2 studied through UTAUT model should be complemented with a qualitative analysis for richer insights.

Global safety in anaesthesia and surgery: Implications for anaesthesia in Pakistan.

The Lancet Commission report in 2015 highlighted the role of safe surgery and anaesthesia. They also gave a framework of recommendations, indicators and targets to achieve universal access to safe surgery. This article explores the applicability of these indicators in Pakistan namely; access to timely surgery, specialist surgical workforce diversity, surgical volumes, perioperative mortality rate, protection against impoverishing expenditure and protection against catastrophic expenditure.

Haar Wavelet Pyramid-Based Melanoma Skin Cancer Identification With Ensemble of Machine Learning Algorithms

Melanoma is a mortal type of skin cancer. Early detection of melanoma significantly improves the patient’s chances of survival. Detection of melanoma at an early juncture demands expert doctors. The scarcity of such expert doctors is a major issue with healthcare systems globally. Computer-assisted diagnostics may prove helpful in this case. This paper proposes a health informatics system for melanoma identification using machine learning with dermoscopy skin images. In the proposed method, the features of dermoscopy skin images are extracted using the Haar wavelet pyramid various levels. These features are employed to train machine learning algorithms and ensembles for melanoma identification. The consideration of higher levels of Haar Wavelet Pyramid helps speed up the identification process. It is observed that the performance gradually improves from the Haar wavelet pyramid level 4x4 to 16x16, and shows marginal improvement further. The ensembles of machine learning algorithms have shown a boost in performance metrics compared to the use of individual machine learning algorithms.

Learning local descriptors with multi-level feature aggregation and spatial context pyramid

Despite that efforts have shifted to learning local descriptors with convolutional neural network (CNN) from hand-crafted realm, the inherent feature hierarchy within CNN has been rarely explored. To increase both the invariant and discriminative abilities of the CNN-based local descriptors by making use of the complementary representation powers of the feature maps at different levels of CNN, in this paper, we design a multi-level feature aggregation (MLFA) module to communicate information across pyramid levels effectively. Then, each level extracts a feature vector after feature fusion and the final descriptor concatenates these outputs. Moreover, to leverage the spatial structure within a local patch, we propose a novel spatial context pyramid (SCP) module to capture the spatial information. SCP is devised in a residual manner and only several additional parameters are introduced to the model. We implement our algorithm based on the HardNet framework and carry out comprehensive evaluation on the UBC Phototour, HPatches and ETH datasets. The experimental results demonstrate that the proposed method performs favorably against the state-of-the-art ones. Ablation study is also provided to show the effectiveness of each component.

Progressive global perception and local polishing network for lung infection segmentation of COVID-19 CT images

In this paper, a progressive global perception and local polishing (PCPLP) network is proposed to automatically segment the COVID-19-caused pneumonia infections in computed tomography (CT) images. The proposed PCPLP follows an encoder-decoder architecture. Particularly, the encoder is implemented as a computationally efficient fully convolutional network (FCN). In this study, a multi-scale multi-level feature recursive aggregation (mmFRA) network is used to integrate multi-scale features (viz. global guidance features and local refinement features) with multi-level features (viz. high-level semantic features, middle-level comprehensive features, and low-level detailed features). Because of this innovative aggregation of features, an edge-preserving segmentation map can be produced in a boundary-aware multiple supervision (BMS) way. Furthermore, both global perception and local perception are devised. On the one hand, a global perception module (GPM) providing a holistic estimation of potential lung infection regions is employed to capture more complementary coarse-structure information from different pyramid levels by enlarging the receptive fields without substantially increasing the computational burden. On the other hand, a local polishing module (LPM), which provides a fine prediction of the segmentation regions, is applied to explicitly heighten the fine-detail information and reduce the dilution effect of boundary knowledge. Comprehensive experimental evaluations demonstrate the effectiveness of the proposed PCPLP in boosting the learning ability to identify the lung infected regions with clear contours accurately. Our model is superior remarkably to the state-of-the-art segmentation models both quantitatively and qualitatively on a real CT dataset of COVID-19.

Automation Pyramid as Constructor for a Complete Digital Twin, Case Study: A Didactic Manufacturing System.

Nowadays, the concept of Industry 4.0 aims to improve factories’ competitiveness. Usually, manufacturing production is guided by standards to segment and distribute its processes and implementations. However, industry 4.0 requires innovative proposals for disruptive technologies that engage the entire production process in factories, not just a partial improvement. One of these disruptive technologies is the Digital Twin (DT). This advanced virtual model runs in real-time and can predict, detect, and classify normal and abnormal operating conditions in factory processes. The Automation Pyramid (AP) is a conceptual element that enables the efficient distribution and connection of different actuators in enterprises, from the shop floor to the decision-making levels. When a DT is deployed into a manufacturing system, generally, the DT focuses on the low-level that is named field level, which includes the physical devices such as controllers, sensors, and so on. Thus, the partial automation based on the DT is accomplished, and the information between all manufacturing stages could be decremented. Hence, to achieve a complete improvement of the manufacturing system, all the automation pyramid levels must be included in the DT concept. An artificial intelligent management system could create an interconnection between them that can manage the information. As a result, this paper proposed a complete DT structure covering all automation pyramid stages using Artificial Intelligence (AI) to model each stage of the AP based on the Digital Twin concept. This work proposes a virtual model for each level of the traditional AP and the interactions among them to flow and control information efficiently. Therefore, the proposed model is a valuable tool in improving all levels of an industrial process. In addition, It is presented a case study where the DT concept for modular workstations underpins the development of technologies within the framework of the Automation Pyramid model is implemented into a didactic manufacturing system.

S-RPN: Sampling-balanced region proposal network for small crop pest detection

Effective pest management and control are the key factors in the agricultural food safety field. Therefore, the automatic monitoring and precise recognition of crop pests have a high practical value in the process of agricultural planting. Over the years, pest recognition and detection results have been rapidly improved with the development of deep learning-based methods. Although promising, these methods still have limited efficiency and precision to detect crop pests with very small scales, deteriorating their effectiveness. The main reason is that current deep learning-based methods may not be able to extract sufficient detailed appearance features for small pest objects in an image, making it difficult to train a classifier to detect and distinguish small pests from the backgrounds or similar objects. To address the small pest recognition and detection problem, in this paper, we instead seek to recast the current region proposal network and perform more details in different scales for easier small pest detection. Inspired by the visual attention system, we first introduce attention mechanism into the Residual network for obtaining richer pest feature appearance, especially the detailed features of small object pests; Then, to make the region proposal network (RPN) obtain more high-quality object proposals for easier detection, a sampling-balanced region proposal generation network is proposed for improving pest detection accuracy. Furthermore, we devise a novel adaptive region of interest (RoI) selection method to learn features from different levels of the feature pyramid. Several experiments were conducted on the proposed AgriPest21 dataset, and our method can achieve an average recall of 89.0% and mAP of 78.7%, outperforming other state-of-the-art methods, including SSD, RetinaNet, Free-Anchor, PISA, Grid RCNN, and Cascade RCNN detectors.

Biological study of uro-genital trichomonosis in women during gynecological consultation at the prefectoral hospital of Macenta, Guinee

Objectives: The objectives of this study were to: calculate the frequency of urogenital trichomoniasis, describe the socio-demographic characteristics and analyze the biological samples. Methodology: This was a six (6) month cross-sectional prospective study carried out in the maternity ward (gynecological consultation) and in the laboratory of the Prefectural Hospital of Macenta. All the women who came for gynecological consultation were included in the study and among them, all those who presented a laboratory examination report for vaginal secretions and / or urine during the study period. Not included in the study were all the women who came for a gynecological consultation but who had not received a laboratory examination report for vaginal secretions and / or urine and all those already undergoing treatment to imidazole in the 15 days preceding the survey were not included. After applying the selection procedure (inclusion and non-inclusion criteria), we obtained a sample size N = 703 cases. Results: The frequency of urogenital trichomoniasis was 26.46% among urogenital infections. The epidemiological profile was that of: young women aged 22-27 (42.47%), housewife (39.76%), having a primary education (30.43%) and married (28.53%). half of the patients were asymptomatic followed by those with fetid leucorrhoea 32.61%. High positivity was found in vaginal secretions (30.41%). Conclusion: Urogenital trichomoniasis is a sexually transmitted infection, common in women of childbearing or childbearing age. It constitutes a public health problem, one of the control strategies for which it involves sensitization and care for women / couples at all levels of the health pyramid.