Correlation between volumetric CT scans and lung function in lower respiratory tract infection

Computed tomography(CT) is currently the gold standard to monitor lower respiratory tract infection(LRTI), however CT is expensive and involves considerable doses of radiation which prevents monitoring patients with the required frequency. As spirometry is a simple procedure that measures inhaled and exhaled volume of air as a function of time, it may have potential to overcome some of these difficulties. This study aimed to explore the correlation between spirometry and CT parameters in LRTI. Volumetric CT was performed in 34 outpatients with LRTI using a 64 MultiDetector CT. Tracheobronchial tree and lung parenchyma were segmented with Region Growing technique and Morphological Operations to obtain tracheobronchial and bronchial(left and right) trees and lung parenchyma volumes(LPV). Forced expiratory volume in the 1º second percentage predicted(FEV1pp), forced vital capacity percentage predicted(FVCpp) and FEV1/FVC ratio were also collected. Correlations were explored with Pearson’s Correlation(PASW 18.0). Participants’(47.1% males) mean age was 52.7±18.9y. Tracheobronchial tree volume correlated significantly with FEV1pp(r=0.357, p=0.038) and FVCpp(r=0.369, p=0.032). Left and right bronchial tree volumes also correlated significantly with FEV1pp(r=0.514, p=0.02 and r=0.507, p=0.02, respectively) and FVCpp(r=0.503, p=0.002 and r=0.436, p=0.010, respectively). Regarding the FEV1/FVC ratio, a negative and significant correlation with LPV was found(r=0.385, p=0.025). These findings suggest that spirometry should not be used alone to monitor LRTI as correlations found were mainly moderate. Further research is needed to explore other non-invasive measures, e.g., respiratory sound analysis.publishe

ANALISIS TERHADAP OPTIMALISASI VOLUME PENJUALAN MELALUI PENYERAHAN PEKERJAAN KEPADA KARYAWAN OUTSOURCING PADA PT WARNA AGUNG DI BANDAR LAMPUNG

Outsourcing in Indonesian labor law is defined as the contracting of workers and providers of labor services. The legal arrangements for outsourcing in Indonesiaare regulated in Articles 64, 65 and 66 of Law Number 13 of 2003 concerningManpower and Decree of the Minister of Manpower and Transmigration of theRepublic of Indonesia No. Kep.101/Men/VI/2004 of 2004 concerning Procedures forLicensing of Companies Providing Workers/Labourers.The results of the research are the process of handing over work to outsourcingemployees at PT Warna Agung, namely PT Warna Agung contacting PT Bina CiptaAbadi as an outsourcing company, then formulating a Work Agreement for charteringwork to support the company's operations. Optimization of sales volume throughhanding over of work to outsourced employees is an effort by PT Warna Agung toobtain competent and experienced workers, especially in the marketing field. Efforts toprotect the legal rights of outsourced employees at PT Warna Agung in BandarLampung are to create legal certainty regarding the rights of outsourced employees soas to ensure the fulfillment of the rights of outsourced employees.The advice given is that PT Warna Agung should pay more attention tooutsourcing employees in fulfilling employee welfare in the future. Then workers shouldunderstand and understand the rights and obligations agreed in the outsourcing workagreement and the government should carry out stricter supervision so that there are no legal violations of the rights of outsourced employees

STV-based Video Feature Processing for Action Recognition

In comparison to still image-based processes, video features can provide rich and intuitive information about dynamic events occurred over a period of time, such as human actions, crowd behaviours, and other subject pattern changes. Although substantial progresses have been made in the last decade on image processing and seen its successful applications in face matching and object recognition, video-based event detection still remains one of the most difficult challenges in computer vision research due to its complex continuous or discrete input signals, arbitrary dynamic feature definitions, and the often ambiguous analytical methods. In this paper, a Spatio-Temporal Volume (STV) and region intersection (RI) based 3D shape-matching method has been proposed to facilitate the definition and recognition of human actions recorded in videos. The distinctive characteristics and the performance gain of the devised approach stemmed from a coefficient factor-boosted 3D region intersection and matching mechanism developed in this research. This paper also reported the investigation into techniques for efficient STV data filtering to reduce the amount of voxels (volumetric-pixels) that need to be processed in each operational cycle in the implemented system. The encouraging features and improvements on the operational performance registered in the experiments have been discussed at the end

Modeling the Resource Requirements of Convolutional Neural Networks on Mobile Devices

Convolutional Neural Networks (CNNs) have revolutionized the research in computer vision, due to their ability to capture complex patterns, resulting in high inference accuracies. However, the increasingly complex nature of these neural networks means that they are particularly suited for server computers with powerful GPUs. We envision that deep learning applications will be eventually and widely deployed on mobile devices, e.g., smartphones, self-driving cars, and drones. Therefore, in this paper, we aim to understand the resource requirements (time, memory) of CNNs on mobile devices. First, by deploying several popular CNNs on mobile CPUs and GPUs, we measure and analyze the performance and resource usage for every layer of the CNNs. Our findings point out the potential ways of optimizing the performance on mobile devices. Second, we model the resource requirements of the different CNN computations. Finally, based on the measurement, pro ling, and modeling, we build and evaluate our modeling tool, Augur, which takes a CNN configuration (descriptor) as the input and estimates the compute time and resource usage of the CNN, to give insights about whether and how e ciently a CNN can be run on a given mobile platform. In doing so Augur tackles several challenges: (i) how to overcome pro ling and measurement overhead; (ii) how to capture the variance in different mobile platforms with different processors, memory, and cache sizes; and (iii) how to account for the variance in the number, type and size of layers of the different CNN configurations

High volume colour image processing with massively parallel embedded processors

Currently Oc´e uses FPGA technology for implementing colour image processing for their high volume colour printers. Although FPGA technology provides enough performance it, however, has a rather tedious development process. This paper describes the research conducted on an alternative implementation technology: software defined massively parallel processing. It is shown that this technology not only leads to a reduction in development time but also adds flexibility to the design