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Your search keyword '"Guo, Jiashu"' showing total 22 results
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22 results on '"Guo, Jiashu"'

5. An Ultra-Low Quiescent Current Resistor-Less Power on Reset Circuit

Author

Houpeng Chen, Guo Jiashu, Xi Li, Xie Chenchen, Zhitang Song, Yi Lv, Qian Wang, Jie Miao, Lei Yu, and Weili Liu

Subjects
Physics, business.industry, 020208 electrical & electronic engineering, Transistor, Electrical engineering, Biasing, Hardware_PERFORMANCEANDRELIABILITY, 02 engineering and technology, Power-on reset, 020202 computer hardware & architecture, law.invention, Capacitor, CMOS, law, Boost converter, Hardware_INTEGRATEDCIRCUITS, 0202 electrical engineering, electronic engineering, information engineering, Electrical and Electronic Engineering, Resistor, business, Pulse-width modulation, Hardware_LOGICDESIGN
Abstract

An ultra-low quiescent current resistor-less power on reset circuit is presented in this brief. The circuit is used in energy harvesting systems to change boost converter from startup state to PWM modulation state. It integrates power on reset (POR) and brown out detection (BOD) functions without resistors or bandgaps overhead. Dual threshold detection (DTD) technology is used instead of a large size delay cell in generation of pulse signals. The circuit has been implemented in a $0.13{\mu }\text{m}$ standard CMOS technology. The active area and the quiescent current of the circuit are $154.4{\mu }\text{m}\,\,\times 26.8{\mu }\text{m}$ and about 1nA, respectively. Ultra-low quiescent current and small area make the proposed circuit suitable to be applied in low power applications like energy harvesting and beyond.

Published
2021

7. BIST-Based Fault Diagnosis for PCM With Enhanced Test Scheme and Fault-Free Region Finding Algorithm

Author

Yi Lv, Jie Miao, Xi Li, Xie Chenchen, Qian Wang, Zhitang Song, Lei Yu, Houpeng Chen, and Guo Jiashu

Subjects
Very-large-scale integration, Hardware_MEMORYSTRUCTURES, Computer science, 02 engineering and technology, Fault (power engineering), 020202 computer hardware & architecture, Non-volatile memory, Modeling and simulation, Built-in self-test, Hardware and Architecture, Fault coverage, 0202 electrical engineering, electronic engineering, information engineering, Overhead (computing), Electrical and Electronic Engineering, Algorithm, Software
Abstract

As one of the most promising candidates for nonvolatile memory, phase change memory (PCM) technology has shown great performance advantages in market applications. However, the conventional test methods have not kept pace with the development. In this article, focusing on specific PCM faults and others, an enhanced march test algorithm is proposed to achieve 100% fault coverage and diagnostic accuracy in bit-oriented PCM. The proposed algorithm is then converted for word-oriented PCM and equipped with capability to detect potential intraword impact. In addition, to reduce the dependence of memory test on the external devices, a novel storage scheme of fault information is devised. Through the modeling and simulation in C-language, this method is proven to improve the probability of finding the predefined fault-free regions in the tested memory. Finally, combining the enhanced test algorithm and the novel storage scheme, a built-in self-test (BIST) march test scheme is proposed, realizing the independent test of PCM without any external equipment. By comparison, the result of experiments, which are performed with C-language, proves that the proposed test scheme not only increases the fault coverage and diagnostic accuracy, but also reduces the additional area overhead.

Published
2020

12. A self-start circuit with asymmetric inductors reconfigurable technology for dual-output boost converter for energy harvesting

Author

Zhitang Song, Houpeng Chen, Guo Jiashu, Qian Wang, Jie Miao, Lei Yu, Zhang Zhenwei, Xi Li, Xie Chenchen, and Weili Liu

Subjects
Materials science, business.industry, Boost converter, Electrical engineering, Electrical and Electronic Engineering, Condensed Matter Physics, business, Inductor, Energy harvesting, Electronic, Optical and Magnetic Materials, Dual (category theory)
Published
2020

13. Deep Neural Networks for Modeling Sequential Prediction Tasks with Applications in Brain Tumors

Author

Guo, Jiashu and Guo, Jiashu

Abstract

Gliomas are malignant brain tumors that are associated with high neurological morbidity and poor outcomes. Patients diagnosed with low-grade gliomas are typically followed by a sequence of measurements of the tumor size as they visit their medical physician. To optimize the timing of therapy of patients, the effective methodologies are needed to predict the future behavior of gliomas by modeling the mechanisms that mediate the characteristic features of gliomas. Machine learning is deployed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or infeasible, example applications include email filtering, optical character recognition (OCR), and computer vision. In the broad sweep of machine learning’s current worldly ambitions, healthcare applications seem to top the list for funding and press in the last three years. In this thesis, we demonstrate the promise of Long Short-Term Memory Neural Networks (LSTMs), and a 1-Dimensional Convolutional Neural Network (1-D CNN) to address two important clinical questions in low-grade gliomas: 1) classification and prediction of future behavior; and 2) early detection of dedifferentiation to a higher grade or more aggressive growth. The model of brain tumor growth was recently modeled to be a complex system of partial differential equations (PDEs). This system of PDE provides a significant amount of information about the grade of the tumor that cannot be easily measured from the tumor mass. For example, the PDEs have constants that are related to motility, angiogenesis and the mitotic rate; however, a single measurement of the brain tumor mass cannot easily be connected to these constants. This thesis presents a machine learning-based approach to estimate these parameters using a sequence of tumor mass measurements with an LSTM and a 1-D CNN network to solve the inverse problem of PDE parameter estimation. Experimental results show that through working with differen

Published
2018

16. A method for building spatial multi-dimension datasets

Author

Zhang Hong-xia and Guo Jiashu

Subjects
Spatial relation, Octree, Theoretical computer science, Computer science, Spatial database, Object-based spatial database, Data mining, computer.software_genre, computer, Decimal, Data warehouse, Vexel, Coding (social sciences)
Abstract

The star or snowflake type multi-dimension spatial data warehouse is based on the spatial facts as core and geological attributes as dimensions. A method for constructing spatial factual table based on the spatial relations of data is proposed. The construction of spatial facts and the relations analysis of spatial facts is based on the geological attribute dimension and vexel. In order to express the spatial multi-dimension data, an octree subsection vexel model is given. The expression of multi-dimension data and spatial integration of the multilevel vexel geological bodies and geological fields devision based on Morton decimal coding are implemented.

Published
2010

17. The Construction and Key Issues of Spatial Data Warehouse for Oil Exploration and Development

Author

Liu Zhan and Guo Jiashu

Subjects
Spatial data infrastructure, Database, business.industry, Data management, Fossil fuel, InformationSystems_DATABASEMANAGEMENT, computer.software_genre, Data science, Data warehouse, chemistry.chemical_compound, Development (topology), chemistry, Petroleum industry, Petroleum, Data center, business, computer
Abstract

professional database especially the data center management model has achieved the integration, share of data and the separation between data and application in oil exploration and development, but the issues for data analysis and data mining, especially the professional data mining, are not well solved. In this paper, the necessity of spatial data warehouse used in oil and gas exploration and development is analyzed, data management framework based on the spatial data warehouse for oil and gas exploration and development is proposed, the construction steps of spatial data warehouse are provided, the differences between spatial data warehouse and other data management model are given, the key issues the construction of spatial data warehouse faces are analyzed.

Published
2010

21. Molecular Characteristics of Jujube Yellow Mottle-Associated Virus Infecting Jujube (Ziziphus jujuba Mill.) Grown at Aksu in Xinjiang of China.

Author

Guo, Jiashu, Wang, Yanxiang, Wang, Guoping, Hong, Jian, Yang, Zuokun, Bai, Jianyu, Hong, Ni, and Maina, Solomon

Subjects
*JUJUBE (Plant), *PHYTOPLASMAS, *CULTIVATED plants, *NON-coding RNA, *VIRAL proteins
Abstract

Chinese jujube (Ziziphus jujuba Mill.) is a native fruit crop in China. Leaf mottle and dapple fruit disease is prevalent in cultivated jujube plants grown at Aksu in Xinjiang Uygur Autonomous Region of China. Jujube yellow mottle-associated virus (JYMaV), a tentative member in the genus Emaravirus, was recently identified from mottle-diseased jujube plants grown in Liaoning Province in China, but its incidence and genetic diversity in China is unknown. In this study, the genome sequences of three JYMaV isolates from two jujube cultivars and one jujube variant were determined by high-throughput sequencing (HTS) for small RNA and rRNA-depleted RNA coupled with RT-PCR assays. Comparison of these sequences together with sequences of the viral RNA segments derived by primer set 3C/5H-based RT-PCR revealed that genetic diversity was present in the virus populations and high sequence variation occurred at the non-translational regions of each of the viral genomic segments. Field investigation confirmed the close association of the virus with leaf mottle symptoms of jujube plants. Furthermore, this study revealed that P5 encoded in the viral RNA5 displayed a nuclear localization feature differing from the plasmodesma (PD) subcellular localization of the virus movement protein (P4), and the two proteins could interact with each other in the BiFC assays. Our study provides a snapshot of JYMaV genetic diversity in its natural hosts. [ABSTRACT FROM AUTHOR]

Published
2021
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22. Transfection of entomopathogenic Metarhizium species with a mycovirus confers hypervirulence against two lepidopteran pests.

Author

Guo J, Zhang P, Wu N, Liu W, Liu Y, Jin H, Francis F, and Wang X

Subjects
Animals, Virulence genetics, Pest Control, Biological methods, Moths microbiology, Moths virology, Genome, Viral, Phylogeny, Metarhizium pathogenicity, Metarhizium genetics, Fungal Viruses genetics
Abstract

Although most known viruses infecting fungi pathogenic to higher eukaryotes are asymptomatic or reduce the virulence of their host fungi, those that confer hypervirulence to entomopathogenic fungus still need to be explored. Here, we identified and studied a novel mycovirus in Metarhizium flavoviride , isolated from small brown planthopper ( Laodelphax striatellus ). Based on molecular analysis, we tentatively designated the mycovirus as Metarhizium flavoviride partitivirus 1 (MfPV1), a species in genus Gammapartitivirus , family Partitiviridae . MfPV1 has two double-stranded RNAs as its genome, 1,775 and 1,575 bp in size respectively, encapsidated in isometric particles. When we transfected commercial strains of Metarhizium anisopliae and Metarhizium pingshaense with MfPV1, conidiation was significantly enhanced ( t test; P -value < 0. 01), and the significantly higher mortality rates of the larvae of diamondback moth ( Plutella xylostella ) and fall armyworm ( Spodoptera frugiperda ), two important lepidopteran pests were found in virus-transfected strains (ANOVA; P -value < 0.05). Transcriptomic analysis showed that transcript levels of pathogenesis-related genes in MfPV1-infected M. anisopliae were obviously altered, suggesting increased production of metarhizium adhesin-like protein, hydrolyzed protein, and destruxin synthetase. Further studies are required to elucidate the mechanism whereby MfPV1 enhances the expression of pathogenesis-related genes and virulence of Metarhizium to lepidopteran pests. This study presents experimental evidence that the transfection of other entomopathogenic fungal species with a mycovirus can confer significant hypervirulence and provides a good example that mycoviruses could be used as a synergistic agent to enhance the biocontrol activity of entomopathogenic fungi., Competing Interests: Competing interests statement:The authors declare no competing interest.

Published
2024
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