How remouldable computer hardware is speeding up science

Nature ◽  
2021 ◽  
Vol 600 (7888) ◽  
pp. 348-349
Author(s):  
Jeffrey M. Perkel
Keyword(s):  
Computer Hardware

Analysis of 3-d molecular distribution with the digital imaging microscope

1988 ◽  
Vol 46 ◽  
pp. 40-41
Author(s):  
W.A. Carrington ◽  
F.S. Fay ◽  
K.E. Fogarty ◽  
L. Lifshitz
Keyword(s):  
Image Restoration ◽  
Digital Imaging ◽  
Fluorescent Dyes ◽  
Optical Axis ◽  
Computer Hardware ◽  
Computer Algorithms ◽  
Low Contrast ◽  
Specific Proteins ◽  
Effective Use ◽  
Single Living Cells

Advances in digital imaging microscopy and in the synthesis of fluorescent dyes allow the determination of 3D distribution of specific proteins, ions, GNA or DNA in single living cells. Effective use of this technology requires a combination of optical and computer hardware and software for image restoration, feature extraction and computer graphics.The digital imaging microscope consists of a conventional epifluorescence microscope with computer controlled focus, excitation and emission wavelength and duration of excitation. Images are recorded with a cooled (-80°C) CCD. 3D images are obtained as a series of optical sections at .25 - .5 μm intervals.A conventional microscope has substantial blurring along its optical axis. Out of focus contributions to a single optical section cause low contrast and flare; details are poorly resolved along the optical axis. We have developed new computer algorithms for reversing these distortions. These image restoration techniques and scanning confocal microscopes yield significantly better images; the results from the two are comparable.

Assessing Functional Language in School-Aged Children Using Language Sample Analysis

2020 ◽  
Vol 5 (3) ◽  
pp. 622-636
Author(s):  
John Heilmann ◽  
Alexander Tucci ◽  
Elena Plante ◽  
Jon F. Miller
Keyword(s):  
Computer Hardware ◽  
School Age Children ◽  
Functional Language ◽  
School Age ◽  
Clinical Use ◽  
Sample Analysis ◽  
Validity And Reliability ◽  
Language Sample Analysis ◽  
Accuracy And Repeatability ◽  
Language Sample

Purpose The goal of this clinical focus article is to illustrate how speech-language pathologists can document the functional language of school-age children using language sample analysis (LSA). Advances in computer hardware and software are detailed making LSA more accessible for clinical use. Method This clinical focus article illustrates how documenting school-age student's communicative functioning is central to comprehensive assessment and how using LSA can meet multiple needs within this assessment. LSA can document students' meaningful participation in their daily life through assessment of their language used during everyday tasks. The many advances in computerized LSA are detailed with a primary focus on the Systematic Analysis of Language Transcripts (Miller & Iglesias, 2019). The LSA process is reviewed detailing the steps necessary for computers to calculate word, morpheme, utterance, and discourse features of functional language. Conclusion These advances in computer technology and software development have made LSA clinically feasible through standardized elicitation and transcription methods that improve accuracy and repeatability. In addition to improved accuracy, validity, and reliability of LSA, databases of typical speakers to document status and automated report writing more than justify the time required. Software now provides many innovations that make LSA simpler and more accessible for clinical use. Supplemental Material https://doi.org/10.23641/asha.12456719

PENGENALAN HARDWARE KOMPUTER

2020 ◽  
Author(s):  
Adel Syah Pohan
Keyword(s):  
Computer Hardware ◽  
Time Sharing ◽  
Time Sharing System

Sejarah jaringan komputer bermula dari lahirnya konsep jaringan komputer pada tahun 1940-an di Amerika yang digagas oleh sebuah proyek pengembangan komputer MODEL I di laboratorium Bell dan group riset Universitas Harvard yang dipimpin profesor Howard Aiken. Kemudian pada tahun 1950-an ketika jenis komputer mulai berkembang sampai terciptanya super komputer, maka sebuah komputer harus melayani beberapa tempat yang tersedia (terminal), untuk itu ditemukan konsep distribusi proses berdasarkan waktu yang dikenal dengan nama TSS (Time Sharing System). Maka untuk pertama kalinya bentuk jaringan (network) komputer diaplikasikan. Hardware adalah perangkat keras computer yang nampak secara fisik dan dapat di raba. Berdasarkan sifat dan kegunaannya perangkat keras computer (hardware computer) dapat dikelompokan menjadi 4 bagian : Perangkat input, Perangkat keluaran, Perangkat proses, Perangkat penyimpanan. Perangkat keras komputer adalah semua bagian fisikkomputer, dibedakan dengan data yang berada di dalamnya atau yang beroperasi di dalamny sedangkan perangkat lunak merupakan perangkat yang dapat dilihat namun tidak dapat disentuh secara langsung oleh manusia, perangkat lunak yang menyediakan instruksi buat perangkat keras untuk menyelesaikantugasnya.

Makalah Perangkat keras komputer

2020 ◽  
Author(s):  
Maulidia Syahmi
Keyword(s):  
Computer Hardware ◽  
Output Device

ABSTRAKKebanyak orang hanya mengenal hardware dan tidak mampu mendifinisikan pengertian dari hardware itu sendiri. Hardware adalah perangkat keras computer yang nampak secara fisik dan dapat di raba. Berdasarkan sifat dan kegunaannya perangkat keras computer (hardware computer) dapat dikelompokan menjadi 5 bagian :A. Perangkat input/ input divice B. Perangkat keluaran / Output device C. Perangkat proses / Process device D. Perangkat penyimpanan/ Backing Storage F. Perangkat tambahan/ Periferal

PERANGKAT KERAS KOMPUTER

2020 ◽  
Author(s):  
NUR AMINA
Keyword(s):  
Computer Hardware

Perangkat keras computer (hardware) merupakan perangkat yang dapat dilihat dirasah secara langsung oleh manusia. Perangkat keras komputer adalah semua bagian fisik komputer, dibedakan dengan data yang berada didalamnya atau yang beroperasi di dalamnya sedangkan perangkat lunak merupakan perangkat yang dapat dilihat dan tidak dapat disentuh secara langsung oleh manusia, perangkat lunak yang menyediakan instruksi buat perangkat keras untuk menyelesaikan tugasnya. Kata kunci : Perangkat Keras (hardware)

PENERAPAN GREEN COMPUTING PADA KUISIONER INSTRUMEN PENILAIAN KINERJA DOSEN DI POLITEKNIK NEGERI SUBANG

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Nunu Nugraha Purnawan
Keyword(s):  
Data Collection ◽  
Literature Review ◽  
Performance Assessment ◽  
Research Method ◽  
Field Research ◽  
Green Computing ◽  
Assessment Instrument ◽  
Computer Hardware ◽  
Model Free ◽  
Collection Methods

The lecturer's performance assessment by students in the form of an online Lecturer Performance Assessment Instrument Questionnaire (KIPKD) is in line with the work concepts of Green Computing by utilizing computer hardware and software better, more efficiently and more useful. KIPKD online POLSUB uses Google Forms, because it has an attractive and responsive look, provides a fairly complete choice of stuffing model, free, the results are neatly arranged and can be analyzed easily. This research method uses literature review in the form of books, journals that discuss about topics related to the use of Google Forms as a medium in the manufacture of questionnaires for surveys and data collection, as well as related to the concept of Green Computing. While data collection methods used in field research by way of observation of the system running in the academic POLSUB. The use of KIPKD online illustrates that POLSUB participates in preserving the environment, with no 12 paper/year rims, equivalent to 12 tree trunks.

scholarly journals Influence of Agisoft Metashape Parameters on UAS Structure from Motion Individual Tree Detection from Canopy Height Models

Forests ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 250
Author(s):  
Wade T. Tinkham ◽  
Neal C. Swayze
Keyword(s):  
Structure From Motion ◽  
Ponderosa Pine ◽  
Point Cloud ◽  
Image Resolution ◽  
Canopy Height ◽  
Computer Hardware ◽  
Forest Monitoring ◽  
Individual Tree ◽  
Ponderosa Pine Forests ◽  
Tree Detection

Applications of unmanned aerial systems for forest monitoring are increasing and drive a need to understand how image processing workflows impact end-user products’ accuracy from tree detection methods. Increasing image overlap and making acquisitions at lower altitudes improve how structure from motion point clouds represents forest canopies. However, only limited testing has evaluated how image resolution and point cloud filtering impact the detection of individual tree locations and heights. We evaluate how Agisoft Metashape’s build dense cloud Quality (image resolution) and depth map filter settings influence tree detection from canopy height models in ponderosa pine forests. Finer resolution imagery with minimal filtering provided the best visual representation of vegetation detail for trees of all sizes. These same settings maximized tree detection F-score at >0.72 for overstory (>7 m tall) and >0.60 for understory trees. Additionally, overstory tree height bias and precision improve as image resolution becomes finer. Overstory and understory tree detection in open-canopy conifer systems might be optimized using the finest resolution imagery that computer hardware enables, while applying minimal point cloud filtering. The extended processing time and data storage demands of high-resolution imagery must be balanced against small reductions in tree detection performance when down-scaling image resolution to allow the processing of greater data extents.

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