Informationprocessingdriven Interfaces In Hybrid Largearea Electronics Systems

• doi: 10.1109/ISCAS.2017.8050267
• title: Information-processing-driven interfaces in hybrid large-area electronics systems
• publisher: IEEE
• isbn: 978-1-5090-1427-9
• issn: 2379-447X
• rank: 2784
• access_type: LOCKED
• content_type: Conferences
• abstract: In the development of human-centric systems, access to a large number of human information signals is required. Such signals can be acquired from both ambient and on-person (wearable) sensors. Large-area electronics (LAE) provide distinct capabilities for creating the required diverse, distributed and conformal sensors. However, the large volume of and complex correlation to target information within the captured data requires significant processing and inference. This makes an LAE-CMOS hybrid system well-suited to such applications. Interfacing between the two technologies is a challenge in hybrid system design. We demonstrate an emerging solution space based on information-processing-oriented interfaces, through two case studies: 1) an image sensing and compression system based on random projection [1]; 2) an electroencephalogram (EEG) acquisition and biomarker-extraction system using compressive-sensing circuits [2].
• article_number: 8050267
• pdf_url: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8050267
• html_url: https://ieeexplore.ieee.org/document/8050267/
• abstract_url: https://ieeexplore.ieee.org/document/8050267/
• publication_title: 2017 IEEE International Symposium on Circuits and Systems (ISCAS)
• conference_location: Baltimore, MD, USA
• conference_dates: 28-31 May 2017
• publication_number: 8014728
• is_number: 8049747
• publication_year: 2017
• publication_date: 28-31 May 2017
• start_page: 1
• end_page: 4
• citing_paper_count: 1
• citing_patent_count: 0
• download_count: 159
• insert_date: 20170928
• index_terms:
  • ieee_terms:
    • Electroencephalography
    • Feature extraction
    • Image coding
    • Sensor systems
    • Sensor arrays
    • Biomarkers
  • author_terms:
    • Large-area electronics (LAE)
    • hybrid systems
    • thin-film sensors
    • thin-film transistors (TFTs)
  • dynamic_index_terms:
    • Large-area Electronics
    • Hybrid System
    • Wearable Sensors
    • Random Projection
    • Raw Data
    • Thin Films
    • Alumina
    • Reduction In The Number
    • Support Vector Machine
    • Dimensionality Reduction
    • Source Code
    • Data Compression
    • High-level Functions
    • Higher-level Functions
    • High Level Of Performance
    • EEG Data
    • EEG Signals
    • Original Signal
    • System Overview
    • Vector Production
    • Random Matrix
    • Random Matrices
    • Prototype System
    • Single Interface
    • Transimpedance Amplifier
    • Inner Product Of Vectors
    • Signal Compression
    • Restricted Isometry Property
    • Restricted Isometry Constant
    • Seizure Detection
    • Human-computer Interaction
    • User Interaction
    • Linear Approximation
    • Best Linear Unbiased Estimates
    • Linear Estimation
    • Feature Calculation
    • Feature Computation
    • Calculation Of Characteristics
    • Sensor Data
• isbn_formats:
  • format: Print on Demand(PoD) ISBN, value: 978-1-5090-1427-9, isbnType: New-2005
  • format: Electronic ISBN, value: 978-1-4673-6853-7, isbnType: New-2005
• authors:
  • Author Name: Tiffany Moy
    Affiliation: Department of Electrical Engineering, Princeton University, Princeton, NJ, USA
    Author URL: https://ieeexplore.ieee.org/author/37085387927
    ID: 37085387927
    Order: 1
    Author Affiliations:
    • Department of Electrical Engineering, Princeton University, Princeton, NJ, USA
  • Author Name: Warren Rieutort-Louis
    Affiliation: Department of Electrical Engineering, Princeton University, Princeton, NJ, USA
    Author URL: https://ieeexplore.ieee.org/author/38466415000
    ID: 38466415000
    Order: 2
    Author Affiliations:
    • Department of Electrical Engineering, Princeton University, Princeton, NJ, USA
  • Author Name: Liechao Huang
    Affiliation: Department of Electrical Engineering, Princeton University, Princeton, NJ, USA
    Author URL: https://ieeexplore.ieee.org/author/38466425800
    ID: 38466425800
    Order: 3
    Author Affiliations:
    • Department of Electrical Engineering, Princeton University, Princeton, NJ, USA
  • Author Name: Sigurd Wagner
    Affiliation: Department of Electrical Engineering, Princeton University, Princeton, NJ, USA
    Author URL: https://ieeexplore.ieee.org/author/37275442700
    ID: 37275442700
    Order: 4
    Author Affiliations:
    • Department of Electrical Engineering, Princeton University, Princeton, NJ, USA
  • Author Name: James C. Sturm
    Affiliation: Department of Electrical Engineering, Princeton University, Princeton, NJ, USA
    Author URL: https://ieeexplore.ieee.org/author/37270459200
    ID: 37270459200
    Order: 5
    Author Affiliations:
    • Department of Electrical Engineering, Princeton University, Princeton, NJ, USA
  • Author Name: Naveen Verma
    Affiliation: Department of Electrical Engineering, Princeton University, Princeton, NJ, USA
    Author URL: https://ieeexplore.ieee.org/author/37399412400
    ID: 37399412400
    Order: 6
    Author Affiliations:
    • Department of Electrical Engineering, Princeton University, Princeton, NJ, USA

Image Sensor

• sensor_type: CMOS
• resolution: 80x80
• dynamic_range: Not specified
• pixel_size: Not specified
• dark_current: Not specified

Optical Data

• focal_length: Not specified
• aperture: Not specified
• field_of_view: Not specified
• distortion: Not specified

Performance Metrics

Applications & Benefits

• cell_imaging: Not specified
• benefits: Not specified

Supporting Organizations

• supported_by: NSF grants CCF-1218206 and CCF-1253670, Systems on Nanoscale Information fabriCs (SONIC)

Manuscript Details

• publication_date: 28-31 May 2017

Relevancy Score

• score: 8
• missing_fields:
  • dynamic_range
  • power_consumption
  • signal_to_noise_ratio
  • sensitivity
  • shutter_speed
  • noise
  • focal_length
  • aperture
  • field_of_view
  • distortion
  • frame_rate
  • cell_imaging
  • benefits

Processed JSON Filename

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