A Sub-mW IoT-Endnode for Always-On Visual Monitoring and Smart Triggering

Rusci, Manuele; Rossi, Davide; Farella, Elisabetta; Benini, Luca

doi:10.1109/jiot.2017.2731301

Cited by 28 publications

(15 citation statements)

References 37 publications

(50 reference statements)

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“…This wiring also adds significant weight to an aircraft, reducing efficiency and increasing the cost of operation. That is the reason why there is a great interest in implementing wireless SHM system (Noel et al 2017;Lee et al 2016;Dutta et al 2005;Rusci et al 2017;Sutton et al 2017;Li et al 2010;Champaigne and Sumners 2007); in fact, it is estimated that in the lifetime of an aircraft, cost savings of 14-70 million dollars could be achieved (Gao et al 2018). The removal of wires although have weight saving benefits, creates a clear problem: how to power the device in a manageable way?…”

Section: Power Consumptionmentioning

confidence: 99%

Aerospace Requirements

Khodaei¹,

Grigg²

2021

Structural Health Monitoring Damage Detection Systems for Aerospace

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This chapter covers the overview of requirements arising in the aerospace industry for operating a structural health monitoring (SHM) system. The requirements are based on existing standards and guidelines and include both requirements on the physical components of the system (such as sensors, data acquisition systems and connectors) and their functional requirements (such as reliability, confidence measure and probability of detection). Emphasis has been given to on-board and ground-based components because they have different functionality requirements. An important factor in the reliability of the system is the effect of the environment and operational loads on the reliability of the diagnosis and, consequently, prognosis. The recommended guidelines for testing the reliability of the system under varying operational conditions are presented. This chapter is then finalized by reporting on methodologies for optimal sensor number and placement, based on different sensor technologies and different optimization algorithms.

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Section: Power Consumptionmentioning

confidence: 99%

Aerospace Requirements

Khodaei¹,

Grigg²

2021

Structural Health Monitoring Damage Detection Systems for Aerospace

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“…System responsiveness, power consumption and large-scale operation were considered. Rusci et al developed a fully programmable ultra-low power smart camera node for always-on visual monitoring system [27]. An event-driven computational model was exploited to allow the system to stay in the idle mode when no events occur, reducing the power consumption.…”

Section: Related Workmentioning

confidence: 99%

An Event-Triggered Energy-Efficient Wireless Structural Health Monitoring System for Impact Detection in Composite Airframes

Khodaei

Aliabadi

2019

IEEE Internet Things J.

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In this paper, a low-power high-response wireless structural health monitoring system (WSHMS) is designed, implemented and experimentally evaluated for impact detection in composite airframes. Due to the rare, random and transitory nature of impacts, an event-triggered mechanism is adopted for allowing the system to exhibit low power consumption when no impact occurs and high performance when triggered. System responsiveness, robustness and energy efficiency are considered and modelled. Based on system requirements and functions, several modules are proposed, including filtering, impact detecting, local processing and wireless communicating modules. The filtering module increases the system robustness by attenuating background vibration noises. The impact detection module monitors impact categories, and when the impact energy is above a certain threshold, it generates a trigger (wake-up) signal for the local processing module. The local processing module is required to be responsive to impact events, capable of processing multiple sensing inputs and energy-efficient when no impact occurs. The wireless module transmits the processed data to the host station for impact evaluation. The whole design was implemented on a printed circuit board (100 × 65 mm). The response time is around 12 µs with an average current consumption lower than 1 mA when the impact activity is lower than 0.1%. The system exhibits high robustness to ambient vibration noises and is also capable of accurately and responsively capturing multiple sensing input channels (up to 24 channels). This work presents a lowlatency energy-aware WSHMS for impact detection of composite structures. It can be adapted to monitor of other rare, random and ephemeral events in many Internet of Things applications.

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“…Pixel-Based Adaptive Segmenter, whose operation pipeline is shown in Figure 1, went one step further incorporating feedback to estimate the dynamics of the background at pixel level. 3 The segmentation output S n (x i ) of pixel x i at frame n is calculated using (1). Here, B k (x i ) is the kth sample of the background model of pixel x i , I n (x i ) is the pixel intensity, #{dist(I n (x i ), B k (x i )) < R} is the cardinal functional that counts the number of samples inside a sphere of radius R centered in the pixel of interest, and # min the minimum number of samples that should meet this condition to be segmented as background.…”

Section: Hardware-oriented Pbasmentioning

confidence: 99%

“…Foreground detection in video, also commonly known as background subtraction, is usually the first step of more complex computer vision applications such as tracking by detection or surveillance. 1,2 The Pixel-Based Adaptive Segmenter (PBAS) is a well-known state-of-the-art foreground detection algorithm with a random update mechanism of the background model. 3 The good performance metrics of PBAS on the CDNET14 database 4 are met with more than 30 memories per pixel, which leads to a large memory overhead regardless of the underlying circuit implementation.…”

Section: Introduction and Related Workmentioning

confidence: 99%

In‐pixel analog memories for a pixel‐based background subtraction algorithm on CMOS vision sensors

Garcia-Lesta

López

Brea

et al. 2018

Circuit Theory & Apps

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This paper addresses the most suitable in-pixel analog memory bank for running a hardware-oriented approach of the well-known Pixel-Based Adaptive Segmenter algorithm on CMOS vision sensors. The high number of memory accesses, typically up to 200 times, along with the long time elapsed before an analog memory in a pixel is updated, around 5 seconds at 30 fps, constrains the memory topology. This work assesses the impact of nominal and process nonidealities of the 3 main analog memory topologies, namely, open-loop, closed-loop, and integrator architectures for background subtraction over the CDNET14 database. This is the first step towards the implementation of a CMOS vision chip with per-pixel processing to run the Pixel-Based Adaptive Segmenter. KEYWORDS analog memories, background subtraction, CMOS vision sensors, focal plane processing, PBAS *Corrections added on 25 April 2018, after first online publication: several incorrect citations have been corrected and a missing reference [19] has been added.

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A Sub-mW IoT-Endnode for Always-On Visual Monitoring and Smart Triggering

Cited by 28 publications

References 37 publications

Aerospace Requirements

Aerospace Requirements

An Event-Triggered Energy-Efficient Wireless Structural Health Monitoring System for Impact Detection in Composite Airframes

In‐pixel analog memories for a pixel‐based background subtraction algorithm on CMOS vision sensors

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