Low-cost soft error resilience with unified data verification and fine-grained recovery for acoustic sensor based detection

2016 ◽  
Author(s):  
Qingrui Liu ◽  
Changhee Jung ◽  
Dongyoon Lee ◽  
Devesh Tiwarit
Keyword(s):  
Low Cost ◽  
Error Resilience ◽  
Soft Error ◽  
Acoustic Sensor ◽  
Data Verification ◽  
Fine Grained

REPAIR: Control Flow Protection based on Register Pairing Updates for SW-Implemented HW Fault Tolerance

2021 ◽  
Vol 20 (5s) ◽  
pp. 1-22
Author(s):  
Uzair Sharif ◽  
Daniel Mueller-Gritschneder ◽  
Ulf Schlichtmann
Keyword(s):  
Fault Tolerance ◽  
Data Flow ◽  
Fault Injection ◽  
Low Cost ◽  
Computation Time ◽  
Error Resilience ◽  
Control Flow ◽  
Soft Error ◽  
High Coverage ◽  
Computation Path

Safety-critical embedded systems may either use specialized hardware or rely on Software-Implemented Hardware Fault Tolerance (SIHFT) to meet soft error resilience requirements. SIHFT has the advantage that it can be used with low-cost, off-the-shelf components such as standard Micro-Controller Units. For this, SIHFT methods apply redundancy in software computation and special checker codes to detect transient errors, so called soft errors, that either corrupt the data flow or the control flow of the software and may lead to Silent Data Corruption (SDC). So far, this is done by applying separate SIHFT methods for the data and control flow protection, which leads to large overheads in computation time. This work in contrast presents REPAIR, a method that exploits the checks of the SIHFT data flow protection to also detect control flow errors as well, thereby, yielding higher SDC resilience with less computational overhead. For this, the data flow protection methods entail duplicating the computation with subsequent checks placed strategically throughout the program. These checks assure that the two redundant computation paths, which work on two different parts of the register file, yield the same result. By updating the pairing between the registers used in the primary computation path and the registers in the duplicated computation path using the REPAIR method, these checks also fail with high coverage when a control flow error, which leads to an illegal jumps, occurs. Extensive RTL fault injection simulations are carried out to accurately quantify soft error resilience while evaluating Mibench programs along with an embedded case-study running on an OpenRISC processor. Our method performs slightly better on average in terms of soft error resilience compared to the best state-of-the-art method but requiring significantly lower overheads. These results show that REPAIR is a valuable addition to the set of known SIHFT methods.

scholarly journals FIWARE based low-cost wireless acoustic sensor network for monitoring and classification of urban soundscape

2021 ◽  
pp. 108199
Author(s):  
Pau Arce ◽  
David Salvo ◽  
Gema Piñero ◽  
Alberto Gonzalez
Keyword(s):  
Sensor Network ◽  
Low Cost ◽  
Acoustic Sensor

scholarly journals A Machine Vision Approach for Bioreactor Foam Sensing

2021 ◽  
pp. 247263032110088
Author(s):  
Jonas Austerjost ◽  
Robert Söldner ◽  
Christoffer Edlund ◽  
Johan Trygg ◽  
David Pollard ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Vision ◽  
State Of The Art ◽  
Low Cost ◽  
High Accuracy ◽  
Consumer Electronics ◽  
Learning System ◽  
Automotive Applications ◽  
Fine Grained

Machine vision is a powerful technology that has become increasingly popular and accurate during the last decade due to rapid advances in the field of machine learning. The majority of machine vision applications are currently found in consumer electronics, automotive applications, and quality control, yet the potential for bioprocessing applications is tremendous. For instance, detecting and controlling foam emergence is important for all upstream bioprocesses, but the lack of robust foam sensing often leads to batch failures from foam-outs or overaddition of antifoam agents. Here, we report a new low-cost, flexible, and reliable foam sensor concept for bioreactor applications. The concept applies convolutional neural networks (CNNs), a state-of-the-art machine learning system for image processing. The implemented method shows high accuracy for both binary foam detection (foam/no foam) and fine-grained classification of foam levels.

scholarly journals Robust Silent Localization of Underwater Acoustic Sensor Network Using Mobile Anchor(s)

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 727
Author(s):  
Rahul Mourya ◽  
Mauro Dragone ◽  
Yvan Petillot
Keyword(s):  
Channel Model ◽  
Low Cost ◽  
Sensor Nodes ◽  
Noise Model ◽  
Robust Estimator ◽  
Acoustic Sensor ◽  
Underwater Acoustic ◽  
Acoustic Channel ◽  
Localization Scheme ◽  
Mobile Anchor

Underwater acoustic sensor networks (UWASNs) can revolutionize the subsea domain by enabling low-cost monitoring of subsea assets and the marine environment. Accurate localization of the UWASNs is essential for these applications. In general, range-based localization techniques are preferred for their high accuracy in estimated locations. However, they can be severely affected by variable sound speed, multipath spreading, and other effects of the acoustic channel. In addition, an inefficient localization scheme can consume a significant amount of energy, reducing the effective life of the battery-powered sensor nodes. In this paper, we propose robust, efficient, and practically implementable localization schemes for static UWASNs. The proposed schemes are based on the Time-Difference-of-Arrival (TDoA) measurements and the nodes are localized passively, i.e., by just listening to beacon signals from multiple anchors, thus saving both the channel bandwidth and energy. The robustness in location estimates is achieved by considering an appropriate statistical noise model based on a plausible acoustic channel model and certain practical assumptions. To overcome the practical challenges of deploying and maintaining multiple permanent anchors for TDoA measurements, we propose practical schemes of using a single or multiple surface vehicles as virtual anchors. The robustness of localization is evaluated by simulations under realistic settings. By combining a mobile anchor(s) scheme with a robust estimator, this paper presents a complete package of efficient, robust, and practically usable localization schemes for low-cost UWASNs.

scholarly journals Exploiting Existing Comparators for Fine-Grained Low-Cost Error Detection

2014 ◽  
Vol 11 (3) ◽  
pp. 1-24
Author(s):  
Gulay Yalcin ◽  
Oguz Ergin ◽  
Emrah Islek ◽  
Osman Sabri Unsal ◽  
Adrian Cristal
Keyword(s):  
Error Detection ◽  
Low Cost ◽  
Fine Grained

A Fine-Grained Software-Implemented DMA Fault Tolerance for SoC Against Soft Error

2018 ◽  
Vol 34 (6) ◽  
pp. 717-733
Author(s):  
Xiaozhi Du ◽  
Dongyang Luo ◽  
Chaohui He ◽  
Shuhuan Liu
Keyword(s):  
Fault Tolerance ◽  
Soft Error ◽  
Fine Grained

scholarly journals Preliminary Characterization of Three Metallurgical Bauxite Residue Samples

2021 ◽  
Vol 5 (1) ◽  
pp. 66
Author(s):  
Panagiotis Angelopoulos ◽  
Maria Georgiou ◽  
Paschalis Oustadakis ◽  
Maria Taxiarchou ◽  
Hakan Karadağ ◽  
...  
Keyword(s):  
Low Cost ◽  
Bauxite Residue ◽  
Raw Material ◽  
Morphological Observation ◽  
Critical Metals ◽  
Alumina Production ◽  
Preliminary Characterization ◽  
Fine Grained ◽  
Extraction And Separation ◽  
Bulk Chemical

Bauxite Metallurgical Residue (BR) is a highly alkaline and very fine-grained by-product of the Bayer process for alumina production. Its huge global annual production has resulted in increasing accumulation of BR, causing deposition problems and serious environmental issues. RM contains oxides and salts of the main elements Fe, Al, Ca, Na, Si, Ti, and rare earths—REEs (Sc, Nd, Y, La, Ce, Ds)—many of which have been categorised by EU as critical metals (CMs). The valorisation of BR as a low-cost secondary raw material and metal resource could be a route for its reduction, introducing the waste into the economic cycle. REEScue constitutes a research project that aims to instigate the efficient exploitation of European bauxite residues, resulting from alumina production from Greece (MYTILINEOS SA), Turkey (ETI Aluminium), and Romania (ALUM SA), containing appreciable concentrations of scandium and REEs, through the development of a number of innovative extraction and separation technologies that can efficiently address the drawbacks of the existing solution. The consortium consists of three alumina producers from Greece (MYTILINEOS SA), Turkey (ETI Aluminium), and Romania (ALUM SA) and two academic partners from Greece (National Technical University of Athens) and Turkey (Necmettin Erbacan University). We present preliminary characterization results of three different BR samples that originate from the three aluminium industries, in respect of bulk chemical analysis (XRF, ICP), mineralogical investigation (XRD), and morphological observation through microscopy.

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