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

Setting an error detection infrastructure with low cost acoustic wave detectors

2012 ◽  
Vol 40 (3) ◽  
pp. 333-343 ◽  
Author(s):  
Gaurang Upasani ◽  
Xavier Vera ◽  
Antonio González
Keyword(s):  
Acoustic Wave ◽  
Error Detection ◽  
Low Cost

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 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.

scholarly journals Towards a Living Lab for Enhanced Thermal Comfort and Air Quality: Analyses of Standard Occupancy, Weather Extremes, and COVID-19 Pandemic

2021 ◽  
Vol 9 ◽  
Author(s):  
Giulia Ulpiani ◽  
Negin Nazarian ◽  
Fuyu Zhang ◽  
Christopher J. Pettit
Keyword(s):  
Environmental Quality ◽  
Low Cost ◽  
Design Guidelines ◽  
Weather Extremes ◽  
Fine Grained ◽  
Occupancy Patterns ◽  
Living Lab ◽  
Spatial Coverage ◽  
Educational Buildings ◽  
Extreme Variability

Maintaining indoor environmental (IEQ) quality is a key priority in educational buildings. However, most studies rely on outdoor measurements or evaluate limited spatial coverage and time periods that focus on standard occupancy and environmental conditions which makes it hard to establish causality and resilience limits. To address this, a fine-grained, low-cost, multi-parameter IOT sensor network was deployed to fully depict the spatial heterogeneity and temporal variability of environmental quality in an educational building in Sydney. The building was particularly selected as it represents a multi-use university facility that relies on passive ventilation strategies, and therefore suitable for establishing a living lab for integrating innovative IoT sensing technologies. IEQ analyses focused on 15 months of measurements, spanning standard occupancy of the building as well as the Black Summer bushfires in 2019, and the COVID-19 lockdown. The role of room characteristics, room use, season, weather extremes, and occupancy levels were disclosed via statistical analysis including mutual information analysis of linear and non-linear correlations and used to generate site-specific re-design guidelines. Overall, we found that 1) passive ventilation systems based on manual interventions are most likely associated with sub-optimum environmental quality and extreme variability linked to occupancy patterns, 2) normally closed environments tend to get very unhealthy under periods of extreme pollution and intermittent/protracted disuse, 3) the elevation and floor level in addition to room use were found to be significant conditional variables in determining heat and pollutants accumulation, presumably due to the synergy between local sources and vertical transport mechanisms. Most IEQ inefficiencies and health threats could be likely mitigated by implementing automated controls and smart logics to maintain adequate cross ventilation, prioritizing building airtightness improvement, and appropriate filtration techniques. This study supports the need for continuous and capillary monitoring of different occupied spaces in educational buildings to compensate for less perceivable threats, identify the room for improvement, and move towards healthy and future-proof learning environments.

scholarly journals An Efficient Hardware-Based Fault-Tolerant Method for SMS4

2018 ◽  
Vol 208 ◽  
pp. 02005
Author(s):  
Hanguang Luo ◽  
Guangjun Wen ◽  
Jian Su
Keyword(s):  
Error Detection ◽  
Wireless Lan ◽  
Fault Tolerant ◽  
Low Cost ◽  
Primary Objective ◽  
Data Confidentiality ◽  
Detection Scheme ◽  
Detection Delay ◽  
Encryption Decryption ◽  
Online Error Detection

The SMS4 cryptosystem has been used in the Wireless LAN Authentication and Privacy Infrastructure (WAPI) standard for providing data confidentiality in China. So far, reliability has not been considered a primary objective in original version. However, a single fault in the encryption/decryption process can completely change the result of the cryptosystem no matter the natural or malicious injected faults. In this paper, we proposed low-cost structure-independent fault detection scheme for SMS4 cryptosystem which is capable of performing online error detection and can detect a single bit fault or odd multiple bit faults in coverage of 100 percent. Finally, the proposed techniques have been validated on Virtex-7 families FPGA platform to analyze its power consumption, overhead and time delay. It only needs 85 occupied Slices and 8.72mW to run a fault-tolerant scheme of SMS4 cryptosystem with 0.735ns of detection delay. Our new scheme increases in minimum redundancy to enhance cryptosystem’s reliability and achieve a better performance compared with the previous scheme.

An Optimized FPGA Implementation of CAN 2.0 Protocol Error Detection Circuitry

2017 ◽  
Vol 6 (3) ◽  
pp. 602
Author(s):  
Md Farukh Hashmi ◽  
Avinash G. Keskar
Keyword(s):  
Embedded System ◽  
Data Storage ◽  
Error Detection ◽  
Low Cost ◽  
Disk Drive ◽  
Critical Issue ◽  
Subsequent Treatment ◽  
Area Network ◽  
Reconfigurable Platform ◽  
Vhdl Code

Controller Area Network is an ideal serial bus design suitable for modern embedded system based networks. It finds its use in most of critical applications, where error detection and subsequent treatment on error is a critical issue. CRC (Cyclic Redundancy Check) block was developed on FPGA in order to meet the needs for simple, low power and low cost wireless communication. This paper gives a short overview of CRC block in the Digital transmitter based on the CAN 2.0 protocols. CRC is the most preferred method of encoding because it provides very efficient protection against commonly occurring burst errors, and is easily implemented. This technique is also sometimes applied to data storage devices, such as a disk drive. In this paper a technique to model the error detection circuitry of CAN 2.0 protocols on reconfigurable platform have been discussed? The software simulation results are presented in the form of timing diagram.FPGA implementation results shows that the circuitry requires very small amount of digital hardware. The Purpose of the research is to diversify the design methods by using VHDL code entry through Modelsim 5.5e simulator and Xilinx ISE8.3i.The VHDL code is used to characterize the CRC block behavior which is then simulated, synthesized and successfully implemented on Sparten3 FPGA .Here, Simulation and Synthesized results are also presented to verify the functionality of the CRC -16 Block. The data rate of CRC block is 250 kbps .Estimated power consumption and maximum operating frequency of the circuitry is also provided.

scholarly journals Low-Cost Digital Test Solution for Symbol Error Detection of RF ZigBee Transmitters

2019 ◽  
Vol 19 (1) ◽  
pp. 16-24 ◽  
Author(s):  
T. Vayssade ◽  
F. Azais ◽  
L. Latorre ◽  
F. Lefevre
Keyword(s):  
Error Detection ◽  
Low Cost ◽  
Test Solution ◽  
Digital Test

scholarly journals Convolutional Neural Networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Teja Kattenborn ◽  
Jana Eichel ◽  
Fabian Ewald Fassnacht
Keyword(s):  
Neural Networks ◽  
High Resolution ◽  
Convolutional Neural Networks ◽  
Low Cost ◽  
Training Data ◽  
Observation Data ◽  
Visual Interpretation ◽  
Fine Grained ◽  
Wide Range ◽  
Vegetation Species

AbstractRecent technological advances in remote sensing sensors and platforms, such as high-resolution satellite imagers or unmanned aerial vehicles (UAV), facilitate the availability of fine-grained earth observation data. Such data reveal vegetation canopies in high spatial detail. Efficient methods are needed to fully harness this unpreceded source of information for vegetation mapping. Deep learning algorithms such as Convolutional Neural Networks (CNN) are currently paving new avenues in the field of image analysis and computer vision. Using multiple datasets, we test a CNN-based segmentation approach (U-net) in combination with training data directly derived from visual interpretation of UAV-based high-resolution RGB imagery for fine-grained mapping of vegetation species and communities. We demonstrate that this approach indeed accurately segments and maps vegetation species and communities (at least 84% accuracy). The fact that we only used RGB imagery suggests that plant identification at very high spatial resolutions is facilitated through spatial patterns rather than spectral information. Accordingly, the presented approach is compatible with low-cost UAV systems that are easy to operate and thus applicable to a wide range of users.

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