Use of metal oxide semiconductor sensors to measure methane in aquatic ecosystems in the presence of cross-interfering compounds

Monitoring dissolved methane in aquatic ecosystems contributes significantly to advancing our understanding of the carbon cycle in these habitats and capturing their impact on methane emissions. Low-cost metal oxide semiconductors (MOS) gas sensors are becoming an increasingly attractive tool to perform such measurements, especially at the air–water interface. However, the performance of MOS sensors in aquatic environmental sciences has come under scrutiny because of their cross-sensitivity to temperature, moisture, and sulfide interference. In this study, we evaluated the performance and limitations of a MOS methane sensor when measuring dissolved methane in waters. A MOS sensor was encapsulated in a hydrophobic extended polytetrafluoroethylene membrane to impede contact with water but allow gas perfusion. Therefore, the membrane enabled us to submerge the sensor in water and overcome cross-sensitivity to humidity. A simple portable, low-energy, flow-through cell system was assembled that included an encapsulated MOS sensor and a temperature sensor. Waters (with or without methane) were injected into the flow cell at a constant rate by a peristaltic pump. The signals from the two sensors were recorded continuously with a cost-efficient microcontroller. Tests specifically focused on the effect of water temperature and sulfide interference on sensor performance. Our experiments revealed that the lower limit of the sensor was in the range of 0.1–0.2 µmol¿L-1 and that it provided a stable response at water temperatures in the range of 18.5–28°C. Dissolved sulfide at a concentration of 0.4¿mmol¿L-1 or higher interfered with the sensor response, especially at low methane concentrations (0.5 µmol¿L-1 or lower). However, we show that if dissolved sulfide is monitored, its interference can be alleviated.Postprint (published version

Learning of chunking sequences in cognition and behavior

We often learn and recall long sequences in smaller segments, such as a phone number 858 534 22 30 memorized as four segments. Behavioral experiments suggest that humans and some animals employ this strategy of breaking down cognitive or behavioral sequences into chunks in a wide variety of tasks, but the dynamical principles of how this is achieved remains unknown. Here, we study the temporal dynamics of chunking for learning cognitive sequences in a chunking representation using a dynamical model of competing modes arranged to evoke hierarchical Winnerless Competition (WLC) dynamics. Sequential memory is represented as trajectories along a chain of metastable fixed points at each level of the hierarchy, and bistable Hebbian dynamics enables the learning of such trajectories in an unsupervised fashion. Using computer simulations, we demonstrate the learning of a chunking representation of sequences and their robust recall. During learning, the dynamics associates a set of modes to each information-carrying item in the sequence and encodes their relative order. During recall, hierarchical WLC guarantees the robustness of the sequence order when the sequence is not too long. The resulting patterns of activities share several features observed in behavioral experiments, such as the pauses between boundaries of chunks, their size and their duration. Failures in learning chunking sequences provide new insights into the dynamical causes of neurological disorders such as Parkinson's disease and Schizophrenia

Chemical Sensor Systems and Associated Algorithms for Fire Detection: A Review

Indoor fire detection using gas chemical sensing has been a subject of investigation since the early nineties. This approach leverages the fact that, for certain types of fire, chemical volatiles appear before smoke particles do. Hence, systems based on chemical sensing can provide faster fire alarm responses than conventional smoke-based fire detectors. Moreover, since it is known that most casualties in fires are produced from toxic emissions rather than actual burns, gas-based fire detection could provide an additional level of safety to building occupants. In this line, since the 2000s, electrochemical cells for carbon monoxide sensing have been incorporated into fire detectors. Even systems relying exclusively on gas sensors have been explored as fire detectors. However, gas sensors respond to a large variety of volatiles beyond combustion products. As a result, chemical-based fire detectors require multivariate data processing techniques to ensure high sensitivity to fires and false alarm immunity. In this paper, we the survey toxic emissions produced in fires and defined standards for fire detection systems. We also review the state of the art of chemical sensor systems for fire detection and the associated signal and data processing algorithms. We also examine the experimental protocols used for the validation of the different approaches, as the complexity of the test measurements also impacts on reported sensitivity and specificity measures. All in all, further research and extensive test under different fire and nuisance scenarios are still required before gas-based fire detectors penetrate largely into the market. Nevertheless, the use of dynamic features and multivariate models that exploit sensor correlations seems imperative

Improving calibration of chemical gas sensors for fire detection using small scale setups

Chemical sensing may be better suited than conventional smoke-based detectors for the detection of certain type of fires, in particular in fires where smoke appears after gas emissions. However, chemical-based systems also respond to non-fire scenarios that also release volatiles. For this reason, discrimination models need to be trained under different fire and non-fire scenarios. This is usually performed in standard fire rooms, the access to which is very costly. In this work, we present a calibration model combining experiments from standard fire room and small-scale setup. Results show that the use of small-scale setup experiments improve the performance of the system.Postprint (author's final draft

Brunyit ultrasònic de peces còncaves i convexes d'acer inoxidable AISI 316L

El brunyit és un procés d’acabament per a peces mecanitzades de metall, consisteix en aconseguir una acabat superficial millor mitjançant una pressió constant sobre la superfície exterior d’una peça mitjançant una eina de brunyit amb una bola de contacte d’acer endurit. La pressió que exerceix la bola d’acer endurit 100Cr6 (57-66HRC) sobre la superfície acompanyada de la seqüencia de moviments en l’eix y i l’eix x aconsegueix enfonsar les crestes més altes generades pel mecanitzat previ i a la vegada s’empeny el material a les valls per que la rugositat sigui menor a l’anterior, amb això la superfície cada vegada és més plana. Amb aquest treball s’ha buscat un punt més de dificultat ja que s’ha utilitzat un material més dur, un acer inoxidable, i amb una rugositat inicial molt precària, a més a més, no s’ha treballat en superfícies planes sinó que s’ha utilitzat una superfície còncava formada per dos radis, un de 50 mm i un altre de 100 mm i una altra superfície convexa formada pels mateixos radis. Una de les causes de brunyir un material en un acabat tant precari es podia veure durant els moviments generats en l’eix y, com la bola pujava i baixava en cada pic de la rugositat, això significava calor que s’acumulava a la bola d’acer endurit, que quan s’acabava el procés estava a una temperatura considerable. En la part pràctica del TFG s’han agafat diferents paràmetres per veure quina influencia tenen en el brunyit els paràmetres són la força, l’amplitud d’ona i el radi de la superfície

Improving Calibration of Chemical Gas Sensors for Fire Detection Using Small Scale Setups

Chemical sensing may be better suited than conventional smoke-based detectors for the detection of certain type of fires, in particular in fires where smoke appears after gas emissions. However, chemical-based systems also respond to non-fire scenarios that also release volatiles. For this reason, discrimination models need to be trained under different fire and non-fire scenarios. This is usually performed in standard fire rooms, the access to which is very costly. In this work, we present a calibration model combining experiments from standard fire room and small-scale setup. Results show that the use of small-scale setup experiments improve the performance of the system

Relation between quantum advantage in supervised learning and quantum computational advantage

The widespread use of machine learning has raised the question of quantum supremacy for supervised learning as compared to quantum computational advantage. In fact, a recent work shows that computational and learning advantage are, in general, not equivalent, i.e., the additional information provided by a training set can reduce the hardness of some problems. This paper investigates under which conditions they are found to be equivalent or, at least, highly related. The existence of efficient algorithms to generate training sets emerges as the cornerstone of such conditions. These results are applied to prove that there is a quantum speed-up for some learning tasks based on the prime factorization problem, assuming the classical intractability of this problem

Dataset from chemical gas sensor array in turbulent wind tunnel

The dataset includes the acquired time series of a chemical detection platform exposed to different gas conditions in a turbulent wind tunnel. The chemo-sensory elements were sampling directly the environment. In contrast to traditional approaches that include measurement chambers, open sampling systems are sensitive to dispersion mechanisms of gaseous chemical analytes, namely diffusion, turbulence, and advection, making the identification and monitoring of chemical substances more challenging. The sensing platform included 72 metal-oxide gas sensors that were positioned at 6 different locations of the wind tunnel. At each location, 10 distinct chemical gases were released in the wind tunnel, the sensors were evaluated at 5 different operating temperatures, and 3 different wind speeds were generated in the wind tunnel to induce different levels of turbulence. Moreover, each configuration was repeated 20 times, yielding a dataset of 18,000 measurements. The dataset was collected over a period of 16 months. The data is related to "On the performance of gas sensor arrays in open sampling systems using Inhibitory Support Vector Machines", by Vergara et al.[1]. The dataset can be accessed publicly at the UCI repository upon citation of [1]: http://archive.ics.uci.edu/ml/datasets/Gas+sensor+arrays+in+open+sampling+settings.This work has been supported by the California Institute for Telecommunications and Information Technology (CALIT2) under Grant number 2014 CSRO 136

Data set from chemical sensor array exposed to turbulent gas mixtures

A chemical detection platform composed of 8 chemo-resistive gas sensors was exposed to turbulent gas mixtures generated naturally in a wind tunnel. The acquired time series of the sensors are provided. The experimental setup was designed to test gas sensors in realistic environments. Traditionally, chemical detection systems based on chemo-resistive sensors include a gas chamber to control the sample air flow and minimize turbulence. Instead, we utilized a wind tunnel with two independent gas sources that generate two gas plumes. The plumes get naturally mixed along a turbulent flow and reproduce the gas concentration fluctuations observed in natural environments. Hence, the gas sensors can capture the spatio-temporal information contained in the gas plumes. The sensor array was exposed to binary mixtures of ethylene with either methane or carbon monoxide. Volatiles were released at four different rates to induce different concentration levels in the vicinity of the sensor array. Each configuration was repeated 6 times, for a total of 180 measurements. The data is related to "Chemical Discrimination in Turbulent Gas Mixtures with MOX Sensors Validated by Gas Chromatography-Mass Spectrometry", by Fonollosa et al. [1]. The dataset can be accessed publicly at the UCI repository upon citation of [1]: http://archive.ics.uci.edu/ml/datasets/Gas+senso+rarray+exposed+to+turbulent+gas+mixtures.This work has been supported by the California Institute for Telecommunications and Information Technology (CALIT2) under Grant Number 2014 CSRO 136

Quantum multiple hypothesis testing based on a sequential discarding scheme

We consider the quantum multiple hypothesis testing problem, focusing on the case of hypothesis represented by pure states. A sequential adaptive algorithm is derived and analyzed first. This strategy exhibits a decay rate in the error probability with respect to the expected value of measurements greater than the optimal decay rate of the fixed-length methods. A more elaborated scheme is developed next, by serially concatenating multiple implementations of the first scheme. In this case each stage considers as a priori hypothesis probability the a posteriori probability of the previous stage. We show that, by means of a fixed number of concatenations, the expected value of measurements to be performed decreases considerably. We also analyze one strategy based on an asymptotically large concatenation of the initial scheme, demonstrating that the expected number of measurements in this case is upper bounded by a constant, even in the case of zero average error probability. A lower bound for the expected number of measurements in the zero error probability setting is also derived.This work was supported in part by the Agencia Estatal de Investigación, Ministerio de Ciencia e Innovación, of the Spanish Government, under Grant RED2018-102668-T and Grant PID2019-104958RB-C41; in part by the Catalan Government under Grant 2017 SGR 578 AGAUR; and in part by the QuantumCAT within the European Regional Development Fund (ERDF) Program of Catalunya under Grant 001-P-001644.Postprint (published version