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A Preliminary Characterization of a Water Contaminant Detection System Based on a Multi-sensor Microsystem
Abstract
The evaluation of measurement quality is a primary task when performing sensing activities based on low–cost sensor technologies. In detail, in this work electrical impedance measurements on different sensors are proposed in order to have a dataset of raw data to be used to perform classification of possible contaminants in water environment. In detail, the sensor technology is based on a proprietary multi-sensing platform which is arranged in a suitable set-up able to carry out measurements in water for prolonged times. Sensors metalized with different materials are jointly used to exploit sensitivity diversity to different contaminants. An ad–hoc measurement procedure has been designed, including data acquisition during warm–up period, contaminant injection and steady state conditions. Since different releases of the platform have been developed, here we propose a comparison between two versions to demonstrate the technological advance in sensor integration and miniaturization leading to higher reliable results. A metrological analysis of the obtained measurements with two different platform versions is carried out and compared results are reported.
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In the framework of indoor air monitoring, this paper proposes an Internet of Things ready solution to detect and classify contaminants. It is based on a compact and low–power integrated system including both sensing and processing capabilities. The sensing is composed of a sensor array on which electrical impedance measurements are performed through a microchip, named SENSIPLUS, while the processing phase is mainly based on Machine Learning techniques, embedded in a low power and low resources micro controller unit, for classification purposes. An extensive experimental campaign on different contaminants has been carried out and raw sensor data have been processed through a lightweight Multi Layer Perceptron for embedded implementation. More complex and computationally costly Deep Learning techniques, as Convolutional Neural Network and Long Short Term Memory, have been adopted as a reference for the validation of Multi Layer Perceptron performance. Results prove good classification capabilities, obtaining an accuracy greater than 75% in average. The obtained results, jointly with the reduced computational costs of the solution, highlight that this proposal is a proof of concept for a pervasive IoT air monitoring system.
In this paper, a filtering stage based on employing a Savitzky-Golay (SG) filter is proposed to be used in the spectrum sensing phase of a Cognitive Radio (CR) communication paradigm for Vehicular Dynamic Spectrum Access (VDSA). It is used to smooth the acquired spectra, which constitute the input for a spectrum sensing algorithm. The sensing phase is necessary, since VDSA is based on an opportunistic approach to the spectral resource, and the opportunities are represented by the user-free spectrum zones, to be detected through the sensing phase. Each filter typology presents peculiarities in terms of its computational cost, de-noising ability and signal shape reconstruction. The SG filtering properties are compared with those of the linear Moving Average (MA) filter, widely used in the CR framework. Important improvements are proposed.
At a time when the safety and security of services we have typically taken for granted are under question, a real-time or near real-time method of monitoring changes in water quality parameters could provide a critical line of defense in protecting public health. This study was undertaken to determine the feasibility of using water quality sensors as part of a contaminant warning system. Commercially available water quality sensors are capable of triggering a contamination warning system using standard water quality parameters. When contaminants are injected into a water line, they cause the water quality parameters-such as chlorine and total organic carbon-to change. These changes are the mechanism that can be used to detect the presence of contaminants. The result of this research supports the dual benefit of adding on-line water quality monitoring to the distribution system, thereby improving both security and water quality. - LH.
Water Pollution: Causes, Effects And Control Is A Book Providing Comprehensive Information On The Fundamentals And Latest Developments In The Field Of Water Pollution.The Book Is Divided Into 28 Chapters Covering Almost All The Aspect Of Water Pollution Including Water Resources And General Properties Of Water; History Of Water Pollution And Legislation; Origin, Sources And Effects Of Pollutants; Bioaccumulation And Biomagnification; Toxicity Testing And Interaction Of Toxicities In Combination; Water Quality Standards; Biomonitoring Of Water Pollution; Bacteriological Examination And Purification Of Drinking Water; Monitoring And Control Of Pollution In Lakes, Rivers, Estuaries And Coastal Waters; Physical And Biological Structure Of Aquatic Systems; And Structure, Properties And Uses Of Water.Some Important Topics Like Eutrophication, Organic Pollution, Oil Pollution And Thermal Pollution Have Been Discussed In Detail. The Water Pollution Caused By Pesticides, Heavy Metals, Radio Nuclides And Toxic Organics And Inorganic Along With The Water Quality Problems Associated With Water-Borne Pathogens And Nuisance Algae Have Also Been Dealt With Extensively.The Book Covers In Detail The Flow Measurement And Characterization Of Waste Waters In Industries, And Control Of Water Pollution By Employing Various Techniques For Treatment Of Biological And Nonbiological Wastes. The Considerations For Recycling And Utilization Of Waste Waters Have Also Found A Place In The Book. Special Topic Has Also Been Given On Water Pollution Scenario And Water Related Policies And Programmes In India.The Book Shall Be Of Immediate Interest To The Students Of Environmental Science, Life Science And Social Sciences Both At Undergraduate And Postgraduate Levels. People From A Wide Variety Of Other Disciplines Like Civil, Chemical And Environmental Engineering; Pollution Control Authorities; Industries; And Practicing Engineers, Consultants And Researchers Will Also Find The Book Of Great Interest.
Water pollution caused by human activities poses a serious global threat to human health. Sensor technologies enabling water monitoring are an important tool that can help facing this problem. In this work, we propose an embedded IoT-ready system based on a proprietary sensor technology for the detection and recognition of six water contaminants. The system architecture is composed of two layers: (i) a sensing layer based on the SENSIPLUS chip, a proprietary Micro-Analytical Sensing Platform with six interdigitated electrodes metalized through different materials; and (ii) a data collection, communication, and classification layer with both hardware and software components. Being classification the most computationally and resource intensive operation, we evaluated nine machine learning solutions of different complexity and analyzed the trade-off between recognition accuracy, processing time, and memory usage to find a solution suitable to be implemented on an edge node. The highest average accuracy of 95.4% was achieved with K-nearest neighbor classification without constraints on processing time and memory usage, which confirms the potentiality of the system. When such constraints are taken into consideration, the best performance dropped to 86.4% offered by Multi Layer Perceptron.
The challenge to detect contaminants inside water solutions is addressed in this paper, through the use of an integrated, low-cost, smart and IoT platform, namely SENSIPLUS. In particular, the complete process from the sensing phase to classification and results analysis is provided with further investigations about the limitations of the current proposal and the description of a further processing technique that promises to improve classification accuracy. The classification is performed by adopting machine learning techniques, particularly Artificial Neural Network, that well fits the implementation on a low-cost microcontroller, as the one SENSIPLUS platform uses.
Nowadays, an increasing concern about people and environmental health and safety is spreading all over the world, and technologies such as efficient monitoring systems are furthered. In the field of air quality, filtering systems, especially based on activated carbons (ACs), are commonly used. In most cases, their state of health is not monitored, and their time of life is based on statistical and a priori evaluations. This paper proposes a novel microsensor platform for the real-time monitoring of AC filters based on the impedance measurements during gas exposition. A metrological characterization of the novel proposed instrument is provided in this paper, by comparing its output to a reference RLC meter. A calibration and adjustment procedure is developed in order to analyze the device measurement capabilities and obtain correction coefficients. Experiments with different gas typologies are presented, and the analysis of filters impedance dynamics is provided. IEEE
Spectrum sensing is a fundamental task in the complex field of cognitive radio systems. It allows a cognitive terminal to scan a frequency span of interest and sense the presence of other users transmitting over it. Many spectrum sensing methods are present in literature and many interesting algorithms have been proposed. Unfortunately, very few methods allow to know the exact boundaries of the user signal, without knowing any channelization of the spectrum of interest. In this context, the paper proposes two novel algorithms, whose purpose is twofold: to keep a low computational burden and to provide information at the most detailed level with respect to the category the algorithms belong to. Tests, executed on simulated and emulated signals, have demonstrated that both algorithms allow reaching a detection probability greater then 95% and a false alarm probability lower then 5% even in scenarios characterized by SNR as low as −10 dB.
The water quality monitoring plays an important role in water contamination surveillance and guides the water resource protection for safe and clean water. A flexible automated real-time water quality monitoring and alarm system based on the wireless sensor network(WSN) is proposed. The WSN is built in accordance with Zigbee communication protocol, which consists of the sensor nodes, route nodes and coordinator node. The sensor nodes based on cheap and efficient sensors (pH electrode, dissolved oxygen electrode, temperature senor) and wireless transreceiver collect the environmental data and transmit to the coordinator node via the route nodes. The data are sent to the remote monitoring center with the help of GPRS. The time synchronous algorithm is adopted to wake up all the nodes in the network to improve the stability and reliability of the communication. The long-time measurement results verify the real time and accuracy in data acquisition and stability and reliability in communication. The system meets the requirements of water quality monitoring, and has great practical value.
It is well known that most of today's electric and electronic devices frequently work with nonsinusoidal waveforms; then, all the passive R, L, and C components sited in these circuits are involved with nonsinusoidal stimuli. Because of their intrinsic nonlinearity, real resistors, inductors, capacitors, and so on show behaviors that are very different from those expected in a sinusoidal environment. Consequently, a problem of reliable assessment of these components in the presence of nonsinusoidal environments arises. This problem is even more critical in applications such as the creation of hybrid filters, control circuits based on patterns of sensing elements, and circuits of digital protection in power systems, where the design and control of electrical and electronic circuits depend on the correct modeling of the R, L, and C components. In previous research works, the authors proposed a suitable measurement method for the estimation of R, L, and C parameters of passive components in nonsinusoidal conditions. This paper deals with the realization of a measurement instrument, based on a Field Gate Programmable Array, that is able to continuously update the estimated values of the considered components. The instrument realization passes through an optimized implementation of the previously proposed measurement method aimed at minimizing the hardware resources and the computational burden and increasing the measurement rate. After a preliminary tuning of the measurement method, carried out in a simulation environment, the hardware and the software architectures of the realized measurement instrument, together with the measurement strategy, are described and experimentally characterized.
Disease control priorities in developing countries. The international bank for reconstruction and development
- D T Jamison
- J G Breman
- A R Measham
- G Alleyne
- M Claeson
- D B Evans
- P Jha
- A Mills
- P Musgrove