Efficient Indoor Gardening: Discover the Benefits of Hydroponic Grow Systems

Triclocarban and triclosan have been reported to be taken up by several crop species from hydroponic solutions. For example, after exposure to an aqueous solution mixture of triclocarban and triclosan 11 different food crops, cucumber, tomato, cabbage, okra , pepper , potato , beet, onion , broccoli, celery , and asparagus , were capable of taking up both compounds. However, translocation from roots to the aerial tissue was ≤1.9% for triclocarban and ≤ 3.7% for triclosan after 1 month of exposure . Similarly, Wu et al. found triclocarban and triclosan to have a translocation factor < 0.01 in four vegetables cultivated in a hydroponic solution with two initial exposure concentrations . In a greenhouse study, triclocarban and triclosan were taken up in radish, carrot, and soybeans from bio solid-amended soils and, the greatest concentration was observed in the carrot root after 45 d of treatment and decreased thereafter . However, in a three-year field study in which soils were amended with bio solids in accordance with Ontario providence agricultural practices, the concentration of triclosan and triclocarban in the plant tissues was relatively steady and low . Plants have also been shown to metabolize triclosan, forming 33 metabolites in horseradish cell cultures with the majority being phase II conjugates . Further, one transformation product of the triclosan, methyl-triclosan, has been widely detected in environmental samples and is known to have greater toxicity than the parent compound . Parabens are common preservatives used in cosmetics, and among the most commonly detected CECs in TWW and bio-solid. Parabens are of concern due to their endocrine disrupting potential . Parabens have been widely detected in surface waters and sediments . However,raspberry container knowledge of their behavior, uptake, and transformation in terrestrial systems is comparatively limited.

Methyl paraben was unstable in soil after application of bio-solids, with the maximum concentration of 14.1 µg kg-1 reached after 5 h and decreasing to < 1 µg kg-1 after 35 d . In a bio solid amended field, methyl paraben was the lone paraben detected in the bio solids but was not quantifiable in tomatoes, sweet corn, carrot and potatoes . The above studies highlight the potential for CECs to enter the terrestrial environment, accumulate in plant tissues, and undergo transformations in plants. However, the wide variations in plant uptake and translocation rates under different soil and environmental conditions are currently not well understood and warrant further investigation. Further, it must be noted that the majority of currently published studies have focused on many of the same 20 or so CECs and explored their uptake in mostly the same plant species . There are over 1500 pharmaceutical compounds, alone, currently in circulation . Further, many of the current models have been shown to overestimate the concentration of CECs in plant tissues . In addition, no models have been able to take into account plant metabolism when determining the concentration and risk of CECs in terrestrial plants. More research is needed on a wider swath of CECs with different physicochemical properties in a wider range of plants to improve risk assessment. Transformation of CECs in the environment, including through plant metabolism also needs to be further investigated to better understand their fate and risks in the terrestrial environment. Antibiotic exposure in plants has been widely studied due to previously observed phenotypic toxicity. Several studies showed decreases in root length and changes in shoot development of various plants exposed to several different classes of antibiotics including sulfamides, fluoroquinolones, and penicillins . Most of these studies were conducted at antibiotic concentrations greater than those of environmental relevance and/or utilized artificial or hydroponic growth media. For instance, shoot and root growth of pinto beans grown in a nutrient solution spiked with two antibiotics, chlortetracycline and oxytetracycline, significantly decreased in a dose-dependent manner . Enrofloxacin, a fluoroquinolone, induced hormetic and toxic effects on post-germination growth in lettuce, cucumber, radish and barley plants at concentrations ranging from 0.005 to 50 mg L-1 in laboratory conditions . Seed germination has also been studied as a potential biological end-point to assess toxicity to antibiotic exposure . The exposure effects on seed germination vary considerably by plant species and exposure chemical. In filter paper tests, sweet oat , rice and cucumber seeds were negatively impacted when the seeds were exposed to aqueous solutions of increasing concentrations of six antibiotics, i.e., chlortetracycline, tetracycline, tylosin, sulfamethoxazole, sulfamethazine, and trimethoprim .

The EC10 and EC50 for seed germination were, however, significantly different depending on the antibiotic and the plant species. Rice seeds exposed to sulfamethoxazole were the most sensitive with an EC10 of 0.1 mg L-1 but tylosin had an EC10 > 500 mg L.-1 On the other hand, cucumber seeds exposed to sulfamethoxazole had an EC10 > 300 mg L-1 but an EC10 of 0.17 mg L-1 for chlortetracycline . Exposure to antibiotics can also change plant nutrient and chemical compositions. For example, irrigation with water spiked with sulfamethoxazole and trimethoprim increased production in carbohydrate and soluble solid contents in tomatoes as compared to the plants irrigated with untreated water . The mechanisms driving the phytotoxicity of antibiotics have also been explored. Antibiotics can be directly toxic to or indirectly affect plants. Indirect adverse effects can arise from antibiotic exposure that detrimentally affects mycorrhizal fungi, a vital plant-microbe interaction . Direct toxicity can result when antibiotics interfere with plant hormones or chemical synthesis pathways, or damage chloroplasts, etc. For example, sulfamethoxazole was shown to directly disrupt the folate synthesis pathway in plants by blocking the action of dihydropteroate synthase . Tetracyclines was shown to interrupt mitochondrial proteostasis and damage plant chloroplasts . Interactions with plant hormones may also play a role in the observed phenotypic phytotoxicity. Erythromycin and tetracycline can promote the production of abscisic acid in plants . Abscisic acid, a stress hormone, is crucial for plant responses to drought, salinity, heavy metals, among other stressors , but antibiotic-induced production of this hormone can cause premature leaf and fruit detachment and inhibit seed germination. Plants, depending upon species, can also detoxify antibiotics through reactions with phase II metabolic enzymes . However, studies so far have shown significant variations among plant species. For example, the antibiotic chlortetracycline was detoxified by glutathione conjugation via glutathione-Stransferase in maize , but glutathione-S-transferase did not efficiently catalyze the conjugation in pinto beans . These detoxification reactions, likely produce a series of conjugated metabolites that have yet to be characterized. Understanding the extent of such conjugation is crucial for estimating the total antibiotic uptake, accumulation, and translocation of antibiotics in plants as the formation of conjugates may mask the total concentration, even though some of these conjugates may retain biological activity .

Several widely used NSAIDs, such as ibuprofen, acetaminophen, and diclofenac are amongst the most studied pharmaceuticals in the environment. Studies have shown that NSAIDs can induce toxicity to plants . Phytotoxicity, however, is often plant species and NSAID specific. For example, ibuprofen has been shown to inhibited root elongation in Sorghum bicolor at high concentrations, with EC50 of 232.64 mg L-1 . However,plastic plants pots in seed germination tests exposure to a hydroponic solution containing 1 mg L-1 ibuprofen, along with other fenamic acid class NSAIDs, increased the length of the primary root in lettuce but had no effect on radish . In the same study, diclofenac was observed to decrease the root-to-shoot ratio in radish seedlings cultivated in a sand/spiked-nutrient solution , but did not significantly affect the seed germination. However, protein content was not affected in maize cultivated in soils irrigated twice with different concentrations of acetaminophen but grain yields and seed germination were negatively impacted in a dose dependent-manner . Plants can metabolize and detoxify NSAIDs. For example, plants were found to detoxify acetaminophen by conjugation with glutathione followed by conversion to cysteine and acetylcysteine conjugates . Similarly, diclofenac was found to be converted to glucose conjugates in barley and horseradish and glutamic acid conjugates in Arabidopsis thaliana . Arabidopsis thaliana cell cultures can detoxify ibuprofen via conjugation with sugars and amino acids .As mentioned above, pharmaceuticals used to treat psychiatric disorders are another group of frequently detected pharmaceuticals in environmental samples, particularly the anticonvulsant carbamazepine . Carbamazepine exposure has been seen to exhibit mycotoxicity to carrot mycorrhizal endpoints by decreasing the production of fungal spores . Similarly, carbamazepine induced leaf necrosis, altered plant hormones and macronutrient concentrations, and reduced root growth at plant tissue concentrations of 1 to 4 mg kg-1 in zucchini cultivated in soil spiked with chemical at 0.1 – 20 mg kg-1 . Information on the toxicity of benzodiazepines and fluoxetine in terrestrial plants is still limited; however, toxicity has been reported in aquatic plantsfor these compounds, indicating that toxicity may also occur after exposure in terrestrial plants .Antimicrobials and preservatives are often added to personal care products to increase shelf life. They pass from the human body, largely unchanged, and ultimately end up in TWW, bio solids, and sewage sludge. . Antimicrobials and preservatives have been detected in agricultural soils after irrigation with TWW and/or the application of bio solids, and can be taken up by plants . Two antimicrobials, triclosan and triclocarban, have attracted more attention because of their potential for endocrine disruption and phytotoxicity . For example, triclosan significantly inhibited plant growth in cucumber and rice seedlings with EC50 of 108 mg kg-1 and 57 mg kg-1 , respectively . Lettuce shoot mass also decreased in a dose-dependent manner after cultivation in soil amended with triclocarban-spiked bio solids . On the other hand, growth of radish, carrot, soybean, spring wheat, and corn plants grown in soils amended with bio solids containing environmentally relevant concentrations of triclosan and triclocarban, improved compared to un-amended soils; likely due to the positive impacts of bio solids addition . Thus, plant species, concentrations, and growth media can significantly affect phytotoxicity of these CECs. Studies exploring the phytotoxicity of individual pharmaceuticals or classes of pharmaceuticals are useful to highlight high-risk compounds and/or the potential mechanism of toxicity.

CECs are, however, often introduced into the environment in complex mixtures and these mixtures can affect the uptake and translocation of individual compounds . Some studies report positive effects on plants exposed to CEC mixtures under environmentally relevant conditions. For instance, TWW irrigation increased tomato and lettuce yield compared to freshwater irrigation . Exposure of lettuce seedlings to a mixture of 11 CECs significantly altered plant metabolic pathways, including the citric acid cycle and pentose phosphate pathway, and decreased chlorophyll content in a dose-dependent manner . Also, exposure to 18 CECs at concentrations ranging from 5 to 50 µg L,-1 induced oxidative stress in cucumber seedlings and caused up regulation of enzymes associated with detoxification reactions . Literature on the toxicity of a number significant CECs to terrestrial plants is still very limited, and many of the studies have utilized concentrations that are orders of magnitude higher than those seen in the environment. Studies on the toxicity of mixtures in terrestrial plants are also limited, but warrant attention as several studies have indicated that mixtures can induce effects not observed from individual compounds . The ability of plants to detoxify these compounds through metabolism also merit further research. Overall, more research is needed on the toxicity of a wider range of CECs in plants under environmentally relevant conditions to more accurately assess the impacts of CECs in the agro-environment. The potential for exposure to, and toxicity of, CECs has been investigated in several aquatic invertebrate species. Toxicity end-points such as endocrine disruption, changes in growth, time to development, and mortality rates have been considered in these studies . Studies addressing the effects of CECs on terrestrial invertebrates are, however, few. Of the published studies on terrestrial invertebrates, the earthworm Eisenia fetida has been examined mainly due to their increased susceptibility and ecological importance . Literature pertaining to toxicities of various classes of CECs to terrestrial invertebrates is discussed below. Like in terrestrial plants, antibiotics can also induce toxicity in terrestrial invertebrates. Exposure to environmentally relevant concentrations of antibiotics caused mortality to earthworms and/or induced oxidative stress and genotoxicity in E. fetida. For instance, high concentrations of tetracycline and chlortetracycline inhibited antioxidant enzymes superoxide dismutase and catalase while these enzymes were stimulated at lower doses , and DNA damage was induced along a dose-dependent curve . Also, chlortetracycline can reduced juvenile earthworm and cocoon counts in E. fetida .

The prioritization uses language variables to evaluate the management alternatives

The management of today’s complex water supply and demand systems rely on assessment models combining climatic, social, economic, and environmental factors. A model was developed using the concept of risk by identifying hazards, exposure, and vulnerability. Te vulnerability was classified into two domains, i.e., sensitivity and adaptive capacity, and two spheres, natural/built environment and human environment. A geographical information system modeling and satellite data were developed for water management in agricultural areas by modulating the irrigation water demand based on several vegetation indices. Te water allocation rules were evaluated among water user groups considering environmental, economic, and social criteria involving agricultural water user groups across France. Transferring of irrigation management was defined as the complete or partial transfer of responsibility for management and investment in irrigation systems from government institutions to water users and non-governmental organizations. A combination of the Adaptation Pathways approach was used with the Soil and Water Assessment Tool to assess the actions under different climate conditions. Conjunctive management requires a strong institutional capacity, which can be achieved through regional planning,raspberry container based on a sound understanding of the interactions between surface water and groundwater. Sustainability in basins with existing irrigation and drainage networks requires a strategic planning according to sustainable development principles.

Strategic planning refers to an organizational infrastructure that prioritizes plans and maximizes potential opportunities and benefits. Sustainable development achieves present economic, environmental and social needs while fulfilling the needs of future generations. The lack of strategic vision with respect to sustainability practices and goals was discussed. A SWOT analysis consists of well-structured strategic planning to assess the status of a system by evaluating its strengths , weaknesses , opportunities , and threats. A review of works based on SWOT analysis was reported. A strategic approach was applied to water management in Africa with SWOT. Strategic planning approaches were analyzed in Austrian food-risk management by identifying background conditions to facilitate scaling and replication of catchment regional planning tools in food-prone areas. A raster-based regional conservation action planning tool was developed for prioritizing local and regional scale conservation actions in heterogeneous landscapes. A stochastic method was developed to determine the water availability in agricultural lands that resulted from drought management plans. A regional optimization model of crop water consumption using cellular automation , crop suitability , and a regional distributed crop water use model was applied to improve irrigation benefits in the context of regional water management. A study was reported to determine deficiencies in irrigation networks and remediation measures . Multi-criteria decision making is a branch of operations research that provides methods for choosing among alternatives ranked by multiple criteria. The Analytic Hierarchy Process is a widely used decision-making tool in various multi-criteria decision-making problems. The AHP, is an approach that uses ratio comparisons among attributes and alternatives.

A method of scaling ratios using the principal eigenvector of a positive pairwise comparison matrix was proposed. This work defines and measures the consistency of the pairwise comparison matrix by an expression involving the average of the non-principal eigenvalues. The literature on methods and applications of Multiple Attribute Decision Making has been reviewed and classified systematically. A review of the TOPSIS method for decision making was presented. A new step‐wise weight assessment ratio analysis was introduced to determine the criteria weights in decision making problems. The weights of the criteria were calculated using the integrated Stepwise Weight Assessment Ratio Analysis -SWARA-TODIM multi-criteria decision-making method. The weighting methods in decision making process including the DEMATEL and BWM was applied to achieve the importance of supplier criteria in a combined manner. The fuzzy set in the form of a class of objects was introduced with a continuum of grades of membership. The fuzzy extension of the AHP method was introduced. Fuzzy TOPSIS method was applied for decision-making process. The model integrating SWARA and Additive Ratio Assessment methods was introduced under uncertainty. A new decision-making approach was developed by measuring attractiveness through a categorical-based evaluation technique and a new combinative distance-based evaluation method in a supplier selection problem during the COVID-19 pandemic. The Level-based weight assessment in fuzzy environment was developed using actual score measures of the picture fuzzy numbers. A novel extension of a developed multi criteria decision making algorithm known as the preference ranking on the basis of ideal-average distance method in fuzzy environment was applied to address a real-life complex decision making problem in social science research. A comparative analysis of supply chain performances of leading healthcare organizations in India with three MCDM frameworks was reported. Uncertainty analysis was conducted using an integrated fuzzy lambda–tau and fuzzy multi criteria decision making method. The integrated Fermatean fuzzy information-based decision-making method was introduced based on the removal effects of criteria and the additive ratio assessment methods, and applied it to a food waste treatment technology selection problem.

A triangular intuitionistic fuzzy linear programming model was proposed for planning of sustainable production system in Baluchistan, Pakistan. A fuzzy multi-criteria group decision-making model was investigated for watershed ecological risk management. A fuzzy-TOPSIS-world open account -based model was developed to identify the impacts of parameters influencing the water quality failure potential. A scenario-based fuzzy interval programming approach was developed for planning agricultural water, energy, food, and crop area management. Game theory was applied for solving decision making problems. The method was applied to construction site selection, and demonstrated that game theory can be applied for supporting decision in a competitive environment. SWOT analysis can be improved by combining it with MCDM. The Analytic Hierarchy Process and the Analytical Network Process analysis have been combined with SWOT analysis. Multiple criteria group decision making applied for prioritizing SWOT factors. Despite numerous studies on sustainable water management by researchers and research on sustainability principles, sustainable agricultural water management at the local level and scale has received less attention. Studies by the Organization for Economic Co-operation and Development on water sustainability indicators show that analysis at the local level and scale is necessary to demonstrate the effectiveness of the principles of water sustainability. The analysis of large-scale water resource systems involving multiple components, resources, stakeholders, reservoirs, small irrigation reservoirs, and water transfer schemes is a complex process. This work develops and applies a conceptual framework for sustainable agricultural water use and supply by applying regional management alternatives at multiple spatial scales. The framework is applied to a large scale water resources system considering social,plastic plants pots economic and environmental factors. The framework applies conceptual and analytical methods to sustainable agricultural water management relying on strategic planning and regional multi-criteria decision-making. Previous works have evaluated the sustainability of water resources from different perspectives and methods. This study is novel in its introduction of a framework that measures the sustainability of large-scale agricultural water systems relying on regional management plans.The type of available water resources , the crop pattern and quality of soil and water sources vary throughout the study area. Therefore, a database of water-use statistics was prepared to estimate the water use by agricultural lands within the Sefdroud irrigation and drainage network. The water use in the agricultural lands is a function of various factors such as the type of water resources, the method of water conveyance and distribution, the irrigation method, the type of crop products, climatic conditions, soil type, management practice, and others. Therefore, estimating the amount of water use in the agricultural areas in the study area is beset by complexity . The inputs to the agricultural water use model are the cultivated area and crop pattern of irrigated lands, the crop water requirements, the irrigation efficiencies and the surface and ground water withdrawal data. The agricultural water use analytical model calculates water use in each irrigation unit by comparing the water requirements of the crop pattern with the water withdrawals of surface water and groundwater. The outputs from this model are actual water use, the contributions of surface and groundwater to water use and the volumes of return flow.

The details of agricultural water use from different water sources within the irrigated units of the Sefdroud irrigation network are depicted in Fig. 4 and listed in Table 1 for three irrigation management zones. It can be seen in Table 1 that the cultivated area of paddy fields in the Sefdroud irrigation and drainage network has been estimated at about 179,181 hectares. The total annual water use of cultivated area in Sefdroud irrigation and drainage network is about 1.8 billion cubic meters, of which about 1707 million cubic meters are surface water and 90 million cubic meters are groundwater. Of the total volume of surface water use about 1.4 billion cubic meters are from the Sefdroud dam and related canals, 260 million cubic meters from local rivers and farm wastewater, and about 47 million cubic meters from small irrigation reservoirs. The average volume of water use in the 191,141 hectares of irrigated lands of the Sefdroud irrigation and drainage network equals 9404 cubic meters per hectare.The management alternatives to improve the agricultural water demand and supply management in the irrigation management zones in the study area were determined to be: Development/Rehabilitation of the Sefdroud irrigation network; Improve the management of operation and maintenance of the Sefdroud irrigation network; Wastewater management, and Inter-basin water transfer within the Sefdroud irrigation network system . The spatial distribution of the management alternatives within the Sefdroud irrigation and drainage network were defined according to the management alternatives for agricultural water demand and supply management, and are shown in Figs. 5, 6, 7 and 8. Under current conditions the management alternative of development/rehabilitation of the Sefdroud irrigation network’s infrastructure has not been fully implemented. Accordingly, completion and implementation of the main irrigation and drainage network in about 90,000 hectares represents one of the most important priorities in the Sefdroud irrigation network. Carrying out this management alternative would raise the irrigation efficiencies of the Sefdroud irrigation network. Furthermore, in spite of the implementation of the main irrigation and drainage network in 10 irrigation units of the Sefdroud irrigation network, the rehabilitation of the irrigation network in 102,000 hectares is imperative to achieve operational effectiveness. Figure 5 displays the spatial distribution of development and rehabilitation lands in Sefdroud irrigation network. One of the effective management alternatives for maximum use of internal water resources in the study area is using the natural potential of small irrigation reservoirs existing in the Sefdroud irrigation and drainage network. The spatial distribution of small irrigation reservoirs is depicted in Fig. 6. It is seen in Fig. 6 that the total number of small irrigation reservoirs in the study area for agricultural water supply is equal to 527, and the total area of the small irrigation reservoirs is 4935 hectares. The total volume of stored water in small irrigation reservoirs is estimated at 197 million cubic meters under the rehabilitation and improvement conditions.The alternatives and criteria for decision making are determined and a hierarchical structure is formed. The hierarchical structure has a first level consisting of goals to be achieved, the second level consists of the decision criteria, and the third level consists of the management alternatives. The weights of the criteria are determined by the hierarchical analysis method once the hierarchical structure is defined, which involves constructing a pairwise comparison matrix to determine the weights. The comparison matrix’s values are determined using Saaty’s table, and the weights of the criteria are calculated based on the geometric mean values. The next step applies the fuzzy TOPSIS algorithm to evaluate the management alternatives in each of the irrigation management zones of the Sefdroud irrigation and drainage network. Lastly, the management alternatives are prioritized. The fuzzy TOPSIS calculates the CCj indexes of the management alternatives, such that the alternatives’ rank or desirability increases with increasing value of the CCj index. The CCj index is a dimensionless metric in the range [0,1] that measures the closeness of a management alternative to an ideal management alternative or solution.The first line of argumentation suggests that demography is destiny, and expects farmer influence to decline over time along with the sector’s share of the population .

Angular sensors can also be used in some cases to measure linear velocity

In the marketplace, people generally care more about the sensed quantity and how well the sensor performs for their specific application, while academic researchers and sensor designers are also interested in how the sensor measures the quantity. This section is concerned with the latter. The means by which a sensor makes a measurement is called the transduction mechanism. Transduction is the conversion of one source of energy to another, and all sensors utilize some form of energy transformation to make and communicate their measurements. It should be noted that this is not an exhaustive list of transduction mechanisms. This list only covers a small fraction of the many universal laws describing the conversion of one energy form to another. Rather, this list focuses on transduction principles that describe converting one energy type to electrical energy. This is because all electrical sensors must take advantage of at least one of these mechanisms, and often more. What this list does not cover is transduction from any energy type to another type other than electrical. For example, the thermal expansion principle that governs the liquid-in-glass thermometer example at the beginning of this chapter is not described,plastic plant containers because that sensor operates on the principle of converting thermal energy to gravitational energy. This list also does not include modes of biological or nuclear signal transduction mechanisms for the sake of brevity.A potentiometric sensor measures the open-circuit potential across a two-electrode device, such as the one shown in Figure 1.3C. Similar to amperometric sensors, the reference electrode provides ‘electrochemical ground’.

The second electrode is the ion-selective electrode , which is sensitive to the analyte-of-interest. The ISE is connected to a voltage sensor alongside the RE. The voltage sensor must be very sensitive and have a high input impedance, allowing only a very small current to pass. There are four possible mechanisms by which ionophores can interact with ions: dissociated ion exchange, charged carrier exchange, neutral carrier exchange, and reactive carrier exchange. Dissociated ion-exchange ionophores operate by classical ion-exchange over a phase boundary, in which hydrophilic counter-ions are completely dissociated from the ionophore’s lipophilic sites, preserving electroneutrality while allowing sites for the ions in solution to bind to. Charged-carrier ionophores bond with opposite-charged ions to make a neutrally charged molecule, and the ions with which they bond are determined by thermodynamics and the Hofmeister principle. Neutral carrier ionophores are typically macrocyclic, where many organic molecules are chained together to form a large ring-like shape whose gap is close to the molecular radius of the primary ion. Finally, reactive carrier ionophores are mechanistically similar to neutral carrier ISEs, with the only difference being that reactive carriers are based on ion-ionophore covalent bond formation while neutral carriers are based on reversible ion-ionophore electrostatic interaction. Neutral carrier and reactive carrier ion exchange both are dependent on the mobility, partition coefficients, and equilibrium constants of the ions and carriers in the membrane phase. Some examples of the chemical structures of ionophores are shown in Figure 1.4. Positional sensors are some of the most common in the world, and there are likely several within reach of you as you read this. Smartphones and wearable health devices utilize various sensors to track how many steps you take in a day, the intensity of your workouts, and what route to take home from work. Displacement, velocity, and acceleration can sometimes all be found with a single device, as each quantity is the time-derivative of the prior.

In practice, however, it is common to use separate devices for any of these three measurements because the cost of these sensors is relatively cheap, and it is easy to build systematic errors if the timing mechanism is off. The measurements for displacement, velocity, and acceleration must be made with respect to some frame of reference. For example, consider a group of people playing a game of billiards in a moving train car. Observers on the train platform would assign different velocity vectors to the balls during play than observers on the train. Displacement and angle sensors commonly use potentiometers when the value is expected to be suitably small. A potentiometer transduces linear or angular displacement to a change in electrical resistance. For a displacement sensor, a conductive wire is wrapped around a non-conductive rod, and a sliding contact is attached to the object whose displacement is being measured. A known voltage is supplied across the wound wire, and as the object moves, the sliding contact will make contact with the wound wire, shorting that part of the circuit. Then, the output voltage across the wire is measured, which will be proportional to the amount of the wire shorted by the sliding contact, which is proportional to the object’s displacement. The same principles are applied to measure the angle for a potentiometer operating in angular displacement mode. There are other methods for measuring displacement, but these methods can also be used to measure velocity, as described in the following section. Velocity measurements utilize a variety of approaches ranging from radar, laser, and sonic sensor systems. These types of sensors use one of these modulating signals to send a sound or light wave in a direction and measure the time it takes to bounce off of a surface, return to the sensor, and activate a sensing element that is sensitive to that modulating signal. Using this, the device can calculate the distance between the sensor and the reflecting object by dividing lag time by the wave speed. Then, because these devices often operate at a high frequency, the measurement can be made again, and the change in distance divided by the change in the time between measurements yields a linear velocity.In a car, for example, the speedometer is a linear velocity sensor, but it makes its measurement using an angular velocity sensor on the drive shaft and calculates the linear velocity from the assumed tire size.

Acceleration measurements are most commonly made with accelerometers. Accelerometers are most commonly MEMS devices that are extraordinarily cheap, have a low-power requirement, and utilize the capacitance transduction mechanism. The charged electrode of an interdigitated parallel-plate capacitor structure is vibrated at a high mechanical frequency. Then, when acceleration occurs, if it is perpendicular to the gap between the two capacitor plates, the force from the acceleration will cause the moving electrode of the parallel-plate capacitor to deflect towards the other plate, changing the space of the gap between the two, thereby changing the measured capacitance. The operating principle of most pressure sensors is based on the conversion of a pressure exertion on a pressure-sensitive element with a defined surface area. In response, the element is displaced or deformed. Thus, a pressure measurement may be reduced to a measurement of a displacement or a force that results from a displacement. Because of this, many pressure sensors are designed using either the capacitive or the piezoresistive transduction mechanisms. In each, a deformable membrane is suspended over an opening, such that the pressure on one side of the membrane is controlled while the pressure on the other side is the subject of the measurement. As the pressure on the measurement side changes, the membrane will deform proportionally to the difference in pressure. For a piezoresistive transducer, the membrane is designed to maximize stress at the edges, which modulates the resistance proportional to the deformation. For a capacitive transducer, the membrane is made of or modified with a conductive material, while a surface on the pressure-controlled side of the membrane is also conductive, and the pair act as a parallel-plate capacitor. Then, the membrane is designed to maximize deflection at the center of the membrane,blueberry container thereby changing the electrode gap and capacitance.Practically speaking, a sensing element does not function by itself. It is always a part of a larger ‘sensor circuit’: a circuit with other electronics, such as signal conditioning devices, micro-controllers, antennas, power electronics, displays, data storage, and more. Sensor circuits fit within the broader subject of systems engineering, which is a vast field in its own right. Figure 1.5 shows one possible sensor circuit configuration. Depending on the design of the circuit and which components are included in it, the signal that is output by the sensing element might be conditioned to the specifications of a connected micro-controller, saved onto a flash drive, shown on a display, and sent to a phone, saved on a remote server, or many other possibilities. Rather than discuss all possible sensor systems and circuit designs, we have selected the most common – and arguably most essential – components in any given sensor system and summarized them in this section.

In some form or another, all sensor circuits require power to operate. The components of a sensor circuit that generate, attenuate, or store energy to power the other circuit components are called power electronics. This may include batteries, energy harvesters, and various power conditioning devices. A sensor circuit can be made passive, where there is no energy storage within the circuit. The concept is similar to passive sensing elements described in section 1.2: passive sensor circuits use the naturally available energy to operate. This can be done if the quantity that is being measured can also be harnessed to power the device, such as light powering a photovoltaic sensing element. If there is no passive power generation, power electronics are vital for a sensing circuit’s function. This could be as simple as a coin-cell battery connected to the micro controller’s power I/O pins or as complex as a circuit with multiple energy harvesting and energy storage modalities. A sensor is not a sensor if it does not communicate its measured signal to another person or device. Communication electronics are what fulfill this function. Communication electronics can be wired or wireless. When communicating data to a person, wired communications electronics could be displays or speakers that communicate the data through images or audio. When communicating data to another computer, wired communication electronics come in the form of a ‘bus’, a catch-all term for all the hardware, wires, software, and communication protocols used between devices. At the time of this writing, wireless communications must be between the sensor circuit and another electronic device, though perhaps in future years, technology will develop a way for people to directly interface with wireless data transfer. In the meantime, wireless communications generally incorporate an antenna that attenuates an electrical signal into a directional RF frequency following one of many wireless communication protocols such as WiFi, Bluetooth, or RFID.In science and engineering, ‘error’ does not mean a mistake or blunder. Rather, it is a quantitative measurement of the inevitable uncertainty that comes with all measurements. This means errors are not mistakes; they cannot be eliminated merely by being careful. All sensors have some inherent error in their measurement. The best that one can hope for is to ensure that the errors are minimized where possible and to have a reasonable estimate of the magnitude of the error. One of the best ways to assess the reliability of a measurement is to perform it several times and consider the different values obtained. Experience has shown that no measurement – no matter how carefully it is made – will obtain the same values. Error analysis is the study and evaluation of uncertainty in a measurement. Uncertainties can be classified into two groups: random errors and systematic errors. Figure 1.8 highlights these two types of error using a dartboard example. Systematic errors always push the measured results in a single direction, while random errors are equally likely to push the results in any direction. Consider trying to time an event with a stopwatch: one source of error will be the reaction time of the user starting and stopping the watch. The user may delay more in starting the stopwatch, thereby underestimating the duration of the event, but they are equally likely to delay more in stopping the stopwatch, resulting in an overestimate of the event. This is an example of random uncertainty. Consider if the stopwatch consistently runs slow – in this case, all events will be underestimated. This is an example of systematic uncertainty. Systematic errors are hard to evaluate and sometimes even difficult to detect. However, the use of statistics gives a reliable estimate of random error. In the kingdom of electronics, silicon reigns.