Sensors - Science topic

Nihal Sanil you can see this documentation on raspberry PI with NetSim https://github.com/NetSim-TETCOS/NetSim-Raspberry-Pi-Project_v13.3/blob/main/Documentation/IOT-Netsim-Raspberry-Pi-Project.pdf

Sorry Amiaan, Physics dictate that any sensor will measure the local level of vibration whatever the origin, so environment-induced vibration will equally be recorded by the LV. And you have to be further cautious to protect the LV device and mirrors from this environment-induced vibration...

As previously commented, if you use an accelerometer, its mass will also alter the vibration reading, which is not the case of LV - this is the main difference!

There are a variety of data collection methods and protocols for transferring sensor data from IoT devices to a local server. The best method for a particular application will depend on a number of factors, including the type of sensor data being collected, the frequency of data collection, the distance between the IoT devices and the server, and the power constraints of the IoT devices.

Some common data collection methods include:

Some common protocols for transferring sensor data from IoT devices to a local server include:

The best way to choose a data collection method and protocol is to consider the specific requirements of your application. If you are not sure which method and protocol to choose, there are a number of resources available online and in libraries to help you make a decision.

Here are some additional tips for collecting and transferring sensor data from IoT devices to a local server:

Fariz,

There are dozens of models made under the 'Rosemount' brand.

The 3051 (Rosemount™ 3051S) for example can be configured to have a line pressure (static) of anything from 2 bar to 420 bar.

What is the application that you are hoping to measure the pressure in?

Hi Pavan, did you come across with any solution for this issue?? I'm working in a protect, where I need to isolate the effect of plankton turbidity on shrimp ponds. regards.

Electrochemical Sensors provide key information required in precision agriculture: pH and soil nutrient levels. Sensor electrodes work by detecting specific ions in the soil. Currently, sensors mounted to specially designed “sleds” help gather, process, and map soil chemical data. This sensor provides information such as air temperature, soil temperature at various depths, rainfall, leaf wetness, chlorophyll, wind speed, dew point temperature, wind direction, relative humidity, solar radiation, and atmospheric pressure is measured and recorded at scheduled intervals. Precision agriculture sensors are very efficient in agriculture because they transmit data that helps farmers not only to monitor but also to improve their products and keep abreast of changes in the field and ecosystem. It can minimize the use of pesticides, effectively control weeds and pests, and achieve efficient green precision agriculture. WSN can sense and collect real-time data of various information changes in the process of agricultural production and provide timely feedback to the users. In IoT, sensors are used to collect data from various sources and send it to cloud-based platforms for analysis. The data collected by sensors are used to monitor and control various systems, including environmental conditions, traffic patterns, and equipment performance. Microcontroller on Arduino uno and Node MCU ESP8266 platform is used to implement the control unit. The setup uses soil moisture sensors which measure the exact moisture level in soil & also it contains Humidity and Temperature Sensor DHT11 for Online monitoring of system. CropSpec sensors measure plant reflectance to determine chlorophyll content, which correlates to nitrogen concentration in the leaf.n IoT-based smart farming, a system is built for monitoring the crop field with the help of sensors (light, humidity, temperature, soil moisture, etc.) and automating the irrigation system. The farmers can monitor the field conditions from anywhere. These precision agriculture sensors are used to determine the variety, distance, and height of any position within the required area. They take the help of GPS satellites for this purpose. They are installed on tractors and other field equipment to check equipment operations. The data collected by the IoT sensors can provide a real-time picture of what's going on in the field. This means that farmers will be able to know when their crops are ripe, how much water is being used and if an irrigation system is needed, soil health, and whether they need more fertilizer or any other input. This smart agriculture using IOT system is powered by Arduino, it consists of Temperature sensor, Moisture sensor, water level sensor, DC motor and GPRS module. When the IOT based agriculture monitoring system starts it checks the water level, humidity and moisture level. IoT agricultural solutions consist of multiple monitoring, controlling, and tracking applications that measure several types of variables such as air monitoring, temperature monitoring, humidity monitoring, soil monitoring, water monitoring, fertilization, pest control, illumination control, and location tracking. One can connect IoT-based agriculture sensors, such as temperature and moisture sensors in agriculture for environmental monitoring applications. The sensors can ensure fine dust, high-pressure spray, submersion in water, and extreme temperatures. Precision agriculture sensors are very efficient in agriculture because they transmit data that helps farmers not only to monitor but also to improve their products and keep abreast of changes in the field and ecosystem. Smart sensors in agriculture collect data to help farmers in monitoring and optimize their crops while being kept updated on the changing environmental and ecosystem factors. Smart sensors in agriculture collect data to help farmers in monitoring and optimize their crops while being kept updated on the changing environmental and ecosystem factors. The system uses various sensors to monitor environmental conditions in real-time. The data collected is processed by a microcontroller and transmitted wirelessly to a web application that provides farmers with visualized information about their crops. This system uses different components like DHT11 sensor, Soil Moisture sensor, Gsm Modem, ultrasonic sensor etc. and according to this sensor parameters farmer are provided an automated way to irrigate their fields and monitor the tank.To predict production rate of the crop artificial network use information collected by sensors from the farm. This information includes parameters such as soil, temperature, pressure, rainfall, and humidity. The farmers can get an accurate soil data either by the dashboard or a customized mobile application.

I think I have a basic solution to this issue. Not perfect but will be transitioning the existing application to this framework. Code is attached.

Precision agriculture is also known as satellite crop management or site-specific crop management. This is used to develop a decision support system for complete farm management with the goal of optimizing the inputs and outputs of the different systems at regular intervals. The primary benefit of using satellites for precision agriculture is the ability to monitor and manage crop yields in real-time. With satellite imagery, farmers can track crop growth, soil moisture levels, and pest infestations. Satellite Agriculture is also called Precision agriculture, which is an approach to farm management that uses information technology to ensure that the crops and soil receive exactly what they need for optimum health and productivity. The temperature sensor and humidity sensor are used to monitor the weather condition in the agriculture field area. The soil moisture sensor and rain sensor are used to monitor the soil moisture and rainfall. Connect a range of IoT-based agricultural sensors such as temperature, moisture, depth, humidity sensors for agriculture, and more for environmental monitoring applications. Dozens of sensors are available today, but the five most important sensors for the maintenance professional are vibration, gas, temperature, humidity, and security sensors.

El método de INTERPOLACION de Krigin resulta más apropiado por su capacidad espacial.

Agricultural drones allow farmers to monitor crop and livestock conditions from the air to keep watch for potential problems and help optimize field management. There are several functions that farmers and other agribusiness owners can use agricultural drone services for, including: Land imaging. UAVs can be equipped with different data collection sensors, such as RGB, multispectral (MS), hyperspectral or thermal cameras, or Lidar. Plants reflect light at varying levels depending on their chlorophyll content and biomass. The electrochemical sensors aid in the collection, processing, and mapping of the chemical data of the soil. They are usually mounted on specially designed sleds. They supply accurate details required for agriculture. UAV sensors also include Inertial Measurement Units (IMUs), which fuse together information from different sensors such as gyroscopes, accelerometers and magnetometers to provide measurements that can be used to calculate orientation and velocity of the drone. Depending on the specific application of the agricultural robot, they could have a combination of position sensors, optical sensors, pressure and temperature sensors, and location sensors. Drones can assist in precision agriculture by performing variety of agricultural tasks including soil health monitoring, seed planting, fertilizer application, crop stress management, irrigation schedule planning, weed management, crop yield management, and weather analysis. Drones equipped with special imaging equipment called Normalized Difference Vegetation Index (NDVI) use detailed colour information to indicate plant health. This allows farmers to monitor crops as they grow so any problems can be dealt with fast enough to save the plants. Drones are used for field mapping to provide information regarding the irregularities and elevation of the field. This information is essential for gaining knowledge about drainage patterns and dry spots, if any, to optimize the watering of crops. Agricultural drone mapping can clearly identify farm field variations that cause spotty crop performance. These anomalies are often indications of soil variations. Identifying and precisely locating underperforming areas allows farmers to investigate the root cause. Beyond elevating yield potential, multi-rotor drones present a faster, safer application strategy compared to hand spraying. Autonomous loading and distribution capabilities limit human exposure to crop protection solutions, and targeted overhead flight prevents interference with growers or other crops in the field. Drones regularly carry high-resolution cameras, infrared cameras, heat sensors, GPS, sensors that detect movement, and automated license plate readers. These cameras may include facial recognition technology that would make it possible to remotely identify individuals from a distance without their knowledge. Drones are also being used to monitor and map vegetation, particularly in areas that are difficult to access, such as steep slopes, dense forests, and wetlands. This information can be used to study environmental changes, such as deforestation, and develop conservation and restoration strategies.

One can connect IoT-based agriculture sensors, such as temperature and moisture sensors in agriculture for environmental monitoring applications. The sensors can ensure fine dust, high-pressure spray, submersion in water, and extreme temperatures. The technology consists of a sensor or series of sensors that measure the level and moisture content in grains, such as corn. This information is then relayed back to an app on a tablet or computer where it can be viewed by the farmer for analysis. Electrochemical Sensors provide key information required in precision agriculture: pH and soil nutrient levels. Sensor electrodes work by detecting specific ions in the soil. Currently, sensors mounted to specially designed “sleds” help gather, process, and map soil chemical data. In addition to monitoring the plants that are harvested, temperature sensors observe the equipment that gathers these plants. Temperature sensors send out alerts whenever an equipment system requires minor maintenance, is underperforming, or is critically failing.Agriculture through precision agriculture implements IoT through the use of robots, drones, sensors, and computer imaging integrated with analytical tools for getting insights and monitoring the farms. Placement of physical equipment on farms monitors and records data, which is then used to get valuable insights. Sensors play a crucial role by detecting and measuring a variety of parameters such as temperature, pressure, humidity, flow rate, motion, and position. They convert physical signals into electric signals and provide information in real-time to the control system, thereby making production intelligent and automated.

Dear doctor

Go To

Sensors and Their Application in Precision Agriculture

Mladen Jurišić*, Ivan Plaščak, Željko Barač, Dorijan Radočaj, Domagoj Zimmer

"CONCLUSIONS In addition to the navigation system, precision agriculture requires the support of other systems to be completely functional. The most important systems are the system for the application of artificial fertilizers and plant protection. They are equipped with various sensors that read and send data to a processing unit that makes decisions based on the collected data. Technology development has resulted in a wide availability of precision farming. Besidesthe navigation of aggregates, every bigger farm uses a system of precision agriculture including the growing use of sensors. Nowadays, every new machine comes with a range of various integrated sensors. They convert analogue signals to the digital ones, read and processed through a computer. The data collected by sensors determine further steps of agrotechnical activities with the aim of production improvement. One of the most well- known sensors is OptRx with aim to calculate the vegetative indices NDVI and NDRE at the wavelengths of 670, 730, and 760 nm. These sensors can perform in difficult field conditions, unlike other sensor types, which failed to produce accurate data in the same conditions. The advantage of the plant sensor PRO Active is the driver’s ability to change the settings even in movement, and to adjust the steps of calculations in one work phase. Using application of variabale rate technology (VRT) farmers have immediate insight into the condition of the crops and simultaneous application of the product. Farmers must deploy modern agricultural systems to ensure the survival of their production. The large EU market provides ways of easy placement and export of agricultural products of differing quality. The quality as well as the product price can be improved through the application of modern systems and a wise use of resources, which is the goal of the sustainable agricultural production. Future use in precise agriculture is having autonomus robots with sensors for each agrotechnical operation so farmers will be only for monitoring and sending a robot scout as needed."

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Dear Md. Imtiaz Kamrul, could you find the appropriate template to the given journal? I have faced the same issue.

you can have a measurement on the VDD pins to see how much power it takes on EE side. But as for the ultrasonic power, it may decay through the distance and space, so it's hard to define this if without any constraints on input power and space.

Thank you, Sulaf Waiss for your valuable suggestions.

Joshua Depiver, Thanks for your answer

Thank you very much to Dr. Marcel Nicola for your reply and recommendation！

I'm actually confused about this problem now, I've tried to look for clues in step responses plot, Bode plots, Nyquist plots, and zero-pole distribution plots, but nothing has progressed.

I posted this discussion because I was stuck, maybe I have to put it aside, take time to find the right direction, the right math tool.

6. My discussion idea comes from the problem of machine tool motion control. from the perspective of dynamic characteristics, I'm trying to analyze whether the motion system can be exactly controlled, whether it is easy or difficult to be controlled, and how to control it.

7. I'm directly inspired by the existing works, including the non-minimum phase zeros of non-collocated flexible beam, the zeros of motion system connected of mass, spring, and damping, approximate inverse algorithms (ZPETC, ZMETC, NZI), and motion control of underactuated system.

8. I found that the "closeness" in the discussion has arbitrariness, when we have different foucus on steady-state error of transient error (in the time domain), have different interests on each frequency band, there will be different "distance" values for the same two systems.

9. Perhaps numerical methods are a feasible and practical approach. Given the time domain response or frequency domain response of a non-minimum phase system,set an weighted objective function, and search for a minimum phase system.

很感谢Marcel Nicola博士的回复与推荐

实际上现在我对这个问题更多的是困惑，我尝试着从阶跃响应、Bode图、Nyquist图、零极点图上寻找线索，但没有进展。我贴出这个讨论是因为一时卡住了，也许需要放一放，找到合适的方向，合适的数学工具。

6、我的讨论想法来自于机床运动控制问题，尝试动态特性的角度分析运动系统是否能够精确控制、是否易于控制，以及如何控制。

7、直接受到已有工作的启发，包括非同位控制的柔性梁的非最小相位零点分析、集中参数系统（质量弹簧阻尼联接）的零点分析、近似求逆算法（ZPETC、ZMETC、NZI）、欠驱动系统的运动控制。

8、我发现讨论中的“最近”有很大的任意性，当我们对稳态误差、瞬态误差（时域上的）有不同倾向时，或对各频段有不同重视程度时，同样的两个系统间会出现不同的“距离”。

9、也许数值方法是一种可行的实用办法。给出非最小相位系统的时域响应或频域响应作为目标，设定有权的优化目标，搜索找到一个最小相位系统。

mg/kg =ppm. so ppm x 2.24= kg/ha

Aparna Sathya Murthy thanks a lot

The wavefront sensor (much like other interferometers) measures only small perturbations from a plane wave. In other words, it works in collimated space. Your lens system has finite conjugates, so it wouldn’t be possible to directly measure the entire lens system in the configuration it will be used. You have two choices.

Typically what is done is adding a null lens. put the focal plane at the focus of another optic which will conjugate it back to collimated space so you can measure the wavefront. The problem is that the null lens is now part of the measured wavefront. Therefore it has to be trusted to be perfect to much better than the errors you want to measure. For that reason people usually use a precision OAP.

The second choice is to measure the lens system in pieces. It looks like you have a region that is intended to be truly collimated. You can split the lens system there and measure the two halves separately. That approach has two problems. First, it may not be mechanically convenient to build the two subassemblies separately. Second, even if you can measure them, you will still have a question of how well you put the two halves together and whether errors in that step that introduced aberrations.

Usually, many COMSOL users export the global variable ewfd.neff to some processing software like MATLAB and then evaluate sensitivity.

Aparna Sathya Murthy Thank you for your answer. I found how to do so is just obtaining the phase difference of the IQ signal using arctan2(Q/I), then to obtain the relative distance = phase_difference/2pi x wavelength/2.

I would expect the size of the protein, and other aspects of the construct, to matter due to the fact that resonance energy transfer has a very strong sensitivity to the distance between the donor and acceptor. The closer together they are, the greater the energy transfer.

The Global Disposable Medical Sensor Market size is expected to reach $25.4 billion by 2028, rising at a market growth of 16.8% CAGR during the forecast period.

Disposable medical sensors are easy-to-use and economical. Medical sensors, under which disposable medical sensors fall, are devices that aid in the detection of physical, biological, and chemical signals. These devices offer a way for these signals to be recorded and measured. The concentrations of substances in liquid, solid or gaseous forms along with the amplitude of electronic and magnetic fields are measured by these sensors.

Microstrip moisture sensor can be used to detect dissolved salt and sugar in liquid.

Ref: Wireless Personal Communications 122 (2022) 593-601

Here's a more detailed explanation of the process:

In your case, if you have measured a sensitivity of 600 mV/Pa at a frequency of 10 kHz, follow these steps:

Remember to perform these steps for multiple frequencies to fully characterize the sensor's phase response and sensitivity.

There are couple of sensors might be used for different purpose in automobile, for temperature, rain, speed, fuel, break and stability direction, knock, transmission, oxygen, proximation, traffic etc. depends on your requirements. You can use all or call on car is inbuilt with which sensors.

I am not sure if you can remove noise from the data. Usually the mweasurement contains of a signal over a given time and then the noise at no signal. Thesed can be compare, usually in the frequency domain and plotted together. There are routines for adjusting for the noise, but it is only possible down to a given s/n difference, I guess about -6dB. The noise, if it has an equal frequency distribution as the signal part, will influence the signal down to - 10 dB using 1 dB digit resolution.

An alternative to an oven could be to use a hot plate. Depending on the film thickness you will need more or less time. For me worked perfectly!

Hafijur Rahman Hafijur, The quality of the lens (glass and arrangement of the glass) and the amount of light (aperture) it acquires is one factor. Small apertures result in less light and reduced sharpness. Size of pixels and their arrangement is another. That can add noise. Made worse at high ISO. Another factor is the dynamic range (DR) of light that’s captured. The greater the range the easier it will be to have a sharp image with clearly defined details.

The more important aspects regarding use, among these two sensors, are the following:

The Hall-effect sensor is suitable where contactless measurement of angle, for rotational motion, is needed.

Whereas, digital rotary incremental encoder requires contact with the rotating axle.

I thought about near field tags (which indeed are not actually RF tags). In case of 860..960MHz one may think about using RF rectifier driving low frequency micropower on tag oscillator. That oscillator may modulate impedance switch connected to the antenna therefore communicating (retromodulation communication) the low frequency which contains temperature information. The t freq dependant oscillator can be made using t dependant piezocrystall or even simply thermistor.

To calculate the maximum possible error in the measured pressure, we need to consider the errors from the load resistor and the ADC. Let's break down the calculations step by step:

1 - Load Resistor Error:

The load resistor's specified value is RL = 250Ω (±0.01%). To calculate the maximum possible error introduced by the load resistor, we use the following formula:

Load Resistor Error = (RL * Load Resistor Error Percentage) = 250Ω * (0.01/100) = 0.025Ω

2 - ADC Error:

The ADC has an error of ±0.5% FSO (Full Scale Output). Since the ADC measures the voltage VL, which is related to the current output of the pressure sensor, we need to calculate the maximum possible error in VL. The ADC measures VL with an error of ±0.5% FSO, which means:

ADC Error = VL * (0.5/100)

3 - Maximum Error in Pressure Calculation:

Now, we can calculate the maximum possible error in the pressure calculation using the given equation P = 6250(V/R) - 25. Since the load resistor and ADC introduce errors, the maximum error in pressure is obtained by taking the absolute value of the sum of the errors:

Maximum Error in Pressure = |6250 * (VL + ADC Error) / (RL + Load Resistor Error) - 25|

Note: Since the load resistor error and ADC error are specified as percentages, we calculate the error contribution by multiplying them with the corresponding variables (VL and RL) in the equation.

Plugging in the values calculated in steps 1 and 2, we can determine the maximum possible error in the measured pressure using the provided equation.

Sensitivity of a sensor or transducer is defined as the 'absolute limit of detection' or 'absolute minimum threshold of detection' - in short what is the lowest level of the parameter in question that the sensor or measuring system can detect without there being a high degree of error in the measurement.

In the case of a gas (above e.g) this needs to be established as the lowest concentration level at which the sensor can produce a constant output which excludes signals interfering with the actual limit of detection. The value of the limit of detection is determined by comparing the 'detector's' output deviation under static conditions against a level of gas concentrations at which the detector or sensor gives an undeviating output over the interfering noise equivalent factor.

For any given sensor there is no absolute benchmark other than the one specific to the measurement in question.

Hello,

You can look through the comprehensive documentation provided by NetSim support. It explains the different nuances involved in obtaining the radio communication range or transmission range.

Here is the link:

Thanks,

Anthony

Farmers use manual intervention to control the greenhouse environment. The use of IoT sensors enables them to get accurate real-time information on greenhouse conditions such as lighting, temperature, soil condition, and humidity. Thermistors are really popular IoT temperature sensors due to their small size and low power consumption. They are also highly accurate and reliable. Other types of temperature sensors are thermocouples, resistance temperature detectors (RTDs), and infrared temperature sensors. The soil moisture sensor is a simple device for measuring the moisture level in soil and similar materials. The soil moisture sensor is straight forward to use. The two large exposed pads function as probes for the sensor, together acting as a variable resistor. Volumetric moisture sensors for soil determine the water-to-soil volume percentage. Two common varieties of volumetric sensors are neutron probes and electromagnetic sensors. Agriculture sensors such as air temperature and humidity, soil moisture, soil pH, light intensity, and carbon dioxide are often used to collect data in all aspects of crop growth such as nursery, growth, and harvest. Agricultural conductivity and agricultural pH sensors are used to monitor water and fertilizer. The Dragino N95S31is a NB-IoT Temperature and Humidity Sensor for Internet of Things solution. It is used to measure the surrounding environment temperature and relative air humidity precisely, and then upload to IoT server via NB-IoT network. An IoT ecosystem consists of web-enabled smart devices that use embedded systems, such as processors, sensors and communication hardware, to collect, send and act on data they acquire from their environments. The applications of IoT in environmental monitoring are broad − environmental protection, extreme weather monitoring, water safety, endangered species protection, commercial farming, and more. In these applications, sensors detect and measure every type of environmental change.

One effective technique for early leak detection is the use of pipeline leak detection systems that rely on sensors installed along the pipeline. These sensors can detect changes in pipeline pressure, temperature, or flow rate (internal parameter measurements), which can indicate the presence of a leak. Fibre-optic sensing technologies (external) can also detect changes in the physical properties of the pipeline, such as strain or vibration, that a leak may cause.

(External techniques are expensive but more realistic in the case of offshore)

However, false alarms can be a problem with sensor-based leak detection systems. False alarms can be caused by external factors, such as changes in weather or tidal conditions, or by internal factors, such as changes in the composition or viscosity of the fluid being transported in the pipeline.

To minimize false alarms, it is essential to use reliable sensors and implement effective filtering and analysis algorithms. Machine learning algorithms can be trained to identify patterns in the sensor data that are indicative of a leak and to distinguish those patterns from false alarms.

Note that all the above discussion is related to continuous monitoring techniques.

Well, you already said it yourself: transition state theory. You need quantum chemical calculation for the educt (adsorbed state), the transition state (i.e. with one imaginary frequency) and the product (desorbed state), all with a calculation of vibrational frequencies, e.g. by the aoforce tool in Turbomole. These can then be used for the statistical calculation of v and E_A (which is the kinetic activation energy for the desorption, not the thermodynamic adsorption energy).

The minimum number of detections required for applying statistical models to estimate the density from camera traps for unmarked individuals depends on several factors, including the study area, the species being monitored, and the specific statistical method being used.

In general, a higher number of detections leads to more precise density estimates, allowing for better estimation of the detection probability and reducing the effects of random variation in the data. However, there is no specific minimum number of detections that is universally applicable to all situations.

As a general rule of thumb, some studies suggest that a minimum of 10-15 independent detections is required for reliable density estimation using camera trap data. However, this can vary widely depending on the specific situation and statistical method being used.

It is important to note that the number of detections required for reliable density estimation can also depend on other factors, such as the size of the study area and the spatial distribution of the animals being monitored. In some cases, collecting data over multiple seasons or years may be necessary to obtain a sufficient number of detections for reliable density estimation.

Statistical analysis of high-frequency time series data for two temperature loggers?

To perform a statistical analysis of high-frequency time series data for two temperature loggers, you can follow these steps:

Overall, statistical analysis of high-frequency time-series data for two temperature loggers involves collecting and processing data, visualizing the data, performing statistical analysis, interpreting the results, and reporting the findings.

Clustering will require some kind of static routing to be set up between the nodes and the cluster heads. If the clustering is not changing dynamically - that is if the sensors are not mobile - then this can be set up initially prior to the start of the simulation.

Check out the symbolic computation in Matlab. You should be able to define "s" as a symbolic variable and define the transfer function in terms of "s". You will find good examples if you search online.

I do not believe the AI will be utilized to describe heart rhythms. Heart Rhythm is unique from so many variations and variables in human physiology. I also do not believe heart rhythm any time soon with be detected without electrode of at least one hook up. Although optical readers can detect blood flow and pulse , electrical activity of the heart is a sign of the heart muscle activity where flow may or may not be occurring. It unlikely to deduce these minute variations of electrical activity without body contact due to impedance. Impedance to electrical current flow make it very unlikely to see the heart electrical activity in the precise measurement required without touching the body.

I think drug delivery, microfabrication, and nanotechnology are promising. Chunguang Hu

The temperature in the troposphere decreases with increase in altitudes but the rate of decrease in temperature changes according to seasons. The decrease of temperatures is known as vertical temperature gradient or normal lapse rate which is 1000 times more than the horizontal lapse rate. The distribution of temperature across the latitudes over the surface of the earth is called its horizontal distribution. On maps, the horizontal distribution of temperature is commonly shown by “Isotherms”, lines connecting points that have equal temperatures.The rate of change of air temperature with height is called the "lapse rate". In the troposphere, the lapse rate is generally about 6.5 deg C per kilometer increase in altitude. The temperature can increase with height in the lower troposphere. When this happens, it is called an "inversion".Horizontal temperature variation the most fundamental horizontal temperature variation is the slow decrease in air temperature from the equator towards the poles. This is the normal effect of latitude on temperature, since the amount of insolation received on the earth's surface largely depends on the latitudes. If more air is packed into the same length vertical column, then the air column will weigh more and, hence, the air pressure will be greater. So we may have horizontal variations of pressure if two air columns are next to one another, both of the same height but one with more molecules packed into it than the other. hermocouple temperature sensors are the most commonly used in industrial, automotive, and everyday applications in your home. As they are self-powered, they require no excitation, have quick response times, and they can operate over the widest temperature range (-328 to 3182 °F/-200 °C to 1750 °C.).

The PZEM004T energy monitor sensor is a highly useful component for smart microgrid energy monitoring. This sensor can measure various electrical parameters, including voltage, current, power, energy, and frequency, which are essential in monitoring energy consumption and production in a smart microgrid. By measuring these parameters accurately, the sensor can provide insights into energy usage patterns, identify potential energy wastage, and help optimize energy management in the microgrid.

In combination with other sensors, smart devices, and software, the PZEM004T energy monitor sensor can provide a comprehensive and real-time view of the microgrid's energy status. This data can help microgrid operators and energy managers make informed decisions about energy management and help them identify potential problems before they become critical.

To answer your question, The PZEM004T energy monitor sensor is an important component of an IoT-based smart microgrid energy monitoring system, as it provides essential data to optimize energy management, improve energy efficiency, and reduce energy costs.

Thanks for your valuable response, .

ooo

Precision agriculture technologies such as drones and sensors can be used to optimize crop yield and reduce input costs in several ways:

Overall, precision agriculture technologies such as drones and sensors can provide farmers with valuable insights into their crops and soil, allowing them to make more informed decisions about inputs and management practices. This can lead to higher yields, better quality crops, and reduced input costs, making precision agriculture an attractive option for farmers looking to improve their bottom line while also promoting more sustainable agriculture practices.

You can surface modification techniques or release agents.

Based on your final statement it appears that you are already monitoring the power to your sensors and instrumentation. However, a simple amp-meter on the input to the electronics package would work.

The best way to reduce the power consumption is to first eliminate any sensor, and it's related instrumentation, that is not strictly required for your project. The next step would be to evaluate the sensors to determine if they are the most energy efficient type available. Finally, analyze your instrumentation to eliminate any redundancy. For example, if one sensor has a power supply of 5 watts but only requires 3 watts to operate and a second sensor has a power supply of 7 watts but only requires 4 watts to operate. You may be able to eliminate the first power supply and use the second to power both. This reduces the overall power consumption because a power supply rated at 5 watts will actually consume 6 or 7 watts due to internal resistances and parasitic losses in transformers.

A common method to clean surfaces of inert (or close to it) surfaces is using basic oxidative cleaning using 50 mmol KOH + 25% H₂O₂

This is very typical of a system with multiple contributors to interfacial impedance, especially with ferri/ferricyanide redox probe. There will always be instability of impedance in your system, regardless of the electrode composition. Have you done a negative control, incubating with only the carrier fluid containing zero target? I recommend doing several of these, and tracking the impedance over time when exposed to either the carrier fluid or the electrolyte alone. You'll need that to accurately determine the range of impedances when no target is present, and also to quantify the temporal stability of the sensor when exposed to these redox-active fluids. It's not a bad idea to monitor OCP also, so you'll know if the system is at equilibrium before starting EIS.

Nice thinking. This kind of research is already ongoing.

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Thank you.

Dear Paul Macheso, I didn't find yet.

It's going to be difficult beat the ~$50 price of something like the Thermochron 4K. I guess if you need the capabilities of the 8K version, you'll double the price. The main issue in getting something cheaper is getting something that doesn't require $200 of your time to assemble and deploy. It's also going to be hard the beat the ruggedness of the iButton with cheaper solutions.

I would suggest looking at something like a disposable cold chain tag. There are several companies that make them, but Freshliance is readily available through common retailers and on-line stores and can be <$10 for a 30 day tag when purchased in bulk.

Other options are certainly available if you shop around for temperature loggers on-line, but they are sometimes very application specific and you don't really give any detail about your specific requirements (e.g. logging frequency, study duration, physical location requirements, etc...).

Two possible peaks also shows harmonics of the fundamental frequency I.e multiples of the fundamental frequency

and industrial applications. Here are a few options you can consider:

These are just a few examples of companies that offer CMC sensors. You may also find other providers by searching online for "carbon micro coil sensors" or "CMC sensors." It is important to consider the specific requirements of your research work, including the range and resolution of the sensors, the size and form factor of the sensors, and the cost and availability of the sensors.

Yes.. It looks okay. It can be futher denoised but it is better not to do it. You should allow some noise to ML to learn under some practical noisy condition.

Dear Mustafa Kapadia ,

you should share a sketch of your set-up.

You may be able to put the part of the torque sensor, which is mounted on the stator, on a slide being able to carry the torque sensor parallel to the shaft displacement.

Nevertheless a sketch will be helpfull...

Best regards

G.M.

Hi Heba,

I'm not sure if I understand the question but I found the following paper, maybe could be useful as starting point:

Bottleneck around Base Station in Wireless Sensor Network and its Solution (https://doi.org/10.1109/MOBIQW.2006.361777)

The accelerometer of most modern transmitters can provide accurate enough data to determine such behavior in comparisons with previous periods. The problem is that for such a study it will be necessary to continuously take a large number of points and this will drain the battery very quickly.

Hello Ritesh,

Returns from an FMCW radar are shifted in frequency due to two effects: (i) the range delay and (ii) Doppler shift from moving targets. For typical parameters, the range delay frequency is much greater than the Doppler shift (often by a few orders of magnitude). Therefore, the phase of returning samples changes rapidly due to the range delay but the phase due to the Doppler shift changes over a much longer timescale. This allows us to make a useful approximation when the duration of the chirp is short (as is often the case for automotive radars that repeat the chirp many times within a beam dwell time).

Let us consider, as you say, M samples per chirp and N chirps, giving a total of M * N samples. We can designate the samples as x_m,n where the index m refers to the sample number (m=0,1,2...(M-1)) and n refers to the chirp number (n=0,1,2,...(N-1)).

The M samples taken over an individual chirp (say, for example, the n^th chirp, i.e. samples x_{m=0,1,2,...(M-1),n}) exhibit a phase gradient due mainly to the range delay. If we assume the phase variation due to the Doppler to be negligibly small over the duration of the chirp then we can say that any phase variation in the M samples taken during each chirp is due to the range delay. An FFT of these M samples yields a frequency, f_R, that is indicative of range, where:

Range = c*f_R /(2K), where K = B/T and is the gradient of the chirp [Hz per second], B is the total chirp bandwidth and T is the duration of the chirp.

Hence the FFT of the M samples is a frequency plot that scales to a RANGE plot.

If we now consider any one of these samples (say, for example, the m^th sample) across all N chirps (x_{m,n=0,1,2,...(N-1)}), they will have a reasonably consistent phase due to the range delay because the samples are all drawn from the same position within the chirp, but they will now exhibit a slower rate of change of phase due to their Doppler shift. The slower rate of change of phase is evident over the longer timescale of N chirps. The Doppler induced phase gradient is due to a small variation in range (in the order of the wavelength) over the timescale of the N chirps. An FFT of consistent samples across the N chirps will therefore yield the Doppler shift frequency, f_d, from which we can deduce the radial velocity, V_r, of the target as:

V_r = lambda*f_d / 2 where lambda is the wavelength.

Hence the FFT of the N samples is a Doppler frequency plot that scales to a (RADIAL) VELOCITY plot.

The 2-dimensional FFT of your array is therefore a RANGE-VELOCITY plot (after suitable scaling).

Note that the approximation that the Doppler induced phase variation during a single chirp is negligibly small does break down in some radar applications, i.e. those using a long chirp duration and/or where high target velocities can be encountered. In that case we can resolve the range delay frequency and the Doppler frequency over two chirps; an up ramp and a down ramp, giving a triangular frequency modulation.

I hope this helps.

Best regards,

Clive Alabaster

Director & Consultant

White Horse Radar Limited

What is the unknown design? Is the imaged object inside it, or in front of it? If the object is in front of it, you might try projecting an image backward from the camera position or from the known system through the unknown system.

I agree with all the above answeres. From my experience, VWSGs are time taking, and not good for transient or dynamic loads. This is for any kind of gauges, say, embedded, welded to rebar, or other means to transfer the strain from body of material to the gauge. Yes, data loggers are monopoly of manufacturers and quite expensive. But they are reliable and robus. Fiber Optic sensors are new and can give better resolution and least count. I suggest, to have first hand approximate analysis to assess the level of strain expected. Then, suitable gauge within the range can be decided. Without this, entire data logging exercise may go futile. So, one should be careful on this aspect.

João Pedro Jenson de Oliveira

See the theory I used to calculate the diffusion time through the membrane. There is a thickness of the membrane. Use this formula to calculate.

Dear Dr. Emmanuel Enoch Thompson ,

your question does not fall within my specific sector but, in general, the necessary operations are to amplify the signal and reduce its noise.

Depending on the type of sensor, an accurate evaluation of the cost of the operation is necessary.

For more information you can have a look at the following, interesting document:

-Realizes high-sensitivity and high-precision sensing performance

available at:

https://toshiba.semicon-storage.com/ap-en/semiconductor/product/linear-ics/operational-amplifiers-and-comparators/articles/realizes-high-sensitivity-and-high-precision-sensing -performance.html

My best regards, Pierluigi Traverso.

Jessica, check out Adafruit (www.adafruit.com). You can put together your own system for a reasonable cost. It will require some C# coding though.

Check out parts 2652, 4026, 5665, 1733, and 249.