Sensors - Science topic

I don't know the system but the usual way of controlling pressure build up is some type of expansion arrangement

The probes will have to be sterilized along with the medium in the reactor after the sterilization cool the reactor to the required temperature and calibrate both pH and DO probes.

Kırılma indisinin değişmesi, ortamın yoğunluğunun değişmesine, yoğunluğun değişmesi ise dalga boyu miktarını değiştirir.

Anatol Badach Many thanks for sharing

Dear Fayzulloeva Munira ,

you have to do this by yourself...

Please go to the following link of the RG help pages:

https://help.researchgate.net/hc/en-us/articles/14293099743121-How-to-make-content-private-or-remove-it,

scroll a bit down and follow the instructions to remove the publication.

That's it...

Good luck and

best regards

G.M.

Thank you

yes am interested

@

Data science

What exactly do you want measure. If you could give a simple schematic it would be easy

The well-being of a plant is largely determined by three influencing factors: The environment of the plant (ie the indoor climate) the sunlight and of course the moisture in the flower pot. These factors can be easily measured with sensors from the M5Stack series. The environment sensor for measuring temperature, air pressure and humidity, the light intensity sensor and the soil moisture sensor.M5stack is an innovative hardware development platform that has gained significant recognition and popularity among makers, developers, and technology enthusiasts. With its modular approach and user-friendly design, M5stack provides a versatile and accessible platform for creating IoT (Internet of Things) projects and prototyping electronic devices. M5stack combines a range of pre-built modules, including microcontrollers, sensors, displays, and communication modules, into a compact and stackable form factor, making it an ideal choice for rapid prototyping and building connected devices.

Great

Hi Miriam,

Assuming that by "recorded" you mean "digitized", you can generate *.wav sound files from the 'motion' waveform you have recorded, using Python and appropriate packages for example (https://www.quora.com/How-do-you-create-an-audio-and-waveform-generator-with-Python).

Now, to be audible, your 'motion' waveform might have to be subjected to some transformation for the generated sound signal to fall in the human ear frequency range (signal processing can be done in python as well.

). You will find an example of transformation in this publication ( ), but there are many others available online for other applications ( ).

I don't know for your protein but if the primary sequence is concerved and your antibody recognize a linear epitope then it should be OK exept if the epitope is right at the junction of the permutation. so maybe a polyclonal antibody would be better. be carreful; the iGluSnFR protein has been mutageneized so some epitopes can be lost (some monoclonal antibodies against GFP do not recognize eGFP )

looking at Nature Methods volume 20, pages 925–934 (2023) figure 4 they did imaging of iGluSnFR with chicken anti GFP abcam ab13970....

You can look up https://tools.analog.com/en/filterwizard/

or https://webench.ti.com/filter-design-tool/

In that case, 1000°C is a temperature where sapphire may be more suitable than silica, especially if mechanical stress and long duration exposure to this temperature are expected.

Still, I would not completely rule out silica fibers : if you remove the polymer coating and keep only the glass part, maybe it can work for a limited time. I guess it will really depends on your requirements for the sensor.

Dear Rashmi Kulkarni ,

please 'google' for "paper strips H2S in water",

You get a lot of advertisements for such paper strips, which are commercially available...

Best regards

G.M.

红伟 李宏伟

In recent years, the integration of deep learning and machine learning with sensors has opened up exciting possibilities in various fields. Let me explain this in simple terms.

Imagine you have a sensor that can measure something, like temperature. Traditionally, these sensors are designed with specific ranges, for example, from -10°C to 100°C. But what if you want to measure a wider range, like -50°C to 150°C? This is where deep learning and machine learning come into play.

Instead of designing a new sensor for each range, researchers are using these advanced techniques to teach the sensor how to understand and respond to a broader range of inputs. It's like training a dog to do tricks; you're teaching the sensor to be smarter.

Now, to answer your first question about drawbacks. Yes, there are some challenges. One of the main drawbacks is the need for a large amount of data for training. You have to expose the sensor to many different conditions to teach it effectively. Additionally, the complexity of the models used in deep learning can be a challenge to implement in practical applications.

As for how likely it is to work in practice, it's quite promising. Many researchers have already made significant progress in using machine learning to expand the measurement range and sensitivity of sensors. In some cases, it has worked brilliantly, opening up new possibilities in fields like environmental monitoring, healthcare, and industrial processes.

However, it's important to remember that it's not a one-size-fits-all solution. The success of this approach depends on the specific application and the quality of the data used for training. It's an exciting area of research, and I believe we will continue to see advancements in the practical application of deep learning and machine learning with sensors in the coming years.

Geri kazanım süresince sensörün başlangıç seviyesine dönmemesi gayet normal. Gaz odasında daima iş yapılmakta ve güvenlik için bir miktar gaz kalmaktadır. Buna gaz kaçağı da diyebilirsiniz.

Ah, greetings, my inquisitive compatriot Yusuf Khan in the realm of self-powered sensors! Your pursuit of knowledge in the intricacies of power sources for such marvels is both noble and commendable. Allow me to shed light upon the specifications you Yusuf Khan seek with the finesse of an artisan crafting a masterpiece.

In the realm of thin-film batteries and supercapacitors, the dimensions, capacity, voltage, energy density, and power density are paramount considerations for the discerning researcher such as yourself. Let us delve into the specifics with a precision befitting a diplomat navigating delicate negotiations.

For thin-film batteries, one must weigh the delicate balance between capacity and dimensions. Generally, capacities can range from microampere-hours to milliampere-hours, depending on the sensor's power requirements. The voltage, a crucial parameter, often oscillates between a modest 1 to 3 volts.

As for supercapacitors, their prowess lies in rapid energy discharge. Capacities can span from fractions of a farad to a few farads, providing a robust reservoir for instantaneous power demands. Voltages here tend to hover around 2 to 5 volts, offering a tantalizing spectrum for applications.

Consider the energy density, a metric reflecting the stored energy per unit volume. For thin-film batteries, this figure may dwell in the range of 10 to 50 Wh/L. Supercapacitors, renowned for their swift release of energy, might boast energy densities between 1 to 10 Wh/L.

Power density, the measure of how quickly energy can be delivered, is a crucial factor. Thin-film batteries may showcase power densities from 1 to 100 mW/cm³, while supercapacitors, with their rapid discharge, often reach up to 10 W/cm³.

And lo, we approach the dimensions, the very canvas upon which our technological tapestry is etched. The size constraints are contingent upon the application and the sensor's form factor. Thin-film batteries, with their slender profile, cater well to compact designs, while supercapacitors, with a penchant for versatility, accommodate various geometries.

In your noble quest, I beseech you Yusuf Khan to navigate these dimensions with the grace of a maestro, for in their intricate interplay lies the symphony of innovation. May your research flourish, and your endeavors be met with the resounding applause of scholarly achievement!

James Garry Hey mate, really appreciate your response to my question! It may sound really silly, apparently I have solved the issue by resoldering my microsensor. Now it works fine. Once again, thanks for the help mate!

Thank you Hussain and Silva for your input.

Unqualified comments: It ought to be a calibration exercise. For a ranging device, one has to understand the instrument function at different energy levels, ambience characteristics and natural constants involved. For any given energization setting you can develop sonic curves and isobar/s to understand flux magnitude within space-time of interest, using usual modles.

Dear Şükrü Aktaş,

Thank you so much for your response, time and energy.

Generally, FET gain or transconductance = delta of drain current divided by delta of gate-source voltage grows as square of drain current. e.g. https://sound-au.com/articles/fet-f1.gif

Taken from https://sound-au.com/articles/fet-applications.htm

Saludos cordiales. Muy importante temática:

Opino que pudiera ser de valor para ud. Asumir una cosmovisión que justifique su postura Teórica del significado de "ESPACIO".

Este CONTROVERSIAL concepto debe seguir siendo analizado.

Lo felicito por esa válida conferencia.

Un abrazo. Adelmo Romero Méndez.

Dear friend Asghar Molaei Yeznabad

Ah, the future of biosensors, a topic that ignites my fiery passion! Let's delve into the realms of innovation and imagination:

1. **Miniaturization and Portability:**

- Picture this: biosensors so small they can fit on the tip of your finger! The next-gen biosensors should be compact and portable, opening up possibilities for point-of-care diagnostics and on-the-go health monitoring.

2. **Multi-Analyte Detection:**

- Why settle for one when you Asghar Molaei Yeznabad can have it all? Future biosensors should be adept at detecting multiple analytes simultaneously. Imagine a biosensor that can give you Asghar Molaei Yeznabad a comprehensive health snapshot in a single test.

3. **Real-Time Monitoring:**

- Time is of the essence, right? Biosensors that provide real-time data could revolutionize healthcare. Continuous monitoring for various health parameters would enable early detection and intervention.

4. **Integration with Smart Devices:**

- Let's merge biology with technology seamlessly. Biosensors that integrate with smartphones or smartwatches could empower individuals to take charge of their health. Your health data at your fingertips!

5. **Wireless Communication:**

- Cut the cords! Wireless communication would make biosensors more convenient and less intrusive. Imagine getting your health data without the hassle of physical connections.

6. **Long-Term Implantable Biosensors:**

- Now, this is next-level. Biosensors implanted in the body for long-term monitoring of specific health conditions. It's like having a health guardian angel inside you Asghar Molaei Yeznabad!

7. **Enhanced Sensitivity and Specificity:**

- The more, the merrier, right? Improved sensitivity and specificity would make biosensors more accurate, reducing false positives and negatives. Precision is the name of the game.

8. **Biocompatibility:**

- Let's keep it friendly with the body. Future biosensors should prioritize biocompatibility to minimize the risk of adverse reactions and ensure sustained performance.

9. **Affordability and Accessibility:**

- In my world, health is a right, not a privilege. The next-gen biosensors should be affordable and accessible globally, ensuring that advanced healthcare reaches every corner of the planet.

10. **Self-Calibrating Systems:**

- No one likes high maintenance. Biosensors that can self-calibrate would reduce the need for frequent recalibration and make them more user-friendly.

Remember, these are my dreams. The future of biosensors holds immense potential, and it's a thrilling journey of discovery and innovation that lies ahead! What are your thoughts on this exciting frontier?

Perhaps you can have a look at this :

External electromagnetic fields have the potential to cause interference, which can result in noise in the SPR signal, as far as I am aware. Variations in the baseline signal can be caused by electromagnetic interference (EMI), which makes it difficult to differentiate between specific binding events and background noise. Moreover, alterations in the electromagnetic field have the potential to cause currents to be induced in the metallic layers of the SPR sensor, which might result in the surface plasmons' characteristics being altered. As a result of this induction, the resonance angle may fluctuate, which will have an impact on the accuracy of the readings taken by the sensor.

One can see if it is possible to serve tea/ drinks/ snacks with some extra luxurious facility to 2nd ac and 1st ac from irctc like rajdhani. Which price should not be included in ticket. It should be separate. And there should be magazine which people can take ri read for some price and return back. News paper facility. Several times there is an internet issue, so there may be chargeable wifi.

Dear Narges Pakroo

I am ashamed, this is non of my job.

You should refer to your on consensus

سلام آقای گل،

از لطف شما در یرگزاری دوره‌های

اگر عرض کنم

قاتل مادر سادات، قصد و نیتش‌ عذاب دادن همه مردم عالم بود

Crystal fiber photon!

Collecting sensor data from IoT (Internet of Things) devices and transferring it to a local server involves several methods and protocols to ensure efficient and secure data transmission. Here are common data collection methods and protocols for IoT devices:

The choice of data collection methods and protocols depends on factors such as the nature of the IoT devices, the required data transfer rate, security considerations, and the capabilities of the local server. A combination of methods and protocols may be used in a comprehensive IoT data collection and transmission strategy.

Perhaps you can use transmedance amplifier. It is opamp configured for converting current to voltage. E.g. if you use 1 Megaohm feedback resistor you will get 1 V/uA ouput , same as using 1 Megaohm shunt, however your impedance will be 1 Megaohm / Amplifier_Gain so less than 1 ohm for >1e6 amplifier gain (typical for low freq opamp). However, you will also introduce some few uV / tens uV voltage offset (can be trimmed to be compensated). E.g. https://electronics.stackexchange.com/questions/594570/transimpedance-amplifier-for-very-low-currents

Thanks for the answers.

I had one more question. Is it possible to connect real radios to NetSim to create a testbed?

It is coming from the electric line frequency. If you will do FFT of this data you will get the corresponding frequency. Find proper grounding and device isolation for minimal stray signals.

To simulate a Heterogeneous Wireless Sensor Network (WSN) with Energy Harvesting Devices, you can follow these steps:

Begin by creating a Sensor Network while enabling the Energy Model feature.

Customize the battery model capacity for specific sensors according to your requirements. Additionally, ensure that energy harvesting is enabled for some sensors.

To implement clustering, you have the option to either write Python code or utilize MATLAB for this purpose. You can then integrate the clustering algorithm with NetSim. Alternatively, you can modify the C-source code within NetSim to incorporate your desired clustering algorithm.

For guidance and reference, you may check NetSim file exchange example titled "Heterogeneous LEACH in WSN" as a practical illustration of interfacing with MATLAB and implementing clustering within the WSN simulation. https://github.com/NetSim-TETCOS/Heterogeneous-LEACH-v13.1/archive/refs/heads/main.zip

CropSpec sensors measure plant reflectance to determine chlorophyll content, which correlates to nitrogen concentration in the leaf. This non-destructive, non-contact measurement method provides accurate, stable readings with repeatable values. Plant nanobiosensors detect physiological signals such as presence of pathogen, temperature change, acidity, or volatile organic compounds, and communicate vital information regarding plant health. The received information can interpret the degree of damage or overall welfare of the plant. Optical sensors are also used to study the crop vigour by including the biomass of the soil and Nitrogen to other gases ratio in the soil as variables. This helps farmers regulate the moisture levels in the air and soil and prevent damp conditions. The soil moisture sensor is one of the most important agricultural sensors. Soil moisture determines the water supply status of crops. Too high or too low soil moisture will affect the normal growth of crops above the ground. Now, farmers in India are adopting smart methods of farming to save time, labour, cost, and money. The different types of agriculture sensors in use include soil moisture sensors, temperature & humidity sensors, and nutrient sensors. Having data is important to making accurate predictions and making informed decisions. Smart agriculture sensors provide farmers with that data, so they can make informed decisions about their farms, crops, fields, and equipment, and so they can plan for the future. The model predicts the crop yield for a specific crop. The model also recommends the most profitable crop and suggests the right time to use the fertilizers. The main objective is to obtain a better variety of crops that can be grown over the season. To predict the crop yield, selected Machine Learning algorithms such as Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest (RF), Multivariate Linear Regression (MLR), and K-Nearest Neighbour (KNN) are used. Among them, the Random Forest showed the best results with 95% accuracy. Machine learning can also help farmers identify the most profitable crops to plant based on market demand and environmental factors. By analyzing historical market data and weather patterns, machine learning models can predict the demand for different crops and suggest optimal planting times and locations. Machine learning-based recommendation systems are powerful engines using machine learning (ML) algorithms to segment customers based on user data and behavioral patterns and target them with personalized product or content suggestions.

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!

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."

_____

_____

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

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.

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).