Smart Air Supply Anti-Haze masks

Xiang Zheng Tu 

A smart air supply anti-haze mask, as shown in the above figure, comprises a sealed mask, a pollution filter, a micro-electric fan and a POSIFA thermal flow sensor based microcontroller. The mask is sealed to the face during inhalation and creates a breathing space by resting far away from the face. Two one - way valves are connected to the mask which are used to direct air flow in and out respectively. The pollution filter is made of multiple porous membranes and blocks against haze PM2.5 particles in the suctioned air.

The micro-electric fan moves enough filtered air to the mask through the in air flow valve.

The air is required to deliver to the mask according to an air flow waveform that is restored in the microcontroller. In order to do so the micro-electric fan is driven by a PWM signal that is send from the microcontroller. The PWM signal is generated by modulating an air flow rate signal measured by a thermal flow sensor. The thermal flow sensor can be installed in two ways. One is installed on the bask surface of the filter. In the first way the air flow rate is measured by the sensor immediately after passing the filter. In the secondly way the air flow rate is measured by the sensor immediately after the fan blowing.

In the second way the thermal flow sensor is installed in a laminar flow restrictor. Reference to the above figure, the restrictor is positioned in an air flow tube that is located between the micro-electric fan and the in one-way valve. The micro-electric fan produces a turbulent flow to the air flow tube. The restrictor consists of a plurality of collimated channels which are used in dividing the velocity components of the incoming flow stream into smaller components. Some of the velocity components cancel each other thereby presenting a more uniform velocity profile, reducing the turbulence of the flow, and allowing laminar flow passing through the channels.

As well known, laminar flow occurs at low Reynolds numbers, where viscous forces are dominant, and is characterized by smooth, constant fluid motion. For air flow in a channel, the Reynolds number is defined as

Re = (ρvD_H)/μ  = (QD_H) / (NυA)                        (1)

where:

Re is 2300 for air flow.

D_H is the hydraulic diameter of the channels (m).

Q is the volumetric air flow rate (m³/s).

A is the channel's cross-sectional area (m²).

N is the number of channels.

v is the mean velocity of air flow (m/s).

μ is the dynamic viscosity of air equaling to 1.983x10^-5 Pa·s.

ν is the kinematic viscosity of air equaling to 15.11x10^-6m²/s.

ρ is the density air equaling to 1.2754 kg/m³

The hydraulic diameter of the channels can be found by

D_H = 4A/P                                                             (2)

Where A is the cross-sectional area of the channel and P is the total perimeter of all channel walls that contact with the air flow. It should be noted that the length of the channel exposed to the flow is not included in the Reynolds number.

In the second way the air flow passes through a porous material such as a pollution filter. In this case Darcy‟s law is applicable which is stated as

Q = - (κAΔp) / (μL)                                         (3)

Where:

Q (m³/s) is the total discharge,

κ (m²) is the intrinsic permeability of the porous material,

A (m²) is the cross-sectional area to flow,

Δp (Pa) is the total pressure drop,

μ (Pa·s)  is the viscosity, and

L (m) is the length. 

Darcy‟s law is only applied for Re < 1, although it is sometimes not easy to define the pore diameter in a stringent way. Darcy’s law assumes laminar or viscous flow (creep velocity) and it does not involve the inertia term. Darcy’s law also assumes that in a porous material a large surface area is exposed to flow, hence the viscous resistance will greatly exceed acceleration forces in the flow.

So for a smart air supply anti-haze mask the thermal flow sensor is not necessary to install in a laminar flow restrictor. Instead it may directly install on the surface of the filter of the mask because the out flow of the filter is an air laminar flow. The laminar flow tends to flow without lateral mixing, and adjacent layers slide past one another. There are no cross-currents perpendicular to the direction of flow, nor eddies or swirls of flows.

As shown in the Darcy‟s law the total pressure drop (Δp) represents the viscous resistance to the flow. That is why the air flow is reduced while one wears a normal mask. There is no doubt that you want to protect you from air pollution and guard your health you will sacrifice some comfort or even though feel overly suffocating. But when wear a smart air supply anti-haze mask you will feel comfortable as usual and absolutely nothing will happen, because there is a micro electric-fan that can supply enough filtered air to you.

High Reliability of  POSIFA’s Thermal Water Flow Sensors

Xiang Zheeng Tu

 

A POSIFA thermal water flow sensor is fabricated in a silicon substrate. A combination of a heater and two thermopiles is used as sensing element of the sensor. A porous silicon layer is formed in the substrate for thermal insulation between the sensing element and the substrate, while the top layer is made of a SiO₂/Si₃N₄ stack thin film. The mechanism of water flow detection mainly depends on measuring the change in the electrical voltage of the thermopiles, associated with the heat convection transfer caused by the water flow.  In operation the sensor is heated by applying an electric voltage pulse to the heater. The pulse can be rectangular with pulse width 20ms, repeat frequency 1Hz resulting in 1.8mw power consumption. It has been measured that the Instantaneous peak temperature of the sensor is lower than 50⁰C.

It has been proved that Arrhenius' equation can be used for calculation of the failure rate of a semiconductor. The equation is expressed as

L = A exp (Ea / k T)                                                  (1)

Where

L is the lifetime of a semiconductor device

T is the absolute temperature (in kelvin)

A is the pre-exponential factor, a constant for each semiconductor device

Ea is the activation energy for each failure mechanism (in Joules mol-1)

R is the universal gas constant

Activation energy refers to the minimum amount of energy required to trigger a temperature-accelerated failure mechanism. The following table shows some activation energy values obtained for various failure mechanisms commonly encountered in the semiconductor devices.

Failure Mechanism	Accelerating Factors	Activation Energy
Oxide Film Defect	Electric Field, Temperature	0.3- 1.1 eV
Al Wire Corrosion	Humidity, Temperature, Voltage	0.7 - 0.9 eV
Al Wire Electromigration	Temperature, Current Density	0.5 – 0.7 eV

Since the sensors normally driven with electric voltage pulses the mean current density is very low. So Al wire electromigration for the sensor failure can be ignored and the main failure mechanism is Al wire correction. If voltage is applied, the leakage current between Al conductors will be added as a factor for Al corrosion. Al corrosion reaction proceeds as follows:

(a) Reaction on anode side

Under the normal ambient conditions, since the surface of “Al” is covered with oxide film, “Al” is in the passive state and exists stably. At the bias voltage application status, if the surface of the anode side adsorbs the Cl- ions diffused from the inside of the sealed resin, the Al wire protected by the passive state gibbsite may react and finally melt as:

At first, the hydroxide on the surface reacts with the Cl- ions to generate fusible salt.

Al(OH)3 + Cl- → Al(OH)2Cl + OH-                          (2)

The substrate Al exposed by this reaction reacts with the Cl- ions.

Al + 4Cl- → AlCl4 - + 3e-                                          (3)

In addition, when the sealed resin absorbs moisture, reaction with the moisture may start.

AlCl4 - + 3H2O → Al(OH)3 + 3H+ + 4Cl-              (4)

Finally Al(OH)₃ will be generated. Different from the protective oxide film, the generated Al(OH)₃ is not soluble, but has a high enough cubic expansion rate to cause cracking on the protective oxide film. So the generated Al(OH)₃ promotes corrosion.

(b) Reaction on cathode side

As the sealed resin absorbs moisture, the hydroxide ion concentration will be increased near the electrode due to oxygen reduction by application of bias and reaction generates hydrogen as

O2 + 2H2O + 4e- → 4(OH)-                                   (5)

H2O + e- → (OH)- + (1/2)H2                                 (6)

The OH- ions generated by the above reaction are diffused from the defect such as pinhole, void, crack, etc. on the Al protective oxide film to the substrate Al, and then react as:

Al + 3(OH)- → Al(OH)3 + 3e-                                (7)

The reaction on the cathode side also generates aluminum hydroxide.

The graph in above figure shows the relationship between the lifetime and the operation temperature of semiconductor devices. The red slash line is the activity energy of 0.7 eV, which represents Al wire corrosion mechanism and the red vertical line represents the typical operation temperature of the water thermal flow sensors. This means that the lifetime of the sensors is expected to be very high when compare with other semiconductor devices which need to be operated at least at 125⁰C.

Optical Coherence Topography with Tunable Cavity Surface Emitting Laser

Tu Xiang Zheng

  

US Patent 6,602,427 issued to the present author describes a micromachined optical mechanical modulator based WDM transmitter/receiver module. The Fabry-Perot cavity of the mechanical modulator is structured from a three-polysilicon-layer stack formed on the surface of a single crystalline silicon substrate. The polysilicon membrane and its supporting polysilicon beams of the cavity are cut from the top polysilicon layer of the stack and are released by selective etching of their underlying polysilicon. The etched underlying polysilicon layer is heavily doped and then converted into porous polysilicon by anodization in HF solution. The polysilicon membrane and its supporting polysilicon are finally released using a reactive ion etch process to avoid stiction often generated in a wet etch process. A conic hole is formed on the backside of the single crystalline silicon substrate for receiving an optical fiber that can be passively aligned with the Fabry-Perot cavity.

Optical coherence tomography (OCT) is a non-invasive imaging test that uses light waves to take cross-section pictures of your retina, the light-sensitive tissue lining the back of the eye. With OCT, each of the retina’s distinctive layers can be seen, allowing your ophthalmologist to map and measure their thickness. These measurements help with diagnosis and provide treatment guidance for glaucoma and retinal diseases, such as age-related macular degeneration and diabetic eye disease. OCT can also be used for intravascular imaging of plaque to assess heart disease, cancer biopsy imaging, developmental biology research, art preservation, and industrial inspection.

As shown in the above figure, a called swept-source OCT uses a wavelength-swept laser light source, that is, one whose emission sweeps back and forth across a range of wavelengths. A detector and a high speed analog-to-digital (A/D) converter complete the imaging system. The OCT has several fundamental advantages including ultrahigh imaging speeds, deep tissue penetration, Doppler OCT flow analysis, and long imaging range. With such a compact, high-performance, low-cost swept source for OCT it is possible to achieve a combination of ultrahigh sweep speeds, wide spectral tuning range, adjustability in sweep trajectory, and extremely long coherence length.

Wavelength tuning of the micromachined cavity is accomplished by applying a voltage between the top membrane and bottom membrane, across the air gap. A reverse bias voltage is used to provide the electrostatic force, which attracts the top membrane downward to the bottom membrane and shortens the air gap, thus tuning the laser wavelength toward a shorter wavelength (blue shift). It has been shown that the cavity using electrostatic force follows a 1/3 gap size rule. As the voltage is applied, the top membrane is attracted downwards with a displacement approximately equaling to 1/3 gap size. As increases further, the attractive force cannot be balanced by the mechanical spring force, and the membrane collapse onto the bottom membrane. Increasing voltage further at this point results either no movement or capacitor discharge. The top membrane can be brought back to its original position when the voltage is removed if an appropriate mechanical design is used.

The incident light to the micromachined cavity is emitted by a vertical cavity surface emitting laser. The micromachined cavity transmits a narrow band of wavelengths and rejects wavelengths outside of that band. The cavity will resonate when the following condition is met:

nd cosθ = mλ/2                         (1)

where θ is the incident light angle normal to the mirror, λ is wavelength, d is the micromachined cavity length, n is the refractive index of the medium, and m is the fringe order number. For normal incident light, with air as the medium (n = 1), the resonating micromachined cavity equals multiples of a half wavelength.

By driving the micromachined cavity with specially shaped voltage, the wavelength can be swept in time as required for swept source OCT. In classical physics, where the speeds of the top membrane of the micromachined cavity relative to the bottom membrane are lower than the velocity of laser light, the relationship between observed micromachined cavity transmitted light frequency f and the incident light frequency f₀ is expressed as

f = [(c+υ_r)/(c + υ_s)] *f₀                     (2)

  

Where c is the velocity of light, υ_r is the velocity of the top membrane relative to bottom membrane or air and υ_s is the velocity of the incident light relative to air. It can be seem that the transmitted light frequency or wavelength is decreased if two membranes of the cavity is moving away from the other.

It has been reported that the micromachined cavity can be move very fast, allowing the micron-scale cavity length to be tuned rapidly. It has demonstrated a fundamental repetition rate of 600 kHz, which for OCT purposes allows its individual scans to be acquired at rates as high as 1.2 MHz through the use of both forwards and backwards sweeps. 

Ternary Gas Mixture Measurements Using Micromachined Thermal Conductivity Sensors

Xiang Zheng Tu

 

According to Chapman–Enskog theory elastic gases deviation from the Maxwell–Boltzmann distribution in the equilibrium is small and it can be treated as a perturbation.

So the thermal conductivity of the ternary gas mixture can be expressed as  

K_mix = k₁ N₁ / (N₁ + N₂ Φ₁₂ + N₁₃ Φ₁₃) + k₂ N₂ / (N₂ + N₃ Φ₂₃ + N₁ Φ₂₁)

+ k₃ N₃ / (N₃ + N₁ Φ₃₁ + N₂ Φ₃₂)                                                                        (1)

N₁ + N₂ + N₃ = 1                                                                                              (2)

where Φ₁₂, Φ₁₃, Φ ₂₃, Φ ₂₁, Φ ₃₁ and Φ ₃₂ are the Wasiljewa constants, k, k₂, k₃ are the conductivities of air, carbon dioxide and water vapor, and N₁, N₂ and N₃ are the molar fractions of air, carbon dioxide and water vapor.

The Wasiljewa constants can be given by

Φ_αβ = (1/8^1/2) ( 1 + M_α/M_β)^-1/2 [ 1 + (μ_α /μ_β )^1/2 (M_β / M_α )^1/4 ]²                                        (3)

Hear M_α is the molecular weight of species α and μ_α is the viscosity of pure species α. Equations (1),(2) and (3) has been shown to reproduce measured values of the thermal conductivity of mixtures within an average deviation of about 2%.

Equation (1) (2) and (3) are used to predict the thermal conductivity of a gas mixture of CO₂, O₂ and N₂. The following data of the pure CO₂, O₂ and N₂ at 1 atm and 293K can be found from a Physical Handbook.

It is assumed that molecular fractions of CO₂ (1), O₂ (2) and N₂ (3) are 0.133, 0.039 and 0.828 respectively. Using equation (3) it can be found the related values as

                                          N₁+N₂Φ₁₂+N₁₃Φ₁₃=0.763              (4)

N₂+N₃Φ₂₃ +N₁Φ₂₁ =1.057              (5)

N₃+N₁Φ₃₁ +N₂Φ₃₂ =1.049              (6)

Substitution in equation (1) gives

K_mix =(0.133)(383)(10^-7) /0.763+(0.039)(612)(10^-7) /1.057+(0.828)(627)(10^-7) /1.049

        =584(10^-7) cal/cm-s-K                                                      (7)

This is the principle of thermal conductivity sensors able to measure the concentrations of any gas mixtures such as a ternary gas mixture consisting of CO₂, O₂ and N₂. The thermal conductivity sensors manufactured by POSIFA Microsystems Company are shown in the above figure. The sensors are created in a silicon substrate and configured to have a hot plate suspending over a cavity recessed into the substrate, a resistive heater and a plural of hot junctions of a thermopile disposed on the hot plate and a plural of cold junction of the thermopile disposed the frame region of the cavity which is formed by the substrate. An interface circuit of the sensors is also shown in the above figure. The circuit comprises a microcontroller, a pre-amplifier, a measurement thermal conductivity sensor and a reference thermal conductivity sensor. The two sensors are heated by applying PWM to the sensor heaters from the microcontroller. The outputs of the sensors are sent to the pre-amplifier and then to the microcontroller for digital processing. The reference sensor is used to compensate the offset, temperature drift and noise of the measurement sensor.

The quality of air inside a building depends on the concentrations of contaminants which are difficult to measure. However, CO₂ levels, which are easy to measure, can be used in place of other measurements to indicate the indoor air quality. CO₂ is produced when people breathe. Each exhaled breath by an average adult contains 35,000 to 50,000 ppm of CO₂ – 100 times higher than 350 to 500 ppm that is typically found in the outside air.

If a thermal conductivity sensing module is installed in a building it will tell you how clean or polluted your air is, and also actuates a ventilation system to supply the building continuously with fresh air. Other applications of the thermal conductivity sensing modules include:

• 0 – 100% Hydrogen in Air
• 0 – 100% Methane in Air
• 0 – 100% Carbon Dioxide in Methane
• 0 – 100% Helium in Air

Micromachined Thermal Conductivity Sensor

with a Thermopile on a Hot-plate

Xiang Zheng Tu

  

The thermal conductivity sensor with a heater and a thermopile is manufactured by POSIFA Microsystems Company. The sensor is created in a silicon substrate and constructed with a thin membrane suspending over a cavity recessed into the substrate. A resistive heater and a plural of hot junctions of a thermopile are disposed on the membrane and a plural of cold junctions of the thermopile are disposed the top of the substrate which is surrounded the membrane. The cavity is configured to have a bottom surface parallel with the top membrane allowing heat generated by the heater transfers perpendicular through the cavity to the bottom. The path length is optimized to have a maximum heat conduction transfer efficient.

The sensors rely on the thermal conductivity of a gas mixture which affects thermal phenomenon by way of heat conduction transfer that, in turn, is converted into a varying electrical signal capturing the sensor response to its component concentration change. As shown in the top figure, the sensors are thermally isolated so only heat transfer due to thermal conductivity through a cavity. Other heat transfer pathways such as through substrate or electrical leads result in thermal losses that degrade sensor performance and have been minimized in the device design.

In sensor operation the heat P_heat generated inside the heater by a DC voltage U_DC applied to the output terminals of the thermopile sensor follows the equation

P_heat = U ² R_sensor . (1)

We can assume that the thermal contact between the periphery and the ambient is so good that the temperatures of the periphery and the ambient environment are identical. They are equal to T_amb. Since the heat P_heat is generated on the membrane, its temperature T_mem depends on the thermal conductivity λ_mem of the hot plate and the thermal conductivity λ_gas of the gas filled in the cavity as

T_mem = P_heat / (λ_mem + λ_gas) + T_amb. (2)

The generated thermopile voltage U is proportional to the temperature difference between the membrane and the periphery as

U_DC ∝ ΔT = T_mem − T_per ≈ Pheat / (λ_mem + λ_gs) . (3)

Thus,

U_DC ∝ U ² / ( λ_mem + λ_gas) . (4)

Each gas has a known thermal conductivity. The thermal conductivities of some gases can be found in the table below.

Gas	Thermal Conductivity
ACETYLENE	4.400
AMMONIA	5.135
ARGON	3.880
CARBON DIOXIDE	3.393
CARBON MONOXIDE	5.425
CHLORINE	1.829
ETHANE	4.303
ETHYLENE	4.020
HELIUM	33.60
HYDROGEN	39.60
HYDROGEN SULPHIDE	3.045
METHANE	7.200
NEON	10.87
NITRIC OXIDE	5.550
NITROGEN	5.680
NITROUS OXIDE	3.515
OXYGEN	5.700
SULPHUR DIOXIDE	1.950

The sensors can be used not only measure all the gases listed in the table but also to analyze a whole range of binary gas mixtures provided that there are only two gases present and that the two gases have significantly different thermal conductivities.  Example is nitrogen and hydrogen or a pseudo-binary mix. Air is an example of a pseudo-binary mix: it has a fixed proportion of oxygen and nitrogen, both having very similar thermal conductivities and so behaves much like a single gas.

Other examples include:

• 0 – 100% Hydrogen in Air
• 0 – 100% Methane in Air
• 0 – 100% Carbon Dioxide in Air
• 0 – 100% Carbon Dioxide in Methane
• 0 – 100% Helium in Air

Thermal conductivity sensor with a heater and a thermopile utilizes micromachining technology which is amenable to creating micro-heaters and thermal conductivity sensors with no moving parts required, thus simplifying fabrication and operational design requirements. Another reason for the large interest in thermal conductivity sensors is the advantages gained through miniaturization: low power consumption, higher sensitivity to low conductivity, fast response and ease of use with different modes of operation. 

Diaphragm Pump Controlled by Thermal Flow Sensor

Xiang Zheng Tu

  

A diaphragm pump controlled by a thermal flow sensor is shown in the above figure.

The pump assembly has a thermal flow sensor, a microcontroller, a NPN switch and a solenoid driven diaphragm pump. The microcontroller has a 10 bits ADC, due to noise and other accuracy diminishing factors, its true accuracy is less than 10 bits. This application provides a software-based oversampling technique, resulting in 16 bits resolution. When the diaphragm pump is in operation a fluid flow is driven to pass over the thermal flow sensor. The sensor measures the flow rate and output an electronic signal to the microcontroller. After ADC conversion a pulse width modulation (PWM) signal is generated by the microcontroller.  It is send to the NPN switch for applying a current to the solenoid driven diaphragm pump. The electromagnetic core of the diaphragm pump moves against a spring to slide a diaphragm into the discharge position. When current is removed, the diaphragm slides back into the suction position.

There are two ways for controlling the flow rates of the diaphragm pumps. When the used PWM frequency is in the range of 25 to 200 Hz the solenoid responds (full stroke) over the duty cycle range of control. At zero duty cycle the solenoid does not move, the pump is not opened and therefore the flow is zero. At 50% duty cycle the solenoid moves through full stroke and opens the pump to full flow. Since the pump is only allowing full flow for 50% of the time, the time averaged flow in theory will be 50% of maximum flow. This type of control is called “digital” because the pump is fully open or fully closed, “on” or “off”. Other way is the PWM frequency limited in the range of 200 to 1000 Hz which produces the time averaged current and does not allow the solenoid to fully respond as in digital control. In this case linear position control is realized and any flow rate between zero and maximum can be chosen by the user.

Theoretically diaphragm pumps can produce the same flow at a given speed (RPM) no matter what the discharge pressure. However, a slight increase in internal leakage as the pressure increases prevents a truly constant flow rate. The following figure shows the measured flow rates in one-hour intervals for an infusion pump. The X-axis reference lines showed the acceptable flow rate (5 mL/h ± 15%). In all experiments, pumps initially infused at a rate faster than their nominal flow, and then returned closer to their set rates up to the complete deflation. The percentage of the flow rate error (deviation from 5 mL/h ± 15%) was 100% in the first and second hours of infusion, 96% in the third hour, 60% in the 20th hour and zero percent in the rest of the infusion time. Flow rate error in the initial hours of infusion was due to fast pump flows, and in the 20th hour due to slow infusion rates. 

Thermal Flow Sensor Measurement Circuit with PIC Microcontroller

Xiang Zheng Tu

  

As shown in the above figure, a PIC16F1704 is used for a thermal flow sensor measurement circuit provided by POSIFA Microsystems Company. The thermal mass flow sensor consists of upstream and downstream temperature thermopiles and a heater located between the two thermopiles. If no gas flows over the sensor surface, the thermopiles measure the same rise in temperature, resulting in the same output voltage of the two thermopiles. If a non-zero gas flows, the velocity of a fully-developed laminar air flow unbalances the temperature profile around the heater and heat is transferred from upstream thermopiles to the downstream thermopiles, causing a change in the voltages of the thermopiles. Larger gas flow rates result in larger change in the temperature profile.

The sensors are thermally isolated so only heat transfer due to flow can occur. Other heat transfer pathways such as through substrate or electrical leads result in thermal losses is minimized in the device design.

To interface the microcontroller the following I/O pins of the microcontroller are setup as:

• RC0 assigned to OPA1in+, pin 9 (sensor in)
• RC1 assigned to OPA1in-, pin 8 (offset bias)
• RC2 assigned to digital out, pin7
• RC3 assigned to OPA2 out, pin 6
• RC4 assigned to OPA2 in-, pin5
• RC5 assigned to OPA2in+, pin4
• RA2 assigned to DAC1out2, pin 10 (offset setting)
• RA0 assigned to I²C data, pin 12, where I²C pull-up to be provided by host assigned to ANO when not in I²C mode (bat monitor)
• RA1 assigned to I²C CLK, pin 11, where I²C pull-up to be provided by host assigned to AN1 when not in I2C mode (temp monitor)
• RA3 assigned to /MCLR pin 3 as an input assigned to LED as an output
• RA4 assigned to AN3 pin 2 (sensor from op-amp 1)
• RA5 assigned to digital output pin 1 firmware PWM for heater current set up as open drain

The internal devices of the microcontroller are setup as:

• Set OPAMP1out to AN6
• Set DAC reference to VFR at 2.048 and V_ss
• A/D reference tied to VFR at 2.048 and V_ss 

Some concepts are adopted in operation of the microcontroller:

• Processor powered directly off battery – no regulator
• External reference for heater and thermistor
• External ref and current source is lower cost than regulator
• DAC and Analog in referenced internally to 2.048
• Offset and amps are setup to avoid amp saturation
• Calibration with no flow to obtain offset reference value
• During operation, heater is on for 15 ms at which time data is taken 

The circuit supports a battery driven power supply and is capable of time keeping. It senses the signals from the flow sensor, calculates the flow and then accumulates it. The total flow accumulated and the month wise profile of the flow are stored and updated in the memory. The user key available on the board can be used to display the flow accumulated in a month and the date on the LCD. The design also supports wireless communication with another handheld device. Thus, the device supports the AMR where

The software design matching the circuit consists mainly of the flow calculation, database, user interface, and communication modules. The software has following main modules:

• Flow Calculation Module

• Database Management Module

• User Interface Module

• Communication Module

PIC Microcontrollers - Programming in C is a Microchip site where you can browse and download free software / firmware code examples for your PIC projects. You'll find code for controlling simple timers and UARTs, low power modes, Fast Fourier Transforms, LCD displays, motor-control algorithms, and many more. These examples are better proof that program writing is neither a privilege nor a talent issue, but the ability of simple putting puzzle pieces together using directives. Design and development of devices mainly boil down to the ‘test-correct-repeat’ method. Of course, the more you are in it, the more complicated it gets since the puzzle pieces are put together by both children and first-class architects.

Example 1: Module CCP1 as PWM signal generator

Example 2: Using A/D converter

Example 3: Using EEPROM Memory

 

Example 4: Using LCD display