raspbian lcd displays random characters for sale

Many of these LCD controllers differ slightly from the HD44780 controller in things like the initialization sequence, minimum delay between commands and maybe other ways I don"t know of.

try putting a 1n4148 diode between the 5V supply and the 5V of the LCD, so the LCD will get around 4.5V, that is enough for the LCD, and lowers its logical input levels enough so that it sees 3V3 logic highs as "1" reliably. The garbled letters, and blocks are due to the LCD sometimes seeing a "1" as a zero and that corrupts all communication between the PI and the LCD.

Its a well know problem when trying to drive an LCD with 3V3 levels, it actually needs minimally 3.5V when its powered with 5V (70 % of VCC = 0.7 x 5.0 = 3.5).

In the photos above the Pi is a Model B Rev 2 so should work fine with my tutorial. Random characters is usually a sign it has not been initialized correctly. Again this could be caused by intermittent connections.

I was having the same problem as the OP on my current build with an I2C back-packed LCD getting corrupted. I"d tried all kinds of things to fix the problems - changing the delays, trying different libraries, and pulling out hair with no joy in fixing it.

For anyone else with this problem, you may have what I have if you"re doing event-driven code e.g. from from a switch or rotary encoder. I was getting events triggered while the python code was trying to communicate with the LCD screen - which themselves tried to write to the LCD.

I also had the same problem with scrambled characters on my display. I found that there was a short between the data lines on the expander board where it is soldered to the display board. Once I cleaned up the messy solder, the display worked perfectly.

Previous examples connect the white LED backlight to power. The following example is specifically for those using an LCD with a RGB LED backlight. The only difference between the connection is the LED"s backlight on pins 15-18.

The Matrix Orbital Parallel display series offers a low cost display solution utilizing an industry standard communication interface for simple integration into a wide variety of new and existing applications.  The Light Emitting Diode backlight with configurable brightness and voltage controlled contrast allows the MOP Liquid Crystal Display line to offer a professional display solution with low power impact for any project.  The standard alphanumeric font set also allows up to eight custom characters to be saved in display Random Access Memory for a custom design touch.

CategorySub CategoryFile NameRevisionSizeDescriptionNotesFeaturesChange LogCategorySub CategoryFile NameRevisionSizeDescriptionNotesFeaturesChange LogMOP SeriesMOP Character LCD1.0278.43KiBAll of our Matrix Orbital Alphanumeric displays have HD44780U compatible drivers.

The Arduino board has a wide variety of compatible displays that you can use in your electronic projects. In most projects, it’s very useful to give the user some sort of feedback from the Arduino.

This is a tiny display with just 1 x 0.96 Inch. This display has a black background, and displays characters in white. There are other similar displays that can show the characters in other colors.

This particular post will cover a bit of technology that most people simply know as "the LCD display". I"m not talking about the computer monitor type of LCD display; no, I"m talking about that small screen on many common pieces of electronics, the one that looks like this.

I"m going to talk about the LCM1602C LCD. The purpose of this post is to get away from a lot of the esoteric "how tos" of this particular display. Ya see, this display is based on the Hitachi HD44780 LCD controller. The nice thing about that is the specifications are readily available. The problem is that the specifications are not readily understood. Yes, I believe that this is an example of "lost in translation".

Let"s talk about the display first. This particular display is a 16 character by 2-line display. Its memory can hold up to 80 characters, 40 on each line of the display. The actual part that displays the characters can only show 16 characters across at any one time. Using the SHIFT command, you can move the part of the lines that are displayed.

I used an Arduino to drive my LCD display simply so that I could drive the various logic lines. I didn"t use any of the specialized Arduino libraries for driving a LCD; instead, I simply used the standard digitalWrite command to send the commands and data. The digitalWrite command merely sets the targetted output pin to either 0 V (GND, or LOW) or to +5 V (HIGH). This is equivalent to opening (LOW) or closing (HIGH) a switch. The difference is that using the Arduino means you don"t have the "bounce" problem. The hard part is that you have to code each HIGH and LOW of each line.

To simplify the steps, I"m going to use a table in which each HIGH is replaced by a 1, and a LOW is replaced by a 0. After each step, the EN (enable) line on the system has to be set HIGH, then LOW. This tells the LCD to read in the data or instructions.

If you happen to be using the normal LCD library for an Arduino, or programming the LCD using a C or C++ based system, then when you program it to, say, show the letter "H", or program it to have the cursor blink, then your programming has to be setting the various lines in a manner similar to what we just did above. Think of this as "assembly language for the LCD".

The luma.OLED library interfaces with many OLED controllers (SSD1306, SSD1309, SSD1322, SSD1325, SSD1327, SSD1331, SSD1351, SSD1362, SH1106, and WS0010), so with a few adjustments (changing the device define to match your display’s controller, and having a compatible breakout board) this tutorial can be used for many of our OLED displays.

To understand the limitations, take a piece of graph paper and draw out a set of 5x7 rectangles. See if you can define ANY recognizable Japanese or Chinese characters on your graph paper.

You need a display with a lot more resolution, like an OLED graphics display. You are also struggling against a microcontroller that doesn"t have support for Unicode. It"s just not up to the job. You"d be much better off with a Raspberry Pi and a screen like a smart-phone color LCD screen. Install Linux and you can have native support for Unicode.

some are pretty obvious as to their function, the smaller sub buttons load various images I happen to randomly pick off the internet to demonstrate the ability to load to the LCD SPI screen as well as the HDMI (See the video for a demo)

In previous posts I’ve covered using HD44780 16×2 and 20×4 LCD screens with the Raspberry Pi using Python. These are relatively easy to use but require a number of connections to be made to the Pi’s GPIO header. This setup can be simplified with the use of an I2C enabled LCD screen.

The example script will allow you to send text to the screen via I2C. It is very similar to my scripts for the normal 16×2 screen. To download the script directly to your Pi you can use :wget https://bitbucket.org/MattHawkinsUK/rpispy-misc/raw/master/python/lcd_i2c.py

Once you’ve played with LEDs, switches and stepper motors the next natural step is 16×2 alphanumeric LCD modules. These modules are cheap (less than $10) and easy to interface to the Raspberry Pi. They have 16 connections but you only need to use 6 GPIO pins on your Pi.

Most of the 16×2 modules available are compatible with the Hitachi HD44780 LCD controller. This allows you to buy almost any device and be sure it is going to work in much the same way as any other. There are loads to choose from on eBay with different coloured backlights. The one I purchased had a blue backlight.

This script can be downloaded using this link or directly to your Pi using the following command :wget https://bitbucket.org/MattHawkinsUK/rpispy-misc/raw/master/python/lcd_16x2.py

Additional Notes : RS is low when sending a command to the LCD and high when sending a character. RW is always low to ensure we only ever input data into the module. 8 bit bytes are sent 4 bits at a time. Top 4 bits first and the last 4 bits second. Delays are added between certain steps to ensure the module can react to the signal before it changes.

Computers seem good at random number generation. But in truth their random numbers are merely pseudorandom because computer programs are inherently deterministic. Here, Dev presents a random number generator that uses machine vision to acquire true randomness from a physical source.

In the time before computers (BC), our ancestors used simple methods, such as throwing dice, to generate random numbers. Although our computers seem to be capable of spewing random numbers at a much higher rate, these numbers are not random, they’re pseudorandom. Computer programs are deterministic algorithms that are completely predictable at some base level. In this article, I share my design of a random number generator based on the Raspberry Pi that uses machine vision to harvest true randomness from a physical source.

An important part of my childhood education was frequent trips to the public library. Our city with a population of 100,000 had a huge public library that was well stocked with books and periodicals. I enjoyed randomly browsing the stacks—the yesteryear equivalent of evoking the random page feature on Wikipedia [1]. In my adventures among the stacks, I chanced upon an article that described an early attempt at using random numbers to generate poetry using a computer. This work was done at Bell Labs, where random numbers were likewise used to create art and computer music.

Reading that article was one of several things that sparked my interest in random numbers and random number generation that continued throughout my career. In the days of impact printing, I used random numbers to program the daisy wheel printer in my office in a simulation that made it sound as if I was hard at work typing when my office door was closed. In that case, the randomness was 1/f noise—a type of noise that’s less random than white noise.

Human culture has embraced randomness in such activities as reading tea leaves and flipping Tarot cards for predicting the future. Throwing dice in gaming has been done for millennia, and randomness is built into our modern slot machines. There are non-gaming uses of randomness in computer simulation, such as the Monte Carlo method used in physics, statistical sampling and statistical analysis of experiments.

Most computer applications will perform adequately using simple random number generators, but due care must be taken in the generation of cryptographic quality random numbers—the ones that are used to encode secret communications against technologically advanced adversaries. Just as an individual would be unwise to use “1234” as a PIN number, cryptographers want to “seed” their random number generators with unique starting values to produce better randomness.

Programming languages have always had random number functions, and these have improved over the decades. The earliest of these used a simple algorithm called a linear congruential generator (LCG). The GNU C compiler uses an improved version of this generator, but LCGs should not be used when good quality random numbers are needed. Many of today’s languages, including Python and PHP, use a much more complicated algorithm called the Mersenne Twister, which, as its name implies, involves the Mersenne primes [2].

Aside from its being a generalized feedback shift register, it’s somewhat hard to describe. It’s used as the random number generator not only in Python and PHP, but also the popular Mathworks MATLAB environment, the R statistical package and some other programming languages. It has a 32-bit implementation, called MT19937, based on the 24th Mersenne prime, that has a period 219937 – 1. There is also a 64-bit version called MT19937-64 [3].

As computer pioneer, John von Neumann, so famously stated in 1951: “Anyone who considers arithmetical methods of producing random digits is, of course, in a state of sin.” He realized that computers just follow some algorithm to generate their random numbers, so these apparently random numbers are completely predictable if the algorithm and its initial state is known. That didn’t stop him from using such random numbers for mathematical modeling of complex systems, since they’re good enough for such things. However, their potential predictability makes them not well suited for cryptography. That’s why sources of true randomness are important.

Nature has given us many sources of true randomness. In 1926, John B. Johnson of Bell Labs discovered that any resistor at temperatures above absolute zero has a random voltage noise across its terminals. Harry Nyquist, a colleague of his at Bell Labs, was able to explain this phenomenon, and we now call it Johnson-Nyquist noise. This noise is very small. A 1kΩ resistor at room temperature—measured over a 10kHz bandwidth—has an RMS (root mean square) noise voltage of just 400nV, but it can be amplified and used to generate random numbers.

A Zener diode noise source. The amplification needs to be large, and this leads to the addition of amplifier noise to the signal. While truly random noise (“white noise”) is independent of frequency, amplifier noise has a frequency dependence. Amplifier noise is “1/f noise” also called “flicker noise.”

Heisenberg’s uncertainty principle is a well known feature of quantum mechanics, and uncertainty is a source of randomness. This sentiment was echoed by Einstein, who wrote that quantum mechanics was like “God playing dice with the world.” Radioactive decay in the tick-tick of a Geiger counter can be a source of random numbers, because the time interval between ticks is random. Since Geiger counters should click very slowly, scientists at the National Institute of Standards and Technology (NIST) developed a quantum voltage noise source (QVNS) in 1999. Their QVNS uses a superconducting junction based on a quantum mechanical effect known as the Josephson effect.

It’s unlikely that you will stock QVNS chips in your electronics parts bin, given that they only work at cryogenic temperatures. However, NIST has an online Randomness Beacon (https://beacon.nist.gov/home) that produces a 512-bit random number every minute—together with a timestamp of its creation—and a hash with the previous value. The Beacon website does have one caveat: The random numbers should not be used as cryptographic keys.

One novel physical random number generator, created in the late 1990s by Silicon Graphics, is Lavarand. Lavarand generated random numbers from images of active lava lamps. The images are random, since the behavior of the bubbles of simulated lava is not predictable (Figure 2). The method of creating random numbers from images is described in US Patent No. 5,732,138, “Method for seeding a pseudo-random number generator with a cryptographic hash of a digitization of a chaotic system [4].” There was once a Lavarand website that served-up random numbers, but Silicon Graphics is now defunct, and so is the web site.

The lava lamp idea has been picked up by the website-security company, Cloudflare, which has a Wall of Entropy at its San Francisco offices [5]. The Wall of Entropy has multiple rows of lava lamps that are imaged to produce random numbers. These numbers are combined with random numbers from other sources of randomness to produce random numbers presumed to be cryptographically secure. Cloudflare has joined with other international randomness creators to form a “League of Entropy” to distribute random numbers on the Internet [6].

A wall of lava lamps would be a decorative addition to my home office, but there are less expensive ways to produce physical randomness. I searched craft stores and supermarket shelves looking for some suitable candidates. Shown in Figure 3 are 50:50 mixtures of easily obtained black and white components. High contrast between black and white is preferred.

Household sources of image randomness. (a) Black and clear plastic craft beads. (b) Black dyed and white pearled couscous pasta. (c) Black and white microbeads used as fingernail decoration. (c) Oriental black rice and white rice.

Figure 3c shows a much better option. These are 0.6mm microbeads used in fingernail art, not stocked in any of my local stores, but available online. The material that’s easiest to find is rice. I learned from a restaurant visit several years ago that there’s such a thing as black rice, and this rice is found in any oriental grocery store. Mixing the black rice with a white rice produces an excellent random image noise source, since the rice grains have a matte finish that doesn’t reflect light. A quick shake of any of these sources produces a new random image.

Machine vision is simplified by the presence of USB ports on the Raspberry Pi and the availability of inexpensive USB interfaced video cameras. These cameras, typically used for video conferencing, produce high-definition color video images and they operate over a large range of light levels. Our basic machine vision physical random number generator is built around the Raspberry Pi, one of these cameras (a YoLuke HD Webcam), an LED light source and a liquid crystal character display connected to the I2C capable GPIO pins of the Raspberry Pi. Linux—in the form of the popular Raspberry Pi OS operating system (previously called Raspbian)—has utilities that simplify stitching these hardware components together with software [7].

Our first problem is trying to stuff all these components into a small, elegant enclosure. The camera needs a reasonably large offset from the plane of the random elements to focus and image the entire area. To accomplish this, I used a folded optical system in which a mirror bends the image from the top of the device through 90-degrees to a side-looking camera (Figure 4). The random elements are contained in a 2″ square plastic cube that’s placed at the top of the enclosure. There’s a leaf switch at the top that detects when the cube is removed, presumably shaken and then replaced.

Mechanical layout of the machine vision physical random number generator. Fortunately, the video camera could easily focus down to the necessary 4″ optical path length. The micro USB power connector for the Raspberry Pi was attached to a short adapter cable with a standard USB receptacle for connection at the rear of the enclosure for the power source.

While the Raspberry Pi gives an easy way to acquire a digital image, we still need to get a signal from the leaf switch and present the random numbers to the outside world. In this random number generator, communication is accomplished in two ways. First, there’s an interface board the converts the 3.3V GPIO signals of the Raspberry Pi to the 5V signals needed by an LCD display on the front panel. Inexpensive character displays with an I2C digital interface are available from many sources, and a backlit display was used in this project. Since the Raspberry Pi has a built-in Wi-Fi interface, the random numbers are also served as a webpage on your home network. Figure 5 shows the circuit diagram for the interface board, and Figure 6 is a component side view of the circuit board showing the connections.

Circuit diagram for the interface board. This board provides voltage level-shifting from the 3.3V logic of the Raspberry Pi to the 5V logic needed for the LCD display. It also powers the illumination LEDs, the LCD backlight, interfaces to the leaf switch that indicates when the cube of random material has been removed and replaced and a push-button power-off switch explained in the text. Connect “A” to “A” and “B” to “B”.

in which my_program.c is the source code, and the -o option specifies the name of the executable, in this case, myprogram. The -Wall option presents a list of program warnings and errors that need attention. I used C language for the main program because it facilitates the image processing and conversion of the physical randomness of the image. Python is a better language for controlling the I2C display interface and the other GPIO pins. For that reason, the main program calls a Python helper program to accomplish such tasks. The main program likewise produces a data file that’s used to present the random numbers on a web page.

It’s straightforward getting the image data into a numerical array for extraction of the random numbers, but creation of quality randomness takes a little effort. Because the image data is represented by pixel values in the range of 0-255, we can set a mid-range value of 127 and generate a “1” bit for values at that point and larger, and a “0” bit for numbers less than that.

The resultant bitstream is somewhat random, but it’s biased in several ways. For example, the white and black elements might have a slightly different electrostatic attraction to each other, and they might clump. Also, as in the case of using the black and white rice, the individual elements extend over many pixels, so there’s some correlation. For these reasons, we select pixels randomly using the random function of our programming language and go through a “whitening” step. Using software randomness in this case is allowed, since our physical system is unpredictable.

Whitening is a means of extracting randomness from a slightly biased random source, such as loaded dice, and a simple whitening method was invented by computer pioneer, John von Neumann. This von Neumann Whitening operates on two successive bits at a time, and maps them to a random stream as follows: (0,1) gives a 0, (1,0) gives a 1, and (0,0) or (1,1) gives a result that’s ignored. The random number in our machine vision system is created by randomly fetching pixels from the image, applying the threshold function to give a 0 or 1, whitening this bit stream and extracting a 64-bit hexadecimal number.

A 2 line by 16-character LCD display with an I2C serial interface is used to display the random numbers. The display chosen uses a Hitachi HD44780 compatible controller, as does nearly every such display. To make such displays I2C displays, they have an additional Texas Instruments (TI) PCF8574 chip that acts as a serial to parallel converter. Connection to the rest of the circuitry is through just four pins for +5Vt power, ground and the I2C SDA and SCL data lines (Figure 7). The display I used has a blue backlight, white characters and a potentiometer for contrast adjustment. The backlight can be connected through a jumper to the +5V supply, but I connected switched power from the GPIO interface board to the LED terminal of the jumper pins. This allows a power-off of the display backlight when the device is inactive.

Component side view of the LCD display. It’s always nice when a manufacturer clearly labels connections. The PCF8574 interface board had a bright LED on it that interfered with the video capture. I covered it with a blob of opaque silicone.

The only way to communicate the state of the switch that indicates whether the cube of random material has been removed from its platform and replaced is to transfer data from the python helper program to the main program written in C is through use of a file. The python program writes data to a file, and the C program reads it. To prevent excessive wear on the Raspberry Pi solid-state memory card, we use a memory-mapped file.

The Raspberry Pi has built-in Wi-Fi connectivity, so it’s easy to program a web interface to view the random numbers on a home network. In fact, some might decide to forego the LCD display and just use the web interface. The built-in Wi-Fi connectivity, the installed the Apache web server and PHP allows a web interface to view the random numbers on a home network with just one additional program. Web pages are served from the /var/www/html/prng/ folder, where the PHP source file, prng_log.php, is placed.

Web browser interface, accessed on my home network at http://192.168.1.61/prng/prng_log.php. The image is the image from which the latest random number is derived. The column entitled Von Neumann Trials indicates the quality of the raw data. On average, a 64-bit number will require 128 acquisitions of raw bit data for whitening. A good method for creating pin numbers is to select some middle decimal digits.

Random numbers are uniformly distributed, but the appearance of a uniform distribution is not a good test of randomness. That’s because a list of numbers in serial order, which is definitely not random, would produce a uniform distribution. Other non-random sequences, such as all even numbers followed by all odd numbers, and other not so obvious arrangement of numbers, will also show a uniform distribution while not being random.

The Diehard Test Suite, a collection of programs initially developed by computer scientist George Marsaglia is the acknowledged method for testing randomness. This updated test suit, now known as Dieharder, is maintained by Robert G. Brown of the Duke University Physics Department and his colleagues [8]. It’s freely available from the Linux repositories, but don’t run it on the Raspberry Pi! You need a fast desktop computer with a lot of memory for this!

Dieharder needs many random numbers for a valid test. I used 10 million for my randomness checks. Because it’s not possible to shake the cube of random material 10 million times to snap its image, I took the shortcut of doing these tests with one static image, so the results might be considered a worst-case effort. Also, Dieharder looks at 32-bit numbers. I modified the program to produce a simulation program to produce a file of 10 million random 32-bit decimal numbers, 10M_data.txt, with the proper header, and executed the following command to run the test suite:

For each test, Dieharder generates a p-value, which is a statistical measure of the rejection of the “null hypothesis” that the numbers aren’t random. The usual—and often misused measure—is that the null hypothesis is rejected when the p-value is greater than 0.05. Dieharder will also state plainly whether your numbers have passed the test. The results for my simulation are shown in Figure 9. Dieharder automatically runs two of the “runs” and “craps” tests. You can get an explanation of a certain test by executing:

Summary of the Dieharder randomness tests. All passed without comment, except for parking lot, which was noted as “weak.” In the parking lot test, unit circles are randomly placed in a 100×100 square. A circle is successfully parked if it does not overlap an existing successfully parked one. After 12,000 tries, the number of successfully parked circles should follow a certain normal distribution.

As every circuit experimenter knows, unexpected problems present themselves in the development of most electronic devices. One problem I had a difficult time in diagnosis was stray light reflections that reduced the contrast at one edge of the cube of random material. The light that produced the glare obviously originated from the illumination LEDs, so I tried moving their position inside the box. This didn’t really help. The eventual fix was to mask most of the area of the mirror, because reflection of the LED light from the over-sized mirror was the cause of the problem (Figure 10). There was still plenty of mirror left for proper imaging.

The finished project, displaying the two most recent 64-bit random numbers on the LCD display. The panel marking was done using a decal transfer process, as described in the text.

[2] M. Matsumoto and T. Nishimura, “Mersenne Twister: A 623-Dimensionally Equidistributed Uniform Pseudo-Random Number Generator”, ACM Transactions on Modeling and Computer Simulation, vol. 8, no. 1 (January, 1998), pp 3-30, doi:10.1145/272991.272995. https://dl.acm.org/doi/10.1145/272991.272995

[4] Landon Curt Noll, Robert G. Mende and Sanjeev Sisodiya, “Method for seeding a pseudo-random number generator with a cryptographic hash of a digitization of a chaotic system,” US Patent No. 5,732,138, March 24, 1998 (https://patents.google.com/patent/US5732138A )

[5] Joshua Liebow-Feeser, “Randomness 101: LavaRand in Production,” Cloudflare Web Site (https://blog.cloudflare.com/randomness-101-lavarand-in-production ).