kortex tft lcd monitor model free sample

Cross-platform Has no external dependencies and can be compiled for any vendor"s any MCU or MPU, and (RT)OS to drive ePaper, OLED or TFT displays, or even monitors.

The NuMaker-HMI-MA35D1-S1 is an evaluation board for Nuvoton NuMicro MA35D1 series microprocessors, and consists of three parts: a NuMaker-SOM-MA35D16A81 SOM board, a NuMaker-BASE-MA35D1B1 base board and a 7” TFT-LCD daughter...

• (2.4", 2.8", 3.2", 3.5", 4.3", 5.0", 7.0")• TFT 65K RGB Resistive Touchscreen• Onboard Processor and Memory• Simple ASCII Text Based Instruction Set• The Cost-effective HMI Solution with Decreased

Nextion is available in various TFT LCD touchscreen sizes including 2.4”, 2.8”, 3.2”, 3.5”, 4.3”, 5.0”, 7.0”, 10.1” . With a large selection to choose from, one will likely fit your needs. Go Nextion Series and Product Datasheets.

A classic data logger would use a MCU and its GPIO pins, a SD card, a RTC, an LCD status display and many lines of code. Today, I"ll show you that you can have all in one, using a Nextion Intelligent series HMI and thus reduces cost and development time: First, the Intelligent series has everything "on board", the MCU, the GPIO pins, the RTC, the screen, and the SD card. Second, a very powerful component, the Data Record is available for these HMI displays in the Nextion Editor, which saves us, let"s say around 500 lines of C code. But telling you this is one thing, giving you a demo project at hands which covers all functionalities and which you can modify and extend as you need for your project is today"s topic.First of all, a happy new 2023! I"ll use this occasion to introduce a new type of Sunday blog post: From now on, every now and then, I"ll publish a collection of FAQ around a specific topic, to compile support requests, forum posts, and questions asked in social media or by email...Whatever you are currently celebrating, Christmas, Hanukkah, Jul, Samhain, Festivus, or any other end-of-the-civil-year festivities, I wish you a good time! This December 25th edition of the Nextion Sunday Blog won"t be loaded with complex mathematical theory or hyper-efficient but difficult to understand code snippets. It"s about news and information. Please read below...After two theory-loaded blog posts about handling data array-like in strings (Strings, arrays, and the less known sp(lit)str(ing) function and Strings & arrays - continued) which you are highly recommended to read before continuing here, if you haven"t already, it"s big time to see how things work in practice! We"ll use a string variable as a lookup lookup table containing data of one single wave period and add this repeatedly to a waveform component until it"s full.A few weeks ago, I wrote this article about using a text variable as an array, either an array of strings or an array of numbers, using the covx conversion function in addition for the latter, to extract single elements with the help of the spstr function. It"s a convenient and almost a "one fits all" solution for most use cases and many of the demo projects or the sample code attached to the Nextion Sunday Blog articles made use of it, sometimes even without mentioning it explicitly since it"s almost self-explaining. Then, I got a message from a reader, writing: "... Why then didn"t you use it for the combined sine / cosine lookup table in the flicker free turbo gauge project?"105 editions of the Nextion Sunday blog in a little over two years - time to look back and forth at the same time. Was all the stuff I wrote about interesting for my readers? Is it possible at all to satisfy everybody - hobbyists, makers, and professionals - at the same time? Are people (re-)using the many many HMI demo projects and code snippets? Is anybody interested in the explanation of all the underlying basics like the algorithms for calculating square roots and trigonometric functions with Nextion"s purely integer based language? Are optimized code snippets which allow to save a few milliseconds here and there helpful to other developers?

We agree that we did not sufficiently address the differences in visual hierarchy at which the two attended features are processed. In the current version of the manuscript, we now explicitly include this argument as one of the factors contributing to differences in the spatial scale at which the two attended features are processed (see our reply to Essential revision point 2). Indeed, it should be expected that offsets in pRF size should lead to differential pRF changes. Specifically, Gaussian interaction models of attention (Klein et al., 2014) suggest that the larger the stimulus drive (i.e. the pRF outside influence of attention), the greater the impact of attention. In correspondence, we observed that absolute pRF shifts were larger in areas with larger average pRFs (see Figures 3 and 4). Importantly however, our assessment of feature based attentional modulation is a contrast measure (AMI), relative to the absolute pRF modulation resulting from differential spatial attention. This analysis therefore controls for any offset in pRF size. Moreover, any difference in pRF size is also taken into account by the attentional gain field modeling (i.e. it is input to the model). Together, this means that differences in pRF size between visual areas that preferentially process the attended feature should not lead to biases in the assessment of interactions between spatial and feature based attention in our analyses. Nevertheless, (as we argue above) these offsets of pRF size between these regions does contribute to explaining why differential spatial resampling is required when attending color compared to temporal frequency.

“Although we used a Quest procedure to equate difficulty across attention conditions and across different levels of eccentricity, it is possible that this procedure stabilized at a faulty difficulty level. In order to verify whether the Quest procedure successfully equated performance we used a similar Bayesian approach, testing whether a model including attention condition (3 levels) and stimulus eccentricity (3 levels) influenced behavioral performance (Figure 9A).”

For head-fixed experiments, a high-speed camera (IMPERX, IPX-VGA-210-L) was fitted with a 45-mm extension tube, a 50-mm lens (Fujifilm, Fujinon HF50HA-1B) and an infrared pass filter (Edmund Optics, 65-796). Images were acquired at 200 Hz through a frame grabber (National Instrument, PCIe-1427). An infrared hot mirror (Edmund Optics, 43-958) was placed parallel to the antero-posterior axis of the animal (1 inch from the eye) in between the animal and the LCD monitor, and the camera captured the image of the eye through its reflection. The camera was angled at 59° relative to the antero-posterior axis. Three infrared 880-nm LED emitters (Digi-Key, PDI-E803) were used to illuminate the eye.

Visual stimuli were presented on an LCD monitor running at 240 Hz (Gigabyte, AORUS KD25F) to the right eye, contralateral to the hemisphere in which recordings were performed. The monitor was angled at 31° anticlockwise relative to the antero-posterior axis of the animal and tilted 20° towards the animal relative to the gravitational axis. It was positioned such that the tangent point between the plane of the monitor and a sphere around the centre of the eye was in the centre of the monitor. The distance from the centre of the eye to the tangent point was 133 mm, with the monitor covering 128° of the field of view horizontally and 97° vertically. In the experiment described in Fig. 2g (a full-field flash), an LCD monitor running at 75 Hz was used.

Recordings were started 15 min after insertion of the probes. Signals were sampled at 30 kS s–1 using 64 channel headstages (Intan Technologies, C3315) combined with adaptors (NeuroNexus, Adpt.A64-Omnetics32_2x-sm), connected to an RHD USB interface board (Intan Technologies, C3100). The interface board was also used to acquire signals from photodiodes (TAOS, TSL253R) placed on the visual stimulation monitor as well as TTL pulses used to trigger the eye tracking camera and the LED. These signals were used during analyses to synchronize visual stimulus timings, video acquisition timings and LED photostimulation timings with electrophysiological recordings. All raw data were stored for offline analyses. Occasionally, we recorded from the same animal on two successive days, provided no pharmacological manipulation was performed on the first day. In these instances, the craniotomy was resealed with Kwik-Cast after the first recording session. For post hoc histological analysis, brains were fixed in 4% paraformaldehyde (PFA) in PBS overnight at 4 °C.

Intraocular injection of TTX (40 μM) was performed 2 h before recording under isoflurane anaesthesia. A typical procedure lasted less than 5 min. Carbachol (0.011% (wt/vol)) was co-injected with TTX to prevent the pupil from fully dilating, as a fully dilated pupil reduces the accuracy of eye tracking. Immediately before the injection, a drop of proparacaine hydrochloride ophthalmic solution was applied to the eye as a local anaesthetic (Bausch + Lomb; 0.5%). TTX solution was injected intravitreally using a bevelled glass micropipette (tip diameter, ~50 μm) on a microinjector (Nanoject II, Drummond) mounted on a manual manipulator. One microlitre was injected in each eye, at a speed of 46 nl s–1. In some animals, the injection solution also contained NBQX (2,3-dioxo-6-nitro-7-sulfamoyl-benzo[f]quinoxaline; 100 μM) and APV ((2R)-amino-5-phosphonovaleric acid; 100 μM). The animals were head-fixed for recording following a 2-h recovery period in their home cage. Suppression of retinal activity was confirmed for every experiment by a lack of response in visual cortex to a full-field flash of the LCD monitor.

Saccade responses on a vertical grating (the number of evoked spikes within 100 ms of saccade onset) were predicted from (1) pseudo-saccade response, (2) saccade response on a grey screen or (3) the sum of the two responses. All responses were baseline-subtracted values. The model is a linear regression (fivefold cross-validated) with no intercept, followed by thresholding, which ensured that the predicted FR did not fall below 0 Hz. That is, if the predicted decrease in the evoked number of spikes exceeded the baseline FR, the value was adjusted so that the sum of the prediction and the baseline was zero. The explained variance is calculated as the explained sum of squares divided by the total sum of squares.

Training data consisted of the response to selected pseudo-saccades. This set of pseudo-saccades was selected such that the amplitudes and number of events for the nasal and temporal directions were matched. This ensured that the classifier depended on the NT discriminability of each unit, rather than on the difference in pseudo-saccade amplitude or frequency. The training dataset was first standardized and subjected to PCA. We limited the number of principal components to 20% of the total number of saccades in the training dataset to avoid overfitting. We then trained QDA for classification. The resulting models for PCA and QDA were applied to the test dataset, which comprised responses to either real saccades or pseudo-saccades that were excluded from the training dataset (10-fold cross-validation).

To rank the contribution of each unit to the classifier model, we calculated the permutation feature importance. In brief, we permuted the data from one unit at a time in the pseudo-saccade training dataset during 10-fold cross-validation, to break the relationship between unit activity and pseudo-saccade direction. We then calculated the increase in prediction error resulting from the permutation procedure. To calculate the total contribution from single units with the highest feature importance, we permuted the data from the corresponding units at the same time.

This work is motivated partly by the utility of the mouse as a mammalian model organism for cell-type-specific microcircuit dissection. Tools such as fluorescent cell labeling, optogenetics, and chemogenetics, when applied to the problems of multisensory integration, may help elucidate the microcircuitry that integrates cross-modal signals. We hope that this study of visual responses in the ACtx of a genetic model organism will further the use of cell-type-specific tools for microcircuit dissection of multisensory phenomena.

Whereas the direct interface accesses the video memory directly by the address bus of the CPU, the indirect interface requires a more complex communication with the display controller to get access to the video memory. On LCD controller side that interface is often called "MPU" interface. It normally consists of a set of control- and data lines. on emWin side this requires a few simple communication routines. These are getting called for writing and reading operations to/from the LCD controller.

The following table lists the currently available run-time configurable drivers developed for the current interface of emWin:Display driverSupported display controllers / PurposeSupported bits/pixelGUIDRV_BitPlainsThis driver can be used for solutions without display controller. It manages separate bitplains for each color bit. Initially it has been developed to support a solution for an R32C/111 which drives a TFT display without display controller. It can be used for each solution which requires the color bits in separate plains.1 - 8

The TFT Display Shield Board (CY8CKIT-028-TFT) has been designed such that a TFT display, audio devices, and sensors can interface with Infineon"s PSoC 6™ MCUs.

The TFT Display Shield Board is compatible with the PSoC 6™ WiFi-BT Pioneer Kit CY8CKIT-062-WiFi-BT and the PSoC 6™ BLE Pioneer Kit CY8CKIT-062-BLE. Refer to the respective kit guides for more details.