Real Pic Simulator 1.3 PORTABLE

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Real Pic Simulator is the fastest software microcontroller simulator targeting the Microchip(tm) baseline and mid-range flash based PIC microcontrollers. Features : - Integrated disassembler - allows to examine and export the code to assembler code.- Debugger - allows execution of the program in real-time, at selected speed or step-by-step, using breakpoints.

Real Pic Simulator is a Microchip PIC microcontroller simulator capable of real-time simulation. An integrated disassembler allows examining and exporting the code to assembler code. Debugger allows execution of the program in real-time, at selected speed or step-by-step, using breakpoints. RAM and EEPROM viewer allows the user to inspect RAM and EEPROM memory content. Processor viewer allows users to view the microcontroller's pin allocation and characteristics. Visual simulator enables visual simulation of the program with visual components (LEDs and Keypads).

Real Pic Simulator è un simulatore di microcontrollori PIC di Microchip di fascia base e media che consente la simulazione e quindi il test di applicazioni firmware precedentemente sviluppate e compilate praticamente senza disporre di alcuna risorsa hardware. Per una corretta collocazione operativa di Real Pic Simulator è necessario, come per ogni strumento di ausilio alla progettazione di schede a microcontrollore, individuare dapprima quali modelli di controllori esso supporti. Questi ultimi sono raggruppati nella tabella 1, distinti rispettivamente in modelli di fascia base e modelli di fascia media. Tra le caratteristiche del simulatore vi sono la simulazione delle risorse fisiche dei controllori implementati e le funzioni operative di carattere visuale. Le prime sono riassunte, come già detto, in tabella 1 mentre le seconde saranno ampiamente illustrate e discusse nei paragrafi a seguire.

Questi ultimi costituiscono prevalentemente componenti di ingresso e di uscita tra i più comuni nella realizzazione effettiva delle schede a microcontrollore. Tra le risorse hardware simulate interne ai controllori implementati nel simulatore vi sono timer, memoria RAM ed EEPROM, interrupt-on-change, interrupt su pin int, CCP, ADC, interfaccia UART. Tra i Componenti hardware virtuali esterni al controllore e che è possibile connettervi, troviamo invece:

Ogni qualvolta si crea qualche dispositivo, non manca la necessità di avere a disposizione un simulatore, che da sempre le case di processori e/o componentistica attiva, creano per aiutare i progettisti e non, al fine di rendere più rapido lo svolgimento, e con questo bellissimo articolo si rinnova questa necessità, con un simulatore molto utile leggero intuitivo e semplice da utilizzare.

While the design concept had a number of attractive features, General Instrument never strongly marketed the 1600, preferring to deal only with large customers and ignoring the low-end market. This resulted in very little uptake of the system, with the Intellivision being the only really widespread use with about three million units. When GI spun off its chip division to form Microchip Technology in 1985, production of the CP1600 ended. By this time, however, the PIC had developed a large market of customers using it for a wide variety of roles, and the PIC went on to become one of the new company's primary products.

Microchip provides a freeware IDE package called MPLAB X, which includes an assembler, linker, software simulator, and debugger. They also sell C compilers for the PIC10, PIC12, PIC16, PIC18, PIC24, PIC32 and dsPIC, which integrate cleanly with MPLAB X. Free versions of the C compilers are also available with all features. But for the free versions, optimizations will be disabled after 60 days.[31]

Our company designs software and hardware products for car driving education and entertainment: smart AI systems, virtual models of cities, car simulators, special vehicle simulators, industrial car driving simulators etc. We also design car driving computer games, on the basis of our own technologies and experience.

The car driving game named "City Car Driving" is a new car simulator, designed to help users experience car driving in а big city, the countryside and in different conditions or go just for a joy ride. Special stress in the "City Car Driving" simulator has been laid on a variety of different road situations and realistic car driving.

The loss function of the model was designed to make the vector close to the real sample data value. In our model, we used a Euclidean distance formula to calculate the distance between the predicted vector data of the model and the corresponding sample vector data. The following formula shows the detail of the calculation:

In the real vector, the more non-zero elements in the vector there are, the larger the deviation between the sample vector data and the predicted vector data will be. The improved model follows the Euclidean distance formula. The final distance is divided by the number of non-zero elements in the sample vector data; the updated formula is as follows:

The transformation of the visual field check result to the real scenario. The real scenario image was transformed from the central point of the visual field check result to the real scenario image. VF, visual field.

During the transformation of the center point, the attribution of xstart and ystart in the quintuple data will be changed while other attributions remain unchanged. Assuming h is the height value of the real scene image, w is the weight value of the real scene image, h' is the height value of the VF check result image, and w' is weight value of the VF check result image. The attribution of xstart and ystart in the quintuple data can then be recalculated by the following formulas:

After the center point transformation for all quintuple data, the VF check result image and the real scene image had the same center point, and we then resized the range of the area represented by the quintuple data. Assuming r is the view range value of the real scene image and r' is the view range value of the VF check result image, the resize rate can be calculated by the following formula:

In computer vision processing, the area and damage information in the VF damage parameter quintuple data set were mapped into the real scene image, and the darkening effect was adjusted according to the damage parameter; the visual effects in patients were thus simulated into the real scene image. After constructing the visual contour based on the VF damage parameter, the visualization matrix of the VF damaged area and the real scene images processed by the Gaussian smoothing filter were merged.

The artificial intelligence model to simulate the visual effects of patients with visual field defects in the real scenario. (A) The grayscale map; (B) the simulation of visual effects of patients with visual field defects in the real scenario.

We used the VGGNet network structure to build our AI model, which was a CNN developed by researchers at the Visual Geometry Group of Oxford University and Google DeepMind (34). VGGNet has two structures: VGG16 and VGG19. Compared to traditional CNN, the application of VGGNet has a profound influence on DL (35). VGG19 adopts the alternating structure of multiple convolutional layers and nonlinear activation layers, which improves the network depth and extracts image features more effectively than a single convolutional layer structure (36,37). Since the image in each grid of the grayscale map has no fixed features, it cannot be directly assigned. By calculating the average gray values in each grid, the damage type parameter can be obtained. After the construction of the AI model, visual processing is required to transform the data into the real scene. After the visual contour construction based on VF damage parameters was completed, the visual matrix of the VF damaged area and the real scene image were required to be processed with a Gaussian smoothing filter. If smoothing is not performed in AI simulations, the image generated by the initialization matrix will have a distinct segmentation edge between the original background and VF damaged area, thus affecting the simulated effect after fusion (38-40).

After constructing the AI model, the model predicted the damage parameters of the grayscale map in the test sample and calculated the MSE between the predicted value and the real value. The MSE showed a downtrend in the (0.75, 1) and (0, 0.25) intervals (MSE =0.0083 and 0.0079) compared to the (0.5, 0.75) and (0.25, 0.5) intervals (MSE =0.0104 and 0.0116). The results indicated that the AI model had a higher prediction accuracy in the normal VF and VF defects area in the grayscale map. 2b1af7f3a8