ARM7 Development Tools

The development Software Keil for architecture ARM7 TDMI support the following devices ARM7: Atmel, Philips and Analog Devices, Sharp, OKI, ST Microelectronics. Instrument for ARM consist of the following parts: – CAARM Compiler/Assembler Kit Contains AARM Assembler, ARM Utilities, CARM Compiler, LARM Linker/Locator and the integrated medium of the development of Вµ.Vision IDE, which accomplishes complete control Compiler, Assembler; – PKARM – Professional Developer’.s Kit Includes all components CAARM plus of Вµ.Vision Debugger, which supports the complete simulation of devices and OS of the real time RTXA-Tiny; – DKARM Developer’s Kit – is included the integrated medium of the development of Вµ.Vision IDE and ВµVision Debugger; – Ar- ARM advanced Rtx- ARM the operating system of the real time Keil Rtx-ARM, which includes Flash File System and support TCP/IP Networking; – ULINK – adapter Usb-jtag for the connection of interface JTAG ARM7 to port USB of personal computer. Keil ARM Tools Makes it possible to select, which of compilers and assemblers to use in the project: – GNU ARM Compiler, Assembler – freely extended ON, does not have limitations according to the functions and the size of the code and is supported DKARM. It is used, if the purpose of…

Continue reading

Healthy skin reflectance model

This pilot study is intended to investigate possibilities of skin nevus imaging using digital still image camera. The main objective is to develop method of dermatology images interpretation, which enables the looking on the skin lesions and nevus from the optical background of skin coloration. Kubelka-Munk calculation method for light transport and reflection from multilayered complex media is applied in modeling of light reflection spectra of skin. Calculation of model shows that red, green, blue and infrared colors lighting is satisfactory to access distribution of comparative estimates of the following skin parameters: volume fraction of melanin in epidermal layer, volume fraction of hemoglobin in dermal layer, presence of dermal melanin and thickness of papillary layer. Performance of image processing method on fourteen samples of images of common melanocytic nevi, dysphasic melanocytic nevi, Spitz nevus, thrombotic hemangioma and surrounding healthy skin were made. Skin spectral properties Understanding how light interacts with skin, can assist in designing physics based dermatological image processing. The key is understanding how light interacts with skin tissue. Skin consists of different layers with different spectral properties. Fig 1. Skin model and its physical view When incident light is applied to skin layer, the part of it absorbed…

Continue reading

Review on skin lesion imaging, analysis and automatic classification

The goal of any imaging methodology used in dermatology is to diagnose melanoma in early stages because it depends on the effectiveness of treatment. Investigations shows, that early diagnosis is more than 90% curable and late is less than 50% [1]. The diagnosis and successful treatment are often supplemented with permanent monitoring of suspicious skin lesions. Doctor’s diagnosis is reliable, but this procedure takes lots of time, efforts. These routines can be automated. It could save lots of doctor’s time and could help to diagnose more accurately. Besides using computerized means there are excellent opportunity to store information with diagnostic information in order to use it for further investigations or creation of new methods of diagnosis. Skin lesion imaging methods We found that there are number of various imaging methods of skin lesions [2]. The simplest skin visualization method is photography. This method gives only top layer skin image. In order to get deeper layer image there is oil immersion used. It reduces reflections of surface and brightens the image of epidermis – the second skin layer.

Continue reading

Discrete systems in series and parallel

Discrete systems in series Let as say that we have two discrete systems and their impulse responses are h1(n) and h2(n). Then when these discrete systems are connected in series, then overall impulse response: Where: As you noticed there were changing made: This is nothing more than convolution of impulse responses of both discrete systems: Discrete systems in parallel If we have two discrete systems connected in parallel: As we see there is simple sum of output queues of each discrete system. So we can assume, that overall impulse response is as sum of systems connected in parallel:

Continue reading

Impulse Response of discrete system

Impulse signal can be represented as: d[n] = 1, if n=0 d[n] = 0, otherwise it can also be written like d=[1,0,0,0,…] Impulse Response The impulse response h(n) is the response of filter L() at time n to unit impulse occurring at time 0. h(n)=L(d(n)) Lets see how discrete system can be described when impulse response is known We know that: In the linear system this can be written as follows: Because h(n-k)=L(d(n-k)) Then: What do we get? There is obvious, that linear system can be described by its impulse response. The last expression is called convolution. This is the heart of DSP Filtering. To write this sum in more convenient matter is assumed that: Matlab example Matlab example: % Plot an unit impulse signal n = -7:7; x = [0 0 0 0 0 0 0 1 2 3 0 0 0 0 0]; subplot(4,2,1); stem(n, x); limit=[min(n), max(n), 0, 5]; axis(limit); title(‘Input x[n]’); subplot(4,2,3); x0=0*x; x0(8)=x(8); stem(n, x0); axis(limit); h=text(0, x0(8), ‘x[0]’); set(h, ‘horiz’, ‘center’, ‘vertical’, ‘bottom’); subplot(4,2,4); y0=0*x; index=find(x0); for i=index:length(n) y0(i)=x0(index)*exp(-(i-index)/2); end stem(n, y0); axis(limit); h=text(0, x0(8), ‘x[0]*h[n-0]’); set(h, ‘vertical’, ‘bottom’); subplot(4,2,5); x1=0*x; x1(9)=x(9); stem(n, x1); axis(limit); h=text(1, x1(9), ‘x[1]’); set(h, ‘horiz’, ‘center’, ‘vertical’, ‘bottom’); subplot(4,2,6);…

Continue reading

What is a linear system?

Discrete system is nothing more than algorithm, where input is transformed to output. The output is transformed by operator L() which describes discrete system. Lets see few most common operators of discrete systems. Delay This means that output queue is delayed by on sample. Multiplication This operator takes each sample of input queue and multiplies by constant a. Sum operator Takes two or more sample queues and adds them in the output. Assuming we can say, that the system is linear if input sum reaction is equal to sum of inputs reactions: The system has stable parameters if: y(n-k)=L(x(n-k)), this means that output delay should be the same as input. It is obvious that delay, multiply and sum operators are linear and has stable parameters. We will need then for further lessons.

Continue reading

Understanding of discrete signals

Discrete signals can be generated by software or obtained from real world through ADC. Discrete signals are sampled from analog signals. So you get samples in fixed time intervals. Discrete signal is as sequence of numbers. The element number n of sequence is marked as x(n). The most common number rows: Unit sample sequence d[n] = 1, if n=0 d[n] = 0, otherwise You can describe it in Matlab like % Plot an unit impulse signal n = -5:5; x = 0*n; index=find(n==0); x(index)=1; % plot stem(n, x); axis([-inf, inf, -0.2, 1.2]); xlabel(‘n’); ylabel(‘x’); title(‘Unit Impulse Signal delta[n]’); Unit Step Sequence u[n] = 1, if n>=0 u[n] = 0, otherwise You can describe it in Matlab like: % Plot an unit impulse signal n = -5:5; x = 0*n; index=find(n>=0); x(index)=1; % plot stem(n, x); axis([-inf, inf, -0.2, 1.2]); xlabel(‘n’); ylabel(‘x’); title(‘Unit Step Signal u[n]’); As you can see step is nothing more than set of impulses. And impulse can be expressed as d[n]=u(n)-u(n-1); Thus any sequence of numbers can be expressed asset of impulses like this: For example sin() sequence can be written like this: Matlab script would look like this: % Plot a sinusoidal signal n = 0:40;…

Continue reading