Monday, January 8, 2018

Spiceworks network monitoring management software free download

Spiceworks Network Monitoring Management

Spiceworks Network Monitor software is Simple, easy-to-use network monitoring software.It helps to figure out real-time status and alerts for your critical devices. It catches problems before users even notice.


Spiceworks-Network-Monitoring-Software
Spiceworks Network Monitoring Software

Some Features


Start monitoring in minutes
Spiceworks Network Monitor is simple to install and easy to set up. Quickly add devices to the dashboard and see data lightning fast.

Fix issues before they are issues
Get real-time network insights and spot slow, sluggish, or overwhelmed systems and devices long before they crash or users start calling.

Get alerts when you need them.

Don’t sweat IT: Get customizable notifications when it’s time to spring into action and get more sleep at night when it’s time to… sleep!

Dynamic dashboard
Get the latest network info without the clutter. Keep an eye on everything within your realm with a single, easy-to-use dashboard.

Ping check
Verify that IP-enabled devices are online and responding, whether it’s to ping, HTTP, SIP, or a user-defined protocol. Check in on VOIP phones, security cameras, access control systems, and more to give you peace of mind. And then kick back and relax!

Free support
Spiceworks support is entirely free. Online or on the phone, chat with IT pros who speak tech and have walked in your shoes. Let us help you get started!

Learn from the Best
Millions of IT pros in the Spiceworks Community can help you find answers, troubleshoot issues, learn new IT tricks, and discover new ideas and hard-won lessons learned.

So, in conclusion, we learn that Spiceworks is a simple network monitor designed to be quick and easy to setup and use.




Reference: https://www.spiceworks.com
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Sunday, January 7, 2018

PRTG Network Monitor Software with sensor free download

PRTG Network Monitor Software with sensor free download

PRTG Network Monitor - Software by Paessler

PRTG (Paessler Router Traffic Grapher) Network Monitor Software helps you to optimize your network. PRTG Network Monitor is an uptime and bandwidth monitoring software that supports a broad variety of sensor types. The network monitoring software is currently being used by over 200,000 customers worldwide.


PRTG-Network-Monitor-Software
PRTG Network Monitor Software
The benefits are:
  • Increased profits: no losses caused by undetected system failures.
  • Improved customer satisfaction by providing more reliable systems.
  • Peace of mind: As long as they do not hear from the monitoring tool they know everything is running perfectly.
  • Deliver better quality of service to users
  • Identify Shadow IT in your company

Network monitor software is essential for companies of any size and branch, to ensure that their computer systems are running smoothly and that no outages occur. The network monitor software PRTG is inexpensive, flexible to use and easy to deploy.

Requirements for a Network Monitor

A good network monitor should be easy to install and usage should be intuitive so that there is no need for external consultancy and training. Further necessary requirements are:
  • Remote Management via web browser, PocketPC, or Windows client
  • Notifications on downtime by email, ICQ, pager/SMS, and more.
  • Comprehensive sensor type selection
  • Multiple location monitoring
  • All common methods for network usage data acquisition (SNMP, Packet Sniffing, Xflow) ought to be supported.

How to set up PRTG Network Monitor

With the various infrastructures in use today it can sometimes be difficult to decide which monitoring technology is right to solve your problem. The easiest way to configure network monitoring is to use PRTG's autodiscovery. PRTG then scans your network for devices in a given IP range and configures suitable sensor types based on device templates, such as MS Exchange or SQL Server. And of course, you can add or change sensors manually, too. You can find detailed setup instructions in our knowledge base and the manual for which click below on button "Click me for Support".





Reference: https://www.paessler.com/prtg
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Friday, November 17, 2017

What is Matlab find function?

What is Matlab find function?

Matlab Find function

This pretty small Matlab function perform different operation on the matrix in Matlab. The major purpose of Matlab Find is used to find indices and values of nonzero elements. Furthermore, the programmer uses these techniques to solve different complex operation.

Syntax use for find

k = find(X)
k = find(X,n)
k = find(X,n,direction)
[row,col] = find(....)
[row,col,v] = find(....)

Description


1) k = find(X) returns a vector containing the linear indices of each nonzero element in array X.
   If X is a vector, then find returns a vector with the same orientation as X.
   If X is a multidimensional array, then find returns a column vector of the linear indices of the result.
   If X contains no nonzero elements or is empty, then find returns an empty array.

2) k = find(X,n) returns the first n indices corresponding to the nonzero elements in X.

3) k = find(X,n,direction), where direction is 'last', finds the last n indices corresponding to nonzero elements in X. The default for direction is 'first', which finds the first n indices corresponding to nonzero elements.

4) [row,col] = find(....) returns the row and column subscripts of each nonzero element in array X using any of the input arguments in previous syntaxes

5) [row,col,v] = find(....) also returns vector v, which contains the nonzero elements of X.

Reference: https://www.mathworks.com

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Monday, November 6, 2017

Radix-2 FFT Matlab code decimation in frequency

Radix-2 FFT Matlab code decimation in frequency

Radix-2 FFT Matlab code Decimation in frequency

Hello, guys in this post I am going to show you the source code for Radix-2 fft Matlab decimation in frequency in Matlab simulation.
Copy the below code and paste it into edit window of the Matlab. You Input must be enclosed
in brackets "[ ]" E.g  [0 1 3 4 5] be sure you can choose any number.

% Code Page http://telecom-academy.blogspot.com
clc
clear
close all
disp('Please enclose your input in Bracket like [3 2 4 5 1]')
x=input('enter the sequence')
N=length(x)
s=log2(N)
for m=s-1:-1:0
for p=0:1:((2^m)-1)
for k=p:(2^(m+1)):N-1
b=x(k+1)
t=(exp(-pi*1i*p/(2^m)))
x(k+1)=b+x(k+(2^m)+1)
x(k+(2^m)+1)=(b-x(k+(2^m)+1))*t
end
end
end
y=bitrevorder(x)
Reference Blog: 
www.mathworks.com

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Thursday, November 2, 2017

Radix-4 FFT(Fast Fourier Transform) Algorithm

Radix-4 FFT Algorithm

When the number of data points N in the DFT is a power of 4 (i.e., N = 4v), we can, of course, always use a radix-2 algorithm for the computation. However, for this case, it is more efficient computationally to employ a radix-r FFT algorithm.
Let us begin by describing a radix-4 decimation-in-time FFT algorithm briefly. We split or decimate the N-point input sequence into four subsequences, x(4n), x(4n+1), x(4n+2), x(4n+3), n = 0, 1, ... , N/4-1.


Thus the four N/4-point DFTs F(l, q)obtained from the above equation are combined to yield the N-point DFT. The expression for combining the N/4-point DFTs defines a radix-4 decimation-in-time butterfly, which can be expressed in matrix form as
The radix-4 butterfly is depicted in Figure TC.3.9a and in a more compact form in Figure TC.3.9b. Note that each butterfly involves three complex multiplications, since WN0 = 1, and 12 complex additions.
Basic-butterfly-computation-in-a-radix-4-FFT-algorithm
Figure TC.3.9 Basic butterfly computation in a radix-4 FFT algorithm.
By performing the additions in two steps, it is possible to reduce the number of additions per butterfly from 12 to 8. This can be accomplished to expressing the matrix of the linear transformation mentioned previously as a product of two matrices as follows:
Sixteen-point-radix-4-decimation-in-time-algorithm
Figure TC.3.10 Sixteen-point radix-4 decimation-in-time algorithm with input in normal order and output in digit-reversed order
A 16-point, radix-4 decimation-in-frequency FFT algorithm is shown in Figure TC.3.11. Its input is in normal order and its output is in digit-reversed order. It has exactly the same computational complexity as the decimation-in-time radix-4 FFT algorithm.
Sixteen-point-radix-4-decimation-in-frequency-algorithm
Figure TC.3.11 Sixteen-point, radix-4 decimation-in-frequency algorithm with input in normal order and output in digit-reversed order.
For illustrative purposes, let us re-derive the radix-4 decimation-in-frequency algorithm by breaking the N-point DFT formula into four smaller DFTs. We have


From the definition of the twiddle factors, we have


The relation is not a N/4-point DFT because the twiddle factor depends on N and not on N/4. To convert it into an N/4-point DFT we subdivide the DFT sequence into four N/4-point subsequences, X(4k),X(4k+1), X(4k+2), and X(4k+3), k = 0, 1, ..., N/4. Thus we obtain the radix-4 decimation-in-frequency DFT as


where we have used the property WN4kn = WknN/4Note that the input to each N/4-point DFT is a linear combination of four signal samples scaled by a twiddle factor. This procedure is repeated v times, where v = log4N.

Reference Book: Digital Signal Processing Fourth Edition by John G. Proakis and Dimitris G. Manolakis

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Thursday, October 26, 2017

Sniff wireless network with Elcomsoft Wireless Security Auditor (EWSA)

Elcomsoft Wireless Security Auditor (EWSA):

In this post, we will learn about how to sniff wireless network with Elcomsoft Wireless Security Auditor(EWSA). But before we start how to sniff let me tell you about few feature of  Ewsa tool




  • Audit security of your wireless networks by attacking Wi-Fi passwords. 
  • Built-in Wi-Fi sniffer and GPU-accelerated recovery ensure the highest-performance attack on WPA/WPA2-PSK passwords. 
  • Elcomsoft Wireless Security Auditor (EWSA) supports dictionary attacks with an advanced variation facility. 
  • The built-in wireless sniffer supports general Wi-Fi adapters and AirPCap sticks. 
  • The tool can also accept standard tcpdump logs supported by any Wi-Fi sniffer.
  • Supporting both WPA and WPA2 security standards, Elcomsoft Wireless Security Auditor can audit all kinds of Wi-Fi networks by attempting to recover WPA-PSK (Pre-Shared Key) and WPA2-PSK passwords.
    • Main interface of EWSA

Built-in Wi-Fi Sniffer

Elcomsoft Wireless Security Auditor comes with a custom-built Wi-Fi sniffer that can work on ordinary Wi-Fi adapters via a custom NDIS driver (32-bit and 64-bit versions are supplied). AirPCap adapters are also supported. The built-in wireless sniffer intercepts the handshake packet required to start the attack. WinPCap drivers are required to enable Wi-Fi sniffing. Now how to let see step by step

Step 1) Select sniffer at upper left corner of the main interface.
ewsa-wifi-sniffer-type
Ewsa wifi sniffer type
Step 2) Now click on below option detect Networks.

detect-network-with-ewsa

Step 3) Now a bunch of available networks will appear. Now choose your favorite one and click on it to select. After selecting click on below button "Use Selected Channel".

wireless-security-auditor

That's it you can see in below image that Elcomsoft wireless security auditor (EWSA) start capturing packets and we capture 39 packets in 4 seconds.

Elcomsoft-packet-sniffer


Click Here To Download EWSA

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Tuesday, October 24, 2017

Introduction to Radix 2 FFT (Fast Fourier Transform ) algorithm

Radix 2 FFT(Fast Fourier Transform)

As we know that, the discrete Fourier transform (DFT) plays an important role in many applications of digital signal processing, including linear filtering, correlation analysis, and spectrum analysis. A major reason for its importance is the existence of efficient algorithms for computing the DFT. The main topic of this article is the description of computationally efficient algorithms for evaluating the DFT. Two different approaches are used for calculating FFT. One is a divide-and-conquer approach in which a DFT of size N, where N is a composite number, is reduced to the computation of smaller DFTs from which the larger DFT is computed. In particular, we present important computational algorithms, called fast Fourier transform (FFT) algorithms, for computing the DFT.

Let us consider the computation of the N = 2v point DFT by the divide-and-conquer approach. We split the N-point data sequence into two N/2-point data sequences f1(nand f2(n), corresponding to the even-numbered and odd-numbered samples of x(n), respectively, that is,
N/2-point data sequence
Thus f1(nand f2(n) are obtained by decimating x(n) by a factor of 2, and hence the resulting FFT(Fast Fourier Transform) algorithm is called a decimation-in-time algorithm.

TAG: View Matlab code for Radix-2 decimation in Frequency

Now the N-point DFT can be expressed in terms of the DFT's of the decimated sequences as follows:

But WN2 = WN/2With this substitution, the equation can be expressed as


where F1(kand F2(kare the N/2-point DFTof the sequences f1(mand f2(m), respectively. Since F1(kand F2(kare periodic, with period N/2, we have F1(k+N/2) = F1(k) and F2(k+N/2) = F2(k). In addition, the factor WNk+N/2 = -WNkHence the equation may be expressed as
We observe that the direct computation of F1(krequires (N/2)2 complex multiplications. The same applies to the computation of F2(k). Furthermore, there are N/2 additional complex multiplications required to compute WNkF2(k). Hence the computation of X(krequires 2(N/2)2 + N/2 = 2/2 + N/2 complex multiplications. This first step results in a reduction of the number of multiplications from N 2 to 2/2 + N/2, which is about a factor of 2 for N large.
First-step-in-the-decimation-in-time-algorithm
Figure TC.3.1 First step in the decimation-in-time algorithm
By computing N/4-point DFTs, we would obtain the N/2-point DFTF1(kand F2(k) from the relations
The decimation of the data sequence can be repeated again and again until the resulting sequences are reduced to one-point sequences. For N = 2vthis decimation can be performed v = log2N times. Thus the total number of complex multiplications is reduced to (N/2)log2NThe number of complex additions is Nlog2N.
For illustrative purposes, Figure TC.3.2 depicts the computation of N = 8 point DFT. We observe that the computation is performed in three stages, beginning with the computations of four two-point DFTs, then two four-point DFTs, and finally, one eight-point DFT. The combination for the smaller DFTs to form the larger DFT is illustrated in Figure TC.3.3 for N = 8.
Three-stages-in-the-computation-of-an-N=8-point-DFT
Figure TC.3.2 Three stages in the computation of an N = 8-point DFT
Eight-point-decimation-in-time-Fast-Fourier-Transform-algorithm
Figure TC.3.3 Eight-point decimation-in-time Fast Fourier Transform algorithm
butterfly-computation-in-the-decimation-in-time-FFT-algorithm
Figure TC.3.4 Basic butterfly computation in the decimation-in-time FFT algorithm
An important observation is concerned with the order of the input data sequence after it is decimated (v-1) times. For example, if we consider the case where N = 8, we know that the first decimation yields the sequence x(0), x(2), x(4), x(6), x(1), x(3), x(5), x(7), and the second decimation results in the sequence x(0), x(4), x(2), x(6), x(1), x(5), x(3), x(7). This shuffling of the input data sequence has a well-defined order as can be ascertained from observing Figure TC.3.5, which illustrates the decimation of the eight-point sequence.
Shuffling-of-the-data-and-bit-reversal
Figure TC.3.5 Shuffling of the data and bit reversal

Decimation in frequency FFT algorithm

Another important radix-2 FFT algorithm, called the decimation-in-frequency algorithm, is obtained by using the divide-and-conquer approach. To derive the algorithm, we begin by splitting the DFT formula into two summations, one of which involves the sum over the first N/2 data points and the second sum involves the last N/2 data points. Thus we obtain
fft-algorithm


Now, let us split (decimate) X(k) into the even- and odd-numbered samples. Thus we obtain
Radix-2-FFT-algorithm
where we have used the fact that WN2 = WN/2
The computational procedure above can be repeated through the decimation of the N/2-point DFTX(2kand X(2k+1). The entire process involves v = log2N stages of decimation, where each stage involves N/2 butterflies of the type shown in Figure TC.3.7. Consequently, the computation of the N-point DFT via the decimation-in-frequency FFT requires (N/2)log2complex multiplications and Nlog2N complex additions, just as in the decimation-in-time algorithm. For illustrative purposes, the eight-point decimation-in-frequency algorithm is given in Figure TC.3.8.
First stage of the decimation-in-frequency FFT algorithm
Figure TC.3.6 First stage of the decimation-in-frequency FFT algorithm
Basic-butterfly-computation-in-the-decimation-in-frequency
Figure TC.3.7 Basic butterfly computation in the decimation-in-frequency.
N=8-point-decimation-in-frequency-FFT-algorithm
Figure TC.3.8 N = 8-point decimation-in-frequency FFT algorithm
We observe from Figure TC.3.8 that the input data x(n) occurs in natural order, but the output DFT occurs in bit-reversed order. We also note that the computations are performed in place. However, it is possible to reconfigure the decimation-in-frequency algorithm so that the input sequence occurs in bit-reversed order while the output DFT occurs in normal order. Furthermore, if we abandon the requirement that the computations be done in place, it is also possible to have both the input data and the output DFT in normal order. That what all about Radix 2 FFT algorithm.

Reference Book: Digital Signal Processing Fourth Edition by John G. Proakis and Dimitris G. Manolakis

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