thmosqueiro

import numpy as np
from bisect import bisect_left

def binary_search( X, list_values ):

    listVals = np.array( list_values )

    nElem = listVals.shape[0]
    listPos = []

thmosqueiro

import time

key = True
intrval = 0.050

for j in range(10000):
        
    while key:
        now = ( time.time() % 1 ) 
        dif = intrval - now % intrval

thmosqueiro

import numpy as np


def smooth(x, window_len = 9, window = 'hanning', keepSameMax = False):
    """
    Smoothing algorithm especially designed to smooth the instantaneous firing
    rates calculated for Mushroom Bodies activity.
    """

    if not window in ['flat', 'hanning', 'hamming', \

thmosqueiro

## Downloading image
wget http://cdimage.ubuntu.com/releases/12.04/release/ubuntu-12.04-preinstalled-server-armhf+omap4.img.gz
gzip -d ubuntu-12.04-preinstalled-server-armhf+omap4.img.gz

## Checking where the disk is mounted
diskutil list
## Assuming disk mounted on disk1
sudo diskutil unmountDisk disk1

## Writing image with dd

thmosqueiro

#
# Simple Makefile for LaTeX projects
# Suggestions (pull requests) are welcome!!
# 
# 
# Simple example: suppose you want to compile the file paper.tex, then run
#
# make f=paper
# 
# Remember to drop the file extension (.tex, .aux, etc).

thmosqueiro

Pwn2Win CTF 2016: Timekeeper's Lock

This is the challenge.

In order to protect their maximum security facilities, the Club employs an electronic security lock activated by a 256-bit key which changes every minute. When a Club member is authorized to enter some of these facilities, he receives this key in hexadecimal format, the same format which is used to enter the key in the electronic lock’s keyboard. The last year (in 2015),

thmosqueiro

// Ping of the LED's positive leg
int ledPin=7;

int state = 0;
int count;
unsigned long previousMillis = 400L;
double timer;

unsigned long interval = 3L;
unsigned long delta;

thmosqueiro

import numpy as np
import pylab as pl
from polarError import polarError

f, ax = pl.subplots(1, 1, figsize=(4,1.8) )

bsize  = 0.5
theta  = np.array( [np.pi*5./4. - bsize, np.pi/2. - bsize, np.pi*7.5/4. - bsize, 0] )
data   = np.array( [0.7, 0.3, 0.52, 0.8] )
edata  = np.array( [0.2, 0.1, 0.5, 0.2] )

thmosqueiro

import numpy as np
import pylab as pl
from lmfit import minimize, Parameters

# Creating some data with some strong random factor
x = np.linspace(0, 10, 50)
y = (x-3)**2 + np.random.rand( 50 )*5

# Defining residuals using an asymmetric loss function
def residual(params, x, data, eps_data):

thmosqueiro

def convert( image, theta0 = 0. ):
    
    numChannels = image.shape[2]
    numPixelX = image.shape[0]
    numPixelY = image.shape[1]
    
    newImage = np.zeros( (numPixelX, numPixelY, 3) )
    
    normSum = np.zeros( (3) )