2 research outputs found
An algorithm for characterizing skin moles using image processing and machine learning
Melanoma, the most serious type of skin cancer, forms in cells (melanocytes) that produce melanin, the pigment that gives color to the skin. There are low-income regions that lack specialized dermatologists, causing skin cancer to be diagnosed in advanced stages. In Peru, in high Andean communities with low resources, the problem is aggravated by the high incidence of ultraviolet radiation and lack of medical resources to make the diagnosis. Normally, mole images are obtained from dermatoscopes. The present work seeks to use mole images obtained from smartphones to make the classification of them as suspected or not suspected of being melanoma, by means of a feature extraction algorithm. The first step is to make color and lighting corrections. After this, the image is segmented using the K-Means algorithm, and we obtain the areas of the mole and skin. With the segmented mole we proceed to extract the main visual characteristics and then use classification algorithms such as support vector machine (SVM), random forest and naïve bayes, which obtained an accuracy of 0.9473, 0.7368 and 0.6842, respectively. These results show that it is possible to use images obtained from smartphones to develop a classification algorithm with 94.73% accuracy to detect melanoma in skin moles
Time- and Amplitude-Controlled Power Noise Generator against SPA Attacks for FPGA-Based IoT Devices
Power noise generation for masking power traces is a powerful countermeasure against
Simple Power Analysis (SPA), and it has also been used against Differential Power Analysis (DPA) or
Correlation Power Analysis (CPA) in the case of cryptographic circuits. This technique makes use of
power consumption generators as basic modules, which are usually based on ring oscillators when
implemented on FPGAs. These modules can be used to generate power noise and to also extract
digital signatures through the power side channel for Intellectual Property (IP) protection purposes.
In this paper, a new power consumption generator, named Xored High Consuming Module (XHCM),
is proposed. XHCM improves, when compared to others proposals in the literature, the amount of
current consumption per LUT when implemented on FPGAs. Experimental results show that these
modules can achieve current increments in the range from 2.4 mA (with only 16 LUTs on Artix-7
devices with a power consumption density of 0.75 mW/LUT when using a single HCM) to 11.1 mA
(with 67 LUTs when using 8 XHCMs, with a power consumption density of 0.83 mW/LUT). Moreover,
a version controlled by Pulse-Width Modulation (PWM) has been developed, named PWM-XHCM,
which is, as XHCM, suitable for power watermarking. In order to build countermeasures against
SPA attacks, a multi-level XHCM (ML-XHCM) is also presented, which is capable of generating
different power consumption levels with minimal area overhead (27 six-input LUTS for generating
16 different amplitude levels on Artix-7 devices). Finally, a randomized version, named RML-XHCM,
has also been developed using two True Random Number Generators (TRNGs) to generate current
consumption peaks with random amplitudes at random times. RML-XHCM requires less than
150 LUTs on Artix-7 devices. Taking into account these characteristics, two main contributions
have been carried out in this article: first, XHCM and PWM-XHCM provide an efficient power
consumption generator for extracting digital signatures through the power side channel, and on the
other hand, ML-XHCM and RML-XHCM are powerful tools for the protection of processing units
against SPA attacks in IoT devices implemented on FPGAs.Junta de AndaluciaEuropean Commission B-TIC-588-UGR2