Resolution invariant wavelet features of melanoma studied by SVM classifiers

This article refers to the Computer Aided Diagnosis of the melanoma skin cancer. We derive wavelet-based features of melanoma from the dermoscopic images of pigmental skin lesions and apply binary C-SVM classifiers to discriminate malignant melanoma from dysplastic nevus. The aim of this research is to select the most efficient model of the SVM classifier for various image resolutions and to search for the best resolution-invariant wavelet bases. We show AUC as a function of the wavelet number and SVM kernels optimized by the Bayesian search for two independent data sets. Our results are compatible with the previous experiments to discriminate melanoma in dermoscopy images with ensembling and feed-forward neural networks

Entropy of never born protein sequences

BACKGROUND: A Never Born protein is a theoretical protein which does not occur in nature. The reason why some proteins were selected and some were not during evolution is not known. We applied information theory to find similarities and differences in information content in Never Born and natural proteins. FINDINGS: Both block and relative entropies are similar what means that both protein kinds contain strongly random sequences. An artificially generated Never Born protein sequence is closely as random as a natural one. CONCLUSIONS: Information theory approach suggests that protein selection during evolution was rather random/non-deterministic. Natural proteins have no noticeable unique features in information theory sense. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/2193-1801-2-200) contains supplementary material, which is available to authorized users

Concurrent real-time optimization of detecting unexpected tasks in IoT design process using GA

In this paper we present a genetic algorithm for concurrent real-time optimization for detecting unexpected tasks in IoT design process. The process can be split into two phases which impact each other in real-time. Modification of one phase modifies the second one. The algorithm detects unexpected tasks as a connection of parts of predicted tasks. Therefore, there can exist more than one way to resolve the unexpected situation. The algorithm searches for optimal solution making concurrent optimization of two phases of the design process. Use of genetic algorithm helps also to eliminate connections of subtasks which do not give a good result

Segmentation of the melanoma lesion and its border

Segmentation of the border of the human pigmented lesions has a direct impact on the diagnosis of malignant melanoma. In this work, we examine performance of (i) morphological segmentation of a pigmented lesion by region growing with the adaptive threshold and density-based DBSCAN clustering algorithm, and (ii) morphological segmentation of the pigmented lesion border by region growing of the lesion and the background skin. Research tasks (i) and (ii) are evaluated by a human expert and tested on two data sets, A and B, of different origins, resolution, and image quality. The preprocessing step consists of removing the black frame around the lesion and reducing noise and artifacts. The halo is removed by cutting out the dark circular region and filling it with an average skin color. Noise is reduced by a family of Gaussian filters 3×3−7×7 to improve the contrast and smooth out possible distortions. Some other filters are also tested. Artifacts like dark thick hair or ruler/ink markers are removed from the images by using the DullRazor closing images for all RGB colors for a hair brightness threshold below a value of 25 or, alternatively, by the BTH transform. For the segmentation, JFIF luminance representation is used. In the analysis (i), out of each dermoscopy image, a lesion segmentation mask is produced. For the region growing we get a sensitivity of 0.92/0.85, a precision of 0.98/0.91, and a border error of 0.08/0.15 for data sets A/B, respectively. For the density-based DBSCAN algorithm, we get a sensitivity of 0.91/0.89, a precision of 0.95/0.93, and a border error of 0.09/0.12 for data sets A/B, respectively. In the analysis (ii), out of each dermoscopy image, a series of lesion, background, and border segmentation images are derived. We get a sensitivity of about 0.89, a specificity of 0.94 and an accuracy of 0.91 for data set A, and a sensitivity of about 0.85, specificity of 0.91 and an accuracy of 0.89 for data set B. Our analyses show that the improved methods of region growing and density-based clustering performed after proper preprocessing may be good tools for the computer-aided melanoma diagnosis

Car make and model recognition system using rear-lamp features and convolutional neural networks

Recognizing cars based on their features is a difficult task. We propose a solution that uses a convolutional neural network (CNN) and image binarization method for car make and model classification. Unlike many previous works in this area, we use a feature extraction method combined with a binarization method. In the first stage of the pre-processing part we normalize and change the size of an image. The image is then used to recognize where the rear-lamps are placed on the image. We extract the region and use the image binarization method. The binarized image is used as input to the CNN network that finds the features of a specific car model. We have tested the combinations of three different neural network architectures and eight binarization methods. The convolutional neural network with parameters of the highest quality metrics value is used to find the characteristics of the rear lamps on the binary image. The convolutional network is tested with four different gradient algorithms. We have tested the method on two data sets which differ in the way the images were taken. Each data set consists of three subsets of the same car, but is scaled to different image dimensions. Compared to related works that are based on CNN, we use rear view images in different position and light exposure. The proposed method gives better results compared to most available methods. It is also less complex, and faster to train compared to other methods. The proposed approach achieves an average accuracy of 93,9% on the first data set and 84,5% on the second set

Melanocytic globules detection in skin lesion images

In this paper a method is presented for detection of melanin globules often present in melanocytic skin lesions images. The detection is done by performing image analysis similar to the one used in clinical evaluation. The method uses multi-stage image filtering to extract objects present in the dermoscopic image that match globule structure pattern. Classification of the found objects is made based on shape and size of globule structure. The classification is problematic task due to color and scale differences between dermatologic images and is related to differences between image acquisition equipment used in dermatoscopy. First we describe characteristic of globule structure needed for correct classification, along with method for calculating those characteristic. Next we presented a method for globules detection that is a part of computer-aided diagnostic process of melanocytic skin lesions. Evaluation of such lesions is a basis for early detection of malignant lesions

Compact extensible authentication protocol for the internet of things : enabling scalable and efficient security commissioning

Internet of Things security is one of the most challenging parts of the domain. Combining strong cryptography and lifelong security with highly constrained devices under conditions of limited energy consumption and no maintenance time is extremely difficult task. This paper presents an approach that combines authentication and bootstrapping protocol (TEPANOM) with Extensible Authentication Protocol (EAP) framework optimized for the IEEE 802.15.4 networks. The solution achieves significant reduction of network resource usage. Additionally, by application of EAP header compacting approach, further network usage savings have been reached. The EAP-TEPANOM solution has achieved substantial reduction of 42% in the number of transferred packets and 35% reduction of the transferred data. By application of EAP header compaction, it has been possible to achieve up to 80% smaller EAP header. That comprises further reduction of transferred data for 3.84% for the EAP-TEPANOM method and 10% for the EAP-TLS-ECDSA based methods. The results have placed the EAP-TEPANOM method as one of the most lightweight EAP methods from ones that have been tested throughout this research, making it feasible for large scale deployments scenarios of IoT

A Quantum Convolutional Neural Network for Image Classification

Artificial neural networks have achieved great success in many fields ranging from image recognition to video understanding. However, its high requirements for computing and memory resources have limited further development on processing big data with high dimensions. In recent years, advances in quantum computing show that building neural networks on quantum processors is a potential solution to this problem. In this paper, we propose a novel neural network model named Quantum Convolutional Neural Network (QCNN), aiming at utilizing the computing power of quantum systems to accelerate classical machine learning tasks. The designed QCNN is based on implementable quantum circuits and has a similar structure as classical convolutional neural networks. Numerical simulation results on the MNIST dataset demonstrate the effectiveness of our model.Comment: 7 pages, 7 figure

Enhancing thermoelectric properties of single-walled carbon nanotubes using halide compounds at room temperature and above.

Carbon nanotubes (CNTs) are materials with exceptional electrical, thermal, mechanical, and optical properties. Ever since it was demonstrated that they also possess interesting thermoelectric properties, they have been considered a promising solution for thermal energy harvesting. In this study, we present a simple method to enhance their performance. For this purpose, thin films obtained from high-quality single-walled CNTs (SWCNTs) were doped with a spectrum of inorganic and organic halide compounds. We studied how incorporating various halide species affects the electrical conductivity, the Seebeck coefficient, and the Power Factor. Since thermoelectric devices operate under non-ambient conditions, we also evaluated these materials' performance at elevated temperatures. Our research shows that appropriate dopant selection can result in almost fivefold improvement to the Power Factor compared to the pristine material. We also demonstrate that the chemical potential of the starting CNT network determines its properties, which is important for deciphering the true impact of chemical and physical functionalization of such ensembles