Musa Ataş

Aflatoxin contaminated chili pepper detection by hyperspectral imaging and machine learning

Mycotoxins are toxic secondary metabolites produced by fungi. They have been demonstrated to cause various health problems in humans, including immunosuppression and cancer. A class of mycotoxins, aflatoxins, has been studied extensively because they have caused many deaths particularly in developing countries. Chili pepper is also prone to aflatoxin contamination during harvesting, production and storage periods. Chemical methods to detect aflatoxins are quite accurate but expensive and destructive in nature. Hyperspectral and multispectral imaging are becoming increasingly important for rapid and nondestructive testing for the presence of such contaminants. We propose a compact machine vision system based on hyperspectral imaging and machine learning for detection of aflatoxin contaminated chili peppers. We used the difference images of consecutive spectral bands along with individual band energies to classify chili peppers into aflatoxin contaminated and uncontaminated classes. Both UV and halogen illumination sources were used in the experiments. The significant bands that provide better discrimination were selected based on their neural network connection weights. Higher classification rates were achieved with fewer numbers of spectral bands. This selection scheme was compared with an information-theoretic approach and it demonstrated robust performance with higher classification accuracy.

Software technologies, architectures and interoperability in remote laboratories

A remote laboratory aims to provide access to remote lab equipment over the internet. The design and development of the client software is subject to various requirements. In this paper we discuss these requirements and propose solutions. Implementations are demonstrated for some remote experiments developed in the ERRL project. An XML format for experimental results is proposed as an attempt towards standardization of remote laboratory interfaces and enhancing the interoperability of diverse remote laboratory applications.

Classification of aflatoxin contaminated chili pepper using hyperspectral imaging and artificial neural networks

Many foods (such as hazelnut, pistachio nut, almond, corn, wheat, dried fig, and chili pepper) may include carcinogenic aflatoxins that threatens human health. Chili pepper is also prone to aflatoxin contamination during harvesting, production and storage periods. Although Turkey is the third largest chili pepper producer in the world, it has less than three percent international market share due to the high level of aflatoxin contamination in the chili pepper. Various chemical methods are used for detection of aflatoxin. Chemical methods used for detection of aflatoxin contamination give accurate results, but they are slow, expensive and destructive. In this study, intensity histograms of hyper spectral images of chili peppers are extracted under halogen illumination source and aflatoxin detection is made by artificial neural networks.

Hand Tremor Based Biometric Recognition Using Leap Motion Device

In this paper, the applicability of hand tremor-based biometric recognition via leap motion device is investigated. The hypothesis is that the hand tremor is unique for humans and can be utilized as a biometric identification. In order to verify our hypothesis, spatiotemporal hand tremor signals are acquired from subjects. The objective is to establish a live and secure identification system to avoid mimic and cloning of password by attackers. Various feature extraction methods, including statistical, fast Fourier transform, discrete wavelet transform, and 1-D local binary pattern are used. For evaluating recognition performance, Naïve Bayes and Multi-Layer Perceptron are utilized as linear-simple and nonlinear-complex classifiers, respectively. Since the conducted experiments produced promising results (above 95% of classification accuracy rate), it is considered that the proposed approach has the potential to be used as a new biometric identification manner in the field of security.

Open Cezeri Library: A novel java based matrix and computer vision framework

In this paper we introduce the Open Cezeri Library (OCL) framework as a domain specific language (DSL) for researchers, scientists, and engineering students to enable them to develop basic linear algebra operations via simple matrix calculations, image processing, computer vision, and machine learning applications in JAVA programming language. OCL provides a strong intuition of coding for the developer while implementing by means of a fluent interface. The significant aspect of the OCL is to combine the methods of well‐known platforms; MATLAB and JAVA, accordingly. Moreover, OCL supports a fluent interface so that users can extend a single line of codes by putting a dot between the methods because all the methods implemented actually return the host class. It was observed that the learning curve of the OCL is lower than the MATLAB and the native JAVA languages, and makes coding more readable, understandable, traceable, and enjoyable. In addition to this, the experiments revealed that the running performance of the OCL is quite comparable and can be used in a variety of diverse applications.

Chess playing robotic arm

In this study, a chess playing robotic arm system which has 5 degree of freedoms is developed. System comprised with various modules such as; main controller, image processing, machine learning, game engine and motion engine of robotic arm. Image processing unit is triggered only whenever opponent starts to move chessman. Meanwhile, images acquired in a specific time intervals are transmitted to the machine learning unit for classification purpose. After the classification process is taken place, opponent valid move is sent to the game engine as an input in order to generate reasonable output. Generated output is forwarded to the motion engine for positioning the robotic arm. It was observed that, developed system provides an efficient, favorable and immersive experience for player.

Classification of power quality disturbances based on S-transform and image processing techniques

This paper presents a method that combines discrete S-transform (DST) time-frequency distribution (TFD) and local binary pattern (LBP) based image analysis technique for classifying power quality (PQ) disturbances. The purpose of this combination is to extract discriminative features by utilizing from both capability of generating the compact TFD of a non-stationary signal and the efficient image representation capability of LBP. In the proposed method, DST based TFDs of PQ disturbance signals are considered as 2-D images. LBP histograms are used to extract the features from TF images. Initially, the uniform patterns in TF images are obtained by the LBP operator. Next, the occurrence histograms of these patterns are used to produce representative feature vectors that can capture the unique and salient characteristics of each disturbance. The classification performance of the proposed method is evaluated through total 2400 disturbance signals. The experimental results have shown that the rate of correct classification is about 98 % for the different PQ disturbances.

Classification of Turkish spam e-mails with artificial immune system

In this study, it is aimed to detect frequently encountered spam e-mails with artificial immune algorithms. Turkish spam and non-spam e-mail dataset are generated within the scope of the work. Fisher discriminant analysis (FDA) and Euclidean Distance (ED) are utilized in order to extract features from the turkish email dataset. In order to evaluate the classification accuracies, artificial immune algorithms with Bayes as a linear and artificial neural network as a non-linear classifiers are used. Various artificial immune algorithms, including AIRS1, AIRS2, AIRS2PARALLEL, CLONALG and CSCA are investigated. Among them, CSCA reveals the best classification accuracy of 86%. Furthermore, CSCA algorithm classifies spam emails with 81% and non-spam e-mails with 90% accuracies.

Adaptive High Dynamic Range

Investigating High Dynamic Range (HDR) approaches in the literature, a new and adaptive HDR model is developed in this study. HDR is processed based on images taken as a Low Dynamic Range (LDR) scheme that ranges between low exposure and high exposure values. Here, main focus is to present and to interpret challenging scenes or cases without having information loss by extending intensity ranges of a camera. Images, converting to HDR from LDR actually have a good satisfaction with respect to the information content, yet they are subject to effect which may deteriorate their natural quality. With respect to end user view, still it is hard to say HDR images satisfy photo-realistic characteristics. In this study, it is focused on information gain without detriment natural characteristics of the picture and thus a new HDR algorithm was developed. Proposed HDR method was crosschecked with famous methods used in the literature with regard to both photo-realistic picture quality, usability and computational cost criteria. It was observed that proposed method preferable over so called traditional algorithms and located in the bunch of first three methods by applying poll with 30 test subjects.

Prediction of adaptive exposure time in hyperspectral bands for industrial cameras

In this study, a new method for exposure time correction for hyperspectral imaging is introduced. Initially, hardware setup was established. Then, a look-up table holds the minimum and maximum exposure times for each band was built. By using the developed image acquisition system, images having different exposure times for each hyperspectral band were acquired. After that, various features that can represent the exposure state were identified and a dataset was established. Prediction performance of the proposed method was cross validated by artificial neural network and outcomes were interpreted. It is observed that, by using the proposed method desired exposure quality can be determined with 99% accuracy.