Yazar "Yavuz, Erdem" seçeneğine göre listele

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    A cepstrum analysis-based classification method for hand movement surface EMG signals
    (Springer Heidelberg, 2019) Yavuz, Erdem; Eyupoglu, Can
    It is of great importance to effectively process and interpret surface electromyogram (sEMG) signals to actuate a robotic and prosthetic exoskeleton hand needed by hand amputees. In this paper, we have proposed a cepstrum analysis-based method for classification of basic hand movement sEMG signals. Cepstral analysis technique primarily used for analyzing acoustic and seismological signals is effectively exploited to extract features of time-domain sEMG signals by computing mel-frequency cepstral coefficients (MFCCs). The extracted feature vector consisting of MFCCs is then forwarded to feed a generalized regression neural network (GRNN) so as to classify basic hand movements. The proposed method has been tested on sEMG for Basic Hand movements Data Set and achieved an average accuracy rate of 99.34% for the five individual subjects and an overall mean accuracy rate of 99.23% for the collective (mixed) dataset. The experimental results demonstrate that the proposed method surpasses most of the previous studies in point of classification accuracy. Discrimination ability of the cepstral features exploited in this study is quantified using Kruskal-Wallis statistical test. Evidenced by the experimental results, this study explores and establishes applicability and efficacy of cepstrum-based features in classifying sEMG signals of hand movements. Owing to the non-iterative training nature of the artificial neural network type adopted in the study, the proposed method does not demand much time to build up the model in the training phase.
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    An effective approach for breast cancer diagnosis based on routine blood analysis features
    (Springer Heidelberg, 2020) Yavuz, Erdem; Eyupoglu, Can
    Breast cancer is a widespread disease and one of the primary causes of cancer mortality among women all over the world. Computer-aided methods are used to assist medical doctors to make early diagnosis of the disease. The aim of this study is to build an effective prediction model for breast cancer diagnosis based on anthropometric data and parameters collected through routine blood analysis. The proposed approach innovatively exploits principal component analysis (PCA) technique cascaded by median filtering so as to transform original features into a form of containing less distractive noise not to cause overfitting. Since a generalized regression neural network (GRNN) model is adopted to classify patterns of the transformed features, the computational load imposed in the training of artificial neural network model is kept minimized thanks to the non-iterative nature of GRNN training. The proposed method has been devised and tested on the recent Breast Cancer Coimbra Dataset (BCCD) that contains 9 clinical features measured for each of 116 subjects. Outperforming all of the existing studies on BCCD, our method achieved a mean accuracy rate of 0.9773. Experimental results evidence that this study achieves the best prediction performance ever reported on this dataset. The fact that our proposed approach has accomplished such a boosted performance of breast cancer diagnosis based on routine blood analysis features offers a great potential to be used in a widespread manner to detect the disease in its inception phase. Graphical abstract
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    An Electronic Control Unit for Erythrocyte Sedimentation Rate Test Device
    (Institute of Electrical and Electronics Engineers Inc., 2020) Sanver, U.; Yavuz, Erdem
    Erythrocyte sedimentation rate test device, which is expensive as a commercial product, is a commonly used tools in the biomedical field. In this study, a low-cost and effective electronic control unit for erythrocyte sedimentation device was designed and implemented. This microcontroller-based control system manages the circular tray, where blood tubes stay on, for measuring sedimentation level using image processing technique. In order to manage the tray, control unit drives a stepper motor via switching components. Necessary information was transferred to computer via serial port communication in order to display to users. The position of circular tray is sensed for aligning camera and tubes. The measurement number data are taken to microcontroller from RFID Cards using RFID Transceiver. The unit controls also the amount of ambient light in the measurement area. The temperature level of device is measured using temperature sensor and step down by switching on a fan when necessary. © 2020 IEEE.
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    Improving Initial Flattening of Convex-Shaped Free-Form Mesh Surface Patches Using a Dynamic Virtual Boundary
    (C R L Publishing Ltd, 2019) Yavuz, Erdem; Yazici, Rifat; Kasapbasi, Mustafa Cem; Bilgin, Turgay Tugay
    This study proposes an efficient algorithm for improving flattening result of triangular mesh surface patches having a convex shape. The proposed approach, based on barycentric mapping technique, incorporates a dynamic virtual boundary, which considerably improves initial mapping result. The dynamic virtual boundary approach is utilized to reduce the distortions for the triangles near the boundary caused by the nature of convex combination technique. Mapping results of the proposed algorithm and the base technique are compared by area and shape accuracy metrics measured for several sample surfaces. The results prove the success of the proposed approach with respect to the base method.
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    Meme Kanseri Teşhisi İçin Yeni Bir Skor Füzyon Yaklaşımı
    (2019) Yavuz, Erdem; Eyüpoğlu, Can
    Meme kanseri tüm dünyada yaygın bir hastalık olması sebebiyle hastalığın erken teşhisi, hastaların bu hastalıktan tamamen kurtulabilmeleri açısından kritik öneme sahiptir. Hastalığın teşhisini kolaylaştırmak için tıp doktorları bilgisayar destekli uzman sistemlerden yararlanabilmektedir. Bu çalışmada meme kanseri veri örneklerini iyi huylu veya kötü huylu sınıflarına ayırmak için genel regresyon sinir ağı (Generalized Regression Neural Network-GRNN) ve ileri beslemeli sinir ağı (Feed Forward Neural Network-FFNN) temelli bir skor füzyon yöntemi önerilmiştir. Önerilen yöntem Wisconsin Teşhis Meme Kanseri (Wisconsin Diagnostic Breast Cancer-WDBC) veri seti üzerinde test edilmiştir. Bu iki temel ağın ve önerilen yöntemin kullanışlılığı incelenmiş ve performans sonuçları karşılaştırmalı olarak sunulmuştur. Önerilen yöntem sınıflandırma doğruluğu bakımından literatürde WDBC veri setini kullanarak yapılan mevcut çalışmalar ile kıyaslanmıştır. Elde edilen deneysel sonuçlar önerilen yöntemin, meme kanseri teşhisi için umut vadettiğini ve tıp uzmanlarının hastalığa ilişkin karar vermelerinde yardımcı bir araç olarak kullanılabileceğini göstermektedir.
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    A new parallel processing architecture for accelerating image encryption based on chaos
    (Elsevier Ltd, 2021) Yavuz, Erdem
    This study introduces a novel parallel processing architecture for accelerating image encryption based on chaos. In the proposed architecture, whole image data is split into partitions of particular size to create separate encryption threads. As the proposed cryptosystem employs several identical chaotic ciphers running concurrently and independently to process the partitions, it greatly leverages the degree of parallelism to some extent. A powerful output mixing logic based on an additional chaotic function, and simple exclusive-OR and shift operations is innovatively incorporated to ensure inter-partition diffusion. Since there is no dependency on previous data bytes in the introduced logic, blending operations applied on the outputs of independent encryption threads can be concurrently executed by exploiting loop-level parallelism to the extent allowed by data processing units available. The number of blending operations that should be carried out for an image is kept proportional to the partition size which also directly determines the number of separate encryption threads created. In order to measure encryption/decryption runtimes, the proposed architecture has been tested on two different multi-core CPUs, namely 4-core and 8-core. The obtained results show that the proposed cryptosystem parallelising sequential operations by introducing a multi-threaded encryption architecture is much faster than the base cipher and most of the other state-of-the-art algorithms. Having successfully passed various security tests, the proposed cryptosystem manifests its robustness against cryptographic attacks, and hence become evident that it is efficient for secure transmission.