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3. Optimising Yield Estimation for Grapes: Utilising the Sliding Window Technique for Visual Counting of Bunches and Berries.

Author

Falih, Bashar S., Ali, Yaseen Hadi, Alabbas, Amjed Razzaq, and Arica, Sami

Subjects
BERRIES, K-means clustering, GRAPE yields, COUNTING, FRUIT
Abstract

Grapes are a highly significant fruit with the highest production rates, which are consistently increasing annually. Although traditional methods are employed in agriculture for spraying, harvesting, and estimating yield based on workers, these techniques are time-consuming, costly, and inaccurate. Hence, alternative technological options are necessary to address these challenges. This study proposed a method for autonomously detecting and counting visible grapes using a Red, Green and Blue (RGB) sensor in a robotic system for two datasets (grape size and colour). The k-means clustering was utilised to differentiate grape pixels from the input image, and the required cluster was automatically selected. This proposed model also incorporated Histogram-Oriented Gradients (HOG) to compute shape information as a feature. The classifier was then trained to identify grape berries in each window by carefully examining the feature shape by sliding an auto-sized window over a segmented image. Consequently, grape counting yielded an average accuracy of 83%, with red grapes achieving 92% accuracy and white grapes 76%. On the other hand, bunch counting showed higher accuracy, averaging at 94%, with red grapes at 96% accuracy and white grapes at 90%. The reliability of the method in detecting grapes and grape bunches regardless of colour is underlined by these results, which also accentuates its applicability in various counting tasks. [ABSTRACT FROM AUTHOR]

Published
2024
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11. Automatic counting of grapes from vineyard images.

Author

Al-Saffar, Bashar, Arica, Sami, and Tangolar, Semih

Subjects
*BERRIES, *CONVOLUTIONAL neural networks, *GRAPES, *VINEYARDS, *HARVESTING
Abstract

Harvesting, spraying, and yield estimation are difficult activities for farmers. They take time, many workers, and moreover, are not always accurate. Therefore, machines are required to ease and speed up harvesting, spraying, and yield estimation. In this study, automatic recognition of visible grape berries and bunches from Red, Green, and Blue (RGB) images acquired by a camera for harvesting, spraying machines, and yield estimation was investigated. The images of grapes of different sizes and colors were taken under divergent natural light conditions and contrasts. The freely available Iceland dataset containing white grapes and in addition, images of red white, and hybrid types of grape trees were picked and used in the study. Initially, the Local Binary Pattern (LBP), Histogram of Oriented Gradients (HOG) were extracted, individually and their combination were used as feature vectors. Next, the features obtained were categorized with Convolution Neural Network (CNN), Artificial Neural Network (ANN), and Support-Vector-Machine (SVM) separately. The samples of grape berry images in the Iceland dataset were employed to train the ANN and SVM classifiers. Finally, the grape bunches were detected by incorporating Density Based Spatial Clustering of Applications with Noise (DBSCAN) clustering method. The artificial neural network classifier with the combined features provided the best accuracy in single berry recognition. It is faster than SVM and CNN as well. The average accuracy, precision, and recall were 99.6%, 99.7%, and 99.5% respectively. The accuracies of grape berry and bunch detection from test images were obtained as 89.8% and 91.7% respectively. Results show that LPB+HOG as a feature with ANN as a classifier provide an efficient grape detection from images taken under variant natural illumination conditions. [ABSTRACT FROM AUTHOR]

Published
2022
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18. ELECTROMYOGRAPHIC CHARACTERISTICS OF THE QUADRICEPS FEMORIS DURING PERFORMANCE OF THE WINGATE TEST

Author

Aslankeser, Zubeyde, Arica, Sami, and Çukurova Üniversitesi

Subjects
EMG, Mean Power Frequencies, musculoskeletal system, Wingate Anaerobic Test
Abstract

WOS: 000409261000004 The purpose of this study was to investigate the relationship between the Wingate Anaerobic Test (WAT) outputs and the Electromyography (EMG) parameters. Seventeen sedentary college males participated in the study (mean +/- SD, age 20.5 +/- 2.4 y; height 174.2 +/- 4.3 cm; body mass 66.2 +/- 7.6 kg). Surface electromyographic signals of vastus medialis, vastus lateralis and rektus femoris were recorded during WAT. Power, normalized power, cadence, Mean power frequency (MPF) of each muscle and Root Mean Square (RMS) EMG were calculated as 5 s averages. Mean differences in power and cadence, mean EMG frequency and RMS EMG were analyzed by repeated measures one way ANOVA with Bonferroni post hoc adjustment for multiple pairwise comparisons. Pearson's correlation coefficient was used to evaluate the relationship between the WAT performance variables and muscle EMG outputs All data are presented as mean +/- SD. The peak power and cadence decreased significantly (p0.01). There was a correlation between peak power, normalized power, cadence and MPF of the quadriceps muscles also. The results suggest that there was a correlation between mean power frequencies of vastus medialis, vastus lateralis, rectus femoris and WAT performance. The decreases of the peak power and cadence should be related to the decreases of the mean frequencies of the quadriceps muscles.

Published
2017

24. Prediction of pharmacologically induced baroreflex sensitivity from noninvasive baroreflex, heart rate and systolic blood pressure variability indices

Author

Arica, Sami, Ince, N. Firat, Birand, Ahmet, Bozkurt, Abdi, Tewfik, Ahmed, and Çukurova Üniversitesi

Subjects
fungi
Abstract

IEEE 17th Signal Processing and Communications Applications Conference -- APR 09-11, 2009 -- Antalya, TURKEY WOS: 000273935600046 Assessment of baroreflex sensitivity (BRS) using vasoactive drugs is widely accepted in clinical research. It has been reported that decrease in BRS is an indicative of increased risk of sudden cardiac death. Due to its clinical importance, alternative baroreflex assessment methods have been developed which eliminate use of drugs and since then they are called non-invasive methods. In this study BRS index obtained by drug based assessment was predicted from subset of noninvasive BRS indices extracted from heart rate and systolic pressure time series. A leave one out method was employed to search subset of indices which gives the highest correlation with invasive and predicted BRS. Two predictors provided the highest correlation (0.87). The algorithm selected consistently normalized cross-power in Mayer frequency band and average magnitude square coherence in high frequency band as predictors for all 16 subjects. IEEE

Published
2009

26. Classification of motor imagery EEG recordings with subject specific time-frequency patterns

Author

Ince, Nuri Firat, Arica, Sami, Tewfik, Ahmed, and Çukurova Üniversitesi

Abstract

IEEE 14th Signal Processing and Communications Applications -- APR 16-19, 2006 -- Antalya, TURKEY WOS: 000245347800137 We introduce an adaptive time-frequency plane feature extraction and classification system for the classification of motor imagery EEG recordings in a Brain Computer Interface task. First the EEG is segmented in time axis with a merge/divide strategy. This is followed by a clustering procedure in the frequency domain in each selected time segment to choose the most discriminant frequency features. The resulting adaptively selected time-frequency features are processed by principal component analysis - PCA for dimension reduction and fed to a linear discriminant classifier. The algorithm was applied to all nine subjects of the 2002 BCI Competition. The classification performance of our proposed algorithm varied between 70% and 92.6% for each subject, which gives an average classification accuracy of 80.6%. The algorithm outperformed the reference standard Adaptive Autoregressive model based classification procedure for all subjects. This latter approach had an average error rate of %76.3 on the same subjects. We observed that the time-frequency tiling selected by the algorithm for EEG signal classification differs from subject to subject. Furthermore, the two hemispheres of the same subject are represented by distinct time-frequency segmentations and features. We argue that the method can adapt automatically to physio-anatomical differences and subject specific motor imagery patterns. IEEE

Published
2006

27. On The Classification of Hand Movements with Electromyogram Signals Obtained From Arm Muscles for Controlling Hand Prosthesis.

Author

ARICA, Sami, KIAN ARA, Rouhollah, and ÖZGÜNEN, Kerem Tuncay

Subjects
*ELECTROMYOGRAPHY, *ARM muscles, *ARTIFICIAL hands, *SIGNAL processing, *SUPPORT vector machines, *WRIST flexion
Abstract

The aim of the study is to generate control signals from surface Electromyography signals (EMGs) measured from four hand muscles; Extensor carpi radialis, Palmaris longus, Pronator quadratus and Flexor digitorum superficialis to navigate a prosthetic hand. The EMGs for five hand movements; finger flexion, wrist flexion, wrist extension, pronation, supination have been acquired. The right hand and left hand data recorded from two males and two females. The features have been computed from the windowed EMG of a 0.512 second interval. From each muscle, root mean square value, mean frequency and peak frequency are employed as features. These features and their pairwise combinations have been classified with support vector machine. The classifications have been done for two scenarios: 1. For each subject the right (left) hand movement is classified from the right (left) arm EMG data. 2. The left (right) hand movement of a subject is classified from the right (left) arm EMG data of the same subject. The average right-hand success of the classification was 82.0%, while the left-hand categorization was 83.5%. Interestingly, the left-hand versus right-hand and the righthand versus left-hand classification success was obtained 65.7%. [ABSTRACT FROM AUTHOR]

Published
2017

28. Predicting tumor recurrence in patients with cervical carcinoma treated with definitive chemoradiotherapy: value of quantitative histogram analysis on diffusion-weighted MR images.

Author

Erbay, Gurcan, Onal, Cem, Karadeli, Elif, Guler, Ozan C., Arica, Sami, and Koc, Zafer

Subjects
CANCER relapse, CERVICAL cancer treatment, CHEMORADIOTHERAPY, CERVICAL cancer patients, HEALTH outcome assessment, METASTASIS, CERVIX uteri, DIAGNOSTIC imaging, LONGITUDINAL method, MAGNETIC resonance imaging, COMPUTERS in medicine, PROGNOSIS, RESEARCH evaluation, CERVIX uteri tumors, TREATMENT effectiveness, RETROSPECTIVE studies, TUMOR treatment
Abstract

Background Further research is required for evaluating the use of ADC histogram analysis in more advanced stages of cervical cancer treated with definitive chemoradiotherapy (CRT). Purpose To investigate the utility of apparent diffusion coefficient (ADC) histogram derived from diffusion-weighted magnetic resonance images in cervical cancer patients treated with definitive CRT. Material and Methods The clinical and radiological data of 50 patients with histologically proven cervical squamous cell carcinoma treated with definitive CRT were retrospectively analyzed. The impact of clinicopathological factors and ADC histogram parameters on prognostic factors and treatment outcomes was assessed. Results The mean and median ADC values for the cohort were 1.043 ± 0.135 × 10-3 mm2/s and 1.018 × 10-3 mm2/s (range, 0.787-1.443 × 10-3 mm2/s). The mean ADC was significantly lower for patients with advanced stage (≥IIB) or lymph node metastasis compared with patients with stage

Published
2017
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29. The Best subbands which maximize coding gain with application to still images

Author

Arica, Sami, Çağlar, Hakan, and Diğer

Subjects
Subband coding, Elektrik ve Elektronik Mühendisliği, Coding technique, Data compression, Electrical and Electronics Engineering, Wavelet technique
Abstract

oz DOKTORA TEZİ THE BEST SUBBANDS WHICH MAXIMIZE CODING GAIN WITH APPLICATION TO STILL IMAGES SAMI ARICA ÇUKUROVA ÜNİVERSİTESİ FEN BİLİMLERİ ENSTİTÜSÜ ELEKTRİK-ELEKTRONİK ANABİLİM DALI Danışman : Doç. Dr. Hakan Çağlar Yıl: 1999, Sayfa: 107 Jüri : Doç. Dr. Hakan Çağlar Prof. Dr. Süleyman Güngör Yrd. Doç. Dr. Ulus Çevik Altband kodlama en etkin kayıplı veri sıkıştırma yöntemlerinden biridir. Kodlama performansım altband süzgeçlerim işaretin doğasına uygun olarak seçmekle artırmak mümkündür. Bu çalışmada önerilen alternatif bir metod da önce işareti bir istatistiksel model olan birinci dereceden autoregressive (AR(1)) modeline uyarlamak ve kodlama kazancını en iyi yapacak band oranlarını hesaplamaktır. Bu yöntem iki boyutlu (2D) işaretlere de yani durağan görüntülere de uygulan abilir. Görüntünün sütunları ve satırları bağımsız olarak kabul edilebilir ve AR(1) ile modellenir. Daha sonra sütunları ve satırları ayrı olarak alt bileşenlerine ayırmak için en iyi band oranlarım sağlayacak bir ağaç yapısı oluşturulur. En iyi band oranlan kodlama kazancım en büyük yaptığından altband kodlamanın başarımını bu ayrıştırma ile artırılmış olacaktır. Anahtar Kelimeler: Sıkıştırma, Süzgeç Çatısı, Altband Kodlama, Dal gacık. II ABSTRACT Ph.D. THESIS THE BEST SUBBANDS WHICH MAXIMIZE CODING GAIN WITH APPLICATION TO STILL IMAGES SAMI ARICA DEPARTMENT OF ELECTRICAL AND ELECTRONICS ENGINEERING INSTITUTE OF NATURAL AND APPLIED SCIENCES UNIVERSITY OF ÇUKUROVA Supervisor : Assoc. Prof. Dr. Hakan Çağlar Year: 1999, Pages: 107 Jury : Assoc. Prof. Dr. Hakan Çağlar Prof. Dr. Süleyman Güngör Assis. Prof. Dr. Ulus Çevik Subband coding is an efficient tool for lossy data compression. It. is possible to enhance its performance by changing subband filters adaptive to signal charac teristics. An alternative method which is proposed in this study is first to model signal by the first order autoregressive (AR(1)) statistical model and finding the best band fractions which maximize coding gain. A tree structure is employed to maintain the best band fractions. This method can also be applied to two dimensional (2D) signals like still images. Columns and rows of the image are considered to form an ensemble and the ensemble is modelled by AR(1). Later a tree is adapted to fit the best band fractions and columns and rows are de composed separately. Because the best band fractions maximize the coding gain subband coding performance is improved by the decomposition. Key Words: Compression, Filter Bank, Subband Coding, Wavelet. 107

Published
1999

31. Some investigations of including multiple sound channels in tv systems

Author

Arica, Sami, Balasubramanian, Karuppanan, Çukurova Üniversitesi, Fen Bilimleri Enstitüsü, Elektrik-Elektronik Mühendisliği Anabilim Dalı, and Diğer

Subjects
Modulation, Elektrik ve Elektronik Mühendisliği, Voice, Video, Television receivers, Electrical and Electronics Engineering
Abstract

ÖZ TV alıcılarında daha doğal bir ses elde edebilmek için video işareti ile birlikte en az iki ses bilgisi iletmemiz gereklidir. Birden fazla ses bilgisi içeren video işareti elde etmek için, video işaretine ses bilgilerinin katılması araştırılmıştır. Bu aşağıdaki metodlarla gerçekleştirilebilir. 1. Yatay senkronizasyon darbelerinin genlik modülasyonu ile. 2. Ses işaretlerinin delta modülasyonu ve sonra delta darbelerinin yatay senkronizasyan darberinine eklenmesiyle. Bu tez ek ses kanallarını TV işaretine katmak için önerilmiş bazı yöntemlerden bahsetmektedir. Tasarım, analiz ve simule edilmiş deneylerin sonuçları anlatılmıştır. vın ABSTRACT For realistic sound reproduction of the audio information in the TV receivers, we need to transmit at least two audio informations along with the video signal. Investigations are carried out in mixing the audio informations with the video signal as to obtain the composite video containing more than one audio information. This is achieved in the following ways. 1. Pulse amplitude modulating Hsyncs. 2. Delta modulating sound signals and then adding delta pulses to Hsync pulses. This thesis reports some proposed techniques of including additional sound channels in TV signal. Design, analysis and results of simulated experiments are presented. 104

Published
1993

41. A space-time-frequency analysis approach for the classification motor imagery EEG recordings in a brain computer interface task.

Author

Ince NF, Tewfik AH, and Arica S

Subjects
Discriminant Analysis, Humans, Task Performance and Analysis, User-Computer Interface, Algorithms, Brain Mapping methods, Electroencephalography methods, Evoked Potentials, Motor physiology, Imagination physiology, Motor Cortex physiology, Pattern Recognition, Automated methods
Abstract

We introduce an adaptive space time frequency analysis to extract and classify subject specific brain oscillations induced by motor imagery in a brain computer interface task. The introduced method requires no prior knowledge of the reactive frequency bands, their temporal behavior or cortical locations. The algorithm implements an arbitrary time-frequency segmentation procedure by using a flexible local discriminant base algorithm for given multichannel brain activity recordings to extract subject specific ERD and ERS patterns. Extracted time-frequency features are processed by principal component analysis to reduce the feature set which is highly correlated due to volume conduction and the neighbor cortical regions. The reduced feature set is then fed to a linear discriminant analysis for classification. We give experimental results for 9 subjects to show the superior performance of the proposed method where the classification accuracy varied between 76.4% and 96.8% and the average classification accuracy was 84.9%

Published
2006
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42. Analysis and visualization of ERD and ERS with Adapted Local Cosine Transform.

Author

Firat Ince N and Arica S

Abstract

We apply Adapted Local Cosine Transform to movement EEG, which is a local spectral representation of the signal. It includes best basis method obtained by entropy minimization. The algorithm yields to adaptive time segmentation, where these segments correspond to ERD and ERS events. The algorithm provides short segments in ERD and ERD to ERS transitions. Then we use averaged DCT coefficients to extract the ERD ERS structure of a person.

Published
2004
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