Mühendislik Fakültesi

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Phase-Synchronized Fluidic Oscillator Pair
(AMER INST AERONAUTICS ASTRONAUTICS, 1801 ALEXANDER BELL DRIVE, STE 500, RESTON, VA 22091-4344 USA, 2019) Tomac, Mehmet N; Gregory, James W.
The relative phase of oscillating jets from a pair of fluidic oscillators was synchronized in this work. The means for this synchronization was mutual interaction through a shared feedback channel between the two oscillators. Flow visualization and hot-wire measurements indicated a strong correlation and phase synchronization between the two oscillators. A numerical analysis offered better understanding of the internal flow physics that led to the synchronization phenomenon. A portion of the output jet from one fluidic oscillator was redirected and crossed over into the adjacent oscillator, leading to momentum transfer between the two oscillators. A portion of this cross-oscillator flow was directed into the shared feedback channel and constituted the main feedback flow. In this process, one of the shared feedback channel outlets was blocked by a vortex, allowing only one oscillator to receive feedback flow. The primary mechanism for in-phase synchronization was the cross-oscillator flow, which was divided into phase-modulated momentum injection to the primary jet and modulated flow input to the shared channel feedback channel.
Citation - Scopus: 5
Emotion Detection Using Multivariate Synchrosqueezing Transform via 2D Circumplex Model
(Institute of Electrical and Electronics Engineers Inc., 2018) Ozel, Pinar; Akan, Aydin; Yilmaz, Bulent; Özel, Pınar; Akan, Aydin I.; Yilmaz, Bulent
Emotion detection by utilizing signal processing methods is a challenging area. An open issue in emotional modeling is to obtain an optimum feature set to use for the classification process. This study proposes an approach for emotional state classification by the investigation of EEG signals via multivariate synchrosqueezing transform (MSST). MSST is a post-processing technique to compose a localized time-frequency representation yielding multivariate syncyrosqueezing coefficients. After obtaining these coefficients from EEG signals for 18 subjects from DEAP dataset, coefficients and self-assessment-mannequins (SAM) labels of those subjects are used for emotional state classification by using support vector machines (SVM) nearest neighbor, decision tree, and ensemble methods. The accuracy rate is 70.6% for high valence high arousal (HVHA), 75.4% for low valence high arousal (LVHA), 77.8% for high valence low arousal (HVLA), and 77.2% for low valence low arousal (LVLA) cases using SVM. © 2019 Elsevier B.V., All rights reserved.
In-silico Identification of Papillary Thyroid Carcinoma Molecular Mechanisms
(IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA, 2019) Ersoz, Nur Sebnem; Guzel, Yasin; Bakir-Gungor, Burcu
Representing approximately 70% to 80% of thyroid cancers, papillary thyroid cancer (PTC) is the most common type of thyroid cancers. PTC is seen in all age groups, but it is seen more frequently in women than in men. Detection of biomarker proteins of papillary thyroid cancinoma plays an important role in the diagnosis of the disease. In this study, we aim to find target genes and pathways that are associated with papillar thyroid carcinoma, by integrating different bioinformatics methods. For this purpose, usingin-silico methodologies, candidate genes and pathways that could explain disease development mechanisms are identified. Throughout this study, firstly we identified differentially expressed genes as the amount of their protein product differ between patient and healthy groups. Secondly, by using active subnetworks search algorithms, topologic analyses and functional enrichment tests, candidate proteins,which could be thought as PTC biomarkers, and affected pathways are identified.
A rational utilization of reinforcement material for flexural design of 3D-printed composite beams
(SAGE PUBLICATIONS LTD, 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND, 2019) Ciftci, Cihan; Sas, Hatice S.
Recent developments in composite industry address the adaptation of 3D printing technology to overcome the design and manufacturing challenges of the traditional composite processing techniques. This adaptation can be performed with the development of design methodologies corresponding to the type of structural load-carrying members in a structure. Considering the frequently use of beams in structures, the development of the design methodology of beams is essential for the adaptation of the additive manufacturing. Therefore, in this paper, the flexural loading concept is analytically formulated to derive moment capacity for the flexural behavior of 3D-printed composite beams. Then, the formulation is adapted to develop a design methodology of 3D-printed laminates under flexural loading. Additionally, the analytical solutions developed for the design methodology presented in this paper were verified with a good agreement with experimental studies.
Sustainable Water Management and Rehabilitation in the Mining Lakes, Ilgin-Konya, Turkey
(Agro Arge Danismanlik San ve Tic As, 2016) Delibalta, M. S.; Uzal, N.; Lermi, A.
The processes during the search, production and enrichment of mining operations naturally affects the air, soil, water resources in turn the natural environment and living organisms. In general, the environmental impact of coal opencast mining operations is much more significant than that of underground mining and mineral processing. After stripping of the material filling the holes in coal opencast production, with the rise of surface water and ground water level is composed of large or small ponds. Low pH (acidic characteristic) and high metal concentrations (Al, Ca, Mn, Fe, Cu, Zn, Pb) of these ponds, containing sulfide minerals and the waste materials, for the sustainability of natural resources is one of the biggest environmental problems. This paper is to investigate geochemical characteristics of the pond waters in the Ilgm Coal deposit area. Geochemical analyses were made by ICP-MS in waters taken from ponds in each three-month periods. Highest heavy metal contents 1839 ppb Mn and 9777 ppb Fe, the average pH values 6.49-7.81, turbidity (NTU) 0.1263.6, sulphate content 0.05-2.67 mg SO4/L, chemical oxygen demand 4-136 mg O-2/L, and electrical conductivity 285 mu S/cm4.68 mS/cm have been measured during the monitoring study of five different lignite opencast mine post-production lakes of the TKI GLI Ilgm. Analyses were performed in three-month periods. The results were evaluated within the framework of relevant laws and regulations.
Citation - WoS: 23
Citation - Scopus: 23
Pressure-Induced Amorphization of MOF-5: A First Principles Study
(Wiley-VCH Verlag GmbH, 2018) Erkartal, Mustafa; Durandurdu, Murat; Erkartal, Mustafa; Durandurdu, Murat
Amorphous metal-organic frameworks (MOFs) and the amorphization of crystalline MOFs under mechanical stimuli are attracting considerable interest in last few years. However, we still have limited knowledge on their atomic arrangement and the physical origin of crystalline-to-amorphous phase transitions under mechanical stimuli. In this study, ab initio simulations within a generalized gradient approximation are carried out to investigate the high-pressure behavior of MOF-5. Similar to the previous experimental findings, a pressure-induced amorphization is observed at 2 GPa through the simulations. The phase transformation is an irreversible first order transition and accompanied by around 68% volume collapse. Remarkably, the transition arises from local distortions and, contrary to previous suggestions, does not involve any bond breaking and formation. Additionally, a drastic band gap closure is perceived for the amorphous state. This study has gone some way towards enhancing our understanding of pressure-induced amorphization in MOFs.
Alçaltıcı/Yükseltici Dc/Dc/Ac Eviricilerle Yüksek Performanslı Anahtarlamalı Relüktans Motoru Sürücü Sistemi Tasarımı Ve Gerçeklemesi
(2021) Tekgün, Burak; Boynuegri, Ali Rifat; Yaşa , Yusuf; Alan, Irfan
Anahtarlamalı relüktans motorları (ARM) 1800?lü yılların ortalarında keşfedilmesine rağmen, 1960?lı yıllarda yarı iletken anahtarların icat edilmesine kadar potansiyeli anlaşılamamış makinalardır. Modern yarı iletken teknolojisinin icadı ve gelişmesi ile birlikte ARM?lerin kullanımı yaygınlaşmıştır. ARM?ler basit yapıları, düşük üretim maliyetleri ve sağlamlıklarından dolayı birçok uygulamada tercih edilmişlerdir. Geleneksel olarak ARM, her bir faz için iki yarı iletken anahtar ve iki diyot kullanılarak oluşturulan sürücülerle sabit giriş gerilimi işlenerek, sırasıyla fazlar enerjilendirilmektedir. Faz sargılarındaki akımın enerjilendirilme esnasında yükselme süresi ve enerji kesildiği durumdaki akımın azalma süresi DC bara voltajına bağlıdır. Bu durum uygulamalarda enerjilendirme süresinin akımın sıfıra gitme süresi de göz önüne alınıp kısa tutulmasına, dolayısıyla komütasyon esnasında düşük tork üretimine, yüksek tork salınımına ve ortalama tork üretiminde azalmaya sebep olmaktadır. Bu projede, geleneksel sürücü topolojisinden farklı olarak ARM, bir DC/DC dönüştürücü ve tek fazlı bir tam-köprü evirici yardımı ile ideale yakın bir enerjilendirme akımı oluşturularak ARM daha yüksek performans ile kontrol edilmesi sağlanmıştır. Projede önerilen ARM sürücüsü her bir fazı bir DC/DC dönüştürücü ve bir tam-köprü evirici içeren modüler yapılı sürücülerden oluşmaktadır. Önerilen sürücü yapısını geleneksel ARM topolojilerinden ayıran özelliği DC/DC dönüştürücü devresidir. Burada makinanın faz sargılarının ihtiyaç duyduğu akım dalga şekli DC/DC dönüştürücü ile sağlanmaktadır. Tork üretiminin pozitif olabilmesi için stator ve rotor kutuplarının tam hizalandığı andan kısa bir süre önce negatif gerilim uygulanarak, faz akımının hızlı bir şekilde kesilmesi gerekmektedir. Bu durumda ise gerilim önerilen devredeki tam-köprü devresi yardımıyla tersine çevrilerek ve DC/DC dönüştürücünün çıkış gerilimi en yüksek seviyesine getirilmek suretiyle akımın hızlı şekilde sıfıra inmesi sağlanmıştır ve böylelikle makinanın performansı artmıştır. sadece DC/DC çevirici katında yüksek frekanslı anahtarlama olduğundan anahtarlama kayıplarının azalarak ve geleneksel topolojiye göre daha yüksek verim sağlanmıştır. Önerilen sürücü sisteminin AC motorların sürücüleri olarak yenilenebilir enerji sistemlerinde ara yüz elemanı olarak uygulanabilir olması, arıza giderme zamanında önemli ölçüde azalmaya neden olarak üretimdeki sürdürülebilirliğin artırılmasına destek olacağı öngörülmektedir.
Structure Health Monitoring Using Wireless Sensor Networks on Structural Elements (vol 82, pg 68, 2019)
(ELSEVIER, RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS, 2020) Ayyildiz, Cem; Erdem, H. Emre; Dirikgil, Tamer; Dugenci, Oguz; Kocak, Taskin; Altun, Fatih; Gungor, V. Cagri
This paper presents a system that monitors the health of structural elements in Reinforced Concrete (RC), concrete elements and/or masonry buildings and warn the authorities in case of physical damage formation. Such rapid and reliable detection of impairments enables the development of better risk management strategies to prevent casualties in case of earthquake and floods. Piezoelectric (PZT) sensors with lead zirconate titanate material are the preferred sensor type for fracture detection. The developed sensor mote hardware triggers the PZT sensors and collects the responses they gather from the structural elements. It also sends the collected data to a data center for further processing and analysis in an energy-efficient manner utilizing low-power wireless communication technologies. The access and the analysis of the collected data can be remotely performed via a web interface. Performance results show that the fractures serious enough to cause structural problems can be successfully detected with the developed system.
Karabuğday Nişastasından Yenilebilir Film Üretimi ve Nişastanın Yağ Asitleri ile Modifikasyonunun Film Mekanik Özellikleri Üzerine Etkisi
(TÜBİTAK, 2022) Kahraman, Kevser; Aydemir, Levent Yurdaer; Koca, Esra; Oskaybaş Emlek, Betül
Bu proje, karabuğdaydan yüksek saflıkta ve verimde nişasta üretilmesini, üretilen nişastanın çeşitli yağ asitleri (10C, 14C, 18C) kullanılarak elde edilen amiloz-lipit kompleksinden film üretimini amaçlamaktadır. Amiloz-lipit kompleksi üretiminde herbir yağ asidi için en etkili iki reaksiyon parametresi (sıcaklık, süre, pH, yağ asidi/nişasta oranı) belirlenmiştir. Herbir yağ asidi için en etkili iki parametre kullanılarak merkezi kompozit tasarım ile deneme tasarımları oluşturulmuş, komplekslerin kompleks indeks (KI), görünür amiloz, sindirilebilirlik, su bağlama-çözünürlük, çirişlenme, berraklık ve sineresis derecesi gibi özellikleri açısından karakterize edilmiştir. Kompleks oluşumunun nişastanın yapısına etkisi XRD ve FT-IR ile incelenmiştir. Amiloz-lipit kompleks oluşumu ile nişastanın enzime dirençli nişasta miktarı artmış, şişme gücü azalmıştır. En yüksek KI değerine sahip amiloz-kompleksi içeren nişastalar kullanılarak gliserol varlığında filmler üretilmiştir. Gliserol konsantrasyonu, çözelti sıcaklığı ve pH?nın film mekanik özelliklerine etkisinin araştırılması amacıyla, bu parametreler kullanılarak yanıt yüzey yöntemi ile deneme tasarımları oluşturulmuş, film üretimi gerçekleştirilmiş ve optimum film karakteristiklerine sahip film üretiminin sağlandığı parametreler belirlenmiştir. Optimum koşullarda amiloz-lipit kompleksi kullanılarak üretilen filmlerin gerilme dirençleri miristik (1,09 MPa) ve stearik asit (3,360 MPa) için kontrol filme göre daha yüksek, uzama değeri ise kaprik asit (%114,59) için daha yüksek bulunmuştur. Amiloz-lipit kompleksi kullanılarak üretilen filmlerin çözünürlük, nem ve kalınlık değerleri kontrollere göre daha yüksek bulunmuştur. En iyi su buharı bariyer özelliğine amiloz-miristik asit kompleks filmi (0,394 g.mm/m2.h.kPa) ile elde edilmiştir. SEM ve AFM görüntüleri amiloz-lipit kompleksi kullanımının film morfolojik ve topografik özelliklerine etki ettiğini göstermiştir. Su ile yüzey temas açısı sonuçlarına göre sadece amiloz-stearik asit kompleksi filmi hidrofobik yüzeye sahip olmuşlardır. Tüm sonuçlar genel olarak değerlendirildiğinde proje kapsamında karabuğday nişastasından enzime dirençli nişasta kaynağı olma potansiyeline sahip amiloz-lipit kompleksi oluşturmuş; oluşturulan bu komplekslerden mekanik özellikleri yüksek yenilebilir film üretimi gerçekleştirilebilmiştir.
Citation - WoS: 5
Near- and Far-Field Characterization of Planar mm-Wave Antenna Arrays With Waveguide-to Transition
(Springer, 2016) Salhi, Mohammed Adnan; Kazemipour, Alireza; Gentille, Gennaro; Spirito, Marco; Kleine-Ostmann, Thomas; Schrader, Thorsten
We present the design and characterization of planar mm-wave patch antenna arrays with waveguide-to-microstrip transition using both near- and far-field methods. The arrays were designed for metrological assessment of error sources in antenna measurement. One antenna was designed for the automotive radar frequency range at 77 GHz, while another was designed for the frequency of 94 GHz, which is used, e.g., for imaging radar applications. In addition to the antennas, a simple transition from rectangular waveguide WR-10 to planar microstrip line on Rogers 3003 (TM) substrate has been designed based on probe coupling. For determination of the far-field radiation pattern of the antennas, we compare results from two different measurement methods to simulations. Both a far-field antenna measurement system and a planar near-field scanner with near-to-far-field transformation were used to determine the antenna diagrams. The fabricated antennas achieve a good matching and a good agreement between measured and simulated antenna diagrams. The results also show that the far-field scanner achieves more accurate measurement results with regard to simulations than the near-field scanner. The far-field antenna scanning system is built for metrological assessment and antenna calibration. The antennas are the first which were designed to be tested with the measurement system.
Ball Lens Based Mobile Microscope
(Gazi Univ, 2016) Icoz, Kutay
In this paper we report a low cost, simple and mobile microscope based on attachment of a ball lens to a cell phone. The system's noise and parameters affecting the image quality is investigated. The ball lens provides approximately 100X magnification and together with the cell phone's integrated lens and image sensor, 3,4-micron resolution is reached. The field-of-view of the system is 1500x1500 mu m where the price of the ball lens and the holder is less than 10 cents. By using this system as an optical light microscope, we are able to acquire images of micro particles and micro sensors. When combined with image processing methods, this optical system is capable of doing complex analysis as an alternative to commercial optical light microscopes.
Hydroponic Agriculture with Machine Learning and Deep Learning Methods
(Gazi Mühendislik, 2023) Bulut,Nurten; Hacıbeyoğlu, Mehmet
In the face of the rapidly increasing population of our world today, researchers have turned to studies that use existing resources more effectively and efficiently in addition to searching for new resources in order to meet the rapidly decreasing needs such as raw materials and nutrients. The use of hydroponic agriculture, which is one of the alternative methods that can be used to meet the need for nutrients, which is one of the greatest needs of humanity, has become more popular day by day. The use of nutrient solution water instead of soil, the fact that it is not affected by weather conditions, that it can be applied indoors and that it can be vertically oriented are the characteristics that make hydroponic agriculture different from other agricultural methods. In addition, the lack of soil in this agricultural method brings with it the need for more observation and supervision. The aim of this study is to show that the observation and surveillance needs necessary to increase yield in hydroponic agriculture can be achieved using machine learning and deep learning methods. For this purpose, it has been observed that the efficiency of hydroponic agriculture has been increased in experimental studies conducted using five machine learning and deep learning methods. The deep learning method has achieved better results with 99.7% success compared to other methods.
Ensemble Churn Prediction for Internet Service Provider with Machine Learning Techniques
(IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA, 2020) Goy, Gokhan; Kolukisa, Burak; Bahcevan, Cenk; Gungor, Vehbi Cagri
With the developing technology in every fields, a competitive marketing environment has been arised In this competitive environment analyzing customer behavior has become vital In particular, the ability to easily change any service provider has become vet) , critical for the company to continue its existence At the same time, the amount of financial resources spent on retaining instituters much less than to obtain new clients. In this context, the traditional methods of examining vast amount of data obtained today for establishing decision support systems have lost their validities In this study. we used a dataset which is provided by TurkNet serving as an internet service provider in Turkey. Various preprocessing steps has performed on this dataset and then classification algorithms ran. Afterwards results have obtained and compared. The results of these experiments analyzed in terms of the area under the curve value In this context the aunt successful classifier algorithm has been determined as the Random Trees algorithm with a value of 0.936.
Citation - Scopus: 2
Street Vendor Detection: Helping Municipalities Make Decisions With Actionable Insights
(IEEE, 2021) Agba, Hatice Nur; Tahir, Abdullah
Street vendors are quite common in countries across the world. By the prevalence of mobile surveillance systems, increasing demand for automatic detection of street vendors for further decisions and planning by the city administrators emerged. In this paper, an object detector is developed using a MobileNet SSD object detection algorithm to detect vendors on the street. For this study images were used, however, in the future this technique could be used for real time video footage from street cameras. Since this is the first study tackling this issue, a data set was created from scratch. The accuracy achieved by the algorithm is promising considering the size of the data set and the minimal computational power available. The goal of this research is to pave the way for more work to be done in this area and help municipalities improve their decision making process regarding street vendor activities in countries like Mexico, Pakistan, China, Turkey, etc.
Citation - WoS: 6
Citation - Scopus: 6
Experimental Measurements of Some Thermophysical Properties of Solid CdSb Intermetallic in the Sn-Cd Ternary Alloy
(Springer, 2016) Ozturk, Esra; Aksoz, Sezen; Altintas, Yemliha; Keslioglu, Kazum; Marasli, Necmettin
The equilibrated grain boundary groove shapes of solid CdSb in equilibrium with Sn-Cd-Sb eutectic liquid were observed from a quenched sample by using a radial heat flow apparatus. The Gibbs-Thomson coefficient, solid-liquid interfacial energy and grain boundary energy of the solid CdSb intermetallic were determined from the observed grain boundary groove shapes. The thermal conductivity of the eutectic solid and the thermal conductivity ratio of eutectic liquid to the eutectic solid in the Sn-35.8 at.%Cd-6.71 at.%Sb eutectic alloy at its eutectic melting temperature were also measured with a radial heat flow apparatus and a Bridgman-type growth apparatus, respectively.
Evaluation of Hybrid Classification Approaches: Case Studies on Credit Datasets
(Springer Verlag service@springer.de, 2018) Cetiner, Erkan; Güngör, Vehbi Çağrı; Kocak, Taskin
Hybrid classification approaches on credit domain are widely used to obtain valuable information about customer behaviours. Single classification algorithms such as neural networks, support vector machines and regression analysis have been used since years on related area. In this paper, we propose hybrid classification approaches, which try to combine several classifiers and ensemble learners to boost accuracy on classification results. We worked with two credit datasets, German dataset which is a public dataset and a Turkish Corporate Bank dataset. The goal of using such diverse datasets is to search for generalization ability of proposed model. Results show that feature selection plays a vital role on classification accuracy, hybrid approaches which shaped with ensemble learners outperform single classification techniques and hybrid approaches which consists SVM has better accuracy performance than other hybrid approaches. © 2018 Elsevier B.V., All rights reserved.
Citation - Scopus: 1
Man-Hour Prediction for Complex Industrial Products
(Institute of Electrical and Electronics Engineers Inc., 2023) Unal, Ahmet Emin; Boyar, Halit; Kuleli Pak, Burcu Kuleli; Cem Yildiz, Mehmet; Erten, Ali Erman; Güngör, Vehbi Çağrı
Accurately predicting the cost is crucial for the success of complex industrial projects. There can be several sources contributing to the cost. Traditional methods for cost estimation may not provide the required accuracy and speed to ensure the success of the project. Recently, machine learning techniques have shown promising results in improving cost estimation in various industrial products. This study investigates the performance of gradient-boosting machine learning models and feature engineering techniques on a private dataset of metal sheet project man-hour costs. A comparison of distinct models is conducted, key aspects influencing cost are identified, and the implications of incorporating domain-specific knowledge, including its advantages and disadvantages, are assessed based on performance outcomes. Experimental results demonstrate that LightGBM and XGBoost outperform other models, and feature selection and synthetic data generation techniques improve the performance. Overall, this study highlights the potential of machine learning in metal sheet sampling projects and emphasizes the importance of feature engineering and domain expertise for better model performance. © 2024 Elsevier B.V., All rights reserved.
Citation - WoS: 251
Citation - Scopus: 263
Surface-Enhanced Raman Spectroscopy (SERS): An Adventure from Plasmonic Metals to Organic Semiconductors as SERS Platforms
(Royal Soc Chemistry, 2018) Demirel, Gokhan; Usta, Hakan; Yilmaz, Mehmet; Celik, Merve; Alidagi, Husniye Ardic; Buyukserin, Fatih; Demirel, Gokhan; Usta, Hakan; Yilmaz, Mehmet; Celik, Merve; Alidagi, Husniye Ardic; Buyukserin, Fatih
The quantitative determination and identification of bio-/chemical molecules at ultra-low concentrations is a hot topic in several fields including medical diagnostics, environmental science, and homeland security. Molecular detection techniques are conventionally based on optical, electrochemical, electronic, or gravimetric methodologies. Among these methods, surface-enhanced Raman spectroscopy (SERS) is considered as one of the most reliable, sensitive and selective techniques for non-destructive molecular analysis through the amplification of electromagnetic fields and/or creation of charge-transfer states between the chemisorbed analyte molecule and SERS active platform. Unfortunately, the applicability of SERS is rather limited, which is mainly due to the lack of highly sensitive SERS platforms with good stability and reproducibility. In line with this, metal nanoparticles (e.g., Au, Ag, and Cu) have been extensively exploited as SERS active platforms. Although the utilization of metallic nanoparticles in SERS is simple and cost-effective, the poor controllability of the structures and limited formation of hot spots in the detection zone leads to discrepancy in the resulting SERS signals. For these reasons, in the past few years, researchers have focused on fabricating 3-dimensional (3D) SERS platforms, which increase the adsorption of analyte molecules and facilitate hot spot formation in all three dimensions. However, the fabrication of 3D SERS platforms is mostly expensive and technologically demanding. Therefore, the discovery of non-metal alternative approaches is of great interest not only to widen SERS applications but to further elucidate fundamental questions. Considering recent developments on the fabrication and application of SERS active platforms, this review is structured in 3 main directions; (1) implementation of the plasmonic nanoparticles having different shapes into SERS-active platforms, (2) highlighting recent developments in the fabrication and application of 3D SERS-active platforms, and (3) examination of recent novel inorganic and organic semiconductor based platforms for SERS applications. At the end, we conclude with the promises and challenges for the future evolution of SERS.
Human identification using palm print images based on deep learning methods and gray wolf optimization algorithm
(SPRINGER, 2024) Alshakree, Firas; Akbas, Ayhan; Rahebi, Javad
Palm print identification is a biometric technique that relies on the distinctive characteristics of a person’s palm print to distinguish and authenticate their identity. The unique pattern of ridges, lines, and other features present on the palm allows for the identification of an individual. The ridges and lines on the palm are formed during embryonic development and remain relatively unchanged throughout a person’s lifetime, making palm prints an ideal candidate for biometric identification. Using deep learning networks, such as GoogLeNet, SqueezeNet, and AlexNet combined with gray wolf optimization, we achieved to extract and analyze the unique features of a person’s palm print to create a digital representation that can be used for identification purposes with a high degree of accuracy. To this end, two well-known datasets, the Hong Kong Polytechnic University dataset and the Tongji Contactless dataset, were used for testing and evaluation. The recognition rate of the proposed method was compared with other existing methods such as principal component analysis, including local binary pattern and Laplacian of Gaussian-Gabor transform. The results demonstrate that the proposed method outperforms other methods with a recognition rate of 96.72%. These findings show that the combination of deep learning and gray wolf optimization can effectively improve the accuracy of human identification using palm print images.
Real-Time Robotic Car Control Using Brainwaves and Head Movement
(IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA, 2018) Ozturk, Nedime; Yilmaz, Bulent; Onver, Ahmet Yasin
Emotiv Epoc Headset is a portable and low-cost device. In this study, Emotiv Epoc headset was used in order to obtain real-time gyro and EEG signals. The aim of this study was to control a robotic car in real-time by using head movement and opening and closing of the eyes. The maximum and minimum amplitude of the gyro signal, and the ratios of the beta waves of O1 and O2 channel to alpha waves of the same channels were used as threshold values. These threshold values were used to determine the direction of the robotic car. Because of its low-cost and easy implementation, Arduino Uno was used to manage the robotic car. This study has shown that brain waves and head movements can control a device in real time. This system has the potential to be used in neurofeedback and brain-computer interface applications.

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