Yavuz Erol

The field of lighting technology is rapidly evolving. Among these technologies, Light Emitting Diode (LED) systems stand out as highly efficient light sources. LEDs are manufactured with various characteristics and structures. For instance, RGB (Red Green Blue) LEDs are capable of producing a wide spectrum of colors. By using RGB LEDs, it is also possible to achieve white light with different color temperatures Color temperature plays a crucial role in how our eyes perceive the colors of objects. We can observe the true colors of an object under daylight illumination with a color temperature of 6500K. The same object may appear differently under lighting with various color temperatures. Consequently, there has arisen a need to adjust different color temperatures within a single lighting fixture. Existing LED lighting systems often struggle with achieving precise control over color temperature and brightness due to limitations in hardware integration and computational efficiency. Additionally, these systems may require costly sensors or lack the flexibility to adapt to varying environmental conditions. To address these gaps, the proposed system integrates PSoC technology with an AS7261 XYZ Chromatic White Color Sensor, enabling real-time adjustments of color temperature and brightness with enhanced precision and cost-effectiveness. In this study, a design of an adjustable LED luminaire in terms of both brightness and color temperature has been developed using PSoC (Programmable System on Chip) technology, which is gaining significant traction and importance in the field of embedded systems. The necessary XYZ data for the color temperature of the luminaire, which consists of RGB LEDs, were obtained from the AS7261 XYZ Chromatic White Color Sensor. The XYZ information gathered from this sensor was utilized in the algorithm of the designed system. By adjusting the brightness levels at which the RGB LEDs operate according to the desired color temperature and illumination level, the system was successfully implemented. This innovative approach has practical applications in energy-efficient lighting systems, industrial production processes, and customizable illumination solutions for residential, commercial, and automotive environments, highlighting its potential for widespread industrial adoption.

In this study, we present a visible light communication (VLC) system that analyzes the performance of an optical camera communication (OCC) system, utilizing a mobile phone camera as the receiver and a computer monitor as the transmitter. By creating color channels in the form of a 4 × 4 matrix within a frame, we determine the parameters that affect the successful transmission of data packets. Factors such as the brightness or darkness of the test room, the light color of the lamp in the illuminated environment, the effects of daylight when the monitor is positioned in front of a window, and issues related to dead pixels and light bleed originating from the monitor’s production process have been considered to ensure accurate data transmission. In this context, we utilized the PyCharm, Pydroid, Python, Tkinter, and OpenCV platforms for programming the transmitter and receiver units. Through the application of image processing techniques, we mitigated the effects of daylight on communication performance, thereby proposing a superior system compared to standard VLC systems that incorporate photodiodes. Additionally, considering objectives such as the maximum number of channels and the maximum distance, we regulated the sizes of the channels, the distances between the channels, and the number of channels. The NumPy library, compatible with Python–Tkinter, was employed to determine the color levels and dimensions of the channels. We investigate the effects of RGB and HSV color spaces on the data transmission rate and communication distance. Furthermore, the impact of the distance between color channels on color detection performance is discussed in detail.

Image processing and embedded systems are used in many applications such as object recognition, robotic applications, autonomous and remote control systems developed for the defense industry, medical applications, face recognition, and vehicle license plate recognition. Many vehicle license number plate detection methods are not effective under vehicle license plate images have a degree of rotation or low resolution images. Thus, we used MVSR normalization algorithm to detect vehicle license plate recognition for better accuracy and lower computational cost. The MVSR normalization algorithm, Mean–Variance-Softmax-Rescale processes respectively is applied for high-accuracy real-time vehicle license plate detection.

Bu çalışmada görünür ışık haberleşme sistemlerinde fotodiyot dizisi kullanımının haberleşme performansına etkileri incelenmiştir. Optik verici kısmında farklı dalga boylarında ışıma yeteneğine sahip yeni nesil power LED’ler ve farklı açılı lensler; optik alıcı kısmında ise görünür ışık bandında ışığa duyarlı PIN fotodiyotlardan oluşan bir algılayıcı dizisi, opamplı karşılaştırma devresi ve ortalama gerilim izleyici bloğu bulunmaktadır. Tasarlanan haberleşme sistemiyle LED akımının büyüklüğünün, lens açısının değerinin, optik verici ile optik alıcı arasındaki mesafenin veri transfer hızına etkileri incelenmiştir.

COVID-19 is still a fatal disease, which has threatened all people by affecting the human lungs. Chest X-Ray or computed tomography imaging is commonly used to make a fast and reliable medical investigation to detect the COVID-19 virus. These medical images are remarkably challenging because it is a full-time job and prone to human errors. In this paper, a new normalization algorithm that consists of Mean–Variance-Softmax-Rescale (MVSR) processes respectively is proposed to provide facilitation pre-assessment and diagnosis Covid-19 disease. In order to show the effect of MVSR normalization technique, the algorithm of proposed method is applied to chest X-ray and Sars-Cov-2 computed tomography images dataset. The normalized X-ray images with MVSR are used to recognize Covid-19 virus via Convolutional Neural Network (CNN) model. At the implementation stage, the MVSR algorithm is executed on MATLAB environment, then all the arithmetic operations of the MVSR normalization are coded in VHDL with the help of fixed-point fractional number representation format on FPGA platform. The experimental platform consists of Zynq-7000 Development FPGA Board and VGA monitor to display the both original and MVSR normalized chest X-ray images. The CNN model is constructed and executed using Anaconda Navigator interface with python language. Based on the results of this study, infections of Covid-19 disease can be easily diagnosed with MVSR normalization technique. The proposed MVSR normalization technique increased the classification accuracy of the CNN model from 83.01, to 96.16% for binary class of chest X-ray images.

The chaotic systems are preferred in secure communication systems as well as used in many different implementation areas. Real time chaos synchronization is also required for secure communication systems. In this study, the...