Son yıllarda, içten yanmalı motorlu araçların neden olduğu çevresel sorunların oldukça artması nedeniyle elektrikli araç teknolojisindeki gelişmeler hız kazanmıştır. Ancak elektrikli araçlar henüz içten yanmalı motorlu araçların yerini tam olarak alamamıştır. Çünkü elektrikli araçların menzil ve şarj süresi sorunları bulunmaktadır. Bu iki sorun batarya parametresi ile ilgilidir. Bu yüzden elektrikli araçlarla ilgili çalışmalar batarya gruplarına yoğunlaşmıştır. Bu çalışmada elektrikli araçlar ve batarya sistemleri incelenmiştir. Ayrıca MATLAB/Simulink ortamında bir elektrikli araç için lityum iyon batarya paketi modellemesi yapılmıştır.

Recent advancements in communication technologies have highlighted the pivotal role of information security for all individuals and entities. In response, researchers are increasingly focusing on cryptographic solutions to ensure the reliability of confidential information. Recognizing the superiority of chaotic systems preference as entropy source of cryptographic systems, this paper proposes a novel true random number generator (TRNG) design by combining four different chaotic systems outputs, tailored for real-time video encryption application. These chaotic systems are continuous-time Lorenz and fractional-order Chen-Lee systems, as well as discrete-time Logistic and Tent maps. This study generates true random bit (TRB) sequences at a high bit rate (25 Mbps) through the hardware implementations of four distinct chaotic systems to have the best statistical randomness in the resulting output. Then, the cryptographic true random key bits (8-bit at 25 MHz frequency) are employed in the post-processing with real-time video data by using the XOR operation, a fundamental post-processing algorithm. The real-time video encryption application is executed on an experimental assembly, composed of a Field Programmable Gate Array (FPGA) development kit, an OV7670 camera module, a VGA monitor, and prototype circuit boards for the chaotic systems. To evaluate the effectiveness of the proposed encryption system, several security assessments are conducted. These include NIST SP 800 − 22 statistical tests, FIPS 140-1 standards, chi-square tests, histogram and correlation analysis, and NPCR and UACI differential attack resilience tests. Consequently, the findings suggest that the presented real-time embedded cryptosystem is robust and suitable for secure communications, particularly in the realm of video transmission.

Background: Psoriasis is a chronic, immune-mediated skin disease characterized by lifelong persistence and fluctuating symptoms. The clinical similarities among its subtypes and the diversity of symptoms present challenges in diagnosis. Early diagnosis plays a vital role in preventing the spread of lesions and improving patients’ quality of life. Methods: This study proposes a hybrid model combining multiple transfer learning and ensemble learning methods to classify psoriasis subtypes accurately and efficiently. The dataset includes 930 images labeled by expert dermatologists from the Dermatology Clinic of Fırat University Hospital, representing four distinct subtypes: generalized, guttate, plaque, and pustular. Class imbalance was addressed by applying synthetic data augmentation techniques, particularly for the rare subtype. To reduce the influence of nonlesion environmental factors, the images underwent systematic cropping and preprocessing steps, such as Gaussian blur, thresholding, morphological operations, and contour detection. DenseNet-121, EfficientNet-B0, and ResNet-50 transfer learning models were utilized to extract feature vectors, which were then combined to form a unified feature set representing the strengths of each model. The feature set was divided into 80% training and 20% testing subsets and evaluated using a hard voting classifier consisting of logistic regression, random forest, support vector classifier, k-nearest neighbors, and gradient boosting algorithms. Results: The proposed hybrid approach achieved 93.14% accuracy, 96.75% precision, and an F1 score of 91.44%, demonstrating superior performance compared to individual transfer learning models. Conclusions: This method offers significant potential to enhance the classification of psoriasis subtypes in clinical and real-world settings.