Autonomous driving technologies are rapidly advancing, driven by the need for safer, more efficient, and intelligent transportation systems. A fundamental component of these systems is the perception module, which enables vehicles to understand and react to their surrounding environment. Object detection, in particular, is essential for identifying dynamic and static elements on the road, such as pedestrians, vehicles, traffic signs, and obstacles. In this work, we explore and enhance the capabilities of state-of-the-art deep learning-based object detectors within the YOLO (You Only Look Once) family, focusing on the latest versions: YOLOv10 and YOLOv11. We fine-tuned and optimized multiple variants of each model—namely, nano (n), small (s), medium (m), and large (l)—to improve detection accuracy and computational efficiency for real-time autonomous driving applications. The models were trained and evaluated on a diverse road object, and performance was measured using key metrics including precision, mean Average Precision (mAP), and Precision-Recall curves. Experimental results reveal that the fine-tuned YOLOv10n achieved a peak class-level precision of 1.00 at a specific confidence threshold (0.997), indicating that perfect precision was observed for certain classes under high-confidence conditions, while the overall mean precision and mAP metrics reflect more balanced model performance, while YOLOv11s attained the best result within its group with a precision of 0.91 at a threshold of 0.972. These findings demonstrate the potential of tailored YOLO architectures to meet the demanding requirements of real-world autonomous navigation systems.

This paper proposes an effective and imperceptible method to protect medical images using watermarking and encryption. The approach combines DWT, SVD, AES and a serial Turbo code. The method employs a second-level DWT decomposition on the cover image and specifically chooses the LH2 and HL2 sub-bands for watermark embedding. These sub-bands are further divided into 8x8 sub-blocks, which undergo SVD transformation. The watermark is subsequently embedded into the singular values of the chosen DWT sub-bands. This approach effectively utilizes transform domain techniques, the proposed method provides a more reliable and robust solution. Moreover, the modified AES encryption is included, which has two advantages: it conceals the watermarked material and strengthens overall security. The proposed technique has shown to be highly effective, with significant PSNR (45.9001 dB), WPSNR (50.8099 dB) and NC values very close to 1 even in the presence of major attacks.

This research presents a novel watermarking technique specifically tailored for medical imaging applications. Our approach incorporates SVD, DCT, and DWT methods, complemented by AES to ensure the security and integrity of medical images while preserving their hidden information. The process begins with DWT applied to the medical image at the second level of decomposition. The resulting LL2 component is then divided into sub-bands, each sized $8 \times 8$. DCT is then applied to each sub-band, generating $8 \times 8$ blocks, which undergo SVD analysis. To achieve robust watermark extraction, we adopt an adaptive approach by customizing the watermark based on the SVD components. This adaptability ensures efficient retrieval of the watermark, even in the face of diverse types of attacks. To determine the appropriate “$\alpha$” for the watermark embedding process, we utilize the Weber constant. The proposed system undergoes rigorous testing against various attacks, including compression, noise, filtering, and geometric transformation. Through extensive experiments and analyses, we demonstrate the achievement of low distortion and high robustness in the watermarking process, while simultaneously ensuring enhanced security during the encryption phase.

Summary This article puts forward a fast chaos‐RSA‐based hybrid cryptosystem to secure and authenticate secret images. The SHA‐512 is used to generate a 512‐bit initial key. The RSA system is used to encrypt the initial secret key and signature generation for both the sender and image authentication. In fact, a powerful block‐cipher algorithm is developed to encrypt and decrypt images with a high level of security. At this stage, a strong PRNG based on four chaotic systems is propounded to generate high‐quality keys. Therefore, an improved architecture is suggested. It performs confusion and diffusion of images with low computational complexity. In the final step, the encrypted secret key, signature, and encrypted image are combined together in order to obtain an encrypted signed image. The block‐cipher algorithm is evaluated in‐depth for several ordinary and medical images with different types, content, and size. The obtained simulation results demonstrate that the system enables high‐level security. The entropy has achieved a value of 7.9998 which is the most important feature of randomness. A comparative study against numerous recent encryption algorithms demonstrates that the proposed algorithm provides good results.

Nowadays, traffic sign recognition is the most important task of advanced driver assistance systems since it improves the safety and comfort of drivers. However, it remains a challenging task due to the complexity of road traffic scenes. In this study, a novel two‐stage approach for real‐time traffic sign detection and recognition in a real traffic situation was proposed. The first stage aims to detect and classify the detected traffic signs into circular and triangular shape using HOG features and linear support vector machines (SVMs). The main objective of the second stage is to recognise the traffic signs using a convolutional neural network into their subclasses. The performance of the whole process is tested on German traffic sign detection benchmark (GTSDB) and German traffic sign recognition benchmark (GTSRB) datasets. Experimental results show that the obtained detection and recognition rate is comparable with those reported in the literature with much less complexity. Furthermore, the average processing time demonstrates its suitability for real‐time processing applications.

In this paper we propose a novel and efficient hardware implementation of an image watermarking system based on the Haar Discrete Wavelet Transform (DWT). DWT is used in image watermarking to hide secret pieces of information into a digital content with a good robustness. The main advantage of Haar DWT is the frequencies separation into four subbands (LL, LH, HL, and HH) which can be treated independently. This permits ensuring a better compromise between robustness and visibility factors. A Field Programmable Gate Array (FPGA) that is based on a very large scale integration architecture of the watermarking algorithm is developed to accelerate media authentication. A hardware cosimulation strategy using the Matlab-Xilinx system generator (XSG) was applied to prove the validity of the suggested implementation. The hardware cosimulation results show the effectiveness of the developed architecture in terms of visibility and robustness against several attacks. The proposed hardware system presents also a high performance in terms of the operating speed.

This study presents a novel chaotic–neural network of image encryption and decryption image applied to the domain of medical. The main objective behind the proposed technique is to ensure the safety of medical images with a less complex algorithm compared with the existing methods. In order to improve the robustness, the totality of the pixels related to the host image is XORed with a generation key. After that, with a chaotic system (logistic map), the binary sequence is generated in order to set the weights w ij and bias bi of neuron network with the goal of encrypting the pixels issued from the previous step. Simulation and experiments were carried out on medical images coded on 8 and 12 bits/pixel. The obtained results confirmed the performance and the efficiency of the proposed method, which is compliant with Digital Imaging and Communications in Medicine standards.

In this paper, a design method of direct torque control (DTC) of an induction machine, which is based on fuzzy logic control (FLC), is proposed. The FLC which is introduced to improve the DTC uses a reduced number of fuzzy inference rules that facilitates the reduction of the FLC computation time. The improvement is performed by reducing the torque and stator flux ripples in the induction machine drive. Then, a hardware description that is based on the VHDL hardware description language of the proposed design is presented and discussed. The techniques of the parallel architecture, direct computation, and modular architecture are adopted for the design of the controller. Simulation and experimental results of the DTFC are compared with those of the conventional DTC. The comparison results illustrate the reduction in the torque and stator flux ripples of the DTFC. The validity of the proposed method is confirmed by the simulation and experimental results.

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