ABSTRACT This paper presents a real‐time, vision‐based framework for detecting driver fatigue using a single low‐cost, road‐facing camera, eschewing direct visual monitoring of the driver. Unlike conventional systems that rely on in‐cabin facial or physiological analysis, the proposed architecture prioritizes privacy by inferring fatigue through vehicle dynamics and road interaction alone. Built upon the YOLOP deep learning model, the system performs lane segmentation and object detection to extract two critical indicators: lane deviation and inter‐vehicle distance, both computed from monocular vision. These signals are interpreted via a fuzzy logic module that incorporates trapezoidal, triangular, and Gaussian membership functions, enabling context‐sensitive and explainable fatigue assessment. Comparative evaluation of these functions illustrates trade‐offs in responsiveness and generalization. Initial validation against expert human assessments shows promising alignment in perceived fatigue levels, suggesting the system can meaningfully approximate fatigue‐related judgments. By aligning with emerging ethical frameworks for non‐intrusive AI in mobility, the system marks a step toward socially responsible and practically deployable fatigue monitoring in intelligent transportation.

This paper introduces a plug-and-play descriptor that can be effectively adopted for image retrieval tasks without prior initialization or preparation. The description method utilizes the recently proposed Vision Transformer network while it does not require any training data to adjust parameters. In image retrieval tasks, the use of Handcrafted global and local descriptors has been very successfully replaced, over the last years, by the Convolutional Neural Networks (CNN)-based methods. However, the experimental evaluation conducted in this paper on several benchmarking datasets against 36 state- of-the-art descriptors from the literature demonstrates that a neural network that contains no convolutional layer, such as Vision Transformer, can shape a global descriptor and achieve competitive results. As fine-tuning is not required, the presented methodology’s low complexity encourages adoption of the architecture as an image retrieval baseline model, replacing the traditional and well adopted CNN-based approaches and inaugurating a new era in image retrieval approaches.

The COVID-19 pandemic is stress-testing existing health information exchange systems. There exists an increasing demand for sharing patient information and efficiently responding to patient medial data requests. Current health information technologies lack data fluidity, especially for remotely sharing medical data beyond their protected, local data storage. This paper presents a blockchain-based data-sharing framework that leverages the properties of immutability and decentralization to ensure a secure, user-centric approach for accessing and controlling access to sensitive medical data. The proposed framework builds its foundations on a peer-to-peer network fueled by the distributed InterPlanetary File System combined with on-chain tagging, and on the use of cryptographic generation techniques for enabling a secure way of sharing medical data. The flow of information is orchestrated by a smart-contract deployed on a blockchain-based protocol to ensure traceability and data integrity. The effectiveness of the framework is demonstrated with the implementation of the framework over a pilot study.

This paper focuses on the optimization of the line-tracking and following control technique used in high speed autonomous movable IoT devices such as line-following robotic vehicles. A fusion of a new pro-active / feed-forward control system, based on the simultaneous use of computer vision and the use of an array of analog infrared reflective phototransistors with an optimised PID based feed-back implementation is proposed. Experimental evaluations demonstrate that the proposed approach aids and improves the performance of line-following robots compared to the traditional PID technique.

Data gathering and critical events detection are two essential functionalities for Wireless Sensor Network (WSN). In this paper, we propose double mobile sinks network architecture, where two mobile sink nodes visit the Cluster Heads (CHs) to collect the captured data, which is very energy effective in terms of energy transmission efficiency and reliable compared with the case of having one static sink node. Moreover, the proposed architecture provides a capable scheme for supporting critical and non-critical data, which assures a timely delivery for any critical event to the remote monitoring and decision-making center with minimal interference to the non-critical data. Our proposed architecture shows a superior performance in terms of packets transmission delay, and requires low buffer occupancy for the CHs nodes when compared to related work in the literature. Finally, the paper provides a preliminary hardware design and implementation for the proposed architecture.

The IoT ecosystem is evolving quickly, developing several applications in different sectors. The majority of these applications use centralized infrastructures something that poses several challenges especially related to trust and data security. Recently, blockchain has been introduced as an effective solution to IoT applications trying to provide solutions to these challenges. In this paper, we introduce a new decentralized IoT application using blockchain technology, namely, ParkChain. The ParkChain application operates in a way that no one can delete, revert, hack or question the time a registered vehicle securely entered a parking area. In order to evaluate ParkChain, we have implemented the smart contract on top of Ethereum Blockchain along with a traditional Image Processing / Computer Vision approach for License Plate Recognition, and using a Raspberry Pi we control the access on a parking place. We report on several experiments that assess the performance of ParkChain application.

This paper introduces a novel image descriptor for content based image retrieval tasks that integrates contour and color information into a compact vector. Loosely inspired by the human visual system and its mechanisms in efficiently identifying visual saliency, operations are performed on a fixed lattice of discrete positions by a set of edge detecting kernels that calculate region derivatives at different scales and orientation. The description method utilizes a weighted edge histogram where bins are populated on the premise of whether the regions contain edges belonging to the salient contours, while the discriminative power is further enhanced by integrating regional quantized color information. The proposed technique is both efficient and adaptive to the specifics of each depiction, while it does not need any training data to adjust parameters. Experimental evaluation conducted on seven benchmarking datasets against 13 well known global descriptors along with SIFT, SURF implementations (both in VLAD and BOVW), highlight the effectiveness and efficiency of the proposed descriptor.

Fractions are considered to be a difficult cognitive area for students. Fractions are represented in different ways and various software systems are used to support, as pedagogical agents, the teaching process with the aim to achieve a desired teaching result for students. This article attempts to present the basic features of three e-learning, web-based systems that contain applications to teach basic operations of fractions using multiple representations. The aim of this study is to highlight the basic features, positive elements, and the originality of each system, as well as, critically discussing the various other parameters that are not considered by such software systems. The main finding of this work is a critical comparison between the systems studied with the aim of identifying the elements that could be incorporated into a newly proposed web-based/cloud application specifically designed for teaching fractions.

Summary In this paper, a novel visual Place Recognition approach is evaluated based on a visual vocabulary of the Color and Edge Directivity Descriptor (CEDD) to address the loop closure detection task. Even though CEDD was initially designed so as to globally describe the color and texture information of an input image addressing Image Indexing and Retrieval tasks, its scalability on characterizing single feature points has already been proven. Thus, instead of using CEDD as a global descriptor, we adopt a bottom‐up approach and use its localized version, Local Color And Texture dEscriptor, as an input to a state‐of‐the‐art visual Place Recognition technique based on Visual Word Vectors. Also, we use a parallel execution pipeline based on a previous work of ours using the well established General Purpose Graphics Processing Unit (GPGPU) computing. Our experiments show that the usage of CEDD as a local descriptor produces high accuracy visual Place Recognition results, while the parallelization used allows for a real‐time implementation even in the case of a low‐cost mobile device.

Image indexing refers to describing the visual multimedia content of a medium, using high level textual information or/and low level descriptors. In most cases, images and videos are associated with noisy and incomplete user-supplied textual annotations, possibly due to omission or the excessive cost associated with the metadata creation. In such cases, Content Based Image Retrieval (CBIR) approaches are adopted and low level image features are employed for indexing and retrieval. We employ the Colour and Edge Directivity Descriptor (CEDD), which incorporates both colour and texture information in a compact representation and reassess it for parallel execution, utilizing the multicore power provided by General Purpose Graphic Processing Units (GPGPUs). Experiments conducted on four different combinations of GPU-CPU technologies revealed an impressive gained acceleration when using a GPU, which was up to 22 times faster compared to the respective CPU implementation, while real-time indexing was achieved for all tested GPU models.

Golden Retriever Image Retrieval Engine (GRire) is an open source light weight Java library developed for Content Based Image Retrieval (CBIR) tasks, employing the Bag of Visual Words (BOVW) model. It provides a complete framework for creating CBIR system including image analysis tools, classifiers, weighting schemes etc., for efficient indexing and retrieval procedures. Its eminent feature is its extensibility, achieved through the open source nature of the library as well as a user-friendly embedded plug-in system. GRire is available on-line along with install and development documentation on http://www.grire.net and on its Google Code page http://code.google.com/p/grire. It is distributed either as a Java library or as a standalone Java application, both GPL licensed.

Due to the rapid development of information technology and the continuously increasing number of available multimedia data, the task of retrieving information based on visual content has become a popular subject of scientific interest. Recent approaches adopt the bag-of-visual-words (BOVW) model to retrieve images in a semantic way. BOVW has shown remarkable performance in content-based image retrieval tasks, exhibiting better retrieval effectiveness over global and local feature (LF) representations. The performance of the BOVW approach depends strongly, however, on predicting the ideal codebook size, a difficult and database-dependent task. The contribution of this paper is threefold. First, it presents a new technique that uses a self-growing and self-organized neural gas network to calculate the most appropriate size of a codebook for a given database. Second, it proposes a new soft-weighting technique, whereby each LF is classified into only one visual word (VW) with a degree of participation. Third, by combining the information derived from the method that automatically detects the number of VWs, the soft-weighting method, and a color information extraction method from the literature, it shapes a new descriptor, called color VWs. Experimental results on two well-known benchmarking databases demonstrate that the proposed descriptor outperforms 15 contemporary descriptors and methods from the literature, in terms of both precision at K and its ability to retrieve the entire ground truth.

The SFly project is an EU-funded project, with the goal to create a swarm of autonomous vision controlled micro aerial vehicles. The mission in mind is that a swarm of MAV's autonomously maps out an unknown environment, computes optimal surveillance positions and places the MAV's there and then locates radio beacons in this environment. The scope of the work includes contributions on multiple different levels ranging from theoretical foundations to hardware design and embedded programming. One of the contributions is the development of a new MAV, a hexacopter, equipped with enough processing power for onboard computer vision. A major contribution is the development of monocular visual SLAM that runs in real-time onboard of the MAV. The visual SLAM results are fused with IMU measurements and are used to stabilize and control the MAV. This enables autonomous flight of the MAV, without the need of a data link to a ground station. Within this scope novel analytical solutions for fusing IMU and vision measurements have been derived. In addition to the realtime local SLAM, an offline dense mapping process has been developed. For this the MAV's are equipped with a payload of a stereo camera system. The dense environment map is used to compute optimal surveillance positions for a swarm of MAV's. For this an optimiziation technique based on cognitive adaptive optimization has been developed. Finally, the MAV's have been equipped with radio transceivers and a method has been developed to locate radio beacons in the observed environment.

The Bag-Of-Visual-Words (BOVW) paradigm is fast becoming a popular image representation for Content-Based Image Retrieval (CBIR), mainly because of its better retrieval effectiveness over global feature representations on collections with images being near-duplicate to queries. In this experimental study we demonstrate that this advantage of BOVW is diminished when visual diversity is enhanced by using a secondary modality, such as text, to pre-filter images.

Traditional tools for information retrieval (IR) evaluation, such as TREC's trec_eval, have outdated command-line interfaces with many unused features, or 'switches', accumulated over the years. They are usually seen as cumbersome applications by new IR researchers, steepening the learning curve. We introduce a platform-independent application for IR evaluation with a graphical easy-to-use interface: the TREC_Files Evaluator. The application supports most of the standard measures used for evaluation in TREC, CLEF, and elsewhere, such as MAP, P10, P20, and bpref, as well as the Averaged Normalized Modified Retrieval Rank (ANMRR) proposed by MPEG for image retrieval evaluation. Additional features include a batch mode and statistical significance testing of the results against a pre-selected baseline.

Compact composite descriptors (CCDs) are global image features, capturing more than one types of information at the same time in a very compact representation. Their qual-ity has so far been evaluated in retrieval from several homo-geneous databases containing images of only the type that each CCD is intended for, and has been found better than other descriptors in the literature such as the MPEG-7 de-scriptors. In this study, we consider heterogeneous databases and investigate query-time fusion techniques for CCDs. The results show that fusion is beneficial, even with simple score normalization and combination methods due to the com-patibility of the score distributions produced by the CCDs considered.

In this paper a new set of descriptors appropriate for image indexing and retrieval is proposed. The proposed descriptors address the tremendously increased need for efficient content-based image retrieval (CBIR) in many application areas such as the Internet, biomedicine, commerce and education. These applications commonly store image information in large image databases where the image information cannot be accessed or used unless the database is organized to allow efficient storage, browsing and retrieval. To be applicable in the design of large image databases, the proposed descriptors are compact, with the smallest requiring only 23 bytes per image. The proposed descriptors' structure combines color and texture information which are extracted using fuzzy approaches. To evaluate the performance of the proposed descriptors, the objective Average Normalized Modified Retrieval Rank (ANMRR) is used. Experiments conducted on five benchmarking image databases demonstrate the effectiveness of the proposed descriptors in outperforming other state-of-the-art descriptors. Also, a Auto Relevance Feedback (ARF) technique is introduced which is based on the proposed descriptors. This technique readjusts the initial retrieval results based on user preferences improving the retrieval score significantly. An online demo of the image retrieval system img(Anaktisi) that implements the proposed descriptors can be found at .

LIRe (Lucene Image Retrieval) is a light weight open source Java library for content based image retrieval. It provides common and state of the art global image features and offers means for indexing and retrieval. Due to the fact that it is based on a light weight embedded text search engine, it can be integrated easily in applications without relying on a database server.