Search Results (75)

The growing interest in smart buildings and the integration of IoT-based technologies is driving the development of new tools for monitoring and optimizing indoor environmental quality (IEQ). However, many existing solutions remain expensive, invasive and inflexible. This paper presents the design and validation of a compact, low-cost, and real-time sensor system, conceived for seamless integration into indoor environments. The system measures key parameters—including air temperature, relative humidity, illuminance, air quality, and sound pressure level—and is embeddable in standard office equipment with minimal impact. Leveraging 3D printing and open-source hardware/software, the proposed solution offers high affordability (approx. EUR 33), scalability, and potential for workspace retrofits. To assess the system’s performance and relevance, dynamic simulations were conducted to evaluate metrics such as the Mean Radiant Temperature (MRT) and illuminance in an open office layout. In addition, field tests with a functional prototype enabled model validation through on-site measured data. The results highlighted significant local discrepancies—up to 6.9 °C in MRT and 28 klx in illuminance—compared to average conditions, with direct implications for thermal and visual comfort. These findings demonstrate the system’s capacity to support high-resolution environmental monitoring within IoT-enabled buildings, offering a practical path toward the data-driven optimization of occupant comfort and energy efficiency. Full article

In the context of secure and efficient data transmission over the future Internet, particularly for remote sensing and geospatial applications, reversible data hiding (RDH) in encrypted hyperspectral images (HSIs) has emerged as a critical technology. This paper proposes a novel RDH scheme specifically designed for encrypted HSIs, offering enhanced embedding capacity without compromising data security or reversibility. The approach introduces a multi-layer block labeling mechanism that leverages the similarity of most significant bits (MSBs) to accurately locate embeddable regions. To minimize auxiliary information overhead, we incorporate an Extended Run-Length Encoding (ERLE) algorithm for effective label map compression. The proposed method achieves embedding rates of up to 3.79 bits per pixel per band (bpppb), while ensuring high-fidelity reconstruction, as validated by strong PSNR metrics. Comprehensive security evaluations using NPCR, UACI, and entropy confirm the robustness of the encryption. Extensive experiments across six standard hyperspectral datasets demonstrate the superiority of our method over existing RDH techniques in terms of capacity, embedding rate, and reconstruction quality. These results underline the method’s potential for secure data embedding in next-generation Internet-based geospatial and remote sensing systems. Full article

When a CMOS (Complementary Metal–Oxide–Semiconductor) imaging system operates at a high frame rate or a high line rate, the exposure time of the imaging system is limited, and the acquired image data will be dark, with a low signal-to-noise ratio and unsatisfactory sharpness. Therefore, in order to improve the visibility and signal-to-noise ratio of remote sensing images based on CMOS imaging systems, this paper proposes a low-light remote sensing image enhancement method and a corresponding ZYNQ (Zynq-7000 All Programmable SoC) design scheme called the BGIR (Bilateral-Guided Image Restoration) algorithm, which uses an improved multi-scale Retinex algorithm in the HSV (hue–saturation–value) color space. First, the RGB image is used to separate the original image’s H, S, and V components. Then, the V component is processed using the improved algorithm based on bilateral filtering. The image is then adjusted using the gamma correction algorithm to make preliminary adjustments to the brightness and contrast of the whole image, and the S component is processed using segmented linear enhancement to obtain the base layer. The algorithm is also deployed to ZYNQ using ARM + FPGA software synergy, reasonably allocating each algorithm module and accelerating the algorithm by using a lookup table and constructing a pipeline. The experimental results show that the proposed method improves processing speed by nearly 30 times while maintaining the recovery effect, which has the advantages of fast processing speed, miniaturization, embeddability, and portability. Following the end-to-end deployment, the processing speeds for resolutions of 640 × 480 and 1280 × 720 are shown to reach 80 fps and 30 fps, respectively, thereby satisfying the performance requirements of the imaging system. Full article

With the advancement of technology, the information hiding capacity has significantly increased, allowing a cover image to conceal one or more secret images. However, this high hiding capacity often leads to contour shadows and color distortions, making the high-quality recovery of secret images extremely challenging. Existing image hiding algorithms based on Invertible Neural Networks (INNs) often discard useful information during the hiding process, resulting in poor quality of the recovered secret images, especially in multi-image hiding scenarios. The theoretical symmetry of INNs ensures the lossless reversibility of the embedder and decoder, but the lost information generated in practical image steganography disrupts this symmetry. To address this issue, we propose an INN-based image steganography framework that overcomes the limitations of current INN methods in image steganography applications. Our framework can embed multiple full-size secret images into cover images of the same size and utilize the correlation between the lost information and the secret and cover images to generate the lost information by combining the auxiliary model of the Dense–Channel–Spatial Attention Module to restore the symmetry of reversible neural networks, thereby improving the quality of the recovered images. In addition, we employ a multi-stage progressive training strategy to improve the recovery of lost information, thereby achieving high-quality secret image recovery. To further enhance the security of the hiding process, we introduced a multi-scale wavelet loss function into the loss function. Our method significantly improves the quality of image recovery in single-image steganography tasks across multiple datasets (DIV2K, COCO, ImageNet), with a PSNR reaching up to 50.37 dB (an improvement of over 3 dB compared to other methods). The results show that our method outperforms other state-of-the-art (SOTA) image hiding techniques on different datasets and achieves strong performance in multi-image hiding as well. Full article

Reversible data hiding typically relies on two main techniques: prediction-error expansion and histogram shifting. These techniques complement each other to facilitate effective data embedding by defining non-positive and non-negative thresholds, thereby reducing distortion. The goal is to minimize overflow and underflow pixels by constraining thresholds appropriately. Managing these pixels remains challenging as they must be mapped within the payload. While double modification testing can eliminate the location map for some images, it is highly complex and struggles with images near intensity limits. In this paper, we show that the non-positive and non-negative thresholds for each predicted value are bounded by their infimum and supremum. By restricting the thresholds to these bounds, we maximize the number of embeddable pixels while minimizing the location map size. Moreover, our approach enables the rapid determination of the first operating thresholds and the development of encoding and decoding formulas for RDH without modification. Performance comparisons with established algorithms demonstrate the advantages of our proposed method. Full article

In recent years, the internet has developed rapidly. With the popularity of social media, uploading and backing up digital images has become the norm. A huge number of digital images are circulating on the internet daily, and issues related to information security follow. To protect intellectual property rights, digital watermarking is an indispensable technology. However, the common lossy compression technology in the network transmission process is a big problem for watermarking. This paper describes an innovative semi-blind watermarking method with the use of deep convolutional generative adversarial networks (DCGANs) for hiding and extracting watermarks from JPEG-compressed images. The proposed method achieves an average peak signal-to-noise ratio (PSNR) of 49.99 dB, a structural similarity index (SSIM) of 0.95, and a bit error rate (BER) of 0.008 across varying JPEG quality factors. The process is based on an embedder, decoder, generator, and discriminator. It allows watermarking, decoding, or reconstruction to be symmetric such that there is less distortion and durability is improved. It constructs a specific generator for each image and watermark that is supposed to be protected. Experimental results show that, with the variety of JPEG quality factors, the restored watermark achieves a remarkably low corrupted rate, outstripping recent deep learning-based watermarking methods. Full article

The prediction of the stability of future steps taken by a biped robot is a very important task, since it allows the robot controller to adopt the necessary measures in order to minimize damages if a fall is predicted. We present a classifier to predict the viability of a given planned step taken by a biped robot, i.e., if it will be stable or unstable. The features of the classifier are extracted from a feature engineering process exploiting the useful information contained in the time series generated in the trajectory planning of the step. In order to state the problem as a supervised classification one, we need the ground truth class for each planned step. This is obtained using the Predicted Step Viability (PSV) criterion. We also present a procedure to obtain a balanced and challenging training/testing dataset of planned steps that contains many steps in the border between stable and non stable regions. Following this trajectory planning strategy for the creation of the dataset we are able to improve the robustness of the classifier. Results show that the classifier is able to obtain a 95% of ROC AUC for this demanding dataset using only four time series among all the signals required by PSV to check viability. This allows to replace the PSV stability criterion, which is safe, robust but impossible to apply in real-time, by a simple, fast and embeddable classifier that can run in real time consuming much less resources than the PSV. Full article

Objectives: The main aim of our experiments was to demonstrate the suitability of cell-based biosensors for searching for new anticancer medicinal preparations. Methods: The effect of the substance doxorubicin, doxorubicin embedded in phospholipid nanoparticles, and doxorubicin with phospholipid nanoparticles modified by targeting vectors (cRGD and folic acid) on dsDNA and breast cancer cell lines (MCF-7, MDA-MB-231) was studied. Results: In the obtained doxorubicin nanoforms, the particle size was less than 60 nm. Our study of the percentage of doxorubicin inclusion showed the almost complete embeddability of the substance into nanoparticles for all samples, with an average of 95.4 ± 4.6%. The calculation of the toxicity index of the studied doxorubicin samples showed that all substances were moderately toxic drugs in terms of adenine and guanine. The biosensor analysis using electrodes modified with carbon nanotubes showed an intercalation interaction between doxorubicin and its derivatives and dsDNA, except for the composition of doxorubicin with folic acid with a linker length of 2000 (NPh-Dox-Fol(2.0)). The results of the electroanalysis were normalized to the total cell protein (mg) and cell concentration. The highest intensity of the electrochemical signals was observed in intact control cells of the MCF-7 and MDA-MB-231 cell lines. Conclusions: The proposed electrochemical approach is useful for the analysis of cell line responses to the medicinal preparations. Full article

This study explores the application of retrieval-augmented generation (RAG) to improve the accuracy and reliability of large language models (LLMs) in the context of financial report analysis. The focus is on enabling private investors to make informed decisions by enhancing the question-and-answering capabilities regarding the half-yearly or quarterly financial reports of banks. The study adopts a Design Science Research (DSR) methodology to develop and evaluate an RAG system tailored for this use case. The study conducts a series of experiments to explore models in which different RAG components are used. The aim is to enhance context relevance, answer faithfulness, and answer relevance. The results indicate that model one (OpenAI ADA and OpenAI GPT-4) achieved the highest performance, showing robust accuracy and relevance in response. Model three (MiniLM Embedder and OpenAI GPT-4) scored significantly lower, indicating the importance of high-quality components. The evaluation also revealed that well-structured reports result in better RAG performance than less coherent reports. Qualitative questions received higher scores than the quantitative ones, demonstrating the RAG’s proficiency in handling descriptive data. In conclusion, a tailored RAG can aid investors in providing accurate and contextually relevant information from financial reports, thereby enhancing decision making. Full article

Transportation infrastructure experiences distress due to aging, overuse, and climate changes. To reduce maintenance costs and labor, researchers have developed various structural health monitoring systems. However, the existing systems are designed for short-term monitoring and do not quantify structural parameters. A long-term monitoring system that quantifies structural parameters is needed to improve the quality of monitoring. In this work, a novel Transportation Rf-bAsed Monitoring (TRAM) system is proposed. TRAM is a multi-parameter monitoring system that relies on embeddable backscatter-based, batteryless, and radio-frequency sensors. The system can monitor structural parameters with 3D spatial and temporal information. Laboratory experiments were conducted on a 1D scale to evaluate and examine the sensitivity and reliability of the monitored structural parameters, which are displacement and water content. In contrast to other existing methods, TRAM correlates phase change to the change in concerned parameters, enabling long-term monitoring. Full article

This article discusses applications of technology for sensory-disabled audiences in modern and contemporary art exhibitions. One case study of experimental artistic and curatorial research by The OtherAbilities art collective is discussed: a series of prototype tools for sensory translation from audible sound to vibration were developed to be embeddable in the architecture of spaces where art is presented. In the article, the case study is approached from a curatorial perspective. Based on bibliographical sources, the article starts with a brief historical reference to disability art activism and a presentation of contemporary accessibility solutions for sensory-disabled audiences in museums. The research for the case study was conducted during testing and feedback sessions on the prototypes using open-ended oral interviews, open-ended written comments, and ethnographic observation of visitors’ behavior during exhibitions. The testers were d/Deaf, hard of hearing and hearing. The results focus on the reception of the sensory translation of audible sound to vibration by test users of diverse hearing abilities and on the reception of the prototypes in the context of art and design exhibitions. The article closes with a reflection on how disability scholarship meets art curatorial theory in the example of the article’s case study. Full article

As a booming technique that allows secret data extraction and information carrier recovery without any loss, reversible data hiding in different carriers has attracted more and more concerns in the field of information security. In this paper, a separable reversible data hiding technique for encrypted 3D meshes is proposed based on self-organized blocking and most significant bit (MSB) prediction. The content-owner traverses all faces of the mesh in the ascending index order. Through self-organized blocking, adjacent vertices are concentrated in different small sets. The central vertex is considered as the reference and the others as embedded vertices in each set. Then, multiple most significant bits between the central vertex and others are adaptively predicted and reserved as embeddable bits for secret data embedding. Because vertex coordinates in each set have a high space correlation and most vertices participate in the prediction process, a huge number of most significant bits can be marked as embeddable bits to embed secret data. Experimental results demonstrate that the proposed method can obtain the highest embedding rate compared with representative methods. To our best knowledge, the average embedding rate of the proposed method is about 28 bits per vertex (bpv) higher than the recently proposed method. Most importantly, instead of recovering meshes with higher quality, original meshes with high visual symmetry/quality can be recovered. Full article

Network visualization, in mathematics often referred to as graph visualization, has evolved significantly over time, offering various methods to effectively represent complex data structures. New methods and devices advance the possibilities of visualization both from the point of view of the quality of displayed information and of the possibilities of visualizing a larger amount of data. Immersive visualization includes the user directly in presented visual representation but requires a native 3D environment for direct interaction with visualized information. This article describes an approach to creating a force-directed immersive 3D network visualization algorithm available for application in immersive environments, such as a cave automatic virtual environment or virtual reality. The algorithm aims to address the challenge of creating visually appealing and easily interpretable visualizations by utilizing 3D space and the Unity engine. The results show successfully visualized data and developed interactive visualization methods, overcoming limitations of basic force-directed implementations. The main contribution of the presented research is the force-directed algorithm with springs and controlled placement as an immersive visualization technique that combines the use of springs and attractive forces to stabilize a network in a 3D environment. Full article

Background: While low back pain (LBP) is the leading cause of disability worldwide, its clinical objective assessment is currently limited. Part of this syndrome arises from the abnormal sensorimotor control of back muscles, involving increased muscle fatigability (i.e., assessed with the Biering–Sorensen test) and abnormal muscle activation patterns (i.e., the flexion–extension test). Surface electromyography (sEMG) provides objective measures of muscle fatigue development (median frequency drop, MDF) and activation patterns (RMS amplitude change). This study therefore assessed the sensitivity and validity of a novel and flexible sEMG system (NSS) based on PEVA electrodes and potentially embeddable in textiles, as a tool for objective clinical LBP assessment. Methods: Twelve participants wearing NSS and a commercial laboratory sEMG system (CSS) performed two clinical tests used in LBP assessment (Biering–Sorensen and flexion–extension). Erector spinae muscle activity was recorded at T12-L1 and L4-L5. Results: NSS showed sensitivity to sEMG changes associated with fatigue development and muscle activations during flexion–extension movements (p < 0.05) that were similar to CSS (p > 0.05). Raw signals showed moderate cross-correlations (MDF: 0.60–0.68; RMS: 0.53–0.62). Adding conductive gel to the PEVA electrodes did not influence sEMG signal interpretation (p > 0.05). Conclusions: This novel sEMG system is promising for assessing electrophysiological indicators of LBP during clinical tests. Full article

The reversible data hiding scheme in the encrypted domain is a potential solution to the concerns regarding user privacy in cloud applications. The 3D mesh model is an emerging file format and is widely used in engineering modeling, special effects, and video games. However, studies on reversible data hiding in encrypted 3D mesh models are still in the preliminary stage. In this paper, two novel techniques, multi-group partition (MGP) and closest pair prediction (CPP), are proposed to improve performance. The MGP technique adaptively classifies vertices into reference and embeddable vertices, while the CPP technique efficiently predicts embeddable vertices and generates shorter recovery information to vacate more redundancy for additional data embedding. Experimental results indicate that the proposed scheme significantly improves the embedding rate compared to state-of-the-art schemes and can be used in real-time applications. Full article