Search Results (2,597)

Recent advancements in geophysical exploration have highlighted the importance of integrating electromagnetic (EM) and magnetic data to enhance subsurface target detection. Conventional inversion techniques often struggle with the non-uniqueness of solutions and sensitivity to noise when relying on a single data modality. In this study, we introduce a novel deep learning framework, MagEMNet, designed to jointly invert EM and magnetic responses. This convolutional neural network (CNN)-based model effectively combines these two complementary data types, improving the estimation of target characteristics such as location, orientation, and physical properties. Trained on synthetic datasets generated through forward modeling, MagEMNet leverages the adaptive moment estimation (Adam) optimizer and a dynamic learning rate strategy to enhance convergence. Our results show that MagEMNet not only outperforms traditional inversion techniques in terms of accuracy but also accelerates the inversion process, offering an efficient solution for real-world applications, including unexploded ordnance (UXO) detection and subsurface resource assessment. Full article

Interior design, which integrates art and science, is vulnerable to infringements such as copying and tampering. The unique and often intricate nature of these designs makes them vulnerable to unauthorized replication and misuse, posing significant challenges for designers seeking to protect their intellectual property. To solve the above problems, we propose a deep learning-based zero-watermark copyright protection method. The method aims to embed undetectable and unique copyright information through image fusion technology without destroying the interior design image. Specifically, the method fuses the interior design and a watermark image through deep learning to generate a highly robust zero-watermark image. This study also proposes a zero-watermark verification network with U-Net to verify the validity of the watermark and extract the copyright information efficiently. This network can accurately restore watermark information from protected interior design images, thus effectively proving the copyright ownership of the work and the copyright ownership of the interior design. According to verification on an experimental dataset, the zero-watermark copyright protection method proposed in this study is robust against various image-oriented attacks. It avoids the problem of image quality loss that traditional watermarking techniques may cause. Therefore, this method can provide a strong means of copyright protection in the field of interior design. Full article

Recent advances in deep learning have fostered a transition from the traditional, bit-centric paradigm of Shannon’s information theory to a semantic-oriented approach, emphasizing the transmission of meaningful information rather than mere data fidelity. However, black-box AI-based semantic communication lacks structured discretization and remains dependent on analog modulation, which presents deployment challenges. This paper presents a new semantic-aware digital speech communication system, named Hybrid-DeepSCS, a stepping stone between traditional and fully end-to-end semantic communication. Our system comprises the following parts: a semantic encoder for extracting and compressing structured features, a standard transmitter for digital modulation including source and channel encoding, a standard receiver for recovering the bitstream, and a semantic decoder for expanding the features and reconstructing speech. By adding semantic encoding to a standard digital transmission, our system works with existing communication networks while exploring the potential of deep learning for feature representation and reconstruction. This hybrid method allows for gradual implementation, making it more practical for real-world uses like low-bandwidth speech, robust voice transmission over wireless networks, and AI-assisted speech on edge devices. The system’s compatibility with conventional digital infrastructure positions it as a viable solution for IoT deployments, where seamless integration with legacy systems and energy-efficient processing are critical. Furthermore, our approach addresses IoT-specific challenges such as bandwidth constraints in industrial sensor networks and latency-sensitive voice interactions in smart environments. We test the system under various channel conditions using Signal-to-Distortion Ratio (SDR), PESQ, and STOI metrics. The results show that our system delivers robust and clear speech, connecting traditional wireless systems with the future of AI-driven communication. The framework’s adaptability to edge computing architectures further underscores its relevance for IoT platforms, enabling efficient semantic processing in resource-constrained environments. Full article

Introduction: Alexithymia is a multidimensional construct characterized by difficulties in identifying and describing emotions, distinguishing emotional states from bodily sensations, and an externally oriented thinking style. Although the prevalence in the general population is around 10%, it is significantly higher in clinical groups, including those with autism spectrum disorders, depression, anxiety, and neurological conditions. Neuroimaging research, especially using magnetic resonance imaging, has documented structural and functional alterations in alexithymia; however, electroencephalography (EEG)—an older yet temporally precise method—remains less comprehensively explored. This mechanistic review aims to synthesize EEG-based evidence of the neural correlates of alexithymia and to propose potential neurophysiological mechanisms underpinning its affective and cognitive dimensions. Methods: A thorough literature search was conducted in December 2024 across PubMed/Medline, ResearchGate, Google Scholar, and Cochrane using combined keywords (“EEG”, “QEEG”, “electroencephalography”, “alexithymia”) to identify English-language clinical trials or case studies published from January 1980 to December 2024. Two reviewers independently screened the titles and abstracts, followed by a full-text review. Studies were included if they specifically examined EEG activity in participants with alexithymia. Of the 1021 initial records, eight studies fulfilled the inclusion criteria. Results: Across the reviewed studies, individuals with alexithymia consistently demonstrated right-hemisphere dominance in EEG power and connectivity, particularly in the theta and alpha bands, during both neutral and emotion-eliciting tasks. Many exhibited reduced interhemispheric coherence and disrupted connectivity in the frontal and parietal regions, potentially contributing to difficulties in cognitive processing and emotion labeling. Some studies have also reported diminished gamma band activity and phase synchrony in response to negative stimuli, suggesting impaired higher-order integration of emotional information. Crucially, subjective reports (e.g., valence ratings) often do not differ between alexithymic and non-alexithymic groups, highlighting that EEG measures may capture subtle emotional processing deficits not reflected in self-reports. Conclusions: EEG findings emphasize that alexithymia involves specific disruptions in cortical activation and network-level coordination, rather than merely the absence of emotional experiences. Right-hemisphere over-reliance, reduced interhemispheric transfer, and atypical oscillatory patterns in the alpha, theta, and gamma bands appear to be central to the condition’s pathophysiology. Understanding these neural signatures offers avenues for future research—particularly intervention studies that test whether modulating these EEG patterns can improve emotional awareness and expression. These insights underscore the potential clinical utility of EEG as a sensitive tool for detecting and tracking alexithymic traits in both research and therapeutic contexts. Full article

Mobile edge computing (MEC) is a modern technique that has led to substantial progress in wireless networks. To address the challenge of efficient task implementation in resource-limited environments, this work strengthens system performance through resource allocation based on fairness and energy efficiency. Integration of energy-harvesting (EH) technology with MEC improves sustainability by optimizing the power consumption of mobile devices, which is crucial to the efficiency of task execution. The combination of MEC and an ultra-dense network (UDN) is essential in fifth-generation networks to fulfill the computing requirements of ultra-low-latency applications. In this study, issues related to computation offloading and resource allocation are addressed using the Lyapunov mixed-integer linear programming (MILP)-based optimal cost (LYMOC) technique. The optimization problem is solved using the Lyapunov drift-plus-penalty method. Subsequently, the MILP approach is employed to select the optimal offloading option while ensuring fairness-oriented resource allocation among users to improve overall system performance and user satisfaction. Unlike conventional approaches, which often overlook fairness in dense networks, the proposed method prioritizes fairness-oriented resource allocation, preventing service degradation and enhancing network efficiency. Overall, the results of simulation studies demonstrate that the LYMOC algorithm may considerably decrease the overall cost of system execution when compared with the Lyapunov–MILP-based short-distance complete local execution algorithm and the full offloading-computation method. Full article

We report the topochemical solid-state polymerization of different series of symmetrical diacetylenes (DAs) and asymmetrical chlorodiacetylenes (ClDAs), whose members differ in their alkyl spacing lengths of one to four methylene units (n = 1, 2, 3, 4) between the diyne and carbamate functionalities. Structure determination by single-crystal X-Ray diffraction (SCXRD) confirms that in each of these series, at least 50% of the analyses show monomers with a particular stacking pattern presenting two potential directions of polymerization simultaneously. An organization of a crystalline polydiacetylene (PDA) with an oblique chain orientation with respect to the network of cooperatives hydrogen bonds is rather rare in the literature (only two cases), and here we have obtained two more examples of this type of structural motif (supported by SCXRD analysis of the polymer). Orientation control is essential to optimize the performance of conjugated polymers, and a spacer length modification strategy presents a potential way to achieve this in the case of PDA. Full article

Thin-walled structures are widely used in aeronautical and aerospace engineering due to their light weight and high structural performance. Ensuring their integrity is crucial for safety and reliability, which is why structural health monitoring (SHM) methods, such as guided wave-based techniques, have been developed to detect and characterize damage in such components. This study presents a novel damage identification procedure for guided wave-based SHM using deep neural networks (DNNs) trained with experimental data. This technique employs the so-called wave damage interaction coefficients (WDICs) as highly sensitive damage features that describe the unique scattering pattern around possible damage. The DNNs learn intricate relationships between damage characteristics, e.g., size or orientation, and corresponding WDIC patterns from only a limited number of damage cases. An experimental training data set is used, where the WDICs of a selected damage type are extracted from measurements using a scanning laser Doppler vibrometer. Surface-bonded artificial damages are selected herein for demonstration purposes. It is demonstrated that smart DNN interpolations can replicate WDIC patterns even when trained on noisy measurement data, and their generalization capabilities allow for precise predictions for damages with arbitrary properties within the range of trained damage characteristics. These WDIC predictions are readily available, i.e., ad hoc, and can be compared to measurement data from an unknown damage for damage characterization. Furthermore, the fully trained DNN allows for predicting WDICs specifically for the sensing angles requested during inspection. Additionally, an anglewise principal component analysis is proposed to efficiently reduce the feature dimensionality on average by more than $90\%$ while accounting for the angular dependencies of the WDICs. The proposed damage identification methodology is investigated under challenging conditions using experimental data from only three sensors of a damage case not contained in the training data sets. Detailed statistical analyses indicate excellent performance and high recognition accuracy for this experimental data-based approach. This study also analyzes differences between simulated and experimental WDIC patterns. Therefore, an existing DNN trained on simulated data is also employed. The differences between the simulations and experiments affect the identification performance, and the resulting limitations of the simulation-based approach are clearly explained. This highlights the potential of the proposed experimental data-based DNN methodology for practical applications of guided wave-based SHM. Full article

With a focus on reducing building energy consumption, approaches that simultaneously optimize multiple passive design parameters in industrial buildings have received limited attention. Most existing studies tend to examine building geometry or individual design parameters under limited scenarios, underscoring the potential benefits of adopting a comprehensive, multiparameter approach that integrates climate-responsive and sustainable design strategies. This study bridges that gap by systematically optimizing key passive design parameters—building geometry, orientation, window-to-wall ratio (WWR), and glazing type—to minimize energy loads and enhance sustainability across five distinct climate zones. Fifteen different building geometries with equal floor areas and volumes were analyzed, considering fifteen glazing types and multiple orientations varying by 30° increments. DesignBuilder simulations yielded 16,900 results, and due to the inherent challenges in directly optimizing building geometry within simulation environments, the data were restructured to reveal underlying relationships. An Energy Performance Optimization Model, based on a Particle Swarm Optimization (PSO) algorithm integrated with an Artificial Neural Network (ANN), was developed to identify optimal design solutions tailored to specific climatic conditions. The optimization results successfully determined the optimal combinations of building geometry, orientation, WWR, and glazing type to reduce heating and cooling loads, thereby promoting energy efficiency and reducing carbon emissions in industrial buildings. This study offers a practical design solution set and provides architects with actionable recommendations during the early design phase, establishing a machine learning-based framework for achieving sustainable, energy-efficient, and climate-responsive industrial building designs. Full article

Object detection in remote sensing images is an important research topic in the field of remote sensing intelligent interpretation. Although modern object detectors have made good progress, high-precision oriented object detection still faces severe challenges due to the large-scale variation, strong directional diversity and complex background interference of objects in remote sensing images. Currently, most remote sensing object detectors focus on modeling object characteristics in the spatial domain while ignoring the frequency domain information of the object. Recent studies have shown that frequency domain learning has brought substantial benefits in many visual fields. To this end, we proposed an adaptive dual-domain dynamic interaction network (AD³I-Net) for oriented object detection tasks in remote sensing images. The network has three important components: a spatial adaptive selection (SAS) module, a frequency adaptive selection (FAS) module, and a dual-domain feature interaction (DDFI) module. The SAS module adaptively models spatial context information and dynamically adjusts the feature receptive field to construct more accurate spatial position features for objects of different scales. The FAS module uses the transformation from the spatial domain to the frequency domain to adaptively learn the frequency information of the object, to model direction features, and to make up for the lack of spatial domain information. Finally, through the DDFI module, the features extracted from the two domains are interactively fused, thereby bridging the complementary information to enhance the feature expression of the object and give it rich spatial position and direction information. The AD³I-Net we proposed fully exploits the interaction between the different domains and improves the model’s ability to capture subtle object features. Our method has been extensively experimentally verified on two mainstream datasets, HRSC2016 and DIOR-R. The experimental results demonstrate that this method performs competitively in oriented object detection tasks. Full article

For networks that process 3D data, estimating the orientation and position of 3D objects is a challenging task. This is because the traditional networks are not robust to the rotation of the data, and their internal workings are largely opaque and uninterpretable. To solve this problem, a novel equivariant self-supervised vector network for point clouds is proposed. The network can learn the rotation direction information of the 3D target and estimate the rotational pose change of the target, and the interpretability of the equivariant network is studied using information theory. The utilization of vector neurons within the network lifts the scalar data to vector representations, enabling the network to learn the pose information inherent in the 3D target. The network can perform complex rotation-equivariant tasks after pre-training, and it shows impressive performance in complex tasks like category-level pose change estimation and rotation-equivariant reconstruction. We demonstrate through experiments that our network can accurately detect the orientation and pose change of point clouds and visualize the latent features. Moreover, it performs well in invariant tasks such as classification and category-level segmentation. Full article

This article presents the design of a novel, compact, 4 × 2 planar-array antenna that provides quad-beam radiation in the broadside direction, and it enhances coverage and serviceability for millimeter-wave applications. The antenna utilizes a corporate (parallel) feed network to deliver equal power and phase to all elements. Non-uniform element spacing in the two orthogonal planes, exceeding 0.5${\lambda }_1$ (${\lambda }_1$ being the wavelength at 30 GHz), results in a quad-beam radiation pattern. Two beams are formed in the xz-plane and two in the yz-plane, oriented at angles of $\theta =\pm 54°$. However, this spacing leads to null radiation at the center and splits the radiation energy, reducing the overall gain. The measured half-power beamwidth (HPBW) is 30° in the xz-plane and 35° in the yz-plane, with X-polarization levels of −20.5 dB and −26 dB, respectively. The antenna achieves a bandwidth of 28.5–31.1 GHz and a peak gain of 10.6 dBi. Furthermore, increasing the aperture size enhances the gain and narrows the beamwidth by replicating the structure and tuning the feed network. These features make the proposed antenna suitable for 5G wireless communication systems. Full article

Lung cancer still represents one of the main causes of cancer-related mortality, highlighting the necessity for precise, effective, and minimally intrusive diagnostic methods. This research presents an innovative approach to classifying lung lesions using Doppler ultrasound imagery combined with a feed-forward neural network (FNN). This study integrates Doppler mode ultrasound vascularization features—blood vessel area, tortuosity index, and orientation—into an FNN to classify lung lesions as benign or malignant. A dataset of 565 Doppler ultrasound pictures was extended using augmentation techniques to enhance robustness, yielding a training dataset of 3390 images. The FNN architecture was trained utilizing the Levenberg–Marquardt algorithm, achieving a classification accuracy of 98%, demonstrating its potential as a diagnostic aid. The results indicate that integrating all three vascularization factors significantly improves diagnosis accuracy compared with individual modules. This method offers a non-invasive and cost-effective complementary tool to conventional techniques such as CT scans, with the potential to improve early detection and treatment planning for lung cancer patients. Full article

Fibrous networks are porous materials that can have stochastic and uniform microstructures. Various fibrous networks can be found in nature (e.g., collagens, hydrogels, etc.) or manufactured (e.g., composites and nonwovens). This study focuses on the geometrical characterisation of stochastic fibrous networks with continuous fibres in a 2D domain, discussing their main relevant parameters: basis weight, orientation distribution function, crimp, porosity, spatial distribution of fibres (uniformity), and fibre intersections. The comprehensive review of the literature is combined with original results to understand the effect of the analysed parameters on various features of fibrous networks such as mechanical performance, filtration, insulation, etc. Full article

Synthetic Aperture Radar (SAR) is a remote sensing technology that can realize all-weather and all-day monitoring, and it is widely used in ocean ship monitoring tasks. Recently, many oriented detectors were used for ship detection in SAR images. However, these methods often found it difficult to balance the detection accuracy and speed, and the noise around the target in the inshore scene of SAR images led to a poor detection network performance. In addition, the rotation representation still has the problem of boundary discontinuity. To address these issues, we propose S4Det, a Sinusoidal Single-Stage SAR image detection method that enables real-time oriented ship target detection. Two key mechanisms were designed to address inshore scene processing and angle regression challenges. Specifically, a Breadth Search Compensation Module (BSCM) resolved the limited detection capability issue observed within inshore scenarios. Neural Discrete Codebook Learning was strategically integrated with Multi-scale Large Kernel Attention, capturing context information around the target and mitigating the information loss inherent in dilated convolutions. To tackle boundary discontinuity arising from the periodic nature of the target regression angle, we developed a Sine Fourier Transform Coding (SFTC) technique. The angle is represented using diverse sine components, and the discrete Fourier transform is applied to convert these periodic components to the frequency domain for processing. Finally, the experimental results of our S4Det on the RSSDD dataset achieved 92.2% mAP and 31+ FPS on an RTXA5000 GPU, which outperformed the prevalent mainstream of the oriented detection network. The robustness of the proposed S4Det was also verified on another public RSDD dataset. Full article

Fused filament fabrication (FFF) has recently emerged as a sustainable digital manufacturing technology to fabricate polymer composite parts with complex structures and minimal waste. However, FFF-printed composite parts frequently exhibit heterogeneous structures with low mechanical properties. To manufacture high-end parts with good mechanical properties, advanced predictive tools are required. In this paper, Artificial Neural Network (ANN) models were developed to evaluate the mechanical properties of 3D-printed polyamide 12 (PA) and carbon fiber (CF) and glass fiber (GF) reinforced PA composites. Tensile samples were fabricated by FFF, considering two input parameters, such as printing orientation and infill density, and tested to determine the mechanical properties. Then, single- and multi-target ANN models were trained using the forward propagation Levenberg–Marquardt algorithm. Post-training performance analysis indicated that the ANN models work efficiently and accurately in predicting Young’s modulus and tensile strength of the 3D-printed PA and fiber-reinforced PA composites, with most relative errors being far less than 5%. In terms of mechanical properties, such as Young’s modulus and tensile strength, the 3D-printed composites outperform the unreinforced PA. Printing PA composites with 0° orientation and 100% infill density results in a maximum increase in Young’s modulus (up to 98% for CF/PA and 32% for GF/PA) and tensile strength (up to 36% for CF/PA and 18% for GF/PA) compared to the unreinforced PA. This study underscores the potential of the ANN models to predict the mechanical properties of 3D-printed parts, enhancing the use of 3D-printed PA composite components in structural applications. Full article