Search Results (167)

Lattice-based cryptography (LBC) is an essential direction in the fields of homomorphic encryption (HE), zero-knowledge proofs (ZK), and post-quantum cryptography (PQC), while number theoretic transformations (NTT) are a performance bottleneck that affects the promotion and deployment of LBC applications. Field-programmable gate arrays (FPGAs) are an ideal platform for accelerating NTT due to their reconfigurability and parallel capabilities. High-level synthesis (HLS) can shorten the FPGA development cycle, but for algorithms such as NTT, the synthesizer struggles to handle the inherent memory dependencies, often resulting in suboptimal synthesis outcomes for direct designs. This paper proposes a systematic HLS co-design to progressively guide the synthesis of NTT accelerators. The approach integrates several key techniques: arithmetic module resource optimization, conflict-free butterfly scheduling, memory partitioning, and template-based automated design fusion. It reveals how to resolve pipeline bottlenecks in HLS-based designs and expand parallel processing, guiding microarchitecture iterations to achieve an efficient design space. Compared to existing HLS-based designs, the area-latency product achieves a performance improvement of 1.93 to 191 times, and compared to existing HDL-based designs, the area-cycle product achieves a performance improvement of 1.7 to 10.6 times. Full article

The misuse of drone technology poses significant risks to public and personal safety, emphasizing the need for accurate and efficient aerial target detection to prevent detection failures due to randomly distributed airborne targets and mitigate interference from undesired directions. Unlike conventional beam-synthesis techniques that often require either a large number of array elements or iterative numerical optimization, the proposed method analytically derives the excitation distribution by solving a newly formulated weighted-constraint problem, thereby fully accounting for mutual coupling between elements and ensuring both computational efficiency and design accuracy. In this communication, a 10 × 4 planar microstrip antenna array with a wide flat-top beam and low sidelobe is designed based on the extended method of maximum power transmission efficiency. The optimized distribution of excitations for the antenna array, which achieves a shaped beam with uniform gain over the desired angular range while suppressing sidelobe levels (SLLs) outside the shaped region, is derived by analytically solving a newly formulated weighted constraint problem. To reduce the number of antenna elements and enhance radiation characteristics, the inter-element spacings in the E-plane and H-plane are set to 0.55 λ₀ and 0.75 λ₀, where λ₀ is the free-space wavelength at 3.5 GHz. Measurement results indicate that the flat-top beam in the E-plane has a wide half-power beamwidth (HPBW) of 51.2° and a low SLL of −30.1 dB, while the beam in the H-plane has a narrow HPBW of 20.1° and a low SLL of −30.5 dB, thereby demonstrating its capability in aerial target detection and airborne tracking applications. Full article

Two critical research problems emerge in creative education quality management: the framework misalignment problem, where business-oriented performance metrics inadequately assess design creativity and innovation, and the sustainability integration gap, reflecting limited incorporation of environmental and social sustainability dimensions into excellence models. This review article addresses these problems by developing an initial framework that adapts the Baldrige framework for urban design, architecture, art, and design education with integrated sustainability principles. Drawing on literature review and theoretical synthesis, the article proposes a framework that introduces three key epistemological shifts: prioritizing process over product, supporting non-linear and reflective learning pathways, and recognizing tacit, embodied, and experiential knowledge as central to creative education. The framework incorporates the United Nations Sustainable Development Goals (SDGs) as core design challenges and introduces innovative evaluation tools, including portfolios with iterative review processes, community feedback loops, and SDG mapping rubrics. This research contributes to the educational quality management literature by offering a systematic framework that bridges business excellence models with creative education paradigms while positioning sustainability as a core educational objective rather than a peripheral concern. Full article

An iterative method was developed in this study within a Petri net system (PNS) for flexible manufacturing systems (FMSs) to eliminate deadlocks. The algorithm employs variant tokens, ranging from a few to many, in idle places to identify subnet deadlocks through reachability states. Additionally, it utilizes the place invariant (PI) to resolve deadlocks. An algorithm with a simple example was developed and applied using a complex scenario. The developed algorithm is user-friendly and effectively eliminates deadlocks in the PNS of FMSs, producing optimal results. Full article

Video stabilization is a critical technology for enhancing visual content quality in dynamic shooting scenarios, especially with the widespread adoption of mobile photography devices and Unmanned Aerial Vehicle (UAV) platforms. While traditional digital stabilization algorithms can improve frame stability by modeling global motion trajectories, they often suffer from excessive cropping or boundary distortion, leading to a significant loss of valid image regions. To address this persistent challenge, we propose the View Out-boundary Synthesis Algorithm (VOSA), a symmetry-aware spatio-temporal consistency framework. By leveraging rotational and translational symmetry principles in motion dynamics, VOSA realizes optical flow field extrapolation through an encoder–decoder architecture and an iterative boundary extension strategy. Experimental results demonstrate that VOSA enhances conventional stabilization by increasing content retention by 6.3% while maintaining a 0.943 distortion score, outperforming mainstream methods in dynamic environments. The symmetry-informed design resolves stability–content conflicts and outperforms mainstream methods in dynamic environments, establishing a new paradigm for full-frame stabilization. Full article

Terahertz metamaterial devices (TMDs) have demonstrated promising applications in biomass detection, wireless communications, and security inspection. Nevertheless, conventional design methodologies for such devices suffer from extensive iterative optimizations and significant dependence on empirical expertise, substantially prolonging the development cycle. This study proposes a reverse design framework leveraging a deep neural network (DNN) to enable rapid and efficient TMD synthesis, exemplified through a three-band terahertz metamaterial sensor. The developed DNN model achieves high-fidelity predictions (mean squared error = 0.03) and enables rapid inference for structural parameter generation. Experimental validation across four distinct target absorption spectra confirms high consistency between simulated and target responses, with near-identical triple-band resonance characteristics. Benchmarking against traditional CST-based optimization reveals a 36-fold acceleration in design throughput (200-device parameterization reduced from 36 h to 1 h). This work demonstrates a promising strategy for data-driven reverse design of multi-peak terahertz metamaterials, combining computational efficiency with rigorous electromagnetic performance. Full article

Design Thinking (DT) has emerged as a relevant methodology for addressing global challenges aligned with the United Nations Sustainable Development Goals (SDGs). This study presents a systematic literature review, conducted following PRISMA 2020 guidelines, which analyzes 42 peer-reviewed publications from 2013 to 2023. Through inductive content analysis, 10 core DT principles—such as empathy, iteration, user-centeredness, and systems thinking—I identified and thematically mapped to specific SDGs, including goals related to health, education, innovation, and climate action. The study also presents five real-world cases from diverse sectors such as technology, healthcare, and urban planning, illustrating how DT has been applied to address practical challenges aligned with the SDGs. However, the review identifies persistent gaps in the field: the lack of standardized evaluation frameworks, limited integration across SDG domains, and weak adaptation of ethical and contextual considerations, particularly in vulnerable communities. As a response, this paper recommends the adoption of structured impact assessment tools (e.g., Cities2030, Responsible Design Thinking), integration of design justice principles, and the development of participatory, iterative ecosystems for innovation. By offering both conceptual synthesis and applied insights, this article positions Design Thinking as a strategic and systemic approach for driving sustainable transformation aligned with the 2030 Agenda. Full article

We present the results of a detailed spectroscopic analysis of the double-lined spectroscopic binary system $\alpha$ Equulei. High-resolution spectra obtained with the SOPHIE spectrograph at various orbital phases were used to disentangle the composite spectra into individual components using the spectral line deconvolution (SLD) iterative technique. The atmospheric parameters of each component were refined with the SME (spectroscopy made easy) package and further validated by following methods: SED (spectral energy distribution), the independence of the abundance of individual Fe i–ii lines on the reduced equivalent width and ionisation potential, and fitting with the hydrogen line profiles. Our accurate abundance analysis uses a hybrid technique for spectrum synthesis. This is based on classical model atmospheres that are calculated under the assumption of local thermodynamic equilibrium (LTE), together with non-LTE (NLTE) line formation. This is used for 15 out of the 25 species from C to Nd that were investigated. The primary giant component (G7-type) exhibits a typical abundance pattern for normal stars, with elements from He to Fe matching solar values and neutron-capture elements showing overabundances up to 0.5 dex. In contrast, the secondary dwarf component displays characteristics of an early stage Am star. The observed abundance differences imply distinct diffusion processes in their atmospheres. Our results support the scenario in which chemical peculiarities in Am stars develop during the main sequence and may decrease as the stars evolve toward the subgiant branch. Full article

This paper proposes a Hybrid Recursive Trigonometric (HRT) technique for FPGA-based direct digital frequency synthesizers. The HRT technique integrates a recursive cosine generator with periodic reinitialization via a second-order Taylor polynomial to reduce cumulative errors without requiring ROMs or iterative CORDIC units. A resource-efficient combinational architecture is implemented and validated on the Lattice iCE40HX1K FPGA. The effectiveness of the proposed HRT technique is evaluated through simulation and FPGA-based experiments, with respect to spectral accuracy and resource efficiency, particularly for fixed-point cosine waveform synthesis in low-resource digital systems. Simulation results show that the system has a spurious-free dynamic range (SFDR) of −86.09 dBc and signal-to-noise ratio of 52.74 dB using 16-bit fixed-point arithmetic. Experimental measurements confirm the feasibility, achieving −58.86 dBc SFDR. Full article

In response to the demand for precise measurement of illuminance distribution in the quality control of LED monitoring fill light products and the iterative direction of secondary optical design, distributed photometric sensors have shown advantages, but their measurement uncertainty assessment faces challenges. This paper addresses the problem of uncertainty evaluation in photometric parameter measurement with a two-dimensional horizontal distributed photometric sensor and proposes an uncertainty evaluation framework for this task. We have established an uncertainty analysis model for the measurement system and provided two uncertainty synthesis methods, The Guide to the Expression of Uncertainty in Measurement and the Monte Carlo method. This study designed illuminance measurement experiments to validate the feasibility of the proposed uncertainty evaluation method. The results demonstrate that the actual probability distribution of the measurement data follows a trapezoidal distribution. Furthermore, the expanded uncertainty calculated using the GUM method was 21.1% higher than that obtained by the MCM. This work effectively addresses the uncertainty evaluation challenge for illuminance measurement tasks using a two-dimensional horizontal distributed photometric sensor. The findings offer valuable reference for the uncertainty assessment of other high-precision optical instruments and possess significant engineering value in enhancing the reliability of optical metrology systems. Full article

Automatic Speaker Verification (ASV) systems are increasingly employed to secure access to services and facilities. However, recent advances in speech deepfake generation pose serious threats to their reliability. Modern speech synthesis models can convincingly imitate a target speaker’s voice and generate realistic synthetic audio, potentially enabling unauthorized access through ASV systems. To counter these threats, forensic detectors have been developed to distinguish between real and fake speech. Although these models achieve strong performance, their deep learning nature makes them susceptible to adversarial attacks, i.e., carefully crafted, imperceptible perturbations in the audio signal that make the model unable to classify correctly. In this paper, we explore adversarial attacks targeting speech deepfake detectors. Specifically, we analyze the effectiveness of Basic Iterative Method (BIM) attacks applied in both time and frequency domains under white- and black-box conditions. Additionally, we propose an ensemble-based attack strategy designed to simultaneously target multiple detection models. This approach generates adversarial examples with balanced effectiveness across the ensemble, enhancing transferability to unseen models. Our experimental results show that, although crafting universally transferable attacks remains challenging, it is possible to fool state-of-the-art detectors using minimal, imperceptible perturbations, highlighting the need for more robust defenses in speech deepfake detection. Full article

To address the challenges of image inpainting in scenarios with extensive or irregular missing regions—particularly detail oversmoothing, structural ambiguity, and textural incoherence—this paper proposes an Image Structure-Guided (ISG) framework that hierarchically integrates structural priors with semantic-aware texture synthesis. The proposed methodology advances a two-stage restoration paradigm: (1) Structural Prior Extraction, where adaptive edge detection algorithms identify residual contours in corrupted regions, and a transformer-enhanced network reconstructs globally consistent structural maps through contextual feature propagation; (2) Structure-Constrained Texture Synthesis, wherein a multi-scale generator with hybrid dilated convolutions and channel attention mechanisms iteratively refines high-fidelity textures under explicit structural guidance. The framework introduces three innovations: (1) a hierarchical feature fusion architecture that synergizes multi-scale receptive fields with spatial-channel attention to preserve long-range dependencies and local details simultaneously; (2) spectral-normalized Markovian discriminator with gradient-penalty regularization, enabling adversarial training stability while enforcing patch-level structural consistency; and (3) dual-branch loss formulation combining perceptual similarity metrics with edge-aware constraints to align synthesized content with both semantic coherence and geometric fidelity. Our experiments on the two benchmark datasets (Places2 and CelebA) have demonstrated that our framework achieves more unified textures and structures, bringing the restored images closer to their original semantic content. Full article

This study responds to the urgent need for education that fosters sustainable, self-directed development by introducing a five-phase narrative instruction pre-module grounded within the Life Design Thinking (LDT) framework. Integrating design thinking with narrative theory, LDT promotes learner agency, identity coherence, and adaptive future planning through structured autobiographical reflection. The intervention was conducted in a group setting with 14 adult learners (n = 14) from China. Although participant demographics were not the primary focus, the study emphasized theoretical and methodological development through iterative instructional design. A mixed-methods approach was employed, including rubric-based scoring of 101 written narratives and a thematic analysis of the reflective content. The results demonstrated significant improvements in narrative competence, especially in technical application (+80.91%), reflective depth (+70.0%), with thematic clarity (+58.11%), also showing notable gains related to meaning synthesis and narrative focus. Learners also exhibited enhanced thematic continuity and future-oriented integration. These outcomes highlight the value of narrative pedagogy as a transformative learning approach aligned with SDG 4.7. By cultivating metacognitive awareness and future-readiness, the module contributes to education for sustainable development and provides initial evidence for a potentially scalable and transferable model that supports learners in navigating complexity with meaning and purpose. Full article

National and international aged care frameworks recommend family-integrated care to enhance care quality and outcomes, supported by evidence demonstrating improvements in patient and clinician experiences. Yet uncertainty remains about how to integrate family carers effectively in diverse healthcare models and settings for neurodegenerative and mental health conditions. A systematic integrative review was conducted to answer two research questions: how do the studies describe the integration of family carers in health services design and delivery for older patients with neurodegenerative and mental health conditions? And what is the evidence for family-integrated care models impacting the health and wellbeing of these older patients? Structured and iterative searches of five databases (CINAHL, Medline (Ovid), Web of Science, PsycINFO, and ProQuest) and the Google Scholar search engine identified 2271 records. A Covidence screening process resulted in 14 studies for review, comprising randomised controlled trials, mixed methods studies, qualitative studies, and quasi-experimental designs. The following four themes emerged from the evidence synthesis: (1) family participation in service delivery, (2) health and wellbeing outcomes, (3) satisfaction with care, and (4) service dynamics in enabling family-integrated care successfully. This review highlights that while family-integrated care models contribute to positive health and wellbeing outcomes for older patients with neurodegenerative and mental health conditions, challenges remain for implementation due to the extent and variability in integration strategies, a lack of rigorous evaluation, and an absence of standardised frameworks. Full article

This study investigates the dimensional synthesis of steering mechanisms for front-wheel-drive, two-axle, four-wheeled vehicles using two metaheuristic optimization algorithms: Differential Evolution with golden ratio (DE-gr) and Improved Particle Swarm Optimization (IPSO). The vehicle under consideration has a track-to-wheelbase ratio of 0.5 and an inner wheel steering angle of 70 degrees. The mechanisms synthesized include the Ackermann steering mechanism and two variants (Type I and Type II) of the Watt-I six-bar steering mechanisms, also known as central-lever steering mechanisms. To ensure accurate steering and minimize tire wear during cornering, adherence to the Ackermann steering condition is enforced. The objective function combines the mean squared structural error at selected steering positions with a penalty term for violations of the Grashoff inequality constraint. Each optimization run involved 100 or 200 iterations, with numerical experiments repeated 100 times to ensure robustness. Kinematic simulations were conducted in ADAMS v2015 to visualize and validate the synthesized mechanisms. Performance was evaluated based on maximum structural error (steering accuracy) and mechanical advantage (transmission efficiency). The results indicate that the optimized Watt-I six-bar steering mechanisms outperform the Ackermann mechanism in terms of steering accuracy. Among the Watt-I variants, the Type II designs demonstrated superior performance and convergence precision compared to the Type I designs, as well as improved results compared to prior studies. Additionally, the optimal Type I-2 and Type II-2 mechanisms consist of two symmetric Grashof mechanisms, can be classified as non-Ackermann-like steering mechanisms. Both optimization methods proved easy to implement and showed reliable, efficient convergence. The DE-gr algorithm exhibited slightly superior overall performance, achieving optimal solutions in seven cases compared to four for the IPSO method. Full article