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Bi-stable Meta-rod Structures with Designable Shape Transformation for Catheter-based Medical Devices
No full textFrom large space antennas to medical balloon catheters, we rely on the deployability of rod-shaped structures. Deployable rod structures enable designs that can be compactly assembled into a cylindrical form for transport and then rapidly deployed to achieve the desired shape transformation. For example, in robotic-assisted surgeries, concentric tube continuum robots are designed to provide precise shape transformations, which make it possible to perform intricate maneuvers and complex procedures with greater accuracy. However, the deformations achieved by currently existing compliant mechanisms are highly susceptible to environmental disturbances, particularly in fluid-filled confined spaces. Attaining and maintaining a desired shape requires a triggering mechanism, which takes space, and a constant force, which consumes energy under real-time control. Current rod concepts are less suitable for complex tasks like soft robot motions.
Recent efforts have produced Bi-stable meta-structures with morphing functionalities by mimicking the snap-through mechanism of the Venus Flytrap in nature. This work presents a new class of Bi-stable metastructures named “Meta-rod.” Meta-rod structures can transform their shapes from a rod-shaped stable stage to a desired deployed stable stage, realizing linear, bending, twisting, radial, and volumetric changes, or combinations of them. The desired deformation can be programmed into the layout of the Bi-stable structures. Designing different deformation modes, like translation and twisting, involves studying how building block symmetry relates to possible deformations. The Bi-stable concept enables accurate programmed motion and deployment at the second stable stage, freeing space and energy for shape locking.
Via a combination of numerical simulations and physical experiments, this study developed prototypes that demonstrated effective deformation in a range of shape-reconfigurations. The proposed Meta-rod can achieve linear deformation of 60% of its original length, 45° in bending, and 18° of twisting via one unit cell. An umbrella-like areal model and a balloon-like volumetric structure, both stabilized by a locking mechanism, were developed and tested. Their stability and adaptability were validated through static and dynamic loading, including a left ventricle duplicator that mimics physiological conditions
A Novel Hybrid Deep Learning Approach for RFI Detection
No full textAbstract
A Novel Hybrid Deep Learning Approach for RFI Detection
Amin Nazarian Saralang
Radio Frequency Interference (RFI) remains a persistent and significant challenge to the optimal performance of modern wireless communication systems. While fifth-generation (5G) technologies have expanded capabilities through the use of a broader spectral range, their operation in certain frequency bands, such as the 5 GHz range (5.1-5.9 GHz U-NII bands), faces unique interference issues. This spectrum segment is vital for many 5G services yet is heavily shared with other widely used technologies, such as Wi-Fi and Internet of Things (IoT) devices. Such coexistence increases the likelihood of complex and dynamic interference scenarios, often exceeding the capabilities of conventional RFI detection methods.
To address these challenges, this study proposes and evaluates a novel hybrid deep learning framework designed for robust, real-time RFI detection in demanding 5 GHz environments. The architecture combines the object detection strengths of YOLOv8 in leveraging Feature Pyramid Network (FPN) structures for multiscale feature extraction from spectrograms with the deep feature representation capabilities of a ResNet-50 backbone. These parallel feature extractors are integrated through an adaptive fusion mechanism, followed by refinement via channel and spatial attention modules. A Transformer module is then employed to capture long-range temporal and spectral dependencies, thereby enhancing classification accuracy. In addition, noise suppression techniques are incorporated to improve adaptability to the unique characteristics of this spectrum.
A comprehensive dataset strategy is developed to ensure rigorous training and validation of the proposed framework. This includes generating synthetic RFI signals that simulate a wide variety of interference patterns in the 5 GHz range through direct simulation and data augmentation, alongside integrating real-world RFI signal data. This dual dataset approach is intended to strengthen model robustness, scalability, and real-world applicability. Performance is systematically assessed using established metrics such as detection accuracy, processing latency, and computational efficiency. The experimental results show significant improvements of the proposed framework over traditional techniques, achieving high precision with manageable computational demands. Furthermore, the modular design offers scalability for adaptation to other frequency bands, making this work a forward-looking contribution to enhancing the reliability of current and emerging wireless communication systems
Seismic Crack Propagation and Structural Damage Analysis in Gravity Dams
No full textGravity dams are critical infrastructure for hydropower generation, water supply, irrigation, and flood control. However, their large mass and rigid geometry make them particularly vulnerable to seismic loading. The failure of such dams during earthquakes can result in catastrophic consequences, including loss of life, downstream infrastructure damage, and long-term environmental disruption. In regions of moderate seismicity like Eastern Canada, the risk is often underestimated due to limited historical damage records. This study addresses that gap by evaluating the seismic behavior of two concrete gravity dams using nonlinear finite element modeling techniques, with a specific focus on tensile cracking and structural degradation.
The investigation employed ABAQUS software to simulate the seismic response of concrete gravity dams under combined hydrostatic and earthquake loads. The Concrete Damaged Plasticity (CDP) model was used to represent the nonlinear behavior of concrete, including cracking, crushing, and stiffness degradation. Model validation was performed using the well-documented Koyna Dam in India, which experienced significant damage during the 1967 Mw 6.5 earthquake. The validation process involved modal analysis, crest displacement comparison, and tensile damage correlation to ensure the model's reliability before applying it to Canadian Dams.
Following validation, the same modeling approach was applied to two dams in Eastern Canada, Dam D1 (35 meters high) and Dam D2 (90 meters high) with consistent material properties. Both dams were assumed to have fixed bases, and soil-structure interaction effects were not explicitly included. A total of 22 ground motion records from the 1988 Mw 5.9 Saguenay Earthquake, collected from 11 recording stations, were used as seismic loading. Each record included both longitudinal and transverse components and was scaled to the design-level spectrum to simulate high-magnitude scenarios and observe potential damage thresholds.
The results revealed distinct differences in seismic response between the two dams. Dam D1, being shorter and stiffer, exhibited limited crest displacements and minor, localized tensile cracking, mostly at the upstream heel. In contrast, Dam D2 experienced significantly higher crest displacements exceeding 100 mm in several simulations. And widespread tensile damage at both the crest and the base, especially under scaled acceleration records. The spatial and temporal patterns of damage indicated classic flexural behavior, with tension developing at the crest and heel due to cantilever action and stress wave reflection
These findings underscore the critical influence of dam geometry, mass, and natural frequency characteristics on seismic performance. The results emphasize the need for modal analysis in preliminary seismic safety assessments and demonstrate the value of nonlinear modeling techniques in capturing progressive damage. By applying realistic earthquake inputs from within the region, this study contributes to a better understanding of dam vulnerability in Eastern Canada and provides a framework for future seismic assessments and retrofit prioritization
Investigating Methodological Considerations for Studying the Dynamics of Popularity and Acceptance in Pre-Adolescence
No full textIn early adolescence, social standing within peer relationships becomes a priority to achieve, above and beyond other developmental milestones. Social standing can be conceptualized by both acceptance (i.e., likeability) and popularity (i.e., social prestige, social power, and/or social visibility). The aim of this dissertation was to investigate the methodological considerations that could enhance our assessment and understanding of social constructs in pre-adolescent peer groups in two cross-cultural longitudinal studies. Self-report and sociometric data were collected among fifth and sixth grade students from Canada and Colombia. Study 1 provided empirical support for the utilization of the burst design methodology to yield a more stable and reliable measure of acceptance in comparison to a single data-wave collection while utilizing sociometric assessments. The second study emphasized popularity as a social construct in pre-adolescence. This was done by examining how antisocial and prosocial behaviours at the level of the individual predicted popularity while also examining how these associations vary as a function of normative and cultural characteristics of the peer group. Findings from Study 2 emphasized the utility of a multi-level framework for assessing status within a peer group context while also highlighting the importance of considering both cultural and normative peer group norms when studying social constructs. These studies highlight important methodological considerations in both data collection and data analysis that researchers should consider when studying social constructs in pre-adolescent peer groups
Come Find Me: A Queer Autoethnographic Exploration of Autobiographical Therapeutic Performance
No full textWelcome, fellow traveler! This unconventional academic paper will take you through a journey of my experience engaging with the drama therapy method of Autobiographical Therapeutic Performance (ATP). This paper aims to meaningfully contribute to the field of drama therapy by elaborating on both the therapeutic and transformative potential of ATP from a client and student perspective. This study uses queer autoethnography as a methodological framework to critically investigate the use of ATP in individual therapy, specifically as a means of examining my Jewish and transgender/non-binary (TGNB) identities. The ATP process offers a dynamic and flexible therapeutic method which allowed me to explore and integrate my complex intersectional identities. A deeply trusting therapeutic relationship enabled me to experience an intra- and interpersonal sense of belonging and connection, and the choices required during the ATP process created space for a deep sense of my own queer agency. I hope you enjoy the ride
Design and Analysis of Hybrid Permanent Magnet Variable Flux Motors for Traction Applications
No full textRare-earth based permanent magnet synchronous motors (PMSMs) are increasingly being utilized for traction applications thanks to their high torque/power densities along with high efficiencies. The high power/torque density feature leads to a lower weight and more powerful machine. On the other hand, the high efficiency feature will lead to less overall power consumption and losses. However, PMSMs have the known drawbacks of high and unstable price per kg of the rare-earth PM materials which drove electric machine researchers to investigate alternative technologies for fully or partially limiting the reliance upon rare-earth PMs.
One of the promising solutions is by replacing the rare-earth PM by another abundantly available and rare-earth-free PM. However, full replacement of the rare-earth PM will lead to significant reduction in torque density. Depending on the shape of the demagnetization characteristics (DCs) of the rare-earth-free PM, it is possible to achieve a controllable PM working point throughout the machine operation. These machines are commonly known as Variable Flux Motors (VFMs). To cope up with the low torque density problem of VFMs, hybrid PM VFMs are being investigated. These machines include a blend of rare-earth and rare-earth-free PMs. In general, the combination of the two PMs improves the torque density and can have a strong effect on the de/remagnetization requirements. Therefore, the performance of a hybrid PM VFM relies mainly upon the position/size of the PMs. The design of the hybrid PM VFMs is similar to designing a rare-earth PMSM along with a VFM in a single rotor structure. The result is a machine that has higher torque density than VFM with limited flux regulation range. The range of flux regulation affects the high efficiency improvement gain in hybrid PM VFMs. Therefore, the PM flux linkage in these machines can not be reduced fully to zero. In addition, its demagnetization range is limited by the rare-earth PM DCs. Therefore, as these machines include mixed features of PMSM and VFM, the commonly adopted design methods for PMSM has to be modified this is
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because mixed performance metrics do exist for the hybrid machines. Meanwhile, it is important to specify a unified performance assessment criterion that can be applied to any VFM design which will enable fast yet comprehensive performance evaluation of VFM topologies.
In addition, a systematic PM sizing methodology is presented for deciding the dimensions of the non-rare-earth and rare-earth PMs in a magnetic circuit according to a specified position. Thereby, the scaling-up effects can be investigated by applying the sizing approach and the performance assessment template. Meanwhile, the effect of rare-earth PM irreversible demagnetization is investigated in detail.
Finally, a new hybrid PM VFM design concept is also proposed and applied to a spoke type machine showing superiority in terms of torque density and re/demagnetization performance. The proposed machine contains a hybrid PM magnetic circuit of series-hybrid nature made by a rare-earth PM along with a parallel-hybrid branch made of low-cost PMs of different dimensions and grades
Development of a UV Nanosecond Laser Process for Polyamide Coating Removal from Micro-Scale Platinum Wires
No full textPrecise coating removal from ultra-thin wires is critical in industries such as aerospace, automotive, and biomedical, where maintaining substrate integrity and meeting high-performance standards are essential.
Common insulating materials like polyimides and enamels must be removed without damaging the underlying conductor, often requiring advanced methods such as laser ablation, chemical etching, or
ultrasonic stripping. Among these, laser ablation offers significant advantages in precision, repeatability, and compatibility with automation, while also minimizing environmental and safety concerns. This work investigates the use of UV laser ablation for stripping polyamide insulation from 50 µm platinum wires used in the production of high-sensitivity Resistance Temperature Detector (RTD) sensors. The UV
laser system operates at a wavelength of 355 nm with a 20 µm spot size, a repetition rate ranging from 20 to 200 kHz, and an average power of 3 watts. The UV enables removal of the polyamide coating, without affecting the platinum substrate. Initial experiments were conducted in air ambient, where various laser parameters such as Number of loops, Line distance, and scanning speed were systematically varied. However, thermal effects from localized heating posed challenges, risking damage to the substrate and
reducing surface quality. To overcome these issues, experiments were conducted in water ambient, which provided effective thermal management through a controlled ablation process. Scanning speed of
1200 mm/s; line spacing of 1 µm; and single loop was identified as optimal parameter settings to produce a clean surface comparable to that achieved by chemical stripping.
Further analysis of these parameters using ANOVA in Python highlighted the key influence and their interactions on output parameters such as the Tensile strength and Surface Roughness. Increasing the line distance to 2 µm and introducing an additional loop significantly improved the tensile strength [104 gr.f], and the surface roughness [0.129 µm], as close to that can be achieved by chemical stripping.
These findings contribute to the development of reliable, repeatable laser de-coating protocols for ultra-thin wires. By identifying optimal processing parameters, this work supports the broader implementation of a laser-based process toward automation particularly in SMEs
Physics-based Learning of Photometric Invariance
No full textPhotometric invariance is vital in many computer vision tasks. Achieving robustness to photometric variations in imaging conditions requires the collection of accurate and sufficient ground-truth data for training. However, this task proves to be challenging, and as a result the reliance on synthetic data compromises the capacity of the model to adapt and generalize well to real-world situations. To address this issue, we move beyond purely data-driven paradigms by introducing physics priors as photometric invariants applicable across diverse models, datasets, and vision tasks. Specifically, we extend beyond task-specific physics priors and present a novel framework that systematically analyzes their effectiveness, thereby enabling a rigorous assessment of photometric-invariant physics priors across various vision tasks, such as semantic segmentation, intrinsic image decomposition (IID), color constancy, and image classification. We propose a physics-based self-supervised learning framework that extracts photometric-invariant features from unlabeled real-world images. Our approach integrates multiple physics priors and uncertainty modeling into a U-Net architecture, enabling robust, model-agnostic, and task-agnostic feature representations while facilitating efficient transfer learning under limited data regimes. Furthermore, we introduce IDTransformer, a transformer-based model incorporating photometric-invariant attention. In contrast to prior methods relying on hand-crafted priors or purely data-driven learning, IDTransformer captures reflectance transitions and clusters similar reflectance regions independently of spatial arrangement. By leveraging illumination- and geometry-invariant attention for reflectance mapping and geometry-variant attention for shading estimation, it achieves competitive performance with minimal training data
SpokenWeb Search Engine
No full textAn institutional demo presentation of the SpokenWeb Search Engine at the Blacklight Summit 2025
Migration policy types and modes of politics: A comparative case study of the Points-Based Preferential Immigration Treatment for Highly Skilled Foreign Professionals and the Technical Intern Training Program in Japan
Get PDFThis thesis explores the relationship between different types of immigration policies and modes of politics in Japan. It focuses on two policy streams: one targeting highly skilled workers and one that has effectively become a policy for low-skilled workers. It examines how migration types, policy features, and political mobilization interact. Based on a qualitative study, the research revisits Freeman's (1995) concept of "client politics” in the context of contemporary immigration politics. It argues that immigration policy has evolved over recent decades, moving away from a coherent grand design and incorporating multiples dimensions. The findings suggest that high-skilled and low-skilled migration policies in Japan emerge from different assumptions about workers and are based on distinctive flows. In the context of Japan, high-skilled migration is linked to long-term national goals such as competitiveness and innovation, while low-skilled migration is driven by short-term local needs. These perspectives create policies with different features, which in turn generate varying costs, benefits, and political mobilization. The findings reveal two distinct modes of politics —despite some similarities due to a shared institutional and ideational context— that are captured through the concepts of “entrepreneurial-elite politics” and “clientelist-based interest politics” respectively