Frame Processing Research Articles

Efficiency of the dynamic relaxation method in the stabilisation process of bridge and building frame

More and more complex Civil Engineering problems are being considered in computational mechanics with the invention of high-quality computing techniques. In addition, the computational cost and storage requirement for complex and or large structures have increased dramatically, leading to an increased interest in removing the difficulties using any form of parallel computing. The process of applying the preload for parallel computing to any unstable structures is called a stabilising process, such as the Dynamic Relaxation Method (DRM) is one. This method minimises the energy by a simple vector iteration technique, which ultimately leads the structure to a static equilibrium state. The present study aims to highlight the utility of the DRM in the stabilisation process for small structures like building frames and large and or complicated structures such as bridges before actual transient analysis. Therefore, the present manuscript discusses the computational cost, CPU runtime, multiple increases of mass and rigid body displacement of building frames and bridges. The DRM allows an explicit solver to conduct a dynamic analysis by increasing the damping until the kinetic energy drops to a proposed value. The simulation of the DRM starts to find the equilibrium state with minimal dynamic effect, which is required to apply at the beginning of the solution phase to obtain the initial stress and displacement field before the start of the actual analysis.

Policy Framing, Social Networks, and Teacher Perspectives in a Technology Rollout

Drawing on theories of framing and social networks, we examine how educators define policy goals in a technology rollout, and how this framing process shapes teacher attitudes for technology. We analyzed social network data and 37 interviews with district leaders, digital learning coaches, and school staff from six elementary schools in a mid-sized district in California. These data suggest that school leaders and teachers re-framed district reform messages in different ways depending on the density of dialogue in their networks and who had social influence. These framings supported teacher motivation for using technology in ways that aligned to or built upon the district's instructional goals, or prompted teachers to resist change and/or make compliance-oriented and superficial shifts in their practice. We argue that district leaders should attend to school context when implementing technology rollouts and proactively engage school actors to define the goals of technology. We caution that technology rollouts demand sudden and intensive shifts in teacher work roles and can foster resistance to change without adequate time and support.

Internet of things assisted deep learning enabled driver drowsiness monitoring and alert system using CNN-LSTM framework

Abstract Driver fatigue has been generally viewed as a critical road safety factor and has been cited for contributing to a good percentage of traffic accidents. Developing systems to monitor and alert drowsy drivers is essential in reducing incidents. This research proposes a robust framework for driver drowsiness detection using a CNN-LSTM architecture developed by fusing facial landmark analysis with multiple aspect ratios. It uses, as key metrics in detecting drowsiness, the Eye Aspect Ratio (EAR), Pupil Circularity (PUC), the Mouth Aspect Ratio (MAR), and the Mouth over Eye Aspect Ratio (MOE). CNN-LSTM had been trained on YawDD, NITYMD, FL3D, and custom datasets. Data augmentation techniques such as flipping, scaling, shearing, rotation, brightness and contrast adjustment are used to generalize under different illumination conditions and driver postures. The system is implemented on NVIDIA’s 128-core Jetson Nano GPU platform and does real-time processing of video frames captured by a CSI camera. It detects eye closure and yawning as symptoms of driver fatigue and immediately raises an alert through vibrations in the seatbelt and pre-recorded voice messages. The Internet connectivity allows remote monitoring via mobile applications, making it safer by ensuring that alerts reach the driver and passengers. This CNN-LSTM model has been carefully tested for various scenarios, including day and night conditions, proving its effectiveness. The proposed framework indicated excellent performance concerning accuracy (98%), precision (95%), recall (93%), F1 score (94%), and AUC (99%), thereby revealing its efficiency in practical scenarios. By incorporating EAR, MAR, PUC, and MOE for the early detection of drowsiness in this CNN-LSTM architecture, the system will be able to alert the driver ahead of time so that necessary precautions can be taken to avoid an accident. The proposed approach enhances the driver's safety and is a scalable solution that adapts to different environments and populations.

LC dynamic signal modeling and analysis of resonant frequency measurement error under rapid eye movements interference

Abstract LC resonant wireless passive intraocular pressure (IOP) sensors measure IOPs by mapping them to LC resonance frequencies. However, during the sweeping acquisition process of each LC signal frame, possible rapid eye movement (REM) can interfere with the wireless mutual inductance coupling, leading to the signal distortion and resulting in the error of resonance frequency measuring. Currently, there is a lack of modelling analysis of the errors generated by REM effect, resulting in an absence of guidance for reducing the impact. Therefore, here, we start from the perspective of the signal model and establish the LC dynamic signal model for sweeping acquisition. By means of the limit state analysis and Monte Carlo simulation, we analyze the influence of external REM parameters (including REM range and velocity) and internal parameters of the LC sensing system (including the quality coefficient of LC sensor, diameter, and number of turns of the reader coil, signal sweep acquisition speed and period) on the errors. We demonstrated theoretical relationship between the extreme errors with these parameters and verify it through a computer simulation. Based on the results, we propose to optimize the internal parameters of the LC sensing system to reduce the REMs effect on errors, safeguarding the quality of signal acquisition and improving the measurement accuracy.

End-to-end multiple object tracking in high-resolution optical sensors of drones with transformer models

Drone aerial imaging has become increasingly important across numerous fields as drone optical sensor technology continues to advance. One critical challenge in this domain is achieving both accurate and efficient multi-object tracking. Traditional deep learning methods often separate object identification from tracking, leading to increased complexity and potential performance degradation. Conventional approaches rely heavily on manual feature engineering and intricate algorithms, which can further limit efficiency. To overcome these limitations, we propose a novel Transformer-based end-to-end multi-object tracking framework. This innovative method leverages self-attention mechanisms to capture complex inter-object relationships, seamlessly integrating object detection and tracking into a unified process. By utilizing end-to-end training, our approach simplifies the tracking pipeline, leading to significant performance improvements. A key innovation in our system is the introduction of a trajectory detection label matching technique. This technique assigns labels based on a comprehensive assessment of object appearance, spatial characteristics, and Gaussian features, ensuring more precise and logical label assignments. Additionally, we incorporate cross-frame self-attention mechanisms to extract long-term object properties, providing robust information for stable and consistent tracking. We further enhance tracking performance through a newly developed self-characteristics module, which extracts semantic features from trajectory information across both current and previous frames. This module ensures that the long-term interaction modules maintain semantic consistency, allowing for more accurate and continuous tracking over time. The refined data and stored trajectories are then used as input for subsequent frame processing, creating a feedback loop that sustains tracking accuracy. Extensive experiments conducted on the VisDrone and UAVDT datasets demonstrate the superior performance of our approach in drone-based multi-object tracking.

(Re)framing used goods: marketing strategies in second-hand retail

PurposeThis paper aims to identify marketing strategies incentivizing consumers to purchase used goods and explain how socio-material arrangements frame second-hand retail (SHR). The growing significance and professionalization of SHR underscore the pivotal role of marketing, necessitating an exploration of how second-hand stores can stimulate the consumption of used items.Design/methodology/approachEthnographically inspired fieldwork was conducted across a second-hand shopping mall with 17 stores in Sweden, utilizing actor-network theory (ANT) concepts to examine marketing activities and how these form strategies.FindingsThe findings reveal two marketing strategies: enrolling sustainability supporters and second-hand shoppers. Additionally, the findings provide examples of how SHR can be framed as environmentally friendly, socially sustainable, value for money, creatively experiential and facilitating sustainable consumer behavior.Practical implicationsUnderstanding the framing processes inherent in second-hand retailing presents opportunities to reinforce the transition to a circular economy. Second-hand retailers can leverage marketing to imbue used goods with greater significance for consumers, which necessitates marketing competencies.Originality/valueThe paper delves into activities that enhance the attractiveness of used goods, a research area that warrants increased attention.

Bridging Academia and Practice with Digital Boundary Objects

ABSTRACT Extant research emphasizes the need for research artifacts to span the academic boundaries, to result in societal benefits. Digital boundary objects (DBOs), with their inherent ability of boundary spanning, are the key to translate research artifacts for non-academic audiences like practitioners. We explore the creation and dissemination of DBOs as a mechanism to bridge the epistemic divide between academics and practitioners. We employ the Gioia methodology and conduct in-depth interviews, netnography and archival data analysis. This method is apt, given our exploratory research objective of understanding the creation of DBOs through the lens of Social Construction of Technology (SCOT) theory. Our findings reveal a three-phase cyclical process model, termed epistemic framing for DBO creation. We identify DBOs as key in bridging the epistemic divide and the novelty of our research lies in identifying the iterative epistemic framing process model that helps in knowledge translation for societal advancement.

Hardware-Accelerated Infrared Small Target Recognition Based on Energy-Weighted Local Uncertainty Measure

Infrared small target detection is a key technology with a wide range of applications, and the complex background and low signal-to-noise ratio characteristics of infrared images can greatly increase the difficulty and error rate of small target detection. In this paper, an uncertainty measurement method based on local component consistency is proposed to suppress the complex background and highlight the detection target. The method analyzes the local signal consistency of the image. It then constructs a confidence assignment function and uses the mutation entropy operator to measure local uncertainty. Then, the target energy information is introduced through an energy-weighting function to further enhance the signal. Finally, the target is extracted using an adaptive threshold segmentation algorithm. The experimental results show that the algorithm can effectively detect small infrared targets in complex backgrounds. And, the algorithm is at the leading edge in terms of performance; the processing frame rate can reach 3051 FPS (frame per second), 96 FPS, and 54 FPS for image data with a resolution of 256 × 256, 1920 × 1080, and 2560 × 1440, respectively.

The uphill battle for reinventing post-industrial regions: The case of Greater Manchester's ‘clean growth’ mission

In this paper we enrich the concept of place-based leadership. Building on social movement theory, our analysis of the clean growth mission development in Greater Manchester (UK) reveals the role of place-based leadership in mobilising and coordinating framing processes that linked the global climate change problem with local challenges, articulated local benefits of its resolution, and provided justification for local change efforts. We draw on theories of the policy process (i.e. Multiple Streams Framework) to shed light on how place-based leaders frame problems and solutions strategically to engender policy change. While framing processes had an impact on local policy agendas, we do not find evidence of accelerated implementation, pointing to the important distinctions between problem ownership and solution ownership in the context of wicked problems. Our findings contribute to the ongoing debate on the role of multi-level governance and localised agency in problem-based policymaking for sustainable regional development.

Naturalization of work accidents through political dynamics: A discursive analysis

Over the last two decades a number of devastating work accidents have occurred in Turkey that have resulted in massacre-like deaths and destructions. Although dramatic consequences forced all actors to promise for betterment, no significant improvement has been achieved yet and one accident followed the other. Apart from social and economic factors, political power tends to be attributed to having the most critical responsibility in all incidents. The present government, which has been in power since 2002, has aimed and achieved to create a pro-government capitalist class who can dominate specific sectors, such as construction, energy and mining in which most of the serious accidents occur. By taking the control of media, the government not only attained the majority of communicative channels but also created a manipulative discourse potential that has enabled to shade reality concerning the accidents. In this regard, the purpose of this study is to analyze how these accidents are normalized by the government’s discourses. A multi-stage naturalization model is employed to reveal the production and circulation of discourses in public sphere during the framing process of the accidents. The model can also be used to propose a solution for de-naturalization, which is also one of the aims of this study.

‘Against imposition, we defend education’: teachers’ movements against austerity and neoconservative reforms in the Spanish context

ABSTRACT Between 2011 and 2015, at the dawn of the global financial crisis, Spain went through severe austerity measures that led to social unrest and to the emergence of new expressions of collective action. In the educational field, teachers’ unions and grassroots movements organised against the neoliberal and neoconservative policies promoted by the central government and several regional governments. The case of the Teachers’ Assembly in the Balearic Islands is particularly relevant because it was a highly successful protest experience that triggered unprecedented social mobilisation and broad social support. Drawing on social movement theories and combining semi-structured interviews with document and media analysis, this study finds that the combination of alliance-building, disruptive collective actions, and framing processes was key to explaining the success of the social mobilisation.

Old Habits Die Hard? The Role of Trade Union Identity and Framing Processes in Shaping Strategy

Old Habits Die Hard? The Role of Trade Union Identity and Framing Processes in Shaping Strategy

Parallel Implementation of K-Best Quadrature Amplitude Modulation Detection for Massive Multiple Input Multiple Output Systems

Massive MIMO (Multiple Input Multiple Output) systems impose significant processing burdens along with strict latency requirements. The combination of large-scale antenna arrays and wide bandwidth requirements for next-generation wireless systems creates an exponential increase in frontend to backend data. Balancing the processing latency and reliability is critical for baseband processing tasks such as QAM detection. While linear detection algorithms have low computational complexity, their use in Massive MIMO scenario has heavy degradation in error performance. Nonlinear detection methods such as Maximum Likelihood and Sphere Decoding have good error performance, but they suffer from high, variable, and uncontrollable computational complexity. For such cases, the K-best QAM detection algorithm can provide required control over the system performance while maintaining near-ML error performance. In this paper, hard-output, as well as soft-output K-best QAM detection, is implemented in a CPU by utilizing the multiple cores combined with vector processing. Similarly, hard-output detection in a GPU is implemented by leveraging the SIMD (Single Instruction, Multiple Data) architecture and Warp-based execution model. The processing time per bit and the energy consumption per bit are compared for CPU and GPU implementations for QAM constellation density and MIMO array size. The GPU implementation shows up to 5× processing latency per bit improvement and up to 120× energy consumption per bit improvement over the CPU implementation for typical QAM constellations such as 4, 16, and 64 QAM. GPU implementation also shows up to 125× improvement over CPU implementation in energy consumption per bit for larger MIMO configurations such as 24 × 24 and 32 × 32. Finally, the soft-output detector is combined with a LDPC (Low-Density Parity Check) decoder to obtain the FER (Frame Error Rate) performance for CPU implementation. The FER is then combined with frame processing latency to form a Goodput metric to demonstrate the latency and reliability tradeoff.

Stereo visual-inertial localization algorithm for orchard robots based on point-line features

The vision-based localization of orchard mobile robots is an indispensable component of orchard intelligent management, applicable to scenarios such as navigation and harvesting. Due to the rugged terrain and lack of texture features in orchard environments, the accuracy of Visual-Inertial Odometry (VIO) based on feature point is compromised, making it challenging to meet practical application requirements. Addressing these issues, this paper proposes an algorithm for stereo visual-inertial localization based on point-line features, by enhancing VINS-Fusion. The algorithm incorporates the line feature detector algorithm (LSD) into the odometry front-end to detect line features, addressing the insufficient effective feature points and low-quality feature matching issues in orchard environments. Simultaneously, strategies for length filtering and feature matching are introduced for line features, eliminating poor-quality line features and matches caused by rugged terrain, thereby reducing adverse impacts on localization accuracy. To prevent keyframe redundancy due to a scarcity of stable feature points in areas with missing textures, such as tree trunks and leaves, a keyframe criteria optimization method based on point-line features is designed. Back-end optimization minimizes the cost function containing residuals of point-line features, prior information, and IMU, ultimately improving the localization accuracy of mobile robots. To validate the effectiveness and practicality of the proposed algorithm, a self-built dataset was created in real orchard environments, including scenes from banana plantation, mango orchard, and wampee orchard. Localization experiments were conducted using Rosario, EuRoC public datasets and self-built orchard dataset. Results indicate that the proposed algorithm consistently achieves lower ATE-RMSE than VINS-Fusion, with a maximum reduction of 79.2 % in the Rosario dataset sequence, 23.3 % in the EuRoC dataset sequence, and ATE-RMSE values of 0.091 m, 0.107 m, and 0.093 m in banana plantation, mango orchard, and wampee orchard, respectively. These represent reductions of 22.9 %, 7.6 %, and 27.3 % compared to VINS-Fusion. In the self-built orchard dataset, the average frame processing times for the algorithm front-end is 56.3 ms, while for the back-end is 31 ms, meeting the practical localization requirements. The proposed algorithm effectively enhances the localization accuracy of orchard mobile robots, offering a viable solution for autonomous navigation in orchard robot systems.

Performance Study of Object Tracking with Multiple Kalman Filters in Autonomous Driving Systems

Object tracking is an important and central aspect of autonomous driving, as it underlies the obstacle detection and avoidance systems of any type of autonomous vehicles. A widely used method for tracking is based on Kalman filters, both for linear and non-linear cases, with different computational burden. Unfortunately, object tracking algorithms are computationally intensive, and they may not easily meet the efficiency and responsiveness requirements of real-time applications such as autonomous driving. This issue motivates ad-hoc investigations to speed up the computation and make Kalman filtering available even within limited computational power. This paper carry out a performance evaluation of a Kalman filter based object tracking system taken from a real tramway use-case, and aims at improving its performance efficiency by leveraging parallelization. In particular, this work analyzes the possibilities of execution parallelization on multi-core processors, proposing a target-specific optimization approach and comparing the obtained results, then summing them in general lessons learned. Our technique achieves up to 80% reduction of single frame processing time in the most crowded cases.

48‐2: Research on Mechanical Simulation of Flexible AMOLED Module Bottom Frame

With the development of science and technology and consumers' pursuit of high screen ratio, the frame of terminal flagship products has become an important research direction. However, the thinner the bottom frame is, the higher probability of metal wire breakage in bending area. In this paper, we use finite element analysis to simulate the whole bending process of bottom frame, study the influence of bending path, indenter displacement, bending radius and buffer zone on metal wire, summarize the failure mechanism, which provides theoretical basis for fault analysis, stacking structure design and smaller radius design.

Two tales of Kushtau Hill movement: ethnic environmental mobilisation and perceived costs of neopatrimonialism

In recent years the number of environmental protests in Russian regions has been on the rise, despite the shrinking political opportunity structure. In the Bashkortostan republic, the Bashkir Soda Company's (BSC) decision to develop the Kushtau Hill for soda ash provoked an environmental campaign to save the mountain. The Kushtau Hill movement succeeded, despite the highly repressive response from regional government and its tight patrimonial link to the BSC - two conditions identified in the literature as unfavorable to protesters. How is environmental discontent successfully mobilised under a repressive government and embedded extractive practices? Drawing on semi-structured interviews with activists, I trace two interlinked pathways to successful mobilisation. The first one testifies to the role played by national organisations in sustaining environmental collective action. Under a shrinking opportunity structure for formal ENGOs, the Bashkir national organisation “Bashkort” provided the emerging movement with its experience of informal organisation. Its leadership successfully linked ethnic grievances to environmental mobilisation byclaiming the Bashkirs' special relation to the mountain. However, ethnic and neighbour ties did not prevent a repressive response from the regional government due to a limited scale of the mobilisation. The second story deals with framing processes that expand the scope of potential supporters beyond particularistic ties. Protesters highlighted the perceived costs of neopatrimonialism to justify their demands. This framing put the republic head as a scapegoat who secured interests of the federal centre and BSC, compromising the residents' ecological well-being. Therefore, Kushtau Hill activists attracted new members, not putting themselves into danger of being perceived as extremists that targeted a regime-dimension.

Numerical Simulation Study on Welding Process of Upper Frame of Hydropower Unit

To optimize the welding process of the upper frame of the hydropower unit, a thermal elastic–plastic (TEP) finite element model of the typical T-joint of the upper frame was established, and the effectiveness and accuracy of the model were verified by welding tests. The effect of welding speed and interlayer cooling time on welding residual stress and deformation was analyzed, and a welding process in line with the requirements was obtained. Based on the results of the TEP calculation, the inherent strain was obtained, and the inherent strain method (ISM) was used to predict the overall deformation of the upper frame under three welding sequence schemes, and the optimal welding sequence was obtained.

Catch me if you can: how episodic and thematic multimodal news frames shape policy support by stimulating visual attention and responsibility attributions

Using media coverage of animal welfare as an example, this study examines how the perception of multimodal news frames shapes recipients’ visual attention, attributions of responsibility, emotions, and policy support. To investigate the mechanisms of multimodal-episodic versus thematic framing, we combined eye-tracking measurements with a pre-post survey experiment in which 143 participants were randomly assigned to an episodic or a thematic multimodal framing condition. The results show that episodic multimodal frames are viewed longer than thematic frames, elicit stronger individual and political responsibility attributions, and increase political support for stricter animal-welfare laws. Understanding multimodal framing as a multistep process, a serial mediation model reveals that episodic frames affect viewing time, which leads to stronger attributions of political responsibility and, in turn, stronger policy support. Our results support the idea of a complex interplay between subsequent stages of information perception and processing within a multimodal framing process.

Filtering Empty Video Frames for Efficient Real-Time Object Detection.

Deep learning models have significantly improved object detection, which is essential for visual sensing. However, their increasing complexity results in higher latency and resource consumption, making real-time object detection challenging. In order to address the challenge, we propose a new lightweight filtering method called L-filter to predict empty video frames that include no object of interest (e.g., vehicles) with high accuracy via hybrid time series analysis. L-filter drops those frames deemed empty and conducts object detection for nonempty frames only, significantly enhancing the frame processing rate and scalability of real-time object detection. Our evaluation demonstrates that L-filter improves the frame processing rate by 31-47% for a single traffic video stream compared to three standalone state-of-the-art object detection models without L-filter. Additionally, L-filter significantly enhances scalability; it can process up to six concurrent video streams in one commodity GPU, supporting over 57 fps per stream, by working alongside the fastest object detection model among the three models.