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Research Article
AirMouse-3D: An On-Device TinyML Inertial Mouse for Table-Free Desktop Interaction
Kavya Shah,
Priyam Parikh*
Issue:
Volume 9, Issue 4, December 2025
Pages:
256-276
Received:
29 August 2025
Accepted:
8 September 2025
Published:
26 September 2025
Abstract: Humans prefer unconstrained, free-space movement—so why must the mouse stay on a tabletop? This paper presents the design and development of a novel three-dimensional (3D) motion-based mouse that operates without a surface, built around the Arduino Nano 33 BLE Sense and Google’s Tiny Motion Trainer. The system uses on-board inertial sensing to capture roll, pitch, yaw, and small lateral/vertical translations, and employs TinyML classification to map these motions to discrete desktop actions. Motion-command map used in this study: pitch↑ → scroll up; pitch↓ → scroll down; roll→ → left-click; roll← → right-click; yaw→/yaw← → drag toggle on/off; lateral± → cursor nudge ±Δx; vertical± → cursor nudge ±Δy. The device is housed in a 3D-printed hexagonal-prism casing with ergonomic circular cuts for stable grip and repeatable gestures, and includes an LED and buzzer for immediate user feedback. The development pipeline comprised (i) gyroscope/IMU calibration and real-time motion mirroring in Processing, (ii) enclosure design and 3D printing, (iii) gesture dataset collection and model training in Tiny Motion Trainer, and (iv) Python integration over serial (pyserial) to synthesize OS-level inputs (pynput). Compared to conventional mice, the proposed interface enables multi-dimensional, touch-free interaction from sofas, beds, or standing postures, removing surface constraints while preserving familiar desktop actions. We detail the hardware, firmware, and TinyML workflow, discuss practical considerations (drift, debouncing, gesture separability, and comfort), and outline evaluation protocols and extensions (adaptive thresholds, continuous cursor control, and user-specific calibration) to advance free-motion pointing.
Abstract: Humans prefer unconstrained, free-space movement—so why must the mouse stay on a tabletop? This paper presents the design and development of a novel three-dimensional (3D) motion-based mouse that operates without a surface, built around the Arduino Nano 33 BLE Sense and Google’s Tiny Motion Trainer. The system uses on-board inertial sensing to capt...
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Research Article
Image Reconstruction in Compressive Sensing Using Symlet 8 (sym8) and Lifting Wavelet Transforms with SP, CoSaMP, and ALISTA Algorithms
Issue:
Volume 9, Issue 4, December 2025
Pages:
277-290
Received:
20 October 2025
Accepted:
3 November 2025
Published:
9 December 2025
Abstract: This paper proposes an efficient image reconstruction for compressive sensing (CS) that combines the Lifting Wavelet Transform (LWT) using Symlet 8 (sym8) wavelets with three reconstruction algorithms: Subspace Pursuit (SP), Compressive Sampling Matching Pursuit (CoSaMP), and the Analytic Learned Iterative Shrinkage Thresholding Algorithm (ALISTA). Unlike the conventional Discrete Wavelet Transform (DWT) which relies on computationally intensive convolution operations the LWT provides a faster sparse representation while preserving the sparsity crucial for CS. The proposed approach leverages a key insight: among the four subbands produced by the LWT namely the approximation (CA) and the detail coefficients (LH, HL, HH) only the latter three are inherently sparse. Therefore, compressive sensing is applied exclusively to these detail subbands, while the CA subband is left uncompressed to retain essential low-frequency information. Experiments were conducted on both a natural test image (Lena) and a medical MRI scan, across image resolutions ranging from 200×200 to 512×512 pixels and sampling rates from 10% to 80%. Performance was assessed using the Structural Similarity Index (SSIM) and reconstruction time. Results consistently demonstrate that ALISTA significantly outperforms SP and CoSaMP in both reconstruction fidelity and computational efficiency. At an 80% sampling rate, ALISTA achieves SSIM values of 0.99346 for Lena and 0.98 for the MRI image, compared to approximately 0.97 and 0.94, respectively, for the other two methods. Furthermore, ALISTA maintains remarkably low reconstruction times under 4 seconds even for 512×512-pixel images. These findings confirm that the ALISTA + LWT/sym8 combination offers the best trade-off between image quality and speed, exhibiting robustness across different image types and scales.
Abstract: This paper proposes an efficient image reconstruction for compressive sensing (CS) that combines the Lifting Wavelet Transform (LWT) using Symlet 8 (sym8) wavelets with three reconstruction algorithms: Subspace Pursuit (SP), Compressive Sampling Matching Pursuit (CoSaMP), and the Analytic Learned Iterative Shrinkage Thresholding Algorithm (ALISTA)....
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Research Article
A Comparative Study of Print and Digital Learning Resource Preferences Among Competitive Exam Aspirants at Kannada University
Issue:
Volume 9, Issue 4, December 2025
Pages:
291-297
Received:
29 October 2025
Accepted:
8 November 2025
Published:
9 December 2025
Abstract: This study investigates the preferences for learning resources among competitive exam aspirants at Kannada University, emphasizing the comparative utilization of print versus digital materials. The results indicate a significant trend towards the adoption of digital resources, with platforms such as YouTube coaching channels, online quizzes, and e-books being identified as the most utilized tools. These digital materials are particularly valued for their accessibility, interactivity, and convenience, aligning with the evolving learning demands of students preparing for competitive examinations. Demographic analysis reveals notable variations in resource usage: male students, those residing in urban areas, and individuals from the arts stream demonstrate a higher propensity to engage with digital learning platforms relative to other groups. Nonetheless, despite the increasing preference for online tools, traditional resources such as printed competitive exam books and academic research publications retain importance and continue to be utilized extensively for comprehensive study and conceptual grasp. In light of these findings, the study recommends that Kannada University enhance its library and digital infrastructure to expand access to e-resources, alongside offering targeted digital literacy training. Additionally, integrating online learning platforms and interactive tools within the university’s academic support framework could significantly improve student preparedness for competitive examinations, thereby fostering a more comprehensive and effective learning environment.
Abstract: This study investigates the preferences for learning resources among competitive exam aspirants at Kannada University, emphasizing the comparative utilization of print versus digital materials. The results indicate a significant trend towards the adoption of digital resources, with platforms such as YouTube coaching channels, online quizzes, and e-...
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Methodology Article
Comparative Analysis of Single-Core and Double-Core Optical Fibers
Randriana Heritiana Nambinina Erica*,
Ando Nirina Andriamanalina
Issue:
Volume 9, Issue 4, December 2025
Pages:
277-282
Received:
5 November 2025
Accepted:
18 November 2025
Published:
9 December 2025
Abstract: This work presents a detailed comparative study of single-core and concentric double-core optical fibers, highlighting their potential advantages for telecommunication applications. Using theoretical and numerical analysis, we examine key parameters including numerical aperture, acceptance angle, V-number, mode capacity, guided power fraction, and radial profiles of the fundamental mode. The double-core fiber exhibits a higher numerical aperture of 0.095 and an acceptance angle of 27.1°, compared to 0.078 and 19.6° for the single-core fiber, enabling more efficient light capture. The normalized frequency parameter (V-number) increases from 6.37 in the single-core fiber to 10.4 in the double-core design, resulting in a total of 54 guided modes versus 20 in the single-core fiber. Radial analysis shows that the fundamental mode is primarily confined within the inner core, with partial extension into the outer core. This distribution facilitates controlled modal coupling and flexible power management, which can be beneficial for multimode transmission or high-power applications. Although the guided power fraction remains 100% in both fiber types, the dual-core structure significantly reduces mode leakage and enhances confinement efficiency, highlighting its potential for robust signal transmission. The comparative results suggest that concentric double-core fibers provide a practical approach to increasing mode diversity and improving light confinement without introducing excessive complexity in fiber fabrication. The dual-core design also aligns with the requirements of advanced telecommunication strategies such as space-division multiplexing, where efficient distribution of multiple spatial channels is essential. Moreover, the dual-core architecture serves as a foundation for further optimization, including tailored core spacing, refractive index engineering, and controlled inter-core coupling, to maximize transmission capacity and signal stability. Overall, the findings demonstrate that double-core fibers offer clear advantages in terms of mode management, power confinement, and flexibility for high-capacity optical systems. These results provide valuable insights for future experimental studies and the development of next-generation multimode and high-performance fiber designs.
Abstract: This work presents a detailed comparative study of single-core and concentric double-core optical fibers, highlighting their potential advantages for telecommunication applications. Using theoretical and numerical analysis, we examine key parameters including numerical aperture, acceptance angle, V-number, mode capacity, guided power fraction, and ...
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Methodology Article
Ray-tracing Analysis of Guided Power in Single- and Dual-core Optical Fibers
Randriana Heritiana Nambinina Erica*,
Ando Nirina Andriamanalina
Issue:
Volume 9, Issue 4, December 2025
Pages:
298-303
Received:
7 November 2025
Accepted:
17 November 2025
Published:
19 December 2025
DOI:
10.11648/j.ajist.20250904.15
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Abstract: Optical fibers serve as essential components in modern communication infrastructures, enabling the reliable transmission of large volumes of data across long distances with exceptionally low attenuation. The performance of these fibers is governed by several key factors, including the physical geometry of the fiber, the refractive-index distribution between the core and cladding, and the overall efficiency with which light is confined and guided within the core region. A comprehensive understanding of these parameters is crucial for predicting system behavior and optimizing fiber design for a wide range of applications. In this work, light propagation in both single-core and dual-core optical fibers is investigated using a simplified yet effective ray-tracing model. This modeling approach provides intuitive visualizations of ray trajectories and enables a quantitative analysis of the guided power fraction as a function of the launch angle. By examining how different launch conditions influence the proportion of light that remains confined within the fiber, the model offers insight into the mechanisms underlying mode propagation and coupling behavior in various fiber configurations. The results obtained from the simulations contribute both an instructive and predictive framework for evaluating the optical performance of single-core and dual-core fibers. This framework facilitates direct comparison between the two configurations and highlights their respective advantages, particularly in terms of light-guiding efficiency and potential application domains. Overall, the study demonstrates that simplified ray-based analyses can serve as valuable tools for understanding complex optical processes and guiding the development of improved fiber-based technologies.
Abstract: Optical fibers serve as essential components in modern communication infrastructures, enabling the reliable transmission of large volumes of data across long distances with exceptionally low attenuation. The performance of these fibers is governed by several key factors, including the physical geometry of the fiber, the refractive-index distributio...
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Research Article
Generative Adversarial Network–based Intrusion Detection for Securing In-vehicle Communication in Electric Vehicles
Issue:
Volume 9, Issue 4, December 2025
Pages:
304-323
Received:
16 November 2025
Accepted:
25 November 2025
Published:
20 December 2025
DOI:
10.11648/j.ajist.20250904.16
Downloads:
Views:
Abstract: The increasing connectivity of in-vehicle electronic control systems has intensified the need for robust cybersecurity solutions, especially for the Controller Area Network (CAN) bus. This study proposes a deep learning–based Intrusion Detection System (IDS) utilizing a Generative Adversarial Network (GAN) architecture to detect anomalous CAN bus traffic in real time. The GAN model is trained solely on legitimate CAN messages, enabling it to learn the underlying statistical patterns of normal communication without relying on predefined attack signatures. The proposed GAN-IDS demonstrates strong detection performance, achieving an accuracy of 98.7% and an F1-Score of 98.5%, outperforming conventional deep learning baselines. To assess deployment feasibility, the discriminator is optimized using TensorFlow Lite (TFLite) and deployed on a Raspberry Pi 4 integrated with a PiCAN2 interface. Hardware evaluation confirms real-time operation with a low detection latency of 2.9 milliseconds per message sequence. System interpretability is further enhanced through SHapley Additive exPlanations (SHAP), which identify CAN ID, engine torque, and RPM as the most influential features contributing to anomaly classification. The proposed GAN-based IDS offer a scalable, manufacturer-independent, and non-intrusive cybersecurity solution for modern Electric Vehicles. Its combination of high detection performance, real-time hardware deployment, and interpretable decision-making marks a significant step toward more intelligent and resilient security mechanisms for future connected and autonomous vehicles.
Abstract: The increasing connectivity of in-vehicle electronic control systems has intensified the need for robust cybersecurity solutions, especially for the Controller Area Network (CAN) bus. This study proposes a deep learning–based Intrusion Detection System (IDS) utilizing a Generative Adversarial Network (GAN) architecture to detect anomalous CAN bus t...
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