This paper presents a quantitative and qualitative comparison of three concurrency frameworks for embedded real-time systems: the C-based FreeRTOS kernel and the rust-based real-time interrupt-driven concurrency (RTIC) and Embassy frameworks. All three frameworks are implemented on an STM32F3 ARM Cortex-M4 platform using three representative applications: a single-task Blinky benchmark, a UART-to-LED event-signaling workload, and a Crane low profile vessel (LPV) subsystem integrating UART, CAN, and PWM tasks. Timing metrics, including task latency, periodic jitter, interrupt latency, and frame-to-register latency, are measured using the ARM data watchpoint and trace (DWT) cycle counter, while flash and RAM utilization are obtained through binary analysis. Across the Crane application, FreeRTOS achieves the lowest mean task-switch latency at 1110 cycles, compared with 2308 cycles for RTIC and 18,333 cycles for Embassy. RTIC provides the smallest memory footprint, using 17,224 bytes of flash and 1308 bytes of RAM, while Embassy and FreeRTOS use 20,784 and 20,444 bytes of flash and 5692 and 21,944 bytes of RAM, respectively. Embassy also shows highly repeatable timing behavior in selected application-level measurements, including a constant 442-cycle frame-to-register latency. Qualitatively, the study evaluates memory safety, concurrency reliability, predictability, scalability, and developer ergonomics. The results suggest that FreeRTOS is preferable for applications requiring minimal latency, RTIC is well suited for safety-oriented and RAM-constrained systems, and Embassy is attractive for scalable asynchronous I/O workloads.
Embodied intelligent robots demand comprehensive 3D scene understanding via multimodal sensor fusion for complex physical environments, yet existing methods struggle with insufficient heterogeneous data complementarity and inflexible fusion weight optimization. This study presents Artificial Hummingbird Algorithm (AHA)-fusion, a vision–LiDAR–tactile fusion framework optimized by the AHA for contact-aware embodied 3D scene perception. The framework integrates a multimodal feature extraction pipeline using ResNet-50, PointNet++, and lightweight CNNs; an improved Artificial Hummingbird Algorithm (I-AHA) with chaotic traversal and elite learning to optimize fusion weights globally; and a contact-aware dynamic weight modulation mechanism for real-time weight adjustment based on tactile contact states. A multilevel temporal synchronization strategy achieves 3.2 ± 1.1 ms alignment accuracy (Acc). Experiments on nuScenes, EmbodiedScan, and a self-built tactile manipulation dataset show AHA-Fusion attains 70.2% mean average precision (mAP) and 73.8% nuScenes detection score (NDS) for 3D object detection and 67.4% mean intersection over union (mIoU) for semantic segmentation, outperforming state-of-the-art methods. Ablation studies confirm I-AHA surpasses learnable attention weights by 2.3 percentage points with enhanced stability, while the contact-aware mechanism boosts contact region detection by 5.2 percentage points. A small-scale real-robot pilot experiment further suggests that the proposed framework can support online contact-aware manipulation, although broader real-world validation remains necessary. These results demonstrate the potential of optimization-guided trimodal fusion for robust embodied 3D perception.
Due to the rapid growth of online and technology development, every people have an own mobile to search their doubts and easily find it out within a second. Hypertext induced topic search (HITS) is a link analyzed algorithm utilized to find reliable websites that satisfy broad-topic queries. To identify hub page that link to different authority pages and enhance better topic search recommendations, various conventional approaches have been proposed in recent times. Yet, these methods consume more computational duration and complexity ranges in the training and testing performance, enhancing poor performance. Moreover, it fails to manage with high-dimensional large quantity of input noisy data, resulting inaccurate outcomes. Therefore, a novel deep learning-based HITS model is developed to capture complex patterns in sparse, sequential and contextual data, thereby enabling personalized, dynamic topic search recommendations that customize the individual preferences in social media platforms. The applications of the adaptive HITS (AHITS) algorithm precisely focus on effective hyperlink selection. While analyzing the specific topics, the relevance of content in HITS algorithm is specifically improved using the random search-based sharpbelly fish optimization (RS-SBFO) strategy is designed. Then, the link contents are processed in the preprocessing phase. Here, the provided contents are preprocessed to eliminate noise and other related artifacts. Then, the acquired preprocessed contents are provided to the developed multihead attention-based bidirectional encoder representations from transformer with residual generative long short-term memory (MHA-BRGLSTM) model for topic modeling and refinement. Here, the developed MHA-BRGLSTM provides an effective decision regarding the specific topics and related analysis by utilizing deep context analysis. Thus, the acquired outcomes are topic modeled and refined. Experimental validations are conducted to compare the effectiveness of the developed framework.
The continuous demand for reliable and privacy-sensitive face recognition and detection has led to improvements in machine learning and federated learning (FL) frameworks. While the centralized framework of existing solutions is beneficial for data collection, it presents significant privacy concerns and challenges due to the diversity of data types. To overcome these challenges, this study presents a new, scientifically grounded hybrid approach that combines ensemble deep learning with FL architecture. The authors achieve this by utilizing various deep learning models, such as AlexNet and vision transformers (ViTs), in conjunction with residual networks (ResNets), enhancing the overall efficiency of data prediction and enhancing the versatility of non-independent and identically distributed (Non-IID) datasets. The last component, FL, is the need to store personal user data in centralized repositories. Major developments include the application of secure aggregation techniques and differential privacy to avoid leakage of data, minimizing overhead communication, and designing strategic learning for different clients’ data distribution. Real-world testing on a distributed dataset with 50,000 facial images shows that the proposed framework can achieve a recognition accuracy of up to 95.6%, which is better than the best single-model FL systems and other similar benchmark methods. Due to the framework’s ability to process large volumes of data from noisy and imbalanced data sets, surveillance, healthcare, and the internet of things are viable application areas. This work highlights the possibility of developing such cascaded solutions using ensemble deep learning embedded in a FL architecture.
Fusion of multimodal medical images has become an essential reference for clinical diagnosis and a prominent topic in information fusion research. However, balancing the fusion performance with computational efficiency remains a challenging issue. In this paper, we propose an effective and efficient hybrid fusion method for multimodal medical images that integrates the merits of both spatial and transform domains, rather than relying on a single approach. First, a highly efficient multi-resolution tool, the framelet transform (FT), is employed to decompose medical images from different modalities into a series of low- and high-frequency sub-images. Next, an improved version of the structure tensor is designed to fuse the low-frequency sub-images, while a modified side-window filter (SWF) model is applied to fuse the high-frequency sub-images. Finally, the inverse FT is used to reconstruct the final fused image. Extensive simulation experiments on more than 120 pairs of multimodal medical images validate the performance of the proposed method, with results demonstrating superior visual quality and objective evaluation metrics compared to several recently published state-of-the-art approaches.
