Accurate detection of near-wave field parameters is crucial for safe navigation and efficient offshore operations. However, estimating precise wave parameter remains challenging due to strong nonlinear wave dynamics and inherent measurement uncertainties. Most of existing deep learning methods relying on single modality data face limitations in insufficient accuracy. Motivated by advances in multimodal fusion algorithms, we propose a novel maritime multimodal fusion inversion model, MR-FuNet. The proposed model integrates a Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) in parallel, enabling effective fusion of spatial information from X-band radar sea clutter images and temporal patterns from ship motion data at the feature level. A multi-dimensional attention strategy was employed to enable the model to dynamically calibrate and integrate heterogeneous information across modalities and feature domains which can significantly improve the accuracy of inversion for significant wave height and characteristic wave period. To address the lack of comprehensive and high-quality public datasets in this research area, this study constructs and releases a large-scale and multimodal datasetRadar Images and Ship Motion Dataset(RSD). RSD is a large-scale multimodal dataset generated via numerical simulation and covers 99 representative sea states. It provides a valuable benchmark for future research. Extensive experiments validate the effectiveness of the proposed model, demonstrating substantial performance gains across various metrics. Compared to traditional Artificial Neural Networks (ANN), the proposed multimodal fusion model achieves notable reductions in RMSE by 61.6% and 62.8% for the characteristic period and significant wave height inversion tasks, respectively. Dataset is available at https://github.com/felixfelixXu/Radar-Images-and-Ship-Motion-Dataset.

Surveillance cameras are ubiquitous around us. Emerging full-feature object-detection models can analyze surveillance videos with high accuracy but consume much computation. Directly applying these models for practical scenarios with large-scale cameras is prohibitively expensive. This, however, is wasteful and unnecessary considering that user-defined anomalies occur rarely among these videos. Therefore, we propose FFS-VA, a multi-stage Fast Filtering Mechanism for Video Analytics, to make video analytics much cost-effective. FFS-VA filters out the frames without the user-defined events by two stream-specialized filters and a cheap full-function model, to reduce the number of frames reaching the full-feature model. FFS-VA presents a global feedback-queue approach to balance the processing speeds of different filters in intra-stream and inter-stream processes. FFS-VA designs a dynamic batch technique to achieve a trade-off between throughput and latency. FFS-VA can also efficiently scale to multiple GPUs. We evaluate FFS-VA against the state-of-the-art YOLOv3 under the same hardware and video workloads. The experimental results show that under a 12.88 percent target-object occurrence rate on two GPUs, FFS-VA can support up to 30 concurrent video streams (15× more than YOLOv3) in the online case, and obtain 10× speedup when offline analyzing a stream, with an accuracy loss of less than 2 percent.

Key-value (KV) stores based on multi-stage structures are widely deployed to organize massive amounts of easily searchable user data. However, current KV storage systems inevitably sacrifice at least one of the performance objectives, such as write, read, space efficiency etc., for the optimization of others. To understand the root cause of and ultimately remove such performance disparities among the representative existing KV stores, we analyze their enabling mechanisms and classify them into two fundamental models of data structures facilitating KV operations, namely, the multi-stage tree (MS-tree), and the multi-stage forest (MS-forest). We build SifrDB, a KV store on a novel split forest structure, that achieves the lowest write amplification across all workload patterns and minimizes space reservation for the compaction. To mitigate the read amplification inherent in MS-forest, we introduce a bloom filer mechanism based on Sorted String Tables (SSTs). Furthermore, we also present a highly efficient parallel search approach that fully exploits the access parallelism of modern flash-based storage devices to substantially boost the read performance. Evaluation results show that under both micro and YCSB benchmarks, SifrDB outperforms its closest competitors, i.e., the popular MS-forest implementations, making it a highly desirable choice for the modern KV stores.

Key-value (KV) stores based on multi-stage structures are widely deployed in the cloud to ingest massive amounts of easily searchable user data. However, current KV storage systems inevitably sacrifice at least one of the performance objectives, such as write, read, space efficiency etc., for the optimization of others. To understand the root cause of and ultimately remove such performance disparities among the representative existing KV stores, we analyze their enabling mechanisms and classify them into two models of data structures facilitating KV operations, namely, the multi-stage tree (MS-tree) as represented by LevelDB, and the multi-stage forest (MS-forest) as typified by the size-tiered compaction in Cassandra. We then build a KV store on a novel split MS-forest structure, called SifrDB, that achieves the lowest write amplification across all workload patterns and minimizes space reservation for the compaction. In addition, we design a highly efficient parallel search algorithm that fully exploits the access parallelism of modern flash-based storage devices to substantially boost the read performance. Evaluation results show that under both micro and YCSB benchmarks, SifrDB outperforms its closest competitors, i.e., the popular MS-forest implementations, making it a highly desirable choice for the modern large-dataset-driven KV stores.

Surveillance video cameras are ubiquitous around us. Full-feature object-detection models such as YOLOv2 can automatically analyze surveillance videos in real-time with high accuracy while consuming huge computational resources. Directly applying these models for practical scenarios with large-scale deployed cameras requires prohibitively expensive computation. This, however, is both wasteful and unnecessary considering the fact that the concerned anomalous events occur rarely among these massive volumes of video streams. Therefore, in this paper, we propose a Fast Filtering System for Video Analytics (FFS-VA), a pipelined multi-stage video analyzing system, to make video analytics much cost-effective. FFS-VA is designed to filter out vast but non-target-object frames by two prepositive stream-specialized filters and a small full-function tiny-YOLO model, to drastically reduce the number of video frames arriving at the full-feature model in the back-end. FFS-VA presents a global feedback-queue mechanism to balance the processing rates of different filters in both intra-stream and inter-stream processes. FFS-VA also designs a dynamic batch technique to achieve an adjustable trade-off between throughput and latency. FFS-VA reasonably distributes all tasks on CPUs and GPUs to fully exploit the underlying hardware resources. We implement a FFS-VA prototype and evaluate FFS-VA against the state-of-the-art YOLOv2 under the same hardware and representative video workloads. The experimental results show that under a 10% target-object occurrence rate on two GPUs, FFS-VA can support up to 30 concurrent video streams (7x more than YOLOv2) in the online case, and obtain 3x speedup when offline analyzing a stream, with an accuracy loss of less than 2%.

Deep learning has exhibited high accuracy and applicability in machine learning field recently, by consuming tremendous computational resources processing massive data. To improve the performance of deep learning, GPUs have been introduced to accelerate the training phase. The complex data processing infrastructure demands high-efficient collaboration among underlying hardware components, such as CPU, GPU, memory, and storage devices. Unfortunately, few work has presented a systematic analysis about the impact of hardware configurations on the overall performance of deep learning. In this paper, we aim to make an experimental study on a standalone system to evaluate how various hardware configurations affect the overall performance of deep learning. We conducted a series of experiments using varied configurations on storage devices, main memory, CPU, and GPU to observe the overall performance quantitatively. Based on analyzing these results, we found that the performance greatly relies on the hardware configurations. Specifically, the computation is still the primary bottleneck as double GPUs and triple GPUs shorten the execution time by 44% and 59% respectively. Besides, both CPU frequency and storage subsystem can significantly affect running time while the memory size has no obvious effect on the running time for training neural network models. We believe our experimental results can help shed light on further optimizing the performance of deep learning in computer systems.