With the rapid increase of the aging population, fall detection, as a key technology of intelligent medical treatment, has attracted extensive attention. Compared with the poor comfort of wearable devices and the light sensitivity of visual methods, the wireless sensing scheme based on channel state information (CSI) shows unique advantages with its non-contact and privacy friendly. However, the existing CSI-based methods have the problem of insufficient long-range modeling ability, and fail to establish the deep semantic correlation between time-domain and frequency-domain in the fall process. Therefore, this paper proposes a fall detection method based on hybrid Mamba and Transformer, namely HMT-Fall. Different from the existing methods that simply parallel the time-domain network and the frequency-domain network, HMT-Fall creatively constructs a multi-domain modeling mechanism with clear functional division and collaborative design. Firstly, Mamba network is introduced to process the raw CSI sequences, and its selective state space mechanism is used to achieve efficient long-range timing modeling. Secondly, the short-time Fourier transform (STFT) spectrum of CSI is analyzed by Swin Transformer, and the frequency domain representation with both local details and global context is efficiently extracted through the shifted window self-attention mechanism. Furthermore, a bidirectional cross-attention fusion module is designed to achieve dynamic alignment and mutual constraint between time-domain features and frequency-domain features at the semantic level, rather than simple static splicing or weighting, so as to form a physically consistent and more discriminative joint representation. The experimental results on the self-built dataset HMT-HAR and public datasets show that the detection performance of HMT-Fall is significantly better than that of the existing representative methods, achieving over 99% accuracy, which verifies the effectiveness and superiority of the proposed method.

Blockchain storage platforms reward storage nodes for keeping user-uploaded data for a certain amount of time. These storage nodes are unstable and can go online or offline unpredictably at any time, leading to potential data loss. To prevent data loss, blockchain storage platforms adopt erasure codes on user-uploaded encrypted data. Data repair processes will be performed to recover the lost data. However, the data repair processes heavily rely on time-consuming erasure coding algorithms, mainly consisting of vector-matrix multiplications. The emerging processing-in-memory technique can efficiently speed up the processing of vector-matrix multiplications. It can be integrated into blockchain storage platforms to solve the data repair issue. This paper presents Rapper, a parameter-aware repair-inmemory accelerator for blockchain storage platforms. Rapper utilizes the computing power of emerging processing-in-memory architecture so that data repair processes can be processed in a parallel manner and the overall efficiency can be improved significantly. Specifically, at the hardware level, the ReRAM memory is reorganized into our proposed double bank, XRU, XGroup, and ReRAM crossbars structure. At the software level, a parallel decoding/encoding strategy is proposed to fully exploit the internal parallelism of ReRAM. We also propose an adaptive parameter-aware mapping to handle various sizes of stripes. To demonstrate the viability of the proposed technique, a representative blockchain storage project Storj is adopted as the default storage infrastructure. Experimental results show that Rapper can achieve a 1.96 × speedup on average compared to the representative scheme.

Emerging Shingled Magnetic Recording (SMR) Disk can improve the storage capacity significantly by overlapping multiple tracks with the shingled direction. However, the shingled-like structure leads to severe write amplification caused by RMW operations inner SMR disks. As the mainstream solid-state storage technology, NAND flash has the advantages of tiny size, cost-effective, high performance, making it suitable and promising to be incorporated into SMR disks to boost the system performance. In this hybrid embedded storage system (i.e., the Embedded Flash with SMR disk (EF-SMR) system), we observe that physical flash blocks can contain a mixture of data associated with different SMR data bands; when garbage collecting such flash blocks, multiple RMW operations are triggered to rewrite the involved SMR bands and the performance is further exacerbated. Therefore, in this paper, we for the first time present MU-RMW to guarantee data from different SMR bands will not be mixed up within the flash blocks with an aim at minimizing unnecessary RMW operations. The effectiveness of MU-RMW was evaluated with realistic and intensive I/O workloads and the results are encouraging.

As the mainstream solid-state storage technology, NAND flash has the advantages of tiny size, cost-effective, and high performance, which make it a promising candidate to be embedded into the shingled magnetic recording (SMR) disk to build a faster, denser, and cheaper storage system. However, such an embedded flash with SMR (EF-SMR) disk system suffers from lengthy tail-latency due to “reclamation issues” in both the NAND flash and the SMR disk. Our preliminary observations reveal that tremendous idle time intervals exist in real-world scenarios, and few prior works have focused on addressing the tail-latency issue in the EF-SMR disk. In this article, we propose a novel method termed MAID-Q to fully exploit the idle time intervals to minimize the lengthy tail-latency of the EF-SMR disk based on a lightweight reinforcement learning model (i.e., the$Q$-learning model). In addition, fine-grained block-level space management and a parallel reclamation strategy are proposed to improve the reclamation efficiency and hide the reclamation overheads. The effectiveness of our proposed design was evaluated with realistic I/O traces, and the results show that the proposed design can remedy the tail-latency by 88.31% and improve the overall performance by 79.03%.