Underwater acoustic (UWA) communication is a critical part of information exchange among marine equipment in the Internet of Underwater Things (IoUT). The impact of the marine environment on UWA communication manifests through physical channels, which exhibit multipath propagation, Doppler spread, and time-varying characteristics. The dynamic compressed sensing (DCS) framework, represented by Kalman Filtered CS (KF-CS) algorithm, is introduced to dynamically adapt support sets by real-time tracking of observations. Meanwhile, channel modeling in Delay-Doppler (DD) domain offers a more stable representation for doubly-selective UWA channels, revealing a potential for channel estimation. In this paper, we transform the time-domain channel estimation into the DD domain channel estimation, and formulate the DD domain channel estimation under the DCS framework to exploit the time-varying sparsity of the UWA channel in the DD domain. In addition, to solve the DD model, the classic algorithms need to search for the sparse solution in the extremely large two-dimensional space of the entire UWA channel. Since only a few multipath arrivals within the large time-delay spread of the entire UWA channel have the Doppler value, we propose the multipath arrivals-Doppler function-based DCS channel estimation approach (DCS-MAD) to solve the DD model. The proposed DCS-MAD can remodel the traditional DD model as multipath arrivals and Doppler model, and search for the sparse solution in the two-dimensional space of multipath arrivals, instead of on the entire channel, so as to effectively reduce system complexity and better focus on Doppler value estimation at the UWA channel’s multipath arrivals. Specifically, we first estimate the multipath arrival delay of the UWA channel using the DCS algorithm. Then, the two-dimensional space search is performed specifically at the multipath arrivals and Doppler domains under the DCS framework to exploit the time-varying sparsity. Finally, the results of numerical simulation and sea experiments validate the superiority of the proposed schemes, compared to the benchmark algorithms.

For deep-sea underwater acoustic (UWA) communication, large multipath time delay spread, that caused by spatial divergence effects of acoustic propagation along the deep-sea distance-depth dimension, exert extremely adverse influence on communication performance. Inspired by the fact that the large delay spread deep-sea channels tend to be clustered, and each cluster of which varies independently with respect to receiving element as well as time delay, in this article a multipath clusters (MCs) joint equalization approach is proposed to explore the spatial–temporal diversity of large delay spread deep-sea UWA channels. First we design a compact m-sequence synchronization sequence to extract MCs along different receiving element and time delay while achieving frame synchronization. Then, a sparsity-aware proportionate-type adaptive iterative algorithm is formulated under the recursive least squares (RLS) framework, by which, equalization output of each spatial–temporal-wise MC is aligned and combined to achieve joint equalization gain. Simulation results demonstrate that the proposed approach is capable of yielding superior performance in terms of output signal-to-noise ratio (SNR) and bit error rate (BER) compared to the traditional equalization approaches under large delay spread channels. Finally, in deep-sea trial error-free UWA communication with a maximum data rate of 6000 bps at a distance of 20 km is achieved by a two-element receiver, further demonstrating the effectiveness of the proposed algorithm.

This letter presents the design of a single-input-single-output (SISO) underwater acoustic (UWA) short video communication system for quasi-real-time marine observation, which employing 64-QAM (Quadrature Amplitude Modulation) to yield a peak data rate of 15 kbps and an adaptive decision feedback equalizer (DA-DFE) with embedded phase-locked loop (PLL) to mitigate multipath interference. Meanwhile, Polar channel encoding and MPEG-4 video compression encoding guarantee the communication performance. Two field tests conducted in different UWA channels demonstrated that the proposed system enables single-element UWA communication of 3.5-second short video clip over a distance of 150 meters with an end-to-end delay on the order of minute. This capability has the potential to be utilized for Internet of Underwater Things (IoUT)-driven quasi-real-time marine observation.

Long-range underwater acoustic communication (LR-UWAC) is an essential technique for applications such as the control of unmanned underwater vehicles, gliders, or tactical submarines for long-term monitoring tasks. While techniques for estimating the channel impulse response (CIR) of short-range UWAC have been successfully applied, this is not the case for LR-UWAC. Here, the main challenge is to handle the diverse channel structure, which can be of a sparse or a non-sparse structure. In this paper, we propose a robust channel estimator for LR-UWAC. Our solution includes a switching mechanism to classify the channel as either sparse or non-sparse. In its sparse form, the LR-UWAC channel is characterized by a long time delay spread, time-invariant characteristics, and a block structure. Thus, we propose a non-trivial combination of the block subspace pursuit and the distributed subspace pursuit algorithms to exploit the channel’s block structure. For non-sparse LR-UWAC channel structures, we approximate the received signal as Gaussian, and derive a maximum-likelihood (ML) estimator to separate the non-resolvable multipath. Extensive numerical simulation and results from two long-range sea experiments demonstrate the efficiency of our approach in identifying the channel’s structure, and to accurately estimate the channel compared to state-of-the-art benchmarks.

As a time-frequency doubly selective channel, severe multipath, Doppler, as well as the large time-delay characteristics of the underwater acoustic (UWA) channel pose significant challenge to the research and design of UWA communication and network systems. To mitigate these negative factors, inherent sparsity contained in the UWA channel has been extensively investigated to improve UWA communication via a sparsity exploitation receiver. While the performance of the UWA sensor network is highly dependent on that of the physical layer, there are few investigations reported on exploiting channel sparsity from the viewpoint of UWA networking. In this article, a UWA sensor network adopting the sparsity exploitation physical layer is evaluated based on the network simulator 3 (NS-3) simulation tool. The simulation time-varying channel is generated by incorporating the Bellhop channel model with the statistical characteristics extracted from experimental shallow water channels. Three types of physical layers, i.e., those do not adopt sparse exploitation, adopting compressed sensing (CS), as well as the dynamic CS (DCS) technique, are employed for evaluation and comparison of network behavior under different media access control (MAC) protocols. The evaluation results verify the effectiveness of sparsity exploitation in improving UWA sensor network performance in the presence of time variations, while giving a quantitative comparison between enhancement achieved by the CS and DCS sparsity exploitation.

In existing big data stacks, the processes of analytical processing and knowledge serving are usually separated in different systems. In Alibaba, we observed a new trend where these two processes are fused: knowledge serving incurs generation of new data, and these data are fed into the process of analytical processing which further fine tunes the knowledge base used in the serving process. Splitting this fused processing paradigm into separate systems incurs overhead such as extra data duplication, discrepant application development and expensive system maintenance. In this work, we propose Hologres, which is a cloud native service for hybrid serving and analytical processing (HSAP). Hologres decouples the computation and storage layers, allowing flexible scaling in each layer. Tables are partitioned into self-managed shards. Each shard processes its read and write requests concurrently independent of each other. Hologres leverages hybrid row/column storage to optimize operations such as point lookup, column scan and data ingestion used in HSAP. We propose Execution Context as a resource abstraction between system threads and user tasks. Execution contexts can be cooperatively scheduled with little context switching overhead. Queries are parallelized and mapped to execution contexts for concurrent execution. The scheduling framework enforces resource isolation among different queries and supports customizable schedule policy. We conducted experiments comparing Hologres with existing systems specifically designed for analytical processing and serving workloads. The results show that Hologres consistently outperforms other systems in both system throughput and end-to-end query latency.

As broad adoption of Apache Hadoop [20] accelerates authentication and authorization capabilities are a major concern for data access security. To integrate pluggable authentication providers, enhance desirable single sign on for end users, and enforce centralized access control on the platform, Hadoop community has widely discussed and concluded that token based authentication is the appropriate approach [18]. In this paper we discuss an innovation solution about how to implement the token authentication based on the Kerberos pre-authentication framework [4]. We propose a pre-authentication mechanism for Kerberos [1] that allows users to authenticate to Key Distribution Center (KDC) using a standard token, and develop a plugin for MIT Kerberos that can be deployed separately to employ the new mechanism. Based on that, we develop our token authentication solution for the entire Hadoop stack that helps integrate identity management systems and OAuth 2.0 [6] authorization solutions, meanwhile avoiding complication, risk and deployment overhead.

Although Hadoop MapReduce provides good programming abstractions and horizontal scalability, it is often blamed for its poor single node performance. In the meantime, MapReduce has already achieved a large install base, thus any performance improvement should keep the compatibility. In this paper, we address the challenges via several approaches guided by low-level performance analysis. And we materialize the approaches via NativeTask, a high-performance, fully compatible MapReduce execution engine. We evaluate its performance with representative HiBench workloads. The results show that the speedup NativeTask achieves ranges from 10% to 160%, and it paves the way for a better MapReduce that excels on both single node performance and scalability. In the future, hardware acceleration can also be applied to further improve the system's efficiency.

Power semiconductor devices, such as insulated-gate bipolar transistors, metal-oxide-semiconductor field-effect transistors, and static-induction thyristors, are used in different kinds of pulsed power generators developed for different applications. In addition, the semiconductor opening switch is found to have very effective applications in pulsed power generation by inductive energy storage. Semiconductor switches have greatly extended the scales of pulsed power parameters, especially in repetition rate and lifetime. They have also enabled new areas of pulsed power applications, such as accelerators, flue-gas treatment, and gas lasers.

Pulsed intense ion beams have been developed for applications including surface modification and alloying, and thin-film and nanopowder synthesis. Rapid thermal processing with ions is quite promising for large-scale commercial use, due to the high specific ion energy deposition (joules per cubic centimeter) without reflection, and to the relative efficiency and low cost of the pulsed power ion-beam drivers compared to other high-kinetic energy alternatives. We discuss in this paper the basis for the use of ions in materials processing and the methods of beam formation and impingement on material to be treated, and give examples of recent and ongoing work in materials processing.