Due to obstructions such as trees and buildings, single-modal satellite or aerial images are insufficient for continuous high-precision representation of road features. To address this problem, this article proposes a lightweight cross-modal information interaction for road feature extraction (LCIRE-Net) from high-resolution remote sensing images (HRSIs) and GPS trajectory/LiDAR images. We design two parallel encoders for modality feature learning, using pairs of multimodal information as inputs to the encoders. By designing a cross-modal information dynamic interaction (CMIDI) mechanism, thresholds are used to decide whether to supplement redundant information from another modality, solving the issue of ineffective fusion calculations due to minor differences in multimodal feedback. A multimodal feature fusion module (MFFM) is proposed after the encoder output to achieve effective dual-modal fusion while addressing the interference of redundant noise generated during extraction. Subsequently, we present the feature refinement and enhancement module (FREM), which successfully captures edge features of the image using the receptive field of dilated convolution kernels. Additionally, in terms of lightweight design, we employ a novel SOTA method on D-LinkNet by replacing the original residual blocks with an enhanced ghost basic block. Extensive experiments are conducted on the BJRoad, Porto, and TLCGIS datasets, demonstrating that our network, with smaller parameters and FLOPs, outperforms other road-based semantic segmentation methods.

Computing offloading is a key challenge of new rising computing paradigms of the Internet of Things (IoT) like edge computing, which shifts computations to data sources as near as possible to gain the benefits, such as low latency and energy efficiency. However, the fragmentation problem of IoT devices results in a heterogeneous and disordered ecosystem, hindering the interoperating demands of computing offloading. What we need is an open-enabled ecosystem which allows third-party developers to create and update functions of deployed devices dynamically. We propose Things-representational state transfer (T-REST) which is an extension of the representational state transfer (REST) architectural style to address this problem. It integrates contents and computations together to inherit the uniform interface principle from REST. Three architectural constraints are added to REST: 1) reusable remote evaluation; 2) dynamic time series representation; and 3) computational hypertext. A novel event triggering mechanism is designed to decouple the tight coupling of front-end content accesses and back-end computations for resources. A reference prototype, named T-REST engine, is implemented to verify the proposed architecture with the open-enabled style, distributed semantics, and computing offloading features. Discussions show that T-REST preserves the benefits of REST. In addition, it achieves extremely lightweight footprints and can perform computing offloading through open-enabled architectures.

Edge computing is a continuum that includes the computing resources from cloud to things. Ecosystem of things (EoT) is a subsystem of the ecosystem of edge computing, which potentially contains trillions of devices of things and directly interacts with the physical world. This paper surveys the state of the art of EoT by focusing on the computing infrastructure aspect with a forward-looking perspective. We point out a trend of smart edge computing with four types of smartness and intelligence. We address three fundamental questions. 1) What capabilities and how much energy efficiency are the hardware providing? What is the future growth potential? 2) What abstractions are provided by the system software? Are they adequate to support smart edge computing? 3) What ecosystem architectures have been proposed for the coordination of things, the edge, and the cloud? Are they meeting the needs to encourage innovation but avoid unnecessary ecosystem fragmentation? We examine advances from both industry and academia, including research results, visions, and project concepts. We also point out future research directions.

Data warehouse systems, like Apache Hive, have been widely used in the distributed computing field. However, current generation data warehouse systems have not fully embraced High Performance Computing (HPC) technologies even though the trend of converging Big Data and HPC is emerging. For example, in traditional HPC field, Message Passing Interface (MPI) libraries have been optimized for HPC applications during last decades to deliver ultra-high data movement performance. Recent studies, like DataMPI, are extending MPI for Big Data applications to bridge these two fields. This trend motivates us to explore whether MPI can benefit data warehouse systems, such as Apache Hive. In this paper, we propose a novel design to accelerate Apache Hive by utilizing DataMPI. We further optimize the DataMPI engine by introducing enhanced non-blocking communication and parallelism mechanisms for typical Hive workloads based on their communication characteristics. Our design can fully and transparently support Hive workloads like Intel HiBench and TPC-H with high productivity. Performance evaluation with Intel HiBench shows that with the help of light-weight DataMPI library design, efficient job start up and data movement mechanisms, Hive on DataMPI performs 30% faster than Hive on Hadoop averagely. And the experiments on TPC-H with ORCFile show that the performance of Hive on DataMPI can improve 32% averagely and 53% at most more than that of Hive on Hadoop. To the best of our knowledge, Hive on DataMPI is the first attempt to propose a general design for fully supporting and accelerating data warehouse systems with MPI.

Transmission of high-quality analogue sourced signals over copper or optical fibre links and networks demands a critical choice of modulation format to achieve both high performance and to avoid unnecessary bandwidth overhead penalties. Pulse time modulation (PTM) techniques are being increasingly employed to overcome many of the problems associated with traditional analogue and digital methods. Among the family of pulse time modulation techniques squarewave frequency modulation (SWFM) is particularly well suited to the transmission of high-quality wideband instrumentation and video signals, such as high definition TV, due to its attractive combination of performance and simplicity. This paper presents theoretical and practical aspects of SWFM and describes a number of experimental SWFM systems capable of transmitting signals from DC to 30 MHz.>