With the rapid development of geolocation technology, the volume of spatio-temporal trajectory data has surged. This data is widely used in fields such as geographic information systems and mobile computing, but its storage and query processing present significant challenges. Current methods of offline indexing are inefficient and cannot be updated in real-time. To address this issue, this paper proposes a concept of the online index that supports real-time storage and indexing of trajectory data and significantly reduces indexing time and storage space requirements. Based on this concept, two vector-based online trajectory indexing methods are proposed in this paper. The first is an online trajectory indexing method based on vector extraction (VBIndex), which offers the advantages of high efficiency and less storage space. The second is an online trajectory indexing method based on road-network matching (RAIndex), which further improves the vector-based indexing efficiency when road network involved. Through experiments with real datasets, the proposed algorithms were evaluated, confirming their superiority in terms of indexing construction time and storage space. Furthermore, we have theoretically proven that queries based on this index are accurate, and statistical analysis is feasible. Both algorithms have a time complexity of$O(N)$in indexing construction, demonstrating good performance.

Decoding via sequentially guessing the error pattern in a received noisy sequence has received attention recently, and ORBGRAND has been proposed as one such decoding algorithm that is capable of utilizing the soft information embedded in the received noisy sequence. An information theoretic study is conducted for ORBGRAND, and it is shown that the achievable rate of ORBGRAND using independent and identically distributed random codebooks almost coincides with the channel capacity, for an additive white Gaussian noise channel under antipodal input. For finite-length codes, improved guessing schemes motivated by the information theoretic study are proposed that attain lower error rates than ORBGRAND, especially in the high signal-to-noise ratio regime.

Scaling is a fundamental research issue in the geosciences and plays an essential role in the comparison and integration of datasets and in the calibration and validation of environmental models. Much environmental research suffers from a scale discrepancy between different data sources and models[1]. In the validation of remote sensing products, for instance, soil moisture is typically measured in situ at the scale of several dm3, while satellites measure soil moisture for grid cells at least several km2in size[2]. Different spatial scales (supports) in the input and output cause the issue of scale transformation[3]. Various methods have been proposed to transfer the spatial scale of land surface parameters but most are appropriate only for homogeneity situations. In most real-world situations, heterogeneity is inevitably present. This article focuses on scaling methods for land surface parameters under spatial heterogeneity and develops a methodological framework for `scale transformation'. This framework is composed of two types of methods to handling the issue of scale transformation: (1) from multiple in situ observations at point support to obtain satellite footprint-scale estimates (area support), named as MOPTA; (2) from multiple in situ observations at footprint scale (area support) to obtain another footprint-scale estimate (area support), named as MOATA. Land surface parameters considered are soil moisture and evapotranspiration (ET). As to MOPTA, we investigates two cases of upscaling in situ soil-moisture observations to satellite footprint-scale estimates. The in situ observations are acquired by three types of ecohydrological wireless sensor network (WSNs) deployed in 5 cm depth, varying measurement precision. WSNs covers approximately 16 (4 × 4) MODIS 1-km spatial resolution pixels. In the first case, A block kriging (BK) upscaling strategy is used to scale up soil moisture to MODIS pixel averages with in situ observations of unequal precision[4]. Furthermore, when measurement times of ground-based and satellite-based observations are not the same, temporal variation in soil moisture must be taken into account. At this case, a spatio-temporal regression block kriging (STRBK) is used to upscale in situ soil moisture observations collected as time series at multiple locations to pixel-scale estimates for validating the Polarimetric L-band Multi-beam Radiometer (PLMR) retrieved soil moisture product in the Heihe watershed[5]. As to MOATA, two types of in situ observations are involved: eddy correlation (EC) which measurements are normally a few to hundreds of meters[6][7] and large aperture scintillometer (LAS) which measurements are integrated over a long transect of approximately 500-5000 m from the same or different underlying surfaces[6][7]. For the purpose of cross-validation and comparison, the scale transformation between EC observations and LAS observations needs to be carried out. A area-to-area regression kriging is used to handle the problem of the nonstationarity of a random function and the issue of scale transformation[8]. This framework will be further developed to handling more cases. When land surface parameter show high spatio(-temporal) heterogeneity within the footprint, the upscaling strategy will take this into account through modelling the mean and variance as non-constant values that depend on high-resolution covariates. We will do this both in the spatial and spatio-temporal setting, in the case of the latter making use of space-time geostatistics and the stochastic partial differential equation (PDE) approach.

Modern multi-core processor architectures strive for the highest possible performance of various applications. This paper discusses a triple-based multi-core architecture which supports object-oriented methodology and applications in hardware level. However, the Memory Wall is still the bottleneck which should be resolved to decrease the disparity between how fast a CPU can operate on data and how fast it can get data. We present hierarchical shared memory system architecture (HSM) which is hierarchically constructed memory shared by multi-cores. Moreover, we propose a new approach mapping data among different levels of cache and memory, which is called partially-inclusive cache mapping policy that facilitates the coherence of shared memory. This paper focus on object-oriented systems combined with the HSM and partially-inclusive policy and presents a new objects management model. The analysis based on comparisons between our objects management and link structured object organization methods shows that our method is predominant in spatial and temporal aspects on memory parallel access efficiency and costs less storage space to organize objects.

As current FPGAs grow in logic capacity, they are widely used to implement entire systems. In some specific applications, such as our embedded multi-core processor TriBA[1],user memory models are not limited to single-port or dual-port. Thus, we need a cost-effective way to realize N-port memory on FPGA since most commercial products do not provide N-port physical arrays. In this paper, we propose a hierarchical N-port memory architecture for LUT-based FPGAs. The principle of this architecture is to create a two-level memory hierarchy formed by different resources. We map the memory resources inside LUTs as 1-port memory banks, and interleave these banks to create N-port L1 memory. We also interleave physical dual-port arrays to build N-port L2 memory. We also provide the data transfer between L1 and L2 memories and assume that such data transfer is managed by software control just like the strategy used by SPM. Compared to L1 memory, L2 memory has the advantage in cost and also has several disadvantages, such as longer access time and higher conflict probability. If most accesses are served by its L1 memory portion, hierarchical memory architecture will achieve both goals in cost and access time. We implement this architecture on Xilinx Virtex-II chips to measure its cost and also use the memory trace collected from multi-core simulator to measure its average access time. The product of cost and average access time shows that, hierarchical memory architecture is a cost-effective way to realize N-port memory on FPGA.

Modern multi-core processors are predominant in improving performance of parallel applications. This paper discusses a triple-based multi-core architecture which provides native support for object-oriented methodology and applications in hardware level. The model explicitly represents objects and supports messaging-based communication, which maps well to the standard style of interaction in object oriented languages. However, the Memory Wall is still the bottleneck which should be resolved to decrease the disparity between how fast a CPU can operate on data and how fast it can get data. A hierarchy shared memory system (HSM) working with the partially-inclusive cache mapping policy is proposed. And a new object management model is presented, which use object table and recycle stack scheme to supports explicit dynamic object management. Our cache design presents an innovative solution to handling the costs of cache coherence by allowing applications to control the amount of sharing between cores. Experimental analysis based on comparisons between our objects management and other common link structured object organization methods shows that our method is predominant in spatial and temporal aspects on memory parallel access efficiency and costs less storage space to organize objects.