This paper proposes a frequency-domain model for fast and accurate prediction of frequency modulation (FM) error in frequency-modulated continuous wave (FMCW) radar chirp generators. Obtaining FM error from the output frequency of a phase-locked loop (PLL) requires lengthy simulation times due to the simulator’s limited frequency quantization accuracy, which can result in discrepancies between simulated and actual performance. To address this issue, the feedback signal (DIV) sent to the phase-frequency detector (PFD) is employed in this study to quickly and accurately determine the FM error through spectrum analysis. During frequency sweeps, the feedback signal with a relatively constant frequency receives the modulating signal directly. To validate the effectiveness of the proposed model, a nested PLL-based frequency synthesizer operating from 24-27.52 GHz is designed in a 55-nm CMOS process. Measurement results show good agreement with those obtained from the model, with the difference between the simulated and measured root mean square (RMS) FM error being less than 22 kHz.

A task assigned to space exploration satellites involves detecting the physical environment within a certain space. However, space detection data are complex and abstract. These data are not conducive for researchers' visual perceptions of the evolution and interaction of events in the space environment. A time-series dynamic data sampling method for large-scale space was proposed for sample detection data in space and time, and the corresponding relationships between data location features and other attribute features were established. A tone-mapping method based on statistical histogram equalization was proposed and applied to the final attribute feature data. The visualization process is optimized for rendering by merging materials, reducing the number of patches, and performing other operations. The results of sampling, feature extraction, and uniform visualization of the detection data of complex types, long duration spans, and uneven spatial distributions were obtained. The real-time visualization of large-scale spatial structures using augmented reality devices, particularly low-performance devices, was also investigated. The proposed visualization system can reconstruct the three-dimensional structure of a large-scale space, express the structure and changes in the spatial environment using augmented reality, and assist in intuitively discovering spatial environmental events and evolutionary rules.

Sorting is essential for many scientific and data processing problems. It is significant to improve the efficiency of sorting. Taking advantage of specialized hardware, parallel sorting, e.g., sorting networks and linear sorters, implements sorting in lower time complexity. However, most of them are designed based on the parallelization of algorithms, lacking consideration of specialized hardware structures. In this article, we propose an extended nonstrict partially ordered set-based configurable linear sorter on field-programmable gate arrays (FPGAs). First, we extend nonstrict partial order to the binary tuple and n-tuple nonstrict partial orders. Then, the linear sorting algorithm is defined based on them, with the consideration of hardware performance. It has 4N/n time complexity varying from 4 to 2 N as the tuple size varies. The number of comparisons reduces to N/2 in binary tuple-based sorting, which is half of the state-of-the-art insertion linear sorting. Finally, we implement the linear sorter on FPGAs. It consists of multiple customizable micro-cores, named sorting units (SUs). The SU packages the storage and comparison of the tuple. All the SUs are connected into a chain with simple communication, which makes the sorter fully configurable in length, bandwidth, and throughput. They also act the same in each clock cycle, so that the achieved frequency of the sorter improves. In our experiment, the sorter achieves at most 660-MHz frequency, 5.6 Gb/s throughput, and 87 times speed-up compared with the quick sort algorithm on general processors.

In this work we consider the rate control issue for network coding based multicast among multiple sessions, which can be formulated as a network utility maximization problem. To solve the problem we propose a distributed optimization decomposition approach, which is different from the previous work in the literature in that (1) it is a hierarchical decomposition approach where the primal problem is decomposed recursively, until an independent rate control module is obtained and the decomposed subproblems can be solved by the distributed max-flow algorithm and we emphasize the layered functionality allocation of decomposed subproblems following the framework of "Layering as Optimization Decompositions"; (2) we first separate the primal problem by relaxing the capacity constraint among sessions; (3) to implement end-to-end control, we separate the independent rate control module at end node from the operation in the interior of the network. In this work we intend to propose not only a rate control algorithm but also a possible choice of network architecture for network coding based communication with rate control capability.

BACKGROUND: There has been considerable effort focused on developing efficient programs for tagging single-nucleotide polymorphisms (SNPs). Many of these programs do not account for potential reduced genomic coverage resulting from genotyping failures nor do they preferentially select SNPs based on functionality, which may be more likely to be biologically important. RESULTS: We have developed a user-friendly and efficient software program, Snagger, as an extension to the existing open-source software, Haploview, which uses pairwise r2 linkage disequilibrium between single nucleotide polymorphisms (SNPs) to select tagSNPs. Snagger distinguishes itself from existing SNP selection algorithms, including Tagger, by providing user options that allow for: (1) prioritization of tagSNPs based on certain characteristics, including platform-specific design scores, functionality (i.e., coding status), and chromosomal position, (2) efficient selection of SNPs across multiple populations, (3) selection of tagSNPs outside defined genomic regions to improve coverage and genotyping success, and (4) picking of surrogate tagSNPs that serve as backups for tagSNPs whose failure would result in a significant loss of data. Using HapMap genotype data from ten ENCODE regions and design scores for the Illumina platform, we show similar coverage and design score distribution and fewer total tagSNPs selected by Snagger compared to the web server Tagger. CONCLUSION: Snagger improves upon current available tagSNP software packages by providing a means for researchers to select tagSNPs that reliably capture genetic variation across multiple populations while accounting for significant genotyping failure risk and prioritizing on SNP-specific characteristics.