An FPGA-Based Ultra-High-Speed Object Detection Algorithm with Multi-Frame Information Fusion.

An ultra-high-speed algorithm based on Histogram of Oriented Gradient (HOG) and Support Vector Machine (SVM) for hardware implementation at 10,000 frames per second (FPS) under complex backgrounds is proposed for object detection. The algorithm is implemented on the field-programmable gate array (FPGA) in the high-speed-vision platform, in which 64 pixels are input per clock cycle. The high pixel parallelism of the vision platform limits its performance, as it is difficult to reduce the strides between detection windows below 16 pixels, thus introduce non-negligible deviation of object detection. In addition, limited by the transmission bandwidth, only one frame in every four frames can be transmitted to PC for post-processing, that is, 75% image information is wasted. To overcome the mentioned problem, a multi-frame information fusion model is proposed in this paper. Image data and synchronization signals are first regenerated according to image frame numbers. The maximum HOG feature value and corresponding coordinates of each frame are stored in the bottom of the image with that of adjacent frames'. The compensated ones will be obtained through information fusion with the confidence of continuous frames. Several experiments are conducted to demonstrate the performance of the proposed algorithm. As the evaluation result shows, the deviation is reduced with our proposed method compared with the existing one.

Candidate sex-associated gene identification in Trachinotus ovatus (Carangidae) using an integrated SLAF-seq and bulked segregant analysis approach.

It is difficult to distinguish the sexes of Trachinotus ovatus based on appearance, and little data about sex-determining genes are available for this species. Here, we generated 200 F2 individuals using the parents R404 and R403. DNA samples were collected from 50 individuals of each sex and aggregated into sex-specific DNA pools. Specific-locus amplified fragment sequencing was integrated with bulked segregant analysis to detect candidate sex-associated genes. Approximately 3,153,153 high-quality single-nucleotide polymorphism (SNP) markers and 135,363 high-quality insertion-deletion (Indel) markers were generated. Six candidate regions within chromosome 14, encompassing 132 candidate genes, were identified as closely related to sex. Based on annotations, six genes (EVM0019817, EVM0004192, EVM0001445, EVM0005260, EVM0014734, and EVM0009626) were predicted to be closely associated with sex. These results present an efficient genetic mapping approach that lays a foundation for molecular sex discrimination in T. ovatus.