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In this paper we describe a method to combine dictionary coding and partial LFSR reseeding to improve the compression efficiency for test data compression. We also present a fast matrix calculation method which significantly reduces the computation time to find a solution for partial LFSR reseeding. Experimental results on ISCAS89 benchmark circuits show that our approach is better than either dictionary coding or LFSR reseeding, and outperforms several test data compression methods proposed recently.

Utilizing image data for yield estimation is a key topic in modern agriculture. This paper addresses the difficulty of counting wheat spikelets using images, to improve yield estimation in wheat fields. A wheat spikelet image dataset was constructed with images obtained by a smartphone, including wheat ears in the flowering, filling, and mature stages of reproduction. Furthermore, a modified lightweight object detection method, YOLOv5s-T, was incorporated. The experimental results show that the coefficient of determination (R2) between the predicted and true values of wheat spikelets was 0.97 for the flowering stage, 0.85 for the grain filling stage, and 0.78 for the mature stage. The R2 in all three fertility stages was 0.87, and the root mean square error (RMSE) was 0.70. Compared with the original YOLOv5s algorithm, the spikelet detection counting effect of YOLOv5s-T was not reduced. Meanwhile, the model size was reduced by 36.8% (only 9.1 M), the GPU memory usage during the training process was reduced by 0.82 GB, the inference time was reduced by 2.3 ms, the processing time was reduced by 10 ms, and the calculation amount was also reduced. The proposed YOLOv5s-T algorithm significantly reduces the model size and hardware resource requirements while guaranteeing high detection and counting accuracy, which indicates the potential for wheat spikelet counting in highly responsive wheat yield estimation.

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In the past few decades, quantum computation has become increasingly attractive due to its remarkable performance. Quantum image scaling is considered a common geometric transformation in quantum image processing, however, the quantum floating-point data version of which does not exist. Is there a corresponding scaling for 2-D and 3-D floating-point data? The answer is yes. In this paper, we present a quantum scaling up and down scheme for floating-point data by using trilinear interpolation method in 3-D space. This scheme offers better performance (in terms of the precision of floating-point numbers) for realizing the quantum floating-point algorithms than previously classical approaches. The Converter module we proposed can solve the conversion of fixed-point numbers to floating-point numbers of arbitrary size data with $$p+q$$ p + q qubits based on IEEE-754 format, instead of 32-bit single-precision, 64-bit double-precision and 128-bit extended-precision. Usually, we use nearest-neighbor interpolation and bilinear interpolation to achieve quantum image scaling algorithms, which are not applicable in high-dimensional space. This paper proposes trilinear interpolation of floating-point data in 3-D space to achieve quantum algorithms of scaling up and down for 3-D floating-point data. Finally, the quantum scaling circuits of 3-D floating-point data are designed.