Most presented implementations of the exponential function confine to the single precision format. Increasing data width to the double precision format requires a different approach. The presented novel architecture employs three independent Look-Up Tables (LUTs) together with a short Taylor expansion exp(x)ap1+times. Implementation results show that the double precision exp() function implementation achieves huge performance with satisfactory accuracy, latency and FPGA area consumption.

Highly parallel architecture for local histogram equalisation is studied. Three different kinds of approaches to the parallel architecture are regarded in this paper. (1) Module-level -which focuses on processing as many data as possible within a single module. (2) 1D -Several modules conducting simultaneously histogram equalization on partially overlapping (either horizontally or vertically) frames. (3) 2D -utilizes the same approach as 1D but in two dimensions. Beside above-mentioned solutions, differential processing of overlapping frames was also considered. At the end of this paper the optimal proportion of the above mention solutions are studied and implementation results given.

We present the experiment results to use the YOLOv3 neural network architecture to automatically detect tumor cells in cytological samples taken from the skin in canines. A rich dataset of 1219 smeared sample images with 28,149 objects was gathered and annotated by the vet doctor to perform the experiments. It covers three types of common round cell neoplasms: mastocytoma, histiocytoma, and lymphoma. The dataset has been thoroughly described in the paper and is publicly available. The YOLOv3 neural network architecture was trained using various schemes involving original dataset modification and the different model parameters. The experiments showed that the prototype model achieved 0.7416 mAP, which outperforms the state-of-the-art machine learning and human estimated results. We also provided a series of analyses that may facilitate ML-based solutions by casting more light on some aspects of its performance. We also presented the main discrepancies between ML-based and human-based diagnoses. This outline may help depict the scenarios and how the automated tools may support the diagnosis process.

The issues of video coding based on exceptionally suitable for SHD format Shapiro (1993) EZW (embedded zero wavelet) algorithm is discussed. The main aspect is a possibility of building a real time system which is able to process the algorithm. Thus a dedicated architecture for the purpose is considered. The method presented is based on the EZW method modified such a way to simplify the hardware architecture dedicated for its execution. Such a simplification allows one to use FPGA technology as a target platform for the system. The MISD (multiple instruction-stream single data-stream) architecture is proposed as a solution of the problem. The architecture is characterised by high speed execution of the EZW algorithm. Simplicity and performance classify the algorithm for implementation in high capacity programmable FPGA structures. The paper is the authors contribution in the world's development of custom computing machines (CCM).