FPGA and SoC Devices Applied to New Trends in Image/Video and Signal Processing Fields
Field-programmable gate arrays (FPGAs) and, recently, System on Chip (SoC) devices have been applied in different areas and fields for the past 20 years. [...]
The Improved Division-Less MT-Type Velocity Estimation Algorithm for Low-Cost FPGAs
Advanced motion control applications require smooth and highly accurate high-bandwidth velocity feedback, which is usually provided by an incremental encoder. Furthermore, high sampling rates are also demanded in order to achieve cutting-edge system performance. Such control system performance with high accuracy can be achieved easily by FPGA-based controllers. On the other hand, the well-known MT method for velocity estimation has been well proven in practice. However, its complexity, which is related to the inherent arithmetic division involved in the calculus part of the method, prevents its holistic implementation as a single-chip solution on small-size low-cost FPGAs that are suitable for practical optimized control systems. In order to overcome this obstacle, we proposed a division-less MT-type algorithm that consumes only minimal FPGA resources, which makes it proper for modern cost-optimized FPGAs. In this paper, we present new results. The recursive discrete algorithm has been further optimized, in order to improve the accuracy of the velocity estimation. The novel algorithm has also been implemented on the experimental FPGA board, and validated by practical experiments. The enhanced algorithm design resulted in improved practical performance.
A method for automated individual, species and call type recognition in free-ranging animals
The ability to identify individuals reliably is often a key prerequisite for animal behaviour studies in the wild. In primates, recognition of other group members can be based on individual differences in the voice, but these cues are typically too subtle for human observers. We applied a combined mechanism consisting of a call feature extraction (mel frequency cepstral coefficients) and pattern recognition algorithm (artificial neural networks) to investigate whether automated caller identification is possible in free-ranging primates. The mechanism was tested for its accuracy in recognizing species, call type and caller identity in a large population of free-ranging blue monkeys, Cercopithecus mitis stuhlmanni, in Budongo Forest, Uganda. Classification was highly accurate with 96% at the species, 98% at the call type and 73% at the caller level. It also outperformed conventional discriminant function analysis in the individual recognition task. We conclude that software based on this method will make a powerful tool for future animal behaviour research, as it allows for automatic, fast and objective classifications in different animal species.