Customized Nios II multi-cycle instructions to accelerate block-matching techniques

This study focuses on accelerating the optimization of motion estimation algorithms, which are widely used in video coding standards, by using both the paradigm based on Altera Custom Instructions as well as the efficient combination of SDRAM and On-Chip memory of Nios II processor. Firstly, a complete code profiling is carried out before the optimization in order to detect time leaking affecting the motion compensation algorithms. Then, a multi-cycle Custom Instruction which will be added to the specific embedded design is implemented. The approach deployed is based on optimizing SOC performance by using an efficient combination of On-Chip memory and SDRAM with regards to the reset vector, exception vector, stack, heap, read/write data (.rwdata), read only data (.rodata), and program text (.text) in the design. Furthermore, this approach aims to enhance the said algorithms by incorporating Custom Instructions in the Nios II ISA. Finally, the efficient combination of both methods is then developed to build the final embedded system. The present contribution thus facilitates motion coding for low-cost Soft-Core microprocessors, particularly the RISC architecture of Nios II implemented in FPGA. It enables us to construct an SOC which processes 50×50 @ 180 fps

Model-free functional MRI analysis based on unsupervised clustering

AbstractConventional model-based or statistical analysis methods for functional MRI (fMRI) are easy to implement, and are effective in analyzing data with simple paradigms. However, they are not applicable in situations in which patterns of neural response are complicated and when fMRI response is unknown. In this paper the “neural gas” network is adapted and rigourosly studied for analyzing fMRI data. The algorithm supports spatial connectivity aiding in the identification of activation sites in functional brain imaging. A comparison of this new method with Kohonen’s self-organizing map and with a fuzzy clustering scheme based on deterministic annealing is done in a systematic fMRI study showing comparative quantitative evaluations. The most important findings in this paper are: (1) both “neural gas” and the fuzzy clustering technique outperform Kohonen’s map in terms of identifying signal components with high correlation to the fMRI stimulus, (2) the “neural gas” outperforms the two other methods with respect to the quantization error, and (3) Kohonen’s map outperforms the two other methods in terms of computational expense. The applicability of the new algorithm is demonstrated on experimental data

Determining and interpreting correlations in lipidomic networks found in glioblastoma cells

Background: Intelligent and multitiered quantitative analysis of biological systems rapidly evolves to a key technique in studying biomolecular cancer aspects. Newly emerging advances in both measurement as well as bio-inspired computational techniques have facilitated the development of lipidomics technologies and offer an excellent opportunity to understand regulation at the molecular level in many diseases. Results: We present computational approaches to study the response of glioblastoma U87 cells to gene- and chemo-therapy. To identify distinct biomarkers and differences in therapeutic outcomes, we develop a novel technique based on graph-clustering. This technique facilitates the exploration and visualization of co-regulations in glioblastoma lipid profiling data. We investigate the changes in the correlation networks for different therapies and study the success of novel gene therapies targeting aggressive glioblastoma. Conclusions: The novel computational paradigm provides unique “fingerprints” by revealing the intricate interactions at the lipidome level in glioblastoma U87 cells with induced apoptosis (programmed cell death) and thus opens a new window to biomedical frontiers. Background Glioblastoma are highly invasive brain tumors. Th

Computing H/D-Exchange rates of single residues from data of proteolytic fragments

<p>Abstract</p> <p>Background</p> <p>Protein conformation and protein/protein interaction can be elucidated by solution-phase Hydrogen/Deuterium exchange (sHDX) coupled to high-resolution mass analysis of the digested protein or protein complex. In sHDX experiments mutant proteins are compared to wild-type proteins or a ligand is added to the protein and compared to the wild-type protein (or mutant). The number of deuteriums incorporated into the polypeptides generated from the protease digest of the protein is related to the solvent accessibility of amide protons within the original protein construct.</p> <p>Results</p> <p>In this work, sHDX data was collected on a 14.5 T FT-ICR MS. An algorithm was developed based on combinatorial optimization that predicts deuterium exchange with high spatial resolution based on the sHDX data of overlapping proteolytic fragments. Often the algorithm assigns deuterium exchange with single residue resolution.</p> <p>Conclusions</p> <p>With our new method it is possible to automatically determine deuterium exchange with higher spatial resolution than the level of digested fragments.</p

Local and Global Stability Analysis Methods of Multi-Time Scale Neural Networks

The dynamics of complex neural networks modelling the self--organization process in cortical maps must include the aspects of long and short--term memory. The behaviour of the network is such characterized by an equation of neural activity as a fast phenomenon and an equation of synaptic modification as a slow part of the neural system. We present new methods of analyzing the dynamics of a competitive neural system with different time scales: The K--monotone system theory developped by Kamke in 1932 Hir85 as a global analysis techniques and the theory of singular perturbations Vid93 as a local analysis method. We also show the consequences of the stability analysis on the neural net parameters. Keywords: Cohen--Grossberg model, Theory of K--monotone systems, Theory of singular perturbations, Learning rule 1 INTRODUCTION The dynamics of complex neural networks must include the aspects of long and short--term memory. The behaviour of the network is thus characterized by an equation..

Current Status and Future Perspectives of Artificial Intelligence in Magnetic Resonance Breast Imaging

Recent advances in artificial intelligence (AI) and deep learning (DL) have impacted many scientific fields including biomedical maging. Magnetic resonance imaging (MRI) is a well-established method in breast imaging with several indications including screening, staging, and therapy monitoring. The rapid development and subsequent implementation of AI into clinical breast MRI has the potential to affect clinical decision-making, guide treatment selection, and improve patient outcomes. The goal of this review is to provide a comprehensive picture of the current status and future perspectives of AI in breast MRI. We will review DL applications and compare them to standard data-driven techniques. We will emphasize the important aspect of developing quantitative imaging biomarkers for precision medicine and the potential of breast MRI and DL in this context. Finally, we will discuss future challenges of DL applications for breast MRI and an AI-augmented clinical decision strategy

Artificial Intelligence in Oncology: A Topical Collection in 2022

Artificial intelligence (AI) is considered one of the core technologies of the Fourth Industrial Revolution that is currently taking place [...