Real-Time neural signal decoding on heterogeneous MPSocs based on VLIW ASIPs

An important research problem, at the basis of the development of embedded systems for neuroprosthetic applications, is the development of algorithms and platforms able to extract the patient's motion intention by decoding the information encoded in neural signals. At the state of the art, no portable and reliable integrated solutions implementing such a decoding task have been identified. To this aim, in this paper, we investigate the possibility of using the MPSoC paradigm in this application domain. We perform a design space exploration that compares different custom MPSoC embedded architectures, implementing two versions of a on-line neural signal decoding algorithm, respectively targeting decoding of single and multiple acquisition channels. Each considered design points features a different application configuration, with a specific partitioning and mapping of parallel software tasks, executed on customized VLIW ASIP processing cores. Experimental results, obtained by means of FPGA-based prototyping and post-floorplanning power evaluation on a 40nm technology library, assess the performance and hardware-related costs of the considered configurations. The reported power figures demonstrate the usability of the MPSoC paradigm within the processing of bio-electrical signals and show the benefits achievable by the exploitation of the instruction-level parallelism within tasks

A Method of Protein Model Classification and Retrieval Using Bag-of-Visual-Features

In this paper we propose a novel visual method for protein model classification and retrieval. Different from the conventional methods, the key idea of the proposed method is to extract image features of proteins and measure the visual similarity between proteins. Firstly, the multiview images are captured by vertices and planes of a given octahedron surrounding the protein. Secondly, the local features are extracted from each image of the different views by the SURF algorithm and are vector quantized into visual words using a visual codebook. Finally, KLD is employed to calculate the similarity distance between two feature vectors. Experimental results show that the proposed method has encouraging performances for protein retrieval and categorization as shown in the comparison with other methods

Deep learning in remote sensing: a review

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin

Reconnaissance of the HR 8799 exosolar system. II. Astrometry and orbital motion

This is the final version of the article. Available from the American Astronomical Society / IOP Publishing via the DOI in this record.We present an analysis of the orbital motion of the four substellar objects orbiting HR 8799. Our study relies on the published astrometric history of this system augmented with an epoch obtained with the Project 1640 coronagraph with an integral field spectrograph (IFS) installed at the Palomar Hale telescope. We first focus on the intricacies associated with astrometric estimation using the combination of an extreme adaptive optics system (PALM-3000), a coronagraph, and an IFS. We introduce two new algorithms. The first one retrieves the stellar focal plane position when the star is occulted by a coronagraphic stop. The second one yields precise astrometric and spectrophotometric estimates of faint point sources even when they are initially buried in the speckle noise. The second part of our paper is devoted to studying orbital motion in this system. In order to complement the orbital architectures discussed in the literature, we determine an ensemble of likely Keplerian orbits for HR 8799bcde, using a Bayesian analysis with maximally vague priors regarding the overall configuration of the system. Although the astrometric history is currently too scarce to formally rule out coplanarity, HR 8799d appears to be misaligned with respect to the most likely planes of HR 8799bce orbits. This misalignment is sufficient to question the strictly coplanar assumption made by various authors when identifying a Laplace resonance as a potential architecture. Finally, we establish a high likelihood that HR 8799de have dynamical masses below 13 MJup, using a loose dynamical survival argument based on geometric close encounters. We illustrate how future dynamical analyses will further constrain dynamical masses in the entire system

Reconnaissance of the HR 8799 Exosolar System. II. Astrometry and Orbital Motion

We present an analysis of the orbital motion of the four substellar objects orbiting HR 8799. Our study relies on the published astrometric history of this system augmented with an epoch obtained with the Project 1640 coronagraph with an integral field spectrograph (IFS) installed at the Palomar Hale telescope. We first focus on the intricacies associated with astrometric estimation using the combination of an extreme adaptive optics system (PALM-3000), a coronagraph, and an IFS. We introduce two new algorithms. The first one retrieves the stellar focal plane position when the star is occulted by a coronagraphic stop. The second one yields precise astrometric and spectrophotometric estimates of faint point sources even when they are initially buried in the speckle noise. The second part of our paper is devoted to studying orbital motion in this system. In order to complement the orbital architectures discussed in the literature, we determine an ensemble of likely Keplerian orbits for HR 8799bcde, using a Bayesian analysis with maximally vague priors regarding the overall configuration of the system. Although the astrometric history is currently too scarce to formally rule out coplanarity, HR 8799d appears to be misaligned with respect to the most likely planes of HR 8799bce orbits. This misalignment is sufficient to question the strictly coplanar assumption made by various authors when identifying a Laplace resonance as a potential architecture. Finally, we establish a high likelihood that HR 8799de have dynamical masses below 13 M_(Jup), using a loose dynamical survival argument based on geometric close encounters. We illustrate how future dynamical analyses will further constrain dynamical masses in the entire system