Depth from a Motion Algorithm and a Hardware Architecture for Smart Cameras.

Applications such as autonomous navigation, robot vision, and autonomous flying require depth map information of a scene. Depth can be estimated by using a single moving camera (depth from motion). However, the traditional depth from motion algorithms have low processing speeds and high hardware requirements that limit the embedded capabilities. In this work, we propose a hardware architecture for depth from motion that consists of a flow/depth transformation and a new optical flow algorithm. Our optical flow formulation consists in an extension of the stereo matching problem. A pixel-parallel/window-parallel approach where a correlation function based on the sum of absolute difference (SAD) computes the optical flow is proposed. Further, in order to improve the SAD, the curl of the intensity gradient as a preprocessing step is proposed. Experimental results demonstrated that it is possible to reach higher accuracy (90% of accuracy) compared with previous Field Programmable Gate Array (FPGA)-based optical flow algorithms. For the depth estimation, our algorithm delivers dense maps with motion and depth information on all image pixels, with a processing speed up to 128 times faster than that of previous work, making it possible to achieve high performance in the context of embedded applications.

Electronic Devices That Identify Individuals with Fever in Crowded Places: A Prototype.

Most epidemiological surveillance systems for severe infections with epidemic potential are based on accumulated symptomatic cases in defined geographical areas. Eventually, all cases have to be clinically verified to confirm an outbreak. These patients will present high fever at the early stages of the disease. Here, we introduce a non-invasive low-cost electronic device (bracelet) that measures and reports 24/7, year-round information on the temperature, geographical location, and identification of the subject using the device. The data receiver can be installed in a tower (ground) or a drone (air) in densely populated or remote areas. The prototype was made with low-cost electronic components, and it was tested indoors and outdoors. The prototype shows efficient ground and air connectivity. This electronic device will allow health professionals to monitor the prevalence of fever in a geographical area and to reduce the time span between the presentation of the first cases of a potential outbreak and their medical evaluation by giving an early warning. Field tests of the device, programs, and technical diagrams of the prototype are available as Supplementary Materials.

Real Time Monitoring of Children, and Adults with Mental Disabilities Using a Low-Cost Non-Invasive Electronic Device.

There are a growing number of small children-as well as adults-with mental disabilities (including elderly citizens with Alzheimer's disease or other forms of age-related dementia) that are getting lost in rural and urban areas for various reasons. Establishing their location within the first 72 h is crucial because lost people are exposed to all kinds of adverse conditions and in the case of the elderly, this is further aggravated if prescribed medication is needed. Herein we describe a non-invasive, low-cost electronic device that operates constantly, keeping track of time, the geographical location and the identification of the subject using it. The prototype was made using commercial low-cost electronic components. This electronic device shows high connectivity in open and closed areas and identifies the geographical location of a lost subject. We freely provide the software and technical diagrams of the prototypes.

Identification of proteins associated with amyloidosis by polarity index method.

There is a natural protein form, insoluble and resistant to proteolysis, adopted by many proteins independently of their amino acid sequences via specific misfolding-aggregation process. This dynamic process occurs in parallel with or as an alternative to physiologic folding, generating toxic protein aggregates that are deposited and accumulated in various organs and tissues. These proteinaceous deposits typically represent bundles of ß-sheet-enriched fibrillar species known as the amyloid fibrils that are responsible for serious pathological conditions, including but not limited to neurodegenerative diseases, grouped under the term amyloidoses. The proteins that might adopt this fibrillar conformation are some globular proteins and natively unfolded (or intrinsically disordered) proteins. Our work shows that intrinsically disordered and intrinsically ordered proteins can be reliably identified, discriminated, and differentiated by analyzing their polarity profiles generated using a computational tool known as the polarity index method (Polanco & Samaniego, 2009; Polanco et al., 2012; 2013; 2013a; 2014; 2014a; 2014b; 2014c; 2014d). We also show that proteins expressed in neurons can be differentiated from proteins in these two groups based on their polarity profiles, and also that this computational tool can be used to identify proteins associated with amyloidoses. The efficiency of the proposed method is high (i.e. 70%) as evidenced by the analysis of peptides and proteins in the APD2 database (2012), AVPpred database (2013), and CPPsite database (2013), the set of selective antibacterial peptides from del Rio et al. (2001), the sets of natively unfolded and natively folded proteins from Oldfield et al. (2005), the set of human revised proteins expressed in neurons, and non-human revised proteins expressed in neurons, from the Uniprot database (2014), and also the set of amyloidogenic proteins from the AmyPDB database (2014).

Computational model of abiogenic amino acid condensation to obtain a polar amino acid profile.

In accordance with the second law of thermodynamics, the Universe as a whole tends to higher entropy. However, the sequence of far-from-equilibrium events that led to the emergence of life on Earth could have imposed order and complexity during the course of chemical reactions in the so-called primordial soup of life. Hence, we may expect to find characteristic profiles or biases in the prebiotic product mixtures, as for instance among the first amino acids. Seeking to shed light on this hypothesis, we have designed a high performance computer program that simulates the spontaneous formation of the amino acid monomers in closed environments. The program was designed in reference to a prebiotic scenario proposed by Sydney W. Fox. The amino acid abundances and their polarities as the two principal biases were also taken into consideration. We regarded the computational model as exhaustive since 200,000 amino acid dimers were formed by simulation, subsequently expressed in a vector and compared with the corresponding amino acid dimers that were experimentally obtained by Fox. We found a very high similarity between the experimental results and our simulations.

Using an aliasing operator and a single discrete Fourier transform to down-sample the Fresnel transform.

In Digital Holography there are applications where computing a few samples of a wavefield is sufficient to retrieve an image of the region of interest. In such cases, the sampling rate achieved by the direct and the spectral methods of the discrete Fresnel transform could be excessive. A few algorithmic methods have been proposed to numerically compute samples of propagated wavefields while allowing down-sampling control. Nevertheless, all of them require the computation of at least two 2D discrete Fourier transforms which increases the computational load. Here, we propose the use of an aliasing operator and a single discrete Fourier transform to achieve an efficient method to down-sample the wavefields obtained by the Fresnel transform.

An FPGA implementation to detect selective cationic antibacterial peptides.

Exhaustive prediction of physicochemical properties of peptide sequences is used in different areas of biological research. One example is the identification of selective cationic antibacterial peptides (SCAPs), which may be used in the treatment of different diseases. Due to the discrete nature of peptide sequences, the physicochemical properties calculation is considered a high-performance computing problem. A competitive solution for this class of problems is to embed algorithms into dedicated hardware. In the present work we present the adaptation, design and implementation of an algorithm for SCAPs prediction into a Field Programmable Gate Array (FPGA) platform. Four physicochemical properties codes useful in the identification of peptide sequences with potential selective antibacterial activity were implemented into an FPGA board. The speed-up gained in a single-copy implementation was up to 108 times compared with a single Intel processor cycle for cycle. The inherent scalability of our design allows for replication of this code into multiple FPGA cards and consequently improvements in speed are possible. Our results show the first embedded SCAPs prediction solution described and constitutes the grounds to efficiently perform the exhaustive analysis of the sequence-physicochemical properties relationship of peptides.