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

A Platform for e-Health Control and Location Services for Wandering Patients

Wandering patients frequently have diseases that demand continuous health control, such as taking pills at specific times, constant blood pressure and heart rate monitoring, temperature and stress level checkups, and so on. These could be jeopardized by their wandering behavior. Mobile applications that focus on health care have received special interest from medical specialists. These applications have been widely accepted, due to the availability of smart devices that include sensors. However, sensor-based applications are highly energy demanding and as such, they can be unaffordable in mobile e-health control due to battery constraints. This paper presents the design and implementation of a platform aimed at providing support in e-health control and provision of location services for wandering patients through real-time medical and mobility information analysis. The platform includes a configurable mobile application for heart rate and stress level monitoring based on Bluetooth Low Energy technology (BLE), and a web service for monitoring and control of the wandering patients. Due to battery limitations of smart devices with sensors, the mobile application includes energy-efficient handling and transmission policies to make more efficient the transmission of medical information from the sensor-based smart device to the web service. In turn, the web service provides e-health control services for patients and caregivers. Through the platform functionality, caregivers (and patients) can receive notifications and suggestions in response to emergency, contingency situations, or deviations from health and mobility patterns of the wandering patients. This paper describes a platform that conceals continuous monitoring with energy-efficient applications in favor of e-health control of wandering patients

On-Device Learning of Indoor Location for WiFi Fingerprint Approach

Indoor positioning is a recent technology that has gained interest in industry and academia thanks to the promising results of locating objects, people or robots accurately in indoor environments. One of the utilized technologies is based on algorithms that process the Received Signal Strength Indicator (RSSI) in order to infer location information without previous knowledge of the distribution of the Access Points (APs) in the area of interest. This paper presents the design and implementation of an indoor positioning mobile application, which allows users to capture and build their own RSSI maps by off-line training of a set of selected classifiers and using the models generated to obtain the current indoor location of the target device. In an early experimental and design stage, 59 classifiers were evaluated, using data from proposed indoor scenarios. Then, from the tested classifiers in the early stage, only the top-five classifiers were integrated with the proposed mobile indoor positioning, based on the accuracy obtained for the test scenarios. The proposed indoor application achieves high classification rates, above 89%, for at least 10 different locations in indoor environments, where each location has a minimum separation of 0.5 m

Real-Time Embedded Vision System for Online Monitoring and Sorting of Citrus Fruits

Citrus fruits are the second most important crop worldwide. One of the most important tasks is sorting, which involves manually separating the fruit based on its degree of maturity, and in many cases, involves a task carried out manually by human operators. A machine vision-based citrus sorting system can replace labor work for the inspection of fruit sorting. This article proposes a vision system for citrus fruit sorting implemented on a dedicated and efficient Field Programmable Gate Array (FPGA) hardware architecture coupled with a mechanical sorting machine, where the FPGA performs fruit segmentation and color and size classification. We trained a decision tree (DT) using a balanced dataset of reference images to perform pixel classification. We evaluate the segmentation task using a pixel accuracy metric, defined as the ratio between correctly segmented pixels produced by a DT and the total pixels in the reference image segmented offline using Otsu’s thresholding algorithm. The balance between correctly classified images by color or size and their corresponding labels of that color and size evaluates the color and size classification algorithms. Considering these metrics, the system reaches an accuracy of 97% for fruit segmentation, 94% for color classification, and 90% for size classification, running at 60 fps