Low-power and high-fanout bus design techniques

Low-power techniques pose an important concern, when designing autonomous electronic devices. Most of the upcoming applications increasingly demand high performance and low-power consumption. In this thesis work, two low-power and high-fanout bus design techniques are reviewed. Pulse Width Modulation (PWM) and Time-Domain Conversion (TDC) approaches are elucidated. Schematic simulations (Cadence), quantitative and comparative results of both approaches are included. Additionally, on-chip wire theory is shown as well as some optimized bus simulation models (MATLAB), concluding with a summary of the main application areas for this techniques. Finally , two ready-to-use library cells are generated, as well as Verilog code for the TDC system

Diabetic Cardiomyopathy: Five Major Questions with Simple Answers

Diabetes is a major risk factor for heart disease. Diabetic cardiomyopathy is a long-lasting process that affects the myocardium in patients who have no other cardiac conditions. The condition has a complex physiopathology which can be subdivided into processes that cause diastolic and/or systolic dysfunction. It is believed to be more common than reported, but this has not been confirmed by a large study. Diagnosis can involve imaging; biomarkers cannot be used to identify diabetic cardiomyopathy at an early stage. In people with diabetes, there should be a focus on prevention and, if diabetic cardiomyopathy develops, the objective is to delay disease progression. Further studies into identifying and managing diabetic cardiomyopathy are essential to reduce the risk of heart failure in people with diabetes

A Review of Deep Learning Methods and Applications for Unmanned Aerial Vehicles

Deep learning is recently showing outstanding results for solving a wide variety of robotic tasks in the areas of perception, planning, localization, and control. Its excellent capabilities for learning representations from the complex data acquired in real environments make it extremely suitable for many kinds of autonomous robotic applications. In parallel, Unmanned Aerial Vehicles (UAVs) are currently being extensively applied for several types of civilian tasks in applications going from security, surveillance, and disaster rescue to parcel delivery or warehouse management. In this paper, a thorough review has been performed on recent reported uses and applications of deep learning for UAVs, including the most relevant developments as well as their performances and limitations. In addition, a detailed explanation of the main deep learning techniques is provided. We conclude with a description of the main challenges for the application of deep learning for UAV-based solutions

Effects of Resistance Circuit-Based Training on Body Composition, Strength and Cardiorespiratory Fitness: A Systematic Review and Meta-Analysis

We assessed the effects of resistance circuit-based training (CT) on strength, cardiorespiratory fitness, and body composition. A systematic review with meta-analysis was conducted in three databases, ending on March, 2020. Meta-analysis and subgroup analysis were used to analyze the effects of pre–post-intervention CT and differences from control groups (CG). Of the 830 studies found, 45 were included in the meta-analysis (58 experimental groups (n = 897) and 34 CG (n = 474)). The CT interventions led to increases in muscle mass (1.9%; p < 0.001) and decreases in fat mass (4.3%; p < 0.001). With regard to cardiorespiratory fitness, CT had a favorable effect on VO2max (6.3%; p < 0.001), maximum aerobic speed or power (0.3%; p = 0.04), and aerobic performance (2.6%; p = 0.006) after training. Concerning strength outcome, the CT increased the strength of the upper and lower extremities. Only the magnitude of strength performance appears to be influenced by the training (number of sessions and frequency) and the training status. Moreover, low and moderate intensities and short rest time between exercise increase the magnitude of change in fat mass loss. Therefore, CT has been shown to be an effective method for improving body composition, cardiorespiratory fitness, and strength of the lower and upper limbs

Shortcomings of international standard iso 9223 for the classification, determination, and estimation of atmosphere corrosivities in subtropical archipelagic conditions—The case of the Canary Islands (Spain)

grant ProID2017010042The classification, assessment, and estimation of the atmospheric corrosivity are fixed by the ISO 9223 standard. Its recent second edition introduced a new corrosivity category for extreme environments CX, and defined mathematical models that contain dose–response functions for normative corrosivity estimations. It is shown here that application of the ISO 9223 standard to archipelagic subtropical areas exhibits major shortcomings. Firstly, the corrosion rates of zinc and copper exceed the range employed to define the CX category. Secondly, normative corrosivity estimation would require the mathematical models to be redefined introducing the time of wetness and a new set of operation constants.publishersversionpublishe

Towards fully autonomous landing on moving platforms for rotary Unmanned Aerial Vehicles

Fully autonomous landing on moving platforms poses a problem of importance for Unmanned Aerial Vehicles (UAVs). Current approaches are usually based on tracking and following the moving platform by means of several techniques, which frequently lack performance in real applications. The aim of this paper is to prove a simple landing strategy is able to provide practical results. The presented approach is based on three stages: estimation, prediction and fast landing. As a preliminary phase, the problem is solved for a particular case of the IMAV 2016 competition. Subsequently, it is extended to a more generic and versatile approach. A thorough evaluation has been conducted with simulated and real flight experiments. Simulations have been performed utilizing Gazebo 6 and PX4 Software-In-The-Loop (SITL) and real flight experiments have been conducted with a custom quadrotor and a moving platform in an indoor environment

Hand features extractor using hand contour – a case study

Hand gesture recognition is an important topic in natural user interfaces (NUI). Hand features extraction is the first step for hand gesture recognition. This work proposes a novel real time method for hand features recognition. In our framework we use three cameras and the hand region is extracted with the background subtraction method. Features like arm angle and fingers positions are calculated using Y variations in the vertical contour image. Wrist detection is obtained by calculating the bigger distance from a base line and the hand contour, giving the main features for the hand gesture recognition. Experiments on our own data-set of about 1800 images show that our method performs well and is highly efficient

Psychological and Sleep Effects of Tryptophan and Magnesium-Enriched Mediterranean Diet in Women with Fibromyalgia

Anxiety, mood disturbance, eating and sleep disorders, and dissatisfaction with body image are prevalent disorders in women with fibromyalgia. The authors of this study aimed to determine the effects of tryptophan (TRY) and magnesium-enriched (MG) Mediterranean diet on psychological variables (trait anxiety, mood state, eating disorders, self-image perception) and sleep quality in women with fibromyalgia (n = 22; 49 ± 5 years old). In this randomized, controlled trial, the participants were randomly assigned to the experimental group and the placebo group. The intervention group received a Mediterranean diet enriched with high doses of TRY and MG (60 mg of TRY and 60 mg of MG), whereas the control group received the standard Mediterranean diet. Pittsburgh Sleep Quality Questionnaire, Body Shape Questionnaire, State–Trait Anxiety Inventory (STAI), Profile of Mood States (POMS-29) Questionnaire, Eating Attitudes Test-26, and Trait Anxiety Inventory were completed before and 16 weeks after the intervention. Significant differences were observed between groups after the intervention for the mean scores of trait anxiety (p = 0.001), self-image perception (p = 0.029), mood disturbance (p = 0.001), and eating disorders (p = 0.006). This study concludes that tryptophan and magnesium-enriched Mediterranean diet reduced anxiety symptoms, mood disturbance, eating disorders, and dissatisfaction with body image but did not improve sleep quality in women with fibromyalgia

AOLI: Near-diffraction limited imaging in the visible on large ground -based telescopes

The combination of Lucky Imaging with a low order adaptive optics system was demonstrated very successfully on the Palomar 5m telescope nearly 10 years ago. It is still the only system to give such high-resolution images in the visible or near infrared on ground-based telescope of faint astronomical targets. The development of AOLI for deployment initially on the WHT 4.2 m telescope in La Palma, Canary Islands, will be described in this paper. In particular, we will look at the design and status of our low order curvature wavefront sensor which has been somewhat simplified to make it more efficient, ensuring coverage over much of the sky with natural guide stars as reference object. AOLI uses optically butted electron multiplying CCDs to give an imaging array of 2000 x 2000 pixels.Science and Technology Facilities CouncilThis is the author accepted manuscript. The final version is available from SPIE via http://dx.doi.org/10.1117/12.223090

Laser-Based Reactive Navigation for Multirotor Aerial Robots using Deep Reinforcement Learning

Navigation in unknown indoor environments with fast collision avoidance capabilities is an ongoing research topic. Traditional motion planning algorithms rely on precise maps of the environment, where re-adapting a generated path can be highly demanding in terms of computational cost. In this paper, we present a fast reactive navigation algorithm using Deep Reinforcement Learning applied to multi rotor aerial robots. Taking as input the 2D-laser range measurements and the relative position of the aerial robot with respect to the desired goal, the proposed algorithm is successfully trained in a Gazebo-based simulation scenario by adopting an artificial potential field formulation. A thorough evaluation of the trained agent has been carried out both in simulated and real indoor scenarios, showing the appropriate reactive navigation behavior of the agent in the presence of static and dynamic obstacles