Hybrid Evolutionary Framework for Designing and Implementing Autonomous Modular Robotics

This paper proposes a novel framework for automatically designing feasible robots that are made up of various heterogeneous modules and raw materials already existing in the surrounding environment. Moreover, it highlights the interrelationship between the robot’s morphology, control, and environment by analyzing the coevolution of morphology and control in robots and allowing the initial set of robots to use the available units in the environment to self-assemble, self-reconfigure, and self-repair. In addition, digital fabrication technologies such as 3D printing are utilized to produce new units if needed and available

Autonomous Task-Based Evolutionary Design of Modular Robots

In an attempt to solve the problem of finding a set of multiple unique modular robotic designs that can be constructed using a given repertoire of modules to perform a specific task, a novel synthesis framework is introduced based on design optimization concepts and evolutionary algorithms to search for the optimal design. Designing modular robotic systems faces two main challenges: the lack of basic rules of thumb and design bias introduced by human designers. The space of possible designs cannot be easily grasped by human designers especially for new tasks or tasks that are not fully understood by designers. Therefore, evolutionary computation is employed to design modular robots autonomously. Evolutionary algorithms can efficiently handle problems with discrete search spaces and solutions of variable sizes as these algorithms offer feasible robustness to local minima in the search space; and they can be parallelized easily to reducing system runtime. Moreover, they do not have to make assumptions about the solution form. This dissertation proposes a novel autonomous system for task-based modular robotic design based on evolutionary algorithms to search for the optimal design. The introduced system offers a flexible synthesis algorithm that can accommodate to different task-based design needs and can be applied to different modular shapes to produce homogenous modular robots. The proposed system uses a new representation for modular robotic assembly configuration based on graph theory and Assembly Incidence Matrix (AIM), in order to enable efficient and extendible task-based design of modular robots that can take input modules of different geometries and Degrees Of Freedom (DOFs). Robotic simulation is a powerful tool for saving time and money when designing robots as it provides an accurate method of assessing robotic adequacy to accomplish a specific task. Furthermore, it is difficult to predict robotic performance without simulation. Thus, simulation is used in this research to evaluate the robotic designs by measuring the fitness of the evolved robots, while incorporating the environmental features and robotic hardware constraints. Results are illustrated for a number of benchmark problems. The results presented a significant advance in robotic design automation state of the art

Application for Big Data Visualization using Google BigQuery and Google Charts

Poster also presented at the Northeast Section American Society for Engineering Education 2015 at Northeastern University in Boston, MA on 2015-04-30.Big data visualization conveys information much faster than tables containing numbers and text. Therefore, this poster presents an efficient method for big data visualization on top of Google BigQuery. Google BigQuery is a cloud-based interactive query service for massive datasets built on Dremel. In this project, we used 'natality' dataset that has United States birth data from 1969 to 2008 saved in 137 million rows. For the implementation of dashboard application, we employ Google App Engine using Python and Google Charts with JavaScript

Detecting Bad Posture using Postuino among Engineering Graduate Students

Poster also presented at the Northeast Section American Society for Engineering Education 2015 at Northeastern University in Boston, MA on 2015-04-30.To understand how good posture minimizes computer related injury’s pain, we developed Postuino. A device that warns the computer users if they are leaning too close towards computer screen. Postuino has an accompanying web application that visualizes the collected data and displays a chart to simplify comparing be-tween straight time and slouch time. Also, the app suggests taking frequent breaks to minimize the risk of injuries and to increase productivity. Then, we designed an experiment with different factors to evaluate the efficiency of Postuino. In our study, 24 subjects first use the computer for 3 hours after disabling Postuino’s alert system. Afterwards, they use the computer again for another 3 hours after enabling the alert system. We collected data, analyzed it, and presented the results in this paper

Cloud Computing Algebra Homomorphic Encryption Scheme Based on Fermat's Little Theorem

© ASEE 2013Although cloud computing is growing rapidly, a key challenge is to build confidence that the cloud can handle data securely. Data is migrated to the cloud after encryption. However, this data must be decrypted before carrying out any calculations; which can be considered as a security breach. Homomorphic encryption solved this problem by allowing different operations to be conducted on encrypted data and the result will come out encrypted as well. In this paper, we propose the application of Algebraic Homomorphic Encryption Scheme based on Fermat's Little Theorem on cloud computing for better security

Evolutionary Modular Robotics: Survey and Analysis

This paper surveys various applications of artificial evolution in the field of modular robots. Evolutionary robotics aims to design autonomous adaptive robots automatically that can evolve to accomplish a specific task while adapting to environmental changes. A number of studies have demonstrated the feasibility of evolutionary algorithms for generating robotic control and morphology. However, a huge challenge faced was how to manufacture these robots. Therefore, modular robots were employed to simplify robotic evolution and their implementation in real hardware. Consequently, more research work has emerged on using evolutionary computation to design modular robots rather than using traditional hand design approaches in order to avoid cognition bias. These techniques have the potential of developing adaptive robots that can achieve tasks not fully understood by human designers. Furthermore, evolutionary algorithms were studied to generate global modular robotic behaviors including; self-assembly, self-reconfiguration, self-repair, and self-reproduction. These characteristics allow modular robots to explore unstructured and hazardous environments. In order to accomplish the aforementioned evolutionary modular robotic promises, this paper reviews current research on evolutionary robotics and modular robots. The motivation behind this work is to identify the most promising methods that can lead to developing autonomous adaptive robotic systems that require the minimum task related knowledge on the designer side.https://doi.org/10.1007/s10846-018-0902-

Fully Autonomous Reproduction Robotic System

Self-reproduction robotics systems are capable of producing other robotic systems; such that the resulting systems are fully functional and autonomous. In this poster, a novel method for producing modular robotic systems is presented, where the resulting robots consist of Cubelets - modular robots kit - and the producing robot is built using Lego Mindstorms EV3

Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study

Summary Background Congenital anomalies are the fifth leading cause of mortality in children younger than 5 years globally. Many gastrointestinal congenital anomalies are fatal without timely access to neonatal surgical care, but few studies have been done on these conditions in low-income and middle-income countries (LMICs). We compared outcomes of the seven most common gastrointestinal congenital anomalies in low-income, middle-income, and high-income countries globally, and identified factors associated with mortality. Methods We did a multicentre, international prospective cohort study of patients younger than 16 years, presenting to hospital for the first time with oesophageal atresia, congenital diaphragmatic hernia, intestinal atresia, gastroschisis, exomphalos, anorectal malformation, and Hirschsprung’s disease. Recruitment was of consecutive patients for a minimum of 1 month between October, 2018, and April, 2019. We collected data on patient demographics, clinical status, interventions, and outcomes using the REDCap platform. Patients were followed up for 30 days after primary intervention, or 30 days after admission if they did not receive an intervention. The primary outcome was all-cause, in-hospital mortality for all conditions combined and each condition individually, stratified by country income status. We did a complete case analysis. Findings We included 3849 patients with 3975 study conditions (560 with oesophageal atresia, 448 with congenital diaphragmatic hernia, 681 with intestinal atresia, 453 with gastroschisis, 325 with exomphalos, 991 with anorectal malformation, and 517 with Hirschsprung’s disease) from 264 hospitals (89 in high-income countries, 166 in middleincome countries, and nine in low-income countries) in 74 countries. Of the 3849 patients, 2231 (58·0%) were male. Median gestational age at birth was 38 weeks (IQR 36–39) and median bodyweight at presentation was 2·8 kg (2·3–3·3). Mortality among all patients was 37 (39·8%) of 93 in low-income countries, 583 (20·4%) of 2860 in middle-income countries, and 50 (5·6%) of 896 in high-income countries (p<0·0001 between all country income groups). Gastroschisis had the greatest difference in mortality between country income strata (nine [90·0%] of ten in lowincome countries, 97 [31·9%] of 304 in middle-income countries, and two [1·4%] of 139 in high-income countries; p≤0·0001 between all country income groups). Factors significantly associated with higher mortality for all patients combined included country income status (low-income vs high-income countries, risk ratio 2·78 [95% CI 1·88–4·11], p<0·0001; middle-income vs high-income countries, 2·11 [1·59–2·79], p<0·0001), sepsis at presentation (1·20 [1·04–1·40], p=0·016), higher American Society of Anesthesiologists (ASA) score at primary intervention (ASA 4–5 vs ASA 1–2, 1·82 [1·40–2·35], p<0·0001; ASA 3 vs ASA 1–2, 1·58, [1·30–1·92], p<0·0001]), surgical safety checklist not used (1·39 [1·02–1·90], p=0·035), and ventilation or parenteral nutrition unavailable when needed (ventilation 1·96, [1·41–2·71], p=0·0001; parenteral nutrition 1·35, [1·05–1·74], p=0·018). Administration of parenteral nutrition (0·61, [0·47–0·79], p=0·0002) and use of a peripherally inserted central catheter (0·65 [0·50–0·86], p=0·0024) or percutaneous central line (0·69 [0·48–1·00], p=0·049) were associated with lower mortality. Interpretation Unacceptable differences in mortality exist for gastrointestinal congenital anomalies between lowincome, middle-income, and high-income countries. Improving access to quality neonatal surgical care in LMICs will be vital to achieve Sustainable Development Goal 3.2 of ending preventable deaths in neonates and children younger than 5 years by 2030

Journal of health policy and management : JHPM

© ASEE 2014Wireless Sensor Networks (WSNs) have become an important means of gathering environmental and physical information from a wide range of areas. WSNs could be used in underground, aboveground and underwater applications. In this paper, we focus on underwater transmission. One of the main limitations of WSNs is the power consumption and short lifetime of the sensors. In this paper, we propose a new solution for underwater Wireless Sensor Networks to overcome the problem that is caused by the ionized nature of seawater. This work presents a methodology to improve the lifetime of WSNs. The wireless sensors have three main functions: sensing, processing and transmitting. The first two factors consume very less power compared to the third. Thus, we need to guarantee the successful transmission of signal with nominal and efficient use of power to improve the lifetime of the sensors. Improving the lifetime of these sensors will improve the experience of the end user, as the information-gathering lifetime of the sensors increases. Based on the results presented in this paper, we can reduce the power consumption, thus improving the lifetime and the signal loss rate