Trolls: a novel low-cost controlling system platform for walk-behind tractor

A novel low-cost controlling system platform for walk-behind hand tractors (Quick G3000 and G1000) was designed and developed to solve the fatigue problem faced by farmers when ploughing the rice field. This platform is dedicated to designing and manufacturing mechanical, electrical, and software components. The tractor was modified and added with an embedded control system that functioned as the slave, while the direction of the tractor movement was controlled remotely by humans through Bluetooth communication with the smartphone application as the master. Several servos and direct currents (DCs) were used as the actuator to move some levers and clutches instead of the tractor to make it remotely controllable. This system has been directly tested in the paddy farming land through two tractors: Quick G3000 and G1000. The testing results showed that this system could be used within more or less six hours; there is a cost-efficiency of 21.74% and 84.62% battery usage efficiency. More efficient mechanics caused this cost efficiency, and the reduction in electronic devices affects battery efficiency. A low-cost platform for controlling walk-behind tractors has been successfully developed; this platform assists farmers in ploughing their fields

Adapting Medical Museums: Technology, Education, and Research

In Malaysia, medical museums are transforming, extending their reach beyond conventional medical student training to encompass public education and health awareness. This modernisation incorporates cutting-edge technologies such as 3D printing, QR codes, augmented reality, diversified exhibitions, and hands-on learning experiences. The overarching goal is to captivate a broader audience while advancing medical research and public health education. To achieve this, strategies like interactive exhibits and multi-sector collaborations are employed. This study explores the role of medical museums and the impact of technological innovations on visitor experience and engagement

Data Optimization on Multi Robot Sensing System with RAM Based Neural Network Method

Monitoring the environment activities is an attractive Abstract— Monitoring the environment activities is an attractive thing for development. That is because the human life would affect the surrounding environtment. There\u27s a lot of research of environment has been done, one of those is the changes of air quality in urban areas. To measure the level of air quality, the data and information from field measurements and laboratory analysis result was needed. This paper review the research result that focus on sensor data processing in multi robot using RAM based neural network. There are 11 pattern input data were processed by temperature data optimization from 250C until 350C, humadity data from 20% until 60% and gas data from 350ppm until 450ppm. The obtained result is from 8 bits and 9 bits become 6 bits in certain level with optimazion percentage is25% and 33,3%. This result effect to the computationan load, it\u27s become more simple, the execution time and data communication becomes faster

FPGA synthesis of an stereo image matching architecture for autonomous mobile robots

This paper describes a hardware proposal to speed up the process of image matching in stereo vision systems like those employed by autonomous mobile robots. This proposal combines a classical window-based matching approach with a previous stage, where key points are selected from each image of the stereo pair. In this first step the key point extraction method is based on the SIFT algorithm. Thus, in the second step, the window-based matching is only applied to the set of selected key points, instead of to the whole images. For images with a 1% of key points, this method speeds up the matching four orders of magnitude. This proposal is, on the one hand, a better parallelizable architecture than the original SIFT, and on the other, a faster technique than a full image windows matching approach. The architecture has been implemented on a lower power Virtex 6 FPGA and it achieves a image matching speed above 30 fps.This work has been funded by Spanish government project TEC2015-66878-C3-2-R (MINECO/FEDER, UE)

Data Optimization on Multi Robot Sensing System with RAM based Neural Network Method

Monitoring the environment activities is an attractive Abstract— Monitoring the environment activities is an attractive thing for development. That is because the human life would affect the surrounding environtment. There’s a lot of research of environment has been done, one of those is the changes of air quality in urban areas.  To measure the level of air quality, the data and information from field measurements and laboratory analysis result was needed. This paper review the research result that focus on sensor data processing in multi robot using RAM based neural network. There are 11 pattern input data were processed by temperature data optimization from 250C until 350C, humadity data from 20% until 60% and gas data from 350ppm until 450ppm. The obtained result is from 8 bits and 9 bits become 6 bits in certain level with optimazion percentage is25% and 33,3%. This result effect to the computationan load, it’s become more simple, the execution time and data communication becomes faster.   

The Future of Work In Cities

The latest report in our City of the Future series examines societal shifts and advancements in technology that are impacting the rapidly changing American workforce. The report outlines solutions to help city leaders plan for the fast-approaching future, while forecasting the economic viability of two distinct sectors – retail and office administration – in which a quarter of Americans are currently employed

Credit Scoring Refinement using Optimized Logistic Regression

A poor credit scoring model will give a poor power for predicting defaulted loan. There are many approaches for modeling the default prediction, such as classical logistic regression and Bayesian logistics regression. In this paper, we applied both classical logistic regression and AUC (Area under Curved) optimized using Nelder-Mead Algorithm for refining a credit scoring model that has already been used for several years by an International bank in Indonesia. Both classical logistics regression and AUC optimized method perform well in improving the model, but logistic regression still better in some aspects. AUC Optimized model has higher AUC than logistic regression model but has lower Kolmogorov-Smirnov Score (KS-Score