ABSTRACT
In this paper we propose the use of secure contactless Electronic Voting Machine(EVM). We used a Radio Frequency Identification(RFID) reader to authenticate the voter's identity, and Infrared(IR) sensors to implement contact-less voting to curb the spread of Covid-19. Significant components used are Arduino Uno, FC-51 Infrared Module, EM-18 RFID reader, keypad, and I2C PCF5874T for Liquid Crystal Display(LCD). The software used to program these components is Arduino IDE. This EVM ensures security in two ways - authorizing the identity of the voters before they are allowed to vote, as well as providing a contact-less experience for the voter. An extra layer of security is added, so that when the candidates and Election Commission are present and enter their respective passwords, only then can the results be accessed. © 2021 IEEE.
ABSTRACT
Liquid crystal display 3D printing is rapidly spreading due to the low cost of equipment and the high accuracy of manufactured parts. Particularly, this process is widespread in dental and medical fields to realise customised healthcare solutions. This study presents a parametric cradle-to-gate life cycle assessment of the liquid crystal display 3D printing process based on the part mass and building time. Specifically, the endpoint environmental impact indicators are calculated using the Recipe 2016 v 1.1 methodology with a hierarchist strategy. Also, a cost model of the process is included to assess the economic aspects of the production. This model comprises the cost of material, energy, consumables and equipment. An adaptive slicing strategy for liquid crystal display 3D printing is proposed. This algorithm starts slicing the model with the minimum layer height, then proceeds by collapsing adjacent layers so as to preserve a certain maximum imposed cusp height. The main aim of this research is to investigate the potential sustainability advantages of this strategy over traditional slicing. To this end, the life cycle assessment and cost models of the process are applied to two benchmark parts, namely a patient-specific dental model and an adapter designed to convert snorkel masks in respirators to face the COVID 19 emergency. The results demonstrate that the adaptive slicing strategy effect on part accuracy is negligible. In contrast, this method significantly increases the sustainability of the process by decreasing the building time. Specifically, the impacts on human health and resource depletion are reduced by up to 48.4% and 51.3%, respectively. Moreover, a significant reduction of the cost per part is observed. Finally, the increased productivity determines social benefits, being the produced parts intended for healthcare purposes. Therefore, the use of adaptive slicing instead of traditional strategies is strongly encouraged to advance towards the sustainability of this technology.