Optimization of automated temperature measurement system for formed meat products

The Federal Safety and Inspection Services in accordance with United States Department of Agriculture require that formed meat products be cooked to a specified minimum internal temperature in order to eliminate the possible presence of harmful bacteria. The current system of verifying the internal temperatures of formed meat products consists of manually inserting a temperature probe into the selected sample as it exits the oven. Creating an automated system that measures and records the internal temperatures of formed meat samples as they exit the oven along a meat processing line has been the topic of an ongoing research. The most recent research conducted utilized the knowledge and results presented from previous work to create a 3-D simulation of a proposed automated system. The objective of this thesis was to analyze the previously created simulation and implement the necessary modifications to increase the efficiency of the system. At the conclusion of the research, a new automated system process was designed along with an additional implementation for further optimization

A smart fire detection system using iot technology with automatic water sprinkler

House combustion is one of the main concerns for builders, designers, and property residents. Singular sensors were used for a long time in the event of detection of a fire, but these sensors can not measure the amount of fire to alert the emergency response units. To address this problem, this study aims to implement a smart fire detection system that would not only detect the fire using integrated sensors but also alert property owners, emergency services, and local police stations to protect lives and valuable assets simultaneously. The proposed model in this paper employs different integrated detectors, such as heat, smoke, and flame. The signals from those detectors go through the system algorithm to check the fire's potentiality and then broadcast the predicted result to various parties using GSM modem associated with the system. To get real-life data without putting human lives in danger, an IoT technology has been implemented to provide the fire department with the necessary data. Finally, the main feature of the proposed system is to minimize false alarms, which, in turn, makes this system more reliable. The experimental results showed the superiority of our model in terms of affordability, effectiveness, and responsiveness as the system uses the Ubidots platform, which makes the data exchange faster and reliable

Improving the information flow internal and external traceability in a slaughterhouse

Several global aspects are affecting the world's food supply chain. In this study a slaughterhouse was used as a "laboratory" to get conclusions that can be applied to all the meat industry and in other automated industries. The aim is to provide tools to solve this gap. The global standards for traceability are now beginning to evolve in the market place, and as a result, early adopters and standard setters will take a lead role and the advantages that come from it. The focus is to describe relevant references that help to support the approaches and to support the recommendations, with the tools for a suitable decision analysis. This analysis considers the RFID identification of each unique "Christmas-tree", and the Physical Marking of the "Christmas-trees" as the most relevant approaches in the long term. The Bar-code identification before shipping is also a pertinent approach, especially in the short term

Intelligent Temperature-Controlled Poultry Feed Dispensing System with Fuzzy Logic Algorithm

This study introduces a novel fuzzy logic algorithm tailored to the thermoneutral zone of poultry, offering a precise and adaptive approach to feed dispensation. This involved the utilization of an LCD module to present essential information such as the selected age, real-time ambient temperature, current time, and the dispensed feed quantity. Data gathered during the process were stored in a memory device. The design of the fuzzy logic algorithm centered on the thermoneutral zone of the chicken serves as the determinant for feed dispensed by the system. It's crucial to note that while the system lacked artificial intelligence (AI), its logical analysis operated based on the fuzzy logic algorithm. Rigorous testing ensued, encompassing the comparison of feed dispensation between automated and manual systems and the assessment of feed waste and broiler weight.  Significant feed waste reduction in the first week demonstrated the efficacy of the fuzzy-based method, with consistently low p-values of 0.00069, 0.015195, and 0.034 across subsequent weeks confirming the consistent outperformance in broiler weight compared to the traditional feeding technique. The findings contribute to the advancement of temperature-based poultry feed systems, addressing key challenges in optimizing feed quantity. The study successfully met its objectives, demonstrating the system's capability to dispense feeds effectively across varying ambient temperatures.  Notably, the study revealed a consistent alignment of system outputs with those obtained from a digital thermometer and digital weighing scale, confirming the accuracy and reliability of the temperature-based feed dispensing system

ACR Accreditation for Utah Valley Hospital’s Radiation Oncology Center

Becoming an accredited clinic through the American College of Radiology (ACR) and their Radiation Oncology Practice Accreditation (ROPA) program will provide third-party evaluation of patient care to ensure the best treatment possible for patients. Talk of getting ACR accreditation has occurred in the past for Utah Valley Hospital/American Fork Hospital, but at the time it was seen as something that did not provide sufficient value vs. the cost. The recent One Intermountain restructuring is intended to unify all of the Intermountain Healthcare radiation oncology centers in Utah so the Radiation Oncology Director has set the goal that all Intermountain radiation oncology programs will be accredited. Intermountain Medical Center (IMC) and Dixie Regional Medical Center (DRMC) are currently ACR accredited and can be used as model programs. I started with an in-depth examination of our department’s workflow, documentation, and policies in order to determine where improvements to meet ACR accreditation standards could be made. I followed this up by working on implementing some of these improvements throughout the clinic and made sure they become routine and a standard in the department. An analysis of Dixie Regional Medical Center and Intermountain Medical Center’s ACR documents was performed to provide a baseline of an accredited-ACR program. Finally, a comprehensive checklist of everything that will need to be changed or implemented was presented in order to provide guidance for the future

A Machine Learning approach to Febrile Classification

General health screening is needed to decrease the risk of pandemic in high volume areas. Thermal characterization, via infrared imaging, is an effective technique for fever detection, however, strict use requirements in combination with highly controlled environmental conditions compromise the practicality of such a system. Combining advanced processing techniques to thermograms of individuals can remove some of these requirements allowing for more flexible classification algorithms. The purpose of this research was to identify individuals who had febrile status utilizing modern thermal imaging and machine learning techniques in a minimally controlled setting. Two methods were evaluated with data that contained environmental, and acclimation noise due to data gathering technique. The first was a pretrained VGG16 Convolutional Neural Network found to have F1 score of 0.77 (accuracy of 76%) on a balanced dataset. The second was a VGG16 Feature Extractor that gives inputs to a principle components analysis and utilizes a support vector machine for classification. This technique obtained a F1 score of 0.84 (accuracy of 85%) on balanced data sets. These results demonstrate that machine learning is an extremely viable technique to classify febrile status independent of noise affiliated