Road Sign Board Direction and Location Extraction and Recognition for Autonomous Vehicle.

The problem of direction and location identification is very important in technologies used for Autonomous vehicles. while the navigation systems are that they cannot cover all areas due to a lack of signals or changes made on routes due to maintenance or upgrades. This research will focus on recognizing the sign and extracting address location names and directions from road signs. Moreover, it will help better identify road exits and lane directions for better route planning. In this paper we use YOLOv5 to identify the road board sign location and direction. Then extract the direction of each address location that are included in the road board sign and inform the car about the direction because autonomous car has no any driver so the car must decide by itself witch direction to choose to get the goal address location. This system can be used to continuously cheek the frames of the video that is taken by the car’s camera for road sign boards and analyses the image to find the direction of each location that are explained inside road sign board on the road. The proposed system consists of a camera mounted on top of the front mirror of the vehicle, and also a computer to run the recorded video on the system. In experiments, yolov5 framework achieves the best performance of 98.76% mean average precision (mAP) at Intersection over Union (IoU) threshold of 0.5, evaluated on our new developed dataset. And 91.31% on different IoU thresholds, ranging from 0.5 to 0.95

Deep Learning Based Car Damage Classification and Cost Estimation

Due to the increasing number of people driving cars, the number of insurance claims has also increased. This process involves the manual assessment of the vehicle by an insurance company's service engineer, as well as the physical inspection by a licensed insurance company representative. An end-to-end solution has been proposed that would allow the customer and the insurance company to automate the process of recognizing the damaged area in the vehicles and estimating the cost of the damage. It would allow them to get a better understanding of the condition of the vehicle. For this purpose, A deep learning, Mask Region-based Convolutional Neural Network (Mask RCNN) model was utilized in this work to classify vehicle damages costs. Two Mask RCNN models were utilized, the first one was used to detect the sides of the vehicle, which will affect damage cost estimation and the second was used to find the area of the damage. The Experimental work shows that the proposed model gives reasonable results to estimate the cost of the damage. We achieve an accuracy of 98.5% with the combination of the two Mask RCNN models. And showed that Mask RCNN has a promising result to detect the area of the damage in the car

Matförgiftningar och dess orsaker i Sverige under perioden 2012–2021

Varje år rapporterar Sveriges kommuner och Folkhälsomyndigheten ca 2000–3000 fall av matförgiftning till Livsmedelsverket. Livsmedelsverkets uppskattning är att ca en halv miljon svenskar drabbas per år. Den här studien syftar till att undersöka matförgiftningar under de senaste 10 åren. Genom att undersöka förekomst av matförgiftningar, de vanligaste smittämnena, de vanligaste utpekade smittkällorna och bidragande faktorer kan vi dra slutsatser om hur matförgiftningar kan minimeras. Denna studie baseras på data som Livsmedelsverket samlat in under de senaste tio åren. Informationen inkluderar rapporter av bekräftade matförgiftningar som rapporterats in av kontrollmyndigheter till livsmedelsverket och kompletterats med resultatet av utredningar som utförs av Livsmedelsverket och Folkhälsomyndigheten i samråd. Resultatet visar att totalt antal rapporterade matförgiftningar för perioden 2012-2021 var 3643 med 26 847 fall. För de flesta var orsak ”Okänd” (2962 matförgiftningar), därnäst kommer Calicivirus för 234 matförgiftningar, följd av Histamin för 111 matförgiftningar, följd av Salmonella som rapporterades för 67 matförgiftningar. ”Kycklingkött och produkter därav” var den livsmedelskategorin som rapporterades ha orsakat flest antal till matförgiftningars, följd av livsmedelskategorin ”Sammansatta måltid (ej buffé)” t.ex. kebabtallrik, hamburgare, eller kyckling med rissoto . De flesta rapporterade matförgiftningarna har skett under vintern och sommaren. ”Olämplig temperatur” var den mesta rapporterade orsaken som anges för 616 matförgiftningar, följd av ”Dålig hygien hos personalen” som anges för 523 matförgiftningar, följd av ”Kontaminering” som anges för 453 matförgiftningar. Sommaren var den årstiden där utredare kunde lätt konstatera vilka faktorer kan betraktas som bidragande pga Ordinarie personal brukar inte vara på plats under sommaren och ersätts med vikarier. Flera mobila verksamheter, säsongsanläggningar dyker upp. Det kan göra att dessa anläggningar inte har tydliga rutiner och inte följer hygien praxis.Every year, Sweden's municipalities and the Swedish Public Health Agency report approximately 2,000–3,000 cases of food poisoning to the Swedish Food Agency. The Swedish Food Agency has estimated that about a half million people in Sweden are affected every year. This study aims to investigate food poisoning over the past 10 years. By examining the occurrence of food poisoning, most common causative agents, most commonly identified sources of infection and contributing factors, to conclude how to minimize food poisoning. This study is based on data collected by the Swedish Food Agency for the past ten years. The data is about confirmed food poisoning reported by the control authorities to the Swedish Food Agency and completed with the results of investigations carried out by Swedish Food Agency and Swedish Public Health Agency in consultation. The results showed that the total number of reported food poisonings for the period 2012-2021 was 3643 with 26,847 cases. For most, the cause was "Unknown" (2962 food poisoning), followed by Calicivirus for 234 food poisoning, followed by Histamine for 111 food poisoning, followed by Salmonella, which was reported for 67 food poisoning. "Poultry meat and products thereof" was the common food category reported in most cases, followed by the food category "Compound meal (not buffet)" e.g. kebab plate, burgers, or Chicken with irssoto. Winter and summer are the seasons when most reported food poisonings have occurred. Most reported food poisonings occurred during the winter and summer. "Inappropriate temperature" was the most reported factor reported for 616 food poisoning, followed by "Poor personal hygiene" reported for 523 food poisoning, followed by "Contamination" reported for 453 food poisoning. Summer was the time of year when investigators could easily ascertain which factors could be considered as contributing because Statutory staff are usually not in place during the summer and are replaced by replacement staff. Several mobile and seasonal installations are emerging. This may mean that these establishments do not have clear rutines and do not follow hygiene practices

Utbildning och bedömning av svensk teckenspråksförmåga

Sign language is used widely around the world as a first language for those that are unable to use spoken language and by groups of people that have a disability which precludes them from using spoken language (such as a hearing impairment). The importance of effective learning of sign language and its applications in modern computer science has grown widely in the modern aged society and research around sign language recognition has sprouted in many different directions, some examples using hidden markov models (HMMs) to train models to recognize different sign language patterns (Swedish sign language, American sign language, Korean sign language, German sign language and so on).  This thesis project researches the assessment and skill efficiency of using a simple video game to learn Swedish sign language for children in the ages within the range of 10 to 11 with no learning disorders, or any health disorders. During the experimental testing, 38 children are divided into two equally sized groups of 19 where each group plays a sign language video game. The context of the video game is the same for both groups, where both listened to a 3D avatar speak to them using both spoken language and sign language. The first group played the game and answered questions given to them by using sign language, whereas the other group answered questions given to them by clicking on an alternative on the video game screen. A week after the children have played the video game, the sign language skills that they have acquired from playing the video game are assessed by simple questions where they are asked to provide some of the signs that they saw during the duration of the video game. The main hypothesis of the project is that the group of children that answered by signing outperforms the other group, in both remembering the signs and executing them correctly. A statistical null hypothesis test is performed on this hypothesis, in which the main hypothesis is confirmed. Lastly, discussions for future research within sign language assessment using video games is described in the final chapter of the thesis.Teckenspråk används i stor grad runt om i världen som ett modersmål för dom som inte kan använda vardagligt talsspråk och utav grupper av personer som har en funktionsnedsättning (t.ex. en hörselskada). Betydelsen av effektivt lärande av teckenspråk och dess tillämpningar i modern datavetenskap har ökat i stor utsträckning i det moderna samhället, och forskning kring teckenspråklig igenkänning har spirat i många olika riktningar, ett exempel är med hjälp av statistika modeller såsom dolda markovmodeller (eng. Hidden markov models) för att träna modeller för att känna igen olika teckenspråksmönster (bland dessa ingår Svenskt teckenspråk, Amerikanskt teckenspråk, Koreanskt teckenspråk, Tyskt teckenspråk med flera). Denna rapport undersöker bedömningen och skickligheten av att använda ett enkelt teckenspråksspel som har utvecklats för att lära ut enkla Svenska teckenspråksmönster för barn i åldrarna 10 till 11 års ålder som inte har några inlärningssjukdomar eller några problem med allmän hälsa. Under projektets experiment delas 38 barn upp i två lika stora grupper om 19 i vardera grupp, där varje grupp kommer att få spela ett teckenspråksspel. Sammanhanget för spelet är detsamma för båda grupperna, där de får höra och se en tredimensionell figur (eng. 3D Avatar) tala till dom med både talsspråk och teckenspråk. Den första gruppen spelar spelet och svarar på frågor som ges till dem med hjälp av teckenspråk, medan den andra gruppen svarar på frågor som ges till dem genom att klicka på ett av fem alternativ som finns på spelets skärm. En vecka efter att barnen har utfört experimentet med teckenspråksspelet bedöms deras teckenspråkliga färdigheter som de har fått från spelet genom att de ombeds återuppge några av de tecknena som de såg under spelets varaktighet. Rapportens hypotes är att de barn som tillhör gruppen som fick ge teckenspråk som svar till frågorna som ställdes överträffar den andra gruppen, genom att både komma ihåg tecknena och återuppge dom på korrekt sätt. En statistisk hypotesprövning utförs på denna hypotes, där denna i sin tur bekräftas. Slutligen beskrivs det i rapportens sista kapitel om framtida forskning inom teckenspråksbedömning med tv spel och deras effektivitet

Effektiva Lagringsmetoder för Glesa Matriser

Sparse matrices are often used in numerical algorithms that solve linear equation systems. Many methods for storing sparse matrices have been proposed and implemented during the years. These methods focus primarily on minimizing the total memory consumption and the time that it takes to store a sparse matrix. This report researches the available storage methods for sparse unstructured matrices. The formats that are researched and implemented are COO, CRS and ELL. The comparisons between the formats are made based on the storage memory and time for the sparse matrices with different filling ratios. A numerical algorithm has also been implemented to study the time it takes to solve a sparse matrix with one of the available storage formats, ELL. The results show that the CRS format outperform the other formats in the storage of a sparse matrix. It is concluded that there are storage methods for sparse matrices that avoid taking up unnecessary memory space, simultaneously preserving the matrix structure and doing so within a reasonable time

Språkutvecklande arbetssätt i förskolan : En kvalitativ fältstudie kring två olika förskolor och deras språkutvecklande arbetsätt

The aim of this study was to examine how the activities of two preschools develop the language in matter of the teacher’s work of procedure. The following research questions were essential for the survey and therefore formed the basis of the examination: How do the teachers in both preschools work in a language evolving way? What significance does the environment have for the language development? Are there similarities and differences between the preschools in their language development approach? The study is based on a qualitative method, where the teachers of both preschools were interviewed with the purpose of obtaining information. The teacher’s statements and interviews were analyzed in relation to previous research, where the theories of different writers form the base of the theoretical connection to the study. The teachers view in developing the language regarding to their aspirations in measuring the language an important position in their operation, can be portraied as the conclusions of this study. Both preschools share a common view that the linguistic development is of great importance and should for that reason be highlighted as well as emphasized in the teachers working methods. However, the teachers in both preschools differ in their view considering language development due to their different ideas of witch working methods best stimulate the students linguistic abilities to development. The preschools working methods can be seen from a positive point of view and also from a critical one in relation to the examination, where the teacher’s interviews were analyzed and discussed. Accordingly, the teachers in both preschools aspire to achieve the best results for language development

Jämförelse mellan mammografiprojektionerna cranio-caudal och medial-lateral oblique för att diagnostisera bröstcancertumörer med maskininlärning

Breast cancer is one of the leading causes of death among women. However, an early detection of the disease has been shown to significantly improve the chances of a successful treatment. Hence, there is a great effort in developing modern Computer-aided detection (CAD) technology by making use of deep learning architectures, such as Convolutional Neural Networks (CNNs). This project investigates how the performance of deep learning classification differ between Craniocaudal (CC) and Mediolateral Oblique (MLO) views when diagnosing breast cancer masses from mammography images using the CNN model ResNet-50. Cropped images from the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM) dataset was used. In order to compare the difference between the CC view and the MLO view in terms of performance three different ResNet-50 models were trained. One model trained on a combination of the two views, and one model for each view separately. This study uses binary classification with the labels benign and malignant. The models exhibited signs of overfitting, resulting in test accuracies ranging between 61% and 66%, suggesting that none of the models performed adequately for practical application in breast cancer diagnosis. Although low performance, the results revealed a notable correlation, with the mediolateral oblique (MLO) view demonstrating superiority over the craniocaudal (CC) view. This indicates that the MLO view performs better than the CC view when diagnosing breast cancer masses from the CBIS-DDSM dataset using ResNet-50.Bröstcancer är en av de främsta dödsorsakerna bland kvinnor. En tidig upptäckt av sjukdomen har dock visat sig avsevärt förbättra chanserna för en lyckad behandling. För att möjliggöra tidigare upptäckt utvecklas moderna datorsstödda detektionsteknologier (CAD) med hjälp av djupinlärningsarkitekturer, såsom konvolutionella neurala nätverk (CNNs). Detta projekt undersöker skillnaden mellan att använda projektionerna cranio-caudal (CC) och medial-lateral oblique (MLO) vid diagnostiseringen av bröstcancer-tumörer med hjälp av CNN-modellen ResNet-50. Beskurna bilder från Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM) användes i denna studie. För att kunna jämföra de två olika projektionerna tränades tre olika ResNet-50-modeller: en på en kombination av båda projektionerna, en på enbart CC-bilder och en på enbart MLO-bilder. Studien använde binär klassificering av tumörerna med klasserna godartad och elakartad. Modellerna visade tecken på överanpassning, vilket resulterade i testnoggrannhet mellan 61% och 66%. Detta indikerar att ingen av modellerna presterar tillräckligt väl för att praktiskt kunna användas vid diagnostisering av bröstcancer. Trots den låga prestandan visade resultaten en märkbar korrelation där MLO-projektioner visade sig vara överlägsna jämfört med CC-projektioner. Detta tyder på att MLO presterar bättre än CC vid diagnostisering av bröstcancertumörer från mammografibilder från CBIS-DDSM med användning av ResNet-50

Jämförelse mellan mammografiprojektionerna cranio-caudal och medial-lateral oblique för att diagnostisera bröstcancertumörer med maskininlärning

Breast cancer is one of the leading causes of death among women. However, an early detection of the disease has been shown to significantly improve the chances of a successful treatment. Hence, there is a great effort in developing modern Computer-aided detection (CAD) technology by making use of deep learning architectures, such as Convolutional Neural Networks (CNNs). This project investigates how the performance of deep learning classification differ between Craniocaudal (CC) and Mediolateral Oblique (MLO) views when diagnosing breast cancer masses from mammography images using the CNN model ResNet-50. Cropped images from the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM) dataset was used. In order to compare the difference between the CC view and the MLO view in terms of performance three different ResNet-50 models were trained. One model trained on a combination of the two views, and one model for each view separately. This study uses binary classification with the labels benign and malignant. The models exhibited signs of overfitting, resulting in test accuracies ranging between 61% and 66%, suggesting that none of the models performed adequately for practical application in breast cancer diagnosis. Although low performance, the results revealed a notable correlation, with the mediolateral oblique (MLO) view demonstrating superiority over the craniocaudal (CC) view. This indicates that the MLO view performs better than the CC view when diagnosing breast cancer masses from the CBIS-DDSM dataset using ResNet-50.Bröstcancer är en av de främsta dödsorsakerna bland kvinnor. En tidig upptäckt av sjukdomen har dock visat sig avsevärt förbättra chanserna för en lyckad behandling. För att möjliggöra tidigare upptäckt utvecklas moderna datorsstödda detektionsteknologier (CAD) med hjälp av djupinlärningsarkitekturer, såsom konvolutionella neurala nätverk (CNNs). Detta projekt undersöker skillnaden mellan att använda projektionerna cranio-caudal (CC) och medial-lateral oblique (MLO) vid diagnostiseringen av bröstcancer-tumörer med hjälp av CNN-modellen ResNet-50. Beskurna bilder från Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM) användes i denna studie. För att kunna jämföra de två olika projektionerna tränades tre olika ResNet-50-modeller: en på en kombination av båda projektionerna, en på enbart CC-bilder och en på enbart MLO-bilder. Studien använde binär klassificering av tumörerna med klasserna godartad och elakartad. Modellerna visade tecken på överanpassning, vilket resulterade i testnoggrannhet mellan 61% och 66%. Detta indikerar att ingen av modellerna presterar tillräckligt väl för att praktiskt kunna användas vid diagnostisering av bröstcancer. Trots den låga prestandan visade resultaten en märkbar korrelation där MLO-projektioner visade sig vara överlägsna jämfört med CC-projektioner. Detta tyder på att MLO presterar bättre än CC vid diagnostisering av bröstcancertumörer från mammografibilder från CBIS-DDSM med användning av ResNet-50