{"title":"A Low-Cost Wearable Rehabilitation Device","authors":"Jahedul Anowar, A. Ali, M. Amin","doi":"10.1145/3384613.3384628","DOIUrl":"https://doi.org/10.1145/3384613.3384628","url":null,"abstract":"Stroke or accidental disabilities has increased in number due to various reasons. Physiotherapeutic rehabilitation of any kind is a lengthy and expensive process. Moreover, due to the scarcity of physiotherapist and rehabilitation centers in developing countries like Bangladesh, makes it even more difficult for most patients to get due time support. In this paper, a cheaper alternative to conventional rehabilitation program is proposed with the use of a motion capture device that utilizes an IMU (Inertial Measurement Unit) sensor. This device is capable of recording movements and store the data for further analysis. This enables a patient to perform certain rehabilitation exercises at home without the need for an instructor to be physically present during the exercise to guide or asses the patient. The motion capture sensor used in this project, is an IMU with 6 D.O.F (Degrees of Freedom) to obtain patients' body part (upper limb in this prototype) rotations in 3 dimensions. The sensor is composed of a 3-axis accelerometer that measures the acceleration and force producing it. Also, a 3-axis gyroscope that helps to determine orientation by using Earth's gravity. The sensor data is coupled with an Arduino for data processing and storage. This device is very small, and the sensor accuracy is almost the same as expensive mobile phones. It shows 0.855 correlation with the similar data from Samsung A50 mobile phone. The cost of our first prototype is under 30 USD.","PeriodicalId":214098,"journal":{"name":"Proceedings of the 2020 12th International Conference on Computer and Automation Engineering","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130347080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Amalia Utamima, Torsten Reiners, Amir H. Ansaripoor
{"title":"Automation in Agriculture: A Case Study of Route Planning Using an Evolutionary Lovebird Algorithm","authors":"Amalia Utamima, Torsten Reiners, Amir H. Ansaripoor","doi":"10.1145/3384613.3384621","DOIUrl":"https://doi.org/10.1145/3384613.3384621","url":null,"abstract":"A recent trend in the agricultural sector is the integration of computers to support automation in the operation of small and large-scale farms. The utilization of computers for decision making is critical for farmers wanting to lower their operative costs and control their machines. The focus of this paper is on the optimization of route planning for agricultural machines that are applying fertilizer on fields. The output of this research is expected to support automation in agriculture by helping farmers to choose the most efficient route for their machines. This study formalizes the decisional problem with a mathematical formula and presents a new improved algorithm, Evolutionary Lovebird Algorithm, to solve the problem. The experimental results show that the proposed algorithm can save 8.45% of the non-working distance compared to other algorithms. Moreover, on average, the running time of the proposed algorithm is only one-third of other algorithms, thereby making the Evolutionary Lovebird Algorithm three times more efficient than other algorithms.","PeriodicalId":214098,"journal":{"name":"Proceedings of the 2020 12th International Conference on Computer and Automation Engineering","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132327669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
V. Muresan, M. Abrudean, M. Ungureșan, I. Clitan, V. Sita, T. Colosi
{"title":"Intelligent Temperature Control in an Industrial Furnace","authors":"V. Muresan, M. Abrudean, M. Ungureșan, I. Clitan, V. Sita, T. Colosi","doi":"10.1145/3384613.3384647","DOIUrl":"https://doi.org/10.1145/3384613.3384647","url":null,"abstract":"The paper presents an original solution for the intelligent control of the temperature in a rotary hearth furnace. The controlled heating process is approached as a distributed parameter one. The process dynamics in relation to time is identified based on experimental data obtained from the real plant. For the appropriate temperature control in the furnace, an advanced control system based on intelligent control is designed. The neural networks are used both to adapt the parameters of the main controller and to estimate the values of the disturbances, respectively of the faults which occur in the system. The designed control system can reject the effect of the faults which occur in the furnace operation, being a fault tolerant one. The estimation of the fault value is made by neural networks, based on the model of the spatial temperature distribution between the furnace sectors. The efficiency of the proposed control system is proved through simulation.","PeriodicalId":214098,"journal":{"name":"Proceedings of the 2020 12th International Conference on Computer and Automation Engineering","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129820810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Guobing Sun, Shengchun Sui, Yuwu Wang, Xiaodan Che
{"title":"Study on the Application Domain of Two Traditional Defogging Algorithms Based on Atmospheric degradation model","authors":"Guobing Sun, Shengchun Sui, Yuwu Wang, Xiaodan Che","doi":"10.1145/3384613.3384645","DOIUrl":"https://doi.org/10.1145/3384613.3384645","url":null,"abstract":"The development of artificial machine vision enables further research on image processing. As the direction of image enhancement, research on image defogging is also essential. Base on Atmospheric degradation model, the image defogging algorithm can execute precisely. Dark Channel Prior algorithm (DCP) and Color Attenuation Prior algorithm (CAP) are two classic algorithms in the traditional defogging algorithm. These two algorithms were implemented based on the degraded model and carried out a comparative analysis. By comparison, it was found that DCP performs better on images with no sky in the middle and short distances, while CAP performed better on images with larger sky areas and deeper depth of field. Regarding the difference between the two algorithms, further research was performed and the reason for the difference was attributed to the calculation method of transmittance, and the reasons for the difference between the two defogging algorithms were summarized.","PeriodicalId":214098,"journal":{"name":"Proceedings of the 2020 12th International Conference on Computer and Automation Engineering","volume":"426 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125787463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}