In general, monitoring of animal growth and health is done directly by farmers (invasive measurement methods) which can cause cows to be injured or experience stress. To avoid this, several studies have been conducted on non-invasive methods using image processing technology. In this study, we systematically reviewed several works of literature to identify and synthesize published articles on image processing technology and image processing applications related to weight estimation and individual cattle identification. Analysis of image processing technologies used for weight estimation and individual cattle identification is the main objective of this article. Articles were searched through several databases and studies that met the inclusion criteria were analyzed and used in the review. The studies were divided into three main themes: image processing technologies, applications using image processing, and image processing research on cattle growth and health. It can be concluded that deep learning approaches are increasingly being studied, tested and considered as a viable and promising approach to monitor cattle weight and health in several aspects

Excessive use of household electricity is one of the causes of the largest amount of national electricity consumption coming from households. One way to reduce the amount of household electricity consumption is to automate the operation of electronic devices. This research proposes utilizing Long Short Term Memory (LSTM) algorithm to predict the habit of operating an electronic device. The prediction is then applied to automate the operation of that by exploiting the time series data from the usage. A series of experiments are conducted to capture the data of operating a manual lamp. Then, an LSTM model is built by training the data. The experiment results show the prediction accuracy of 99,28% and Root Mean Square Error of 0,091. Furthermore, the LSTM model is used to automatically operate a lamp in a month. The electricity cost from the automation is 36,38% lower than the manual.

Retinal fundus images are used by ophthalmologists to diagnose eye disease, such as glaucoma disease. The diagnosis of glaucoma is done by measuring changes in the cup-to-disc ratio. Segmenting the optic cup helps petrify ophthalmologists calculate the CDR of the retinal fundus image. This study proposed a deep learning approach using U-Net architecture to carry out segmentation task. This proposed method was evaluated on 650 color retinal fundus image. Then, U-Net was configured using 160 epochs, image input size = 128x128, Batch size = 32, optimizer = Adam, and loss function = Binary Cross Entropy. We employed the Dice Coefficient as the evaluator. Besides, the segmentation results were compared to the ground truth images. According to the experimental results, the performance of optic cup segmentation achieved 98.42% for the Dice coefficient and loss of 1,58%. These results implied that our proposed method succeeded in segmenting the optic cup on color retinal fundus images.

This article by Yogesh Awasthi (Email: dryogeshawasthi@lfu.edu.krd) has been retracted (i.e. withdrawn from the press) for violating our Publication Ethics.

Resepsi merupakan suatu acara yang dihadiri oleh tamu undangan. Ketika memasuki acara resepsi, tamu akan disambut oleh penanti tamu yang akan mempersilahkan tamu untuk mengisi buku tamu dan menyerahkan souvenir kepada tamu. Pada resepsi yang mengundang banyak tamu akan menyebabkan penumpukan antrian tamu di pintu masuk resepsi yang disebabkan oleh waktu yang lama untuk menuliskan nama dan alamat pada buku tamu. Pada penelitian ini dirancang sebuah sistem penerima tamu otomatis pada resepsi dengan cara melakukan pembacaan RFID tag yang ditempelkan ke RFID reader dan menyimpan data tamu ke SD card kemudian souvenir akan keluar secara otomatis. Dengan adanya sistem ini, proses penerimaan tamu resepsi menjadi lebih singkat dan data tamu yang hadir dapat disimpan dan ditampilkan melalui notepad. Hasil pengujian menunjukkan sistem dapat membaca dan menyimpan data tamu dengan tingkat keberhasilan 100 %, dengan rata-rata jarak masksimal RFID tag dan RFID reader 2,5 cm. Pengujian secara keseluruhan dari sistem menunjukkan 2 dari 10 kali percobaan tidak berhasil disebabkan oleh souvenir yang tersangkut pada alat pendorong souvenir atau penampung souvenir sehingga souvenir tidak dapat keluar dari alat, namun data tamu berhasil disimpan sesuai dengan RFID tag yang didekatkan ke RFID reader.

Braking Automation system is needed to make people do their job in braking while driving. This paper aims to design and construct an electromagnetic braking automatic system that able to make car stopped before colliding a barrier by applying of the Proportional Integral Derivative (PID) method. To find out how the effective utilization of PID control on electromagnetic braking automatic will be compared with the results of braking without using PID. Determination tuning result of controller PID parameter obtained by using the trial and error methods and obtained the PID parameter values that suitable are Kp = 2.98, Ki = 0.104 and Kd = 1.35. The ultrasonic sensor used as detection the distance to a barrier. From the testing results of electromagnetic braking system with PID control more effective than with the electromagnetic braking system without PID because it can be seen from the side cessation of the car approaching set point, the results of the electromagnetic braking with PID control better than the electromagnetic braking without PID control.

In this study, a monitoring system was generated to send data about temperature, humidity, and ammonia gas from a dairy cow cage. This system also provides notifications and recommendation to the owner to carry out certain actions based on data obtained from the dairy cow cage. This system consists of three main components, namely the web server monitoring devices and an application on mobile device. Furthermore, monitoring devices consist of a microcontroller, DHT22 sensor, MQ-135, and ESP266. The microcontroller sends data to the web server and the device moves continuously to notify the state of the dairy cage. From the test results, it is known that the system can send data correctly to mobile devices and web servers. Notification of abnormal conditions is given in an average of 39 seconds.