Segmentasi Citra Digital Menggunakan Thresholding Otsu untuk Analisa Perbandingan Deteksi Tepi

Pendeteksian tepi menjadi salah satu tahapan penting pengolahan citra dalam proses segmentasi karena dapat mempertegas batas-batas antara objek dan latar belakang. Banyaknya metode deteksi tepi saat ini menimbulkan keraguan dalam memilih metode deteksi tepi yang tepat dan sesuai dengan kondisi citra. Berdasarkan masalah tersebut dilakukan penelitian untuk menganalisis kinerja metode deteksi tepi Sobel, Prewitt, Roberts dan Canny menggunakan thresholding Otsu berdasarkan nilai threshold, waktu proses dan pengamatan visual. Program dibuat menggunakan perangkat lunak Microsoft Visual C# 2010 Express. Hasil penelitian terhadap tiga citra uji bahwa metode Canny menghasilkan tepian yang tipis dan halus serta tidak menghilangkan informasi penting pada gambar meskipun membutuhkan waktu komputasi yang tidak sedikit. Nilai threshold yang diperoleh dari Otsu merupakan nilai ambang terbaik dan optimal untuk setiap metod

Extending OpenStack Monasca for Predictive Elasticity Control

Traditional auto-scaling approaches are conceived as reactive automations, typically triggered when predefined thresholds are breached by resource consumption metrics. Managing such rules at scale is cumbersome, especially when resources require non-negligible time to be instantiated. This paper introduces an architecture for predictive cloud operations, which enables orchestrators to apply time-series forecasting techniques to estimate the evolution of relevant metrics and take decisions based on the predicted state of the system. In this way, they can anticipate load peaks and trigger appropriate scaling actions in advance, such that new resources are available when needed. The proposed architecture is implemented in OpenStack, extending the monitoring capabilities of Monasca by injecting short-term forecasts of standard metrics. We use our architecture to implement predictive scaling policies leveraging on linear regression, autoregressive integrated moving average, feed-forward, and recurrent neural networks (RNN). Then, we evaluate their performance on a synthetic workload, comparing them to those of a traditional policy. To assess the ability of the different models to generalize to unseen patterns, we also evaluate them on traces from a real content delivery network (CDN) workload. In particular, the RNN model exhibites the best overall performance in terms of prediction error, observed client-side response latency, and forecasting overhead. The implementation of our architecture is open-source

The role of metal ions in the uptake of aspartate aminotransferase and malate dehydrogenase into isolated rat liver mitochondria in vitro

AbstractTo gain further insight into the mitochondrial receptor area which allows selective uptake of both purified aspartate aminotransferase and malate dehydrogenase into mitochondria, the inhibition of metal complexing agents such as bathophenanthroline and tiron on the uptake of both enzymes has been investigated. In view of the nature of the inhibition found, we propose the existence of metal ion(s) at or near the aspartate aminotransferase, but far from the malate dehydrogenase binding site

Predictive auto-scaling with OpenStack Monasca

Cloud auto-scaling mechanisms are typically based on reactive automation rules that scale a cluster whenever some metric, e.g., the average CPU usage among instances, exceeds a predefined threshold. Tuning these rules becomes particularly cumbersome when scaling-up a cluster involves non-negligible times to bootstrap new instances, as it happens frequently in production cloud services. To deal with this problem, we propose an architecture for auto-scaling cloud services based on the status in which the system is expected to evolve in the near future. Our approach leverages on time-series forecasting techniques, like those based on machine learning and artificial neural networks, to predict the future dynamics of key metrics, e.g., resource consumption metrics, and apply a threshold-based scaling policy on them. The result is a predictive automation policy that is able, for instance, to automatically anticipate peaks in the load of a cloud application and trigger ahead of time appropriate scaling actions to accommodate the expected increase in traffic. We prototyped our approach as an open-source OpenStack component, which relies on, and extends, the monitoring capabilities offered by Monasca, resulting in the addition of predictive metrics that can be leveraged by orchestration components like Heat or Senlin. We show experimental results using a recurrent neural network and a multi-layer perceptron as predictor, which are compared with a simple linear regression and a traditional non-predictive auto-scaling policy. However, the proposed framework allows for the easy customization of the prediction policy as needed

Desain Dan Pembuatan Alat Pendeteksi Golongan Darah Menggunakan Mikrokontroler

Sebuah mikrokontroler ATMEGA 8535 dirancang untuk tujuan pendeteksi golongan darah otomatis yang portable sehingga mudah dibawa serta digunakan, dalam pengujiannya metode yang digunakan adalah metode ABO. Untuk mendesain alat, desain framework menjadi landasan dalam pemikiran konsep, selanjutnya diterjemahkan dalam bentuk sket desain dan CAD desain. Langkah selanjutnya adalah perakitan komponen dari segi perangkat keras dan pembuatan program (software). Untuk perangkat keras yang menjadi inti sistem adalah sensor yang memanfaatkan sistem LED dan LDR sedangkan bahasa yang digunakan adalah bahasa C. Pada proses pengujian alat dilakukan menggunakan cara statistik, yaitu dengan mengukur waktu pengujian untuk mendapatkan nilai rata-rata. Nilai rata-rata waktu yang dibutuhkan untuk mendeteksi golongan darah adalah 2 menit 30 detik dengan jumlah sampel yang diambil adalah 12 orang, dengan rincian 3 orang golongan darah A, 3 orang darah B, 3 orang darah AB serta 3 orang O. adapun tingkat akurasi dari alat ini adalah 100

5G-Based Multi-Sensor Platform for Monitoring of Workpieces and Machines: Prototype Hardware Design and Firmware

In this paper, we introduce a 5G-based multi-sensor platform for monitoring workpieces and machines. The prototype is realized within the EU-funded 5G-SMART project, which aims to enable smart manufacturing through 5G, demonstrating and validating new generation network technology in industrial processes. There are already state-of-the-art solutions, but with drawbacks such as limited flexibility, brief real-time capability, and sensors aimed at single applications. The 5G-SMART multi-sensor platform is designed to overcome these points and meet the requirements of Industry 4.0. The device is equipped with different sensors to acquire multiple data from workpieces and machines of the shop floor, wirelessly connected by 5G to the factory cloud. A hardware design description of the prototype is provided, focusing on the electronic components and their interaction with the microcontroller. Verification of the correct functioning of the board is given, with a basic library for the main peripherals used as a basis for the final firmware