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The bone fracture detection using X-rays or CTscan produces accurate images but has harmful effect radiation. This paper presented the use of ultrasonic waves (US) as an alternative to substitute those two instruments. This study used femur bovine and chicken bones in conditions with and without meat. The fractures are artificially made on transverse and oblique patterns. The scanning US probe produces twodimensional (2D) B-mode images. Fracture detection is done using five variations of the Convolutional Neural Network (CNN) architectural design, i.e., CNN1-CNN5. The results showed that the CNN4 is the best design of bone contour recognition and bone fracture classification compared to the other tested designs, with 95.3% accuracy, 95% sensitivity, and 96% specificity. The comparison with the Support Vector Machine (SVM) and k-NN classification methods indicate that CNN has superior performance in accuracy, sensitivity, and specificity. Intisari-Pendeteksian patah tulang dengan X-ray atau CTscan menghasilkan gambar yang akurat tetapi memiliki efek negatif radiasi yang berbahaya. Makalah ini memaparkan penggunaan gelombang ultrasonik (US) sebagai alternatif pengganti kedua instrumen tersebut. Makalah ini menggunakan tulang femur sapi dan ayam dalam kondisi dengan dan tanpa daging, dengan patahan dibuat secara manual dengan pola patah transverse dan oblique. Pemindaian probe US menghasilkan citra B-mode dua dimensi. Pendeteksian tulang patah dilakukan menggunakan lima variasi desain arsitektur Convolutional Neural Network (CNN), yaitu CNN1-CNN5. Hasil uji coba menunjukkan bahwa desain arsitektur CNN4 memberikan hasil pengenalan kontur tulang dan klasifikasi tulang patah yang paling bagus dibandingkan desain arsitektur lain yang diuji, dengan akurasi 95,3%, sensitivitas 95%, dan specificity 96%. Hasil perbandingan dengan metode klasifikasi Support Vector Machine (SVM) dan k-Neural Network (k-NN) menunjukkan bahwa CNN memiliki unjuk kerja yang lebih unggul baik dalam hal akurasi, sensitivitas, maupun specificity. Kata Kunci-Citra ultrasonik B-mode, Convolutional Neural Network, lapisan konvolusi, tulang femur.

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The two-wheeled electric skateboard (TWS) is designed for a personal vehicle. A Fuzzy-PID control strategy is designed and implemented for controlling its motion. Basically, motions control of the TWS is performed by balancing the pitch position of the TWS. Performance of the designed controller is demonstrated experimentally. The Fuzzy algorithm updates the PID gains and therefore it can handle the changing of the TWS load. Contribution of Fuzzy-PID in reducing the electric energy consumption, which is an important issue in electrical system, is also evaluated. The Fuzzy-PID successes to reduce the electric energy consumption of the TWS compared to the conventional PID.

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Detection of vascular areas (blood vessels) using B-Mode ultrasound images is needed for automatic applications such as registration and navigation in medical operations. This study developed the detection of the carotid artery area using Convolution Neural Network Single Shot Network Multibox Detector (SSD) to determine the bounding box ROI of the carotid artery area in B-mode ultrasound images. The data used are B-Mode ultrasound images on the neck that contain the carotid artery area (primary data). SSD method result is 95% of accuracy which is higher than the Hough transformation method, Ellipse method, and Faster RCNN in detecting carotid artery area in the B-Mode ultrasound image. The use of image enhancement with Gaussian filter, histogram equalization, and Median filters in this method can increase detection accuracy. The best process time of the proposed method is 2.09 seconds so that it can be applied in a real-time system. Keywords -object detection; carotid artery; ultrasound B-Mode; convolutional neural network; single shot multibox detector Abstrak -Deteksi area vaskular (pembuluh darah) menggunakan citra ultrasound B-Mode diperlukan untuk aplikasi otomatis seperti registrasi dan navigasi dalam operasi medis. Penelitian ini melakukan kajian deteksi area arteri karotis menggunakan Convolution Neural Network Single Shot Multibox Detector (SSD) untuk menentukan RoI area arteri karotis dengan fitur bounding box pada citra ultrasound B-Mode. Data yang digunakan dalam penelitian adalah citra ultrasound B-Mode pada bagian leher yang mengandung area arteri karotis (data primer). Hasil metode SSD memiliki akurasi 95% dan akurasi yang lebih tinggi dari metode transformasi Hough, metode Ellipse dan Faster RCNN dalam mendeteksi area arteri karotis pada citra ultrasound B-Mode. Penerapan image enhancement dengan filter Gaussian, histogram equalization dan filter Median memberikan pengaruh dalam peningkatan akurasi deteksi. Waktu proses terbaik dari metode yang diusulkan adalah 2,09 detik sehingga dapat diterapkan dalam sistem yang bersifat real-time.Kata kunci -deteksi objek; arteri karotis; ultrasound B-Mode; convolutional neural network; single shot multibox detektor