BOOSTING

• Research ways to efficiently implement machinelearning algorithms on MIPS/PowerVR • Research possible extensions to MIP

SISTEM DETEKSI WAJAH PADA OPEN SOURCE PHYSICAL COMPUTING

Face detection is one of the interesting research area. Majority of this research implemented on a computer. Development of face detection on a computer requires a significant investment costs. In addition to having to spend the cost of procurement of computers, is also required for operational cost such as electricity use, because the computer requires large power/watt.This research is proposed to build a face detection system using Arduino. The system will be autonomous, in other word the role of computer will be replaced by Arduino. Arduino is used is Arduino Mega 2560 with specifications microcontroller AT MEGA 2560, a speed of 16 MHz, 256 KB flash memory, 8 KB SRAM, 4 KB EEPROM. So not all face detection algorithm can be implemented on the Arduino. The limitations of memory owned by the arduino will be resolved by applying the method of template matching using the facial features in the form of a template that is shaped like a mask. Detection rate achieved in this study is 80% - 100%. Where, in the Arduino's success in identifying the face are influenced by the distance between the camera with the human face and human movement

Sistem Deteksi Wajah Pada Open Source Physical Computing

Deteksi wajah merupakan salah satu area penelitian yang menarik. Penelitian deteksi wajah yang dilakukan hingga saat ini mayoritas masih diimplementasikan pada komputer. Pembangunan sistem deteksi wajah pada komputer memerlukan biaya investasi yang tidak sedikit. Selain harus mengeluarkan biaya pengadaan komputer, juga diperlukan biaya untuk operasional seperti penggunaan listrik, karena komputer membutuhkan daya/watt yang besar. Dalam penelitian ini diusulkan untuk membangun sistem deteksi wajah dengan menggunakan arduino. Sistem tersebut akan bersifat autonomous (mandiri), dengan kata lain peran komputer akan digantikan oleh arduino. Arduino yang digunakan adalah arduino Mega 2560 dengan spesifikasi mikrokontroler AT MEGA 2560, kecepatan 16 MHz, flash memory 256 KB, SRAM 8 KB. EEPROM 4 KB. Sehingga tidak semua algoritma deteksi wajah dapat diimplementasikan pada arduino. Untuk mengatasi keterbatasan memori yang dimiliki oleh arduino akan digunakan metode template matching dengan menggunakan fitur wajah berupa template yang berbentuk seperti topeng. Detection rate yang berhasil dicapai dalam penelitian ini adalah sebesar 80%-100%. Dimana, keberhasilan dalam arduino dalam mengidentifikasi wajah dipengaruhi oleh jarak antara wajah manusia dengan kamera dan pergerakan manusia. ============================================================================================================================== Face detection is one of the interesting research area. Majority of this research implemented on a computer. Development of face detection on a computer requires a significant investment costs. In addition to having to spend the cost of procurement of computers, is also required for operational cost such as electricity use, because the computer requires large power/watt. This research is proposed to build a face detection system using Arduino. The system will be autonomous, in other word the role of computer will be replaced by Arduino. Arduino is used is Arduino Mega 2560 with specifications microcontroller AT MEGA 2560, a speed of 16 MHz, 256 KB flash memory, 8 KB SRAM, 4 KB EEPROM. So not all face detection algorithm can be implemented on the Arduino. The limitations of memory owned by the arduino will be resolved by applying the method of template matching using the facial features in the form of a template that is shaped like a mask. Detection rate achieved in this study is 80% - 100%. Where, in the Arduino's success in identifying the face are influenced by the distance between the camera with the human face and human movemen

A scalable, portable, FPGA-based implementation of the Unscented Kalman Filter

Sustained technological progress has come to a point where robotic/autonomous systems may well soon become ubiquitous. In order for these systems to actually be useful, an increase in autonomous capability is necessary for aerospace, as well as other, applications. Greater aerospace autonomous capability means there is a need for high performance state estimation. However, the desire to reduce costs through simplified development processes and compact form factors can limit performance. A hardware-based approach, such as using a Field Programmable Gate Array (FPGA), is common when high performance is required, but hardware approaches tend to have a more complicated development process when compared to traditional software approaches; greater development complexity, in turn, results in higher costs. Leveraging the advantages of both hardware-based and software-based approaches, a hardware/software (HW/SW) codesign of the Unscented Kalman Filter (UKF), based on an FPGA, is presented. The UKF is split into an application-specific part, implemented in software to retain portability, and a non-application-specific part, implemented in hardware as a parameterisable IP core to increase performance. The codesign is split into three versions (Serial, Parallel and Pipeline) to provide flexibility when choosing the balance between resources and performance, allowing system designers to simplify the development process. Simulation results demonstrating two possible implementations of the design, a nanosatellite application and a Simultaneous Localisation and Mapping (SLAM) application, are presented. These results validate the performance of the HW/SW UKF and demonstrate its portability, particularly in small aerospace systems. Implementation (synthesis, timing, power) details for a variety of situations are presented and analysed to demonstrate how the HW/SW codesign can be scaled for any application