Jitter measurement in digital signals by using software defined radio technology

Here, we propose a method using software defined radio (SDR) technology to measure periodic and random jitter in a digital signal. Conventional methods to measure jitter required expensive measurement equipment. We use a cheap (less than US $40) SDR USB dongle to expose random and periodic jitter in the amplitude modulated light source of a range camera. By using Fourier analysis we generate an ideal reference signal to enable to extract jitter at the zero crossings of the signal under test. We measure jitter with SDR receiver and more conventionally a real-time digital oscilloscope. From the SDR receiver, we find the periodic jitter at low frequency and compare to the oscilloscope results. We demonstrate that periodic and random jitter can be detected on a RF signal with consumer priced products provided that the jitter frequency less within the bandwidth of the receiver

Estimating heading direction from monocular video sequences using biologically-based sensor

The determination of one’s movement through the environment (visual odometry or self-motion estimation) from monocular sources such as video is an important research problem because of its relevance to robotics and autonomous vehicles. The traditional computer vision approach to this problem tracks visual features across frames in order to obtain 2-D image motion estimates from which the camera motion can be derived. We present an alternative scheme which uses the properties of motion sensitive cells in the primate brain to derive the image motion and the camera heading vector. We tested heading estimation using a camera mounted on a linear translation table with the line of sight of the camera set at a range of angles relative to straight ahead (0◦ to 50◦ in 10◦ steps). The camera velocity was also varied (0.2, 0.4, 0.8, 1.2, 1.6 and 2.0 m/s). Our biologically-based method produced accurate heading estimates over a wide range of test angles and camera speeds. Our approach has the advantage of being a one-shot estimator and not requiring iterative search techniques for finding the heading

Development of an image recognition systems for crop disease indentification of paddy field in Sri Lanka

The classification and recognition of paddy diseases are of the major technical and economical importance in the agricultural industry. To automate these activities, like texture, color and shape, disease recognition system is feasible. The goal of this research is to develop an image recognition system that can recognize paddy diseases. Images were acquired under laboratory condition using digital camera. Four major diseases commonly found in Sri Lanka, Rice blast (Magnaporthe grisea), Rice sheath blight (Rhizoctonia solani), Brown spot (Cochiobolus miyabeanus) and False smut (Ustilaginoidia virens) were selected for this study. Image processing starts with the digitized a color image of paddy disease leaf. Then a method of mathematics morphology was introduced to segment these images. Then texture, shape and color features of color image of disease spot on leaf were extracted, and a classification method of membership function was used to discriminate between the four types of diseases. The analysis of the results shows over 70 percent classification accuracy over 50 .sample images. The conclusion is that in case of reasonably good images, this approach yields excellent results, Use of powerful RGB camera would allow higher precision of the image color and segmentatio

Signal processing approaches for Jitter extraction in time-of-flight range imaging cameras

The precision and accuracy of time-of-flight full-field range cameras are important for many applications, however there are a number of noise sources that degrade both precision and accuracy. Many of the noise sources such as nonlinearity, multipath inferences and harmonic cancellation are well investigated. Barely investigated is jitter on the camera light and shutter signals. Here we measure periodic and random jitter on the light signal of a camera. We use signal processing techniques to construct a reference signal, hence find the jitter. The performance of the proposed method is examined using the MESA Imaging SwissRanger 4000. We found periodic jitter of two frequencies at 0.12 and 5.04 MHz and the random jitter of 164 ± 4 ps, on the light signal of the camera