This paper presents a new type of electromagnetic interference (EMI) measurement system. An EMI Camera LSI (EMcam) with a 124 on-chip 25050 µm2 loop antenna matrix in 65 nm CMOS is developed. EMcam achieves both the 2D electric scanning and 60 µm-level spatial precision. The down-conversion architecture increases the bandwidth of EMcam and enables the measurement of EMI spectrum up to 3.3 GHz. The shared IF-block scheme is proposed to relax both the increase of power and area penalty, which are inherent issues of the matrix measurement. The power and the area are reduced by 74% and 73%, respectively. EMI measurement with the smallest 3212 µm2 antenna to date is also demonstrated.

In the acquisition of projection data of X-ray CT, logarithm operation is indispensable. But noise distribution is nonlinearly projected by the logarithm operation, and this deteriorates the precision of CT number. This influence becomes particularly remarkable when only a few photons are caught with a detector. It generates a strong streak artifact (SA) in a reconstructed image. Previously we have clarified the influence of the nonlinearity by statistical analysis and proposed a correction method for such nonlinearity. However, there is a problem that the compensation for clamp processing cannot be performed and that the suppression of SA is not enough in photon shortage state. In this paper, we propose a new technique for correcting the nonlinearity due to logarithm operation for noisy data by combining the previously presented method and an adaptive filtering method. The technique performs an adaptive filtering only when the number of captured photons is very few. Moreover we quantitatively evaluate the influence of noise on the reconstructed image in the proposed method by the experiment using numerical phantoms. The experimental results show that there is less influence on spatial resolution despite suppressing SA effectively and that CT number are hardly dependent on the number of the incident photons.

In this study, the frequency and spatial properties of undesired electromagnetic radiation distribution around a simple printed circuit board (PCB) model, which only has the mismatching printed line (PL) and ground, are estimated. Finite difference time domain (FDTD) modeling is developed for the analysis space, which is 500 400 51 mm3 in size, around the PCB. As the driving clock pulse has a very wide frequency bandwidth, ranging from kHz to GHz, basic and precise investigation of the noise emission mechanism from the basic model is performed. The results of the magnetic field Hx on the PCB as determined by FDTD simulation, and those of the experiment, driven by a clock pulse, agree well. The results show that although this approach is basic and simple, it becomes clear that the frequency and spatial characteristics of the electric and magnetic field near the PCB are influenced by the wavelength of the frequency and appling the driving clock pulse, and the low-frequency component of the electromagnetic distribution around the PCB is larger than the high-frequency components. It is suggested that the low-frequency noise problem should be carefully considered.

Under a contamination of background sound noises, it seems difficult especially in a real working situation to evaluate various type statistics of only an objective sound signal fluctuation. In many cases of the noise evaluation, some signal processing method have been employed to eliminate the effect of background sound noises by first measuring emitted sound levels. In this study, a new evaluation method of sound level fluctuation is proposed in principle on the basis of the measurement of heterogeneous physical quantity other than sound pressures or sound levels to eliminate the effect of background sound noises. Though the theoretical analysis on acoustical emission caused by a mechanical vibration seems very difficult in a working situation, the sound noise fluctuation emitted only from an objective sound source can be effectively evaluated through its related vibration measurement by employing a fairly unified stochastic method proposed on the basis of a generalized regression analysis between sound and vibration. Here, the regression coefficients are determined by employing the least squares error method to minimize the mean square of estimation error to illustrate well the sound data by means of vibration data. Finally, the effectiveness of proposed method has been experimentally applied to the sound noise evaluation of a jigsaw.