The identification and localization of large-range, wide-band electromagnetic interference (EMI) sources have always been both costly and time-consuming. The measurements at different times and places are often required before a typical system can locate a target. In this paper, we proposed a 2D electromagnetic imaging system to localize interference sources and identify the EMI frequency in real time. In this system, an offset paraboloid with a diameter of three meters is designed for large-range EMI imaging, while a multi-channel digital signal acquisition system is developed for wide-band EMI localization. The located interference source is segmented by the maximum entropy method based on particle swarm optimization, and the modified generalized regression neural network (MGRNN) is applied to identify the EMI frequency effectively by excluding misleading effects of outliers. The experiment which has been completed on our dataset indicates that our approach not only increases accuracy by 5% compared with the standard generalized regression neural network approaches for identification, but also exerts a large-range wide-band localization of the EMI source detection method.

In this paper, a weighted l₁-norm is proposed in a l₁-norm-based singular value decomposition (L1-SVD) algorithm, which can suppress spurious peaks and improve accuracy of direction of arrival (DOA) estimation for the low signal-to-noise (SNR) scenarios. The weighted matrix is determined by optimizing the orthogonality of subspace, and the weighted l₁-norm is used as the minimum objective function to increase the signal sparsity. Thereby, the weighted matrix makes the l₁-norm approximate the original l₀-norm. Simulated results of orthogonal frequency division multiplexing (OFDM) signal demonstrate that the proposed algorithm has s narrower main lobe and lower side lobe with the characteristics of fewer snapshots and low sensitivity of misestimated signals, which can improve the resolution and accuracy of DOA estimation. Specifically, the proposed method exhibits a better performance than other works for the low SNR scenarios. Outdoor experimental results of OFDM signals show that the proposed algorithm is superior to other methods with a narrower main lobe and lower side lobe, which can be used for DOA estimation of UAV and pseudo base station.

This paper introduces an under-sampling E-field measurement system involving three femtosecond pulse lasers, which can be used to measure a multi-frequency electric field (E-field) in a wide frequency range. Optical under-sampling technology utilizes femtosecond laser pulses to sample the electrical signals and convert the high-frequency signal down to the first pulse repetition frequency (RPF) intervals for measurement, i.e., tens to hundreds of MHz. However, previous work rarely studied the measurement of multi-frequency signals. Therefore, in this work, we use three RPFs and propose the Remainder Matching (RM) method of three RPFs to eliminate false-alarm-triggered frequencies. Moreover, to solve the challenging problem of grouping the down-conversion components of each measured frequency, we suggest the Eigenvalue Matching of Remainder’s Difference (EMRD) method to obtain the correct group accurately. Hence, this work combines the RM and EMRD methods to improve the system’s measurement accuracy. To evaluate the suggested measurement technique, detailed theoretical analysis and simulation are performed to demonstrate the universality of our method that can effectively guide the design of E-field measuring systems using optical under-sampling technology. The proposed scheme manages an error rate less than 0.1% without any measurement error of the down-converted components when six frequencies exist simultaneously and is less than 5% when the measured frequency deviation is ±25kHz. Finally, the proposed system is challenged on noise perturbations, with the results demonstrating that the suggested combined RM and RDEM scheme attains an appealing performance.

This study examines media responses to two important food rationing orders introduced by the Roosevelt administration in the spring of 1943 and subsequent drives for nutrition awareness as part of the nation's war effort. The analysis of news reports, advertising campaigns, trade publications and propaganda strategies shows the media s instrumental role in mobilizing civilian support for the wartime government's food initiatives. Focusing on women as their target audience, news, advertising and propaganda worked in tandem to help condition Americans to changes in food consumption and preparation. The study suggests the political significance of women s pages, an overlooked genre of news where the kitchen patriot emerged as a prominent role model for American women.

In the research of passive millimetre wave (PMMW) imaging, the focal plane array (FPA) can realize fast, wide-range imaging and detection. However, it has suffered from a limited aperture and off-axis aberration. Thus, the result of FPA is usually blurred by space-variant point spread function (SVPSF) and is hard to restore. In this paper, a polar-coordinate point spread function (PCPSF) model is presented to describe the circle symmetric characteristic of space-variant blur, and a log-polar-coordinate transformation (LPCT) method is propagated as the pre-processing step before the Lucy–Richardson algorithm to eliminate the space variance of blur. Compared with the traditional image deblur method, LPCT solves the problem by analyzing the physical model instead of the approximating it, which has proved to be a feasible way to deblur the FPA imaging system.

Microwave camera is a passive focal-plane microwave imaging system which is utilized for large-scale, wide-band electromagnetic interference (EMI) imaging. Microwave camera images the EMIs with their amplitude and location by the reflector and sensor array, like taking a photograph. This camera however only receives the power density of the EMIs within a wide band and the frequency information is aliased and abandoned. To address this issue, in this paper, we propose a frequency estimation method to recover the frequency of the wide-band EMI from its amplitude image without a need to add new hardware. For the developed imaging system, the proposed method realizes the frequency estimation in 3-6GHz frequency range with the resolution of 100KHz consistently in most of the central part of the field of vision (FOV). Our proposed method is also applicable to other passive imaging applications such as passive millimetre radar imaging and infrared imaging, to extract the related information of the detected objects.