Abstract This paper investigates the changes in the waveform of a sinusoidal carrier resulting from amplitude modulation (AM) process. Based on this analysis, a novel method for extracting amplitude information is proposed. The proposed method uses the behaviour of the amplitude limitation which does not significantly affect the slope of the sinusoidal signal near zero crossing points. A simple comparator is used to convert the changes in sinusoidal slope near zero crossing points into pulse width changes. A simple circuit is proposed which keeps the output pulse width of the comparator constant by a simple control loop. The accuracy of the method is evaluated through simulation and is experimentally tested. If the modulation index is high and the amplitude of the input signal to the detector is limited, the proposed method can yield up at least 9 dB improvement in relative error power. However, if the modulation index is small, the improvement in relative error power can be at least 35 dB compared to other conventional types of AM demodulators.

In traffic sign recognition tasks, recognition of road signs by observing synthetic reference images is a human-like ability that can be performed by one-shot learning algorithms. One-shot object recognition is a challenging task for deep neural networks in which a deep model classifies query examples based on support images. It becomes more difficult when there is a domain shift between support and query samples. The generalization of a deep model on an unknown domain with different distributions is another problematic task in on-shot recognition. This work introduces a novel deep network named SeqNet to overcome the aforementioned problems. To the best of our knowledge, this work outperforms all state-of-the-art models in one-shot traffic sign recognition and one-shot logo identification by superior results. Our proposed SeqNet model generalizes to unseen domains without further model fine-tuning on the test data. Also, we show how using transferred knowledge from an irrelevant but large domain could reduce the network parameters that result in model size reduction. By utilizing the power of transferred knowledge from a large deep model the SeqNet becomes smaller and has about 6X fewer parameters than its competitors. The smaller size of the SeqNet architecture enables it to be used in resource-constrained devices in many applications such as smart vehicles. The experimental results depict that our proposed SeqNet performance is ameliorated by large margins, with up to 20% accuracy for one-shot classification and 30% area under the curve (AUC) for image retrieval tasks.

Infrared small target detection in an infrared search and track (IRST) system is a challenging task. This situation becomes more complicated when high gray-intensity structural backgrounds appear in the field of view (FoV) of the infrared seeker. While the majority of the infrared small target detection algorithms neglect directional information, in this paper, a directional approach is presented to suppress structural backgrounds and develop a more effective detection algorithm. To this end, a similar concept to the average absolute gray difference (AAGD) is utilized to construct a novel directional small target detection algorithm called absolute directional mean difference (ADMD). Also, an efficient implementation procedure is presented for the proposed algorithm. The proposed algorithm effectively enhances the target area and eliminates background clutter. Simulation results on real infrared images prove the significant effectiveness of the proposed algorithm.

This paper presents a novel array allocation method for radar and communication integration. Firstly, the math model between the array resource and tracking and communication performance is built and analyzed. The primary objectives of radar and communication are then designed respectively to minimize tracking error and bit error rate in the presence of multiple target tracking and channel communication. Furthermore, we transfer both problems to convex problems which are solved by checking the conditions of Karush-Kuhn-Tucker(KKT). Simulation results on array allocation performance of the proposed method and conventional one are conducted finally, which also prove effectiveness of the proposed method.

Blind source separation (BSS), i.e. extracting unknown sources from mixtures of them, has attracted great interest in various fields of signal processing, for instance in neurophysiological data analysis. By increasing the availability of multiple and complementary data, associated with a given case, many joint BSS (JBSS) algorithms have been developed, which attempt to jointly analyze all datasets and meaningfully integrate information from them. In this letter, we study the special case of multiple datasets analysis, where all datasets share the same set of underlying sources which contribute to different datasets through different mixing matrices. With the aim of efficient dimension reduction, we propose a novel joint preprocessing method to accurate and robust source separation from multipldatasets. We sequentially separate the source with the most significant impact on all datasets on average. To this end, the magnitudes of source projections on the subspaces spanned by each dataset are obtained and sum of their pth power is maximized. Dependency between datasets can be efficiently treated using p. Simulation results show the considerable improvement of the proposed method over existing JBSS algorithms.

The authors study the power spectrum density (PSD) estimation of wideband wide‐sense stationary (WSS) signals with sub‐Nyquist sampling rate. Owing to the large bandwidth, Nyquist rate sampling of such signals needs very high rate analogue‐to‐digital converters. It is important to note that PSD estimation does not necessarily require reconstruction of the original signal. Indeed, the power spectrum can be directly obtained from sub‐Nyquist samples. In this study, a new method for reconstructing the power spectrum in the frequency domain for WSS signals is presented. The main idea is to divide the whole spectrum into N equal‐length segments and calculate the average PSD in each segment using a frequency‐domain representation of sub‐Nyquist samples. In addition, the capability of the proposed method, as a detector of spectrum holes, is studied using receiver operating characteristic (ROC) curves. Then, the analysis of false alarm probability is provided. Simulation results for the additive white Gaussian noise channel and the slowly fading frequency selective channel show that the proposed method considerably outperforms available techniques.

In this letter, we derive formulas for optimal discrete-time pulse shaping for communications in multiband channels. Furthermore, we propose a technique for extending a short signal segment for the purpose of power spectral estimation using the available information about the signal stopbands. The later technique can be also used for minimizing the energy leakage into stopbands of a signal, e.g., as in windowing for orthogonal frequency division modulation (OFDM). Simulation results show that when the pulse duration is long enough, the stopband energy can be practically reduced to zero using the proposed technique. The proposed power spectral estimation technique can improve the resolution and reduce the energy leakage from signal passbands into adjacent stopbands. Moreover, it can reduce the out-of-band power leakage significantly when used for windowing in OFDM.