The motivation of this work is to help outpatients find their corresponding departments or clinics, thus, it needs to provide indoor positioning services with a room-level accuracy. Unlike wireless outdoor localization that is dominated by the global positioning system (GPS), wireless indoor localization is still an open issue. Many different schemes are being developed to meet the increasing demand for indoor localization services. In this paper, we investigated the AoA-based wireless indoor localization for outpatients’ wayfinding in a hospital, where Wi-Fi access points (APs) are deployed, in line, on the ceiling. The target position can be determined by a mobile device, like a smartphone, through an efficient geometric calculation with two known APs coordinates and the angles of the incident radios. All possible positions in which the target may appear have been comprehensively investigated, and the corresponding solutions were proven to be the same. Experimental results show that localization error was less than 2.5 m, about 80% of the time, which can satisfy the outpatients’ requirements for wayfinding.

Radar has played an irreplaceable role in modern warfare. A variety of radar-jamming methods have been applied in recent years, which makes the electromagnetic environment more complex. The classification of radar jamming is critical for electronic counter-countermeasures (ECCM). In the field of signal classification, machine learning-based methods take great effort to find proper features as well as classifiers, and deep learning-based methods depend on large training datasets. For the above reasons, an efficient transfer learning-based method is proposed in this paper. Firstly, one-dimensional radar signals were transformed into time–frequency images (TFIs) using linear and bilinear time–frequency analysis, which is inspired by symmetry theory. Secondly, pretrained AlexNet and SqueezeNet networks were modified to classify the processed TFIs. Finally, performance of this method was evaluated and compared using a simulated data set with nine types of radar-jamming signals. The results demonstrate that our proposed classification method performs well in accuracy and efficiency at a 1% training ratio, which is practical for anti-jamming.

Compared with conventional Class-A, Class-B, and Class-AB amplifiers, Class-D amplifier, also known as switching amplifier, employs pulse width modulation (PWM) technology and solid-state switching devices, capable of achieving much higher efficiency. However, PWM-based switching amplifier is usually designed for low-voltage application, offering a maximum output voltage of several hundred Volts. Therefore, a step-up transformer is indispensably adopted in PWM-based Class-D amplifier to produce high-voltage output. In this paper, a switching amplifier without step-up transformer is developed based on digital pulse step modulation (PSM) and hybrid multilevel converter. Under the control of input signal, cascaded power converters with separate DC sources operate in PSM switch mode to directly generate high-voltage and high-power output. The relevant topological structure, operating principle, and design scheme are introduced. Finally, a prototype system is built, which can provide power up to 1400 Watts and peak voltage up to ±1700 Volts. And the performance, including efficiency, linearity, and distortion, is evaluated by experimental tests.