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A new photonic-assisted instantaneous frequency measurement system is presented. It overcomes the latency problem in the reported structures based on the frequency-to-time mapping technique or the frequency-to-power mapping technique that involves a long length of fiber, and at the same time, enables the incoming microwave signal frequency to be measured over a wide frequency range with only small errors. The system generates three low-frequency signals. The phases of the three low-frequency signals are compared. One of the two low-frequency signal phase differences is used to estimate the incoming microwave signal frequency unambiguously over a wide frequency range and the other is used to provide accurate microwave signal frequency measurement. A proof-of-concept experiment is set up. Experimental results show, by measuring the phase difference of two low-frequency signals, the frequency of the input microwave signal can be determined unambiguously in 15 GHz and 500 MHz frequency ranges with errors below ±220 MHz and ±10 MHz respectively. Hence, by using two low-frequency signal phase differences, the input microwave signal frequency can be determined accurately over a wide frequency range. The new photonic-assisted frequency measurement system has a fast response time, which is an order of magnitude shorter than that of the systems based on the frequency-to-time mapping technique and the frequency-to-power mapping technique with a kilometer-long fiber.

For higher data throughput, the Massive Multi-input Multi-output (MIMO) system has been widely used in modern communication systems. Due to the circuit size and power consumption, it is difficult to integrate one Radio Frequency (RF) chain for each power amplifier (PA). As a result, MIMO transmitters with hybrid beam-forming don't have enough RF chains, and this demands the digital predistortion (DPD) block to deal with several power amplifiers (PA) simultaneously, which causes a shared DPD problem. This article presents an Analog Predistorter Averaged DPD (A2-DPD) for hybrid beam-forming massive MIMO transmitters. To linearize more than one PAs with one shared digital predistortion unit, continuously tunable Analog Predistortion (APD) modules are employed to uniform the nonlinear behavior of PAs in each channel. Based on iterative learning control (ILC) technique, crossed normalized mean square error (CNMSE) is derived to judge the uniformity of PAs. By comparing the CNMSE of each PAs, the best similarity state of PAs can be found. The nonlinear behavior of PAs can be adjusted to a similar state by tuning the APD control voltage. The adjustment makes PAs easier to be linearized by a shared DPD. Furthermore, an averaged DPD algorithm is proposed to improve the total linearity performance for each PA. Simulations are performed to study the relationship between CNMSE and PA uniformity and further simulations show the ability of A2-DPD to deal with a multi-PA scenario. In the end, to verify the feasibility of the proposed A2-DPD, two class-AB PAs with operating frequency at 3.5 GHz with the same type transistor and similar design are tested. Experimental results show the proposed A2-DPD can significantly improve the uniformity of PAs and the Adjacent Channel Power Ratio (ACPR) can be improved by 8 dB compared with using the DPD parameter of the other PA.

In recent years, deep learning has greatly improved the ability of wheatear detection. However, there are still three main problems in wheatear detection based on unmanned aerial vehicle (UAV) platforms. First, dense wheat plants often overlap, and the wind direction will blur the pictures, which obviously interferes with the detection of wheatears; second, due to the different maturity, color, genotype, and head orientation, the appearance will also be different; third, UAV needs to take images in the field and conduct real-time detection, which requires the embedded module to detect wheatears quickly and accurately. Given the above problems, we studied and improved YoloV4, and proposed a robust method for wheatear detection using UAV in natural scenes. For the first problem, we modified the network structure, deleted the feature map with a size of 19×19, and used k-means algorithm to re-cluster the anchors, and we proposed a method of prediction box fusion. For the second problem, we used the pseudo-labeling method and data augmentation methods to improve the generalization ability of the model. For the third problem, we simplified the network structure, replaced the original network convolution with the improved depthwise separable convolution, and proposed an adaptive ReLU activation function to reduce the amount of calculation and speed up the calculation. The experimental results showed that our method can effectively mark the bounding of wheatears. In test sets, our method achieves 96.71% in f1-score, which is 9.61% higher than the state of the art method, and the detection speed is 23% faster than the original method. It can be concluded that our method can effectively solve the problems of wheatear detection based on the UAV platform in natural scenes.

Automatic license plate recognition has a wide range of applications in intelligent transportation systems and is of great significance. However, most of the current work on license plate recognition focuses on the images on the front of license plates. license plate recognition in natural scenes and arbitrary perspective is still a huge challenge. To solve this problem, this work mainly studies the detection and recognition of inclined Chinese license plates in natural scenes. We propose a robust method that can detect and correct multiple license plates with severe distortion or skewing in one image and input them into the license plate recognition module to obtain the final result. Different from the existing methods of license plate detection and recognition, our method performs affine transformation during license plate detection to rectify the distorted license plate image. It can not only avoid the accumulation of intermediate errors but also improve the accuracy of recognition. As an additional contribution, we put forward a challenging Chinese license plate recognition data set, including images obtained from different scenes under a variety of weather conditions. Through a large number of comparative experiments, we have proved the effectiveness of our proposed method.

In this paper, an independently tunable linearizer (ITL) is proposed to offer a continuous tuning and individual compensation of AM/AM and AM/PM distortion of power amplifiers (PAs). Two new operating modes of linearizer have been extended from conventional linearizer which has a correlated expansion characteristic of gain and phase, a derivative factor of the linearizer's forward S-parameter is presented to characterize the modes and aided the ITL design. By synthesizing these modes, an ITL prototype is designed and fabricated at a center frequency of 3.5 GHz. The restrictions of the derivative factors during the design are analyzed and presented first, then the ITL is tested with a continuous wave (CW) and several Long Term Evolution (LTE) signals with different bandwidth and Peak to Average Power Ratio (PAPR), respectively. The results show that the independent tuning range of AM/AM and AM/PM is measured by more than 3.4 dB and 33 degrees, and the ITL can significantly improve the linearity of PAs with wideband stimulated up to a bandwidth of 55 MHz. Two different PAs achieve over -40 dBc of Adjacent Channel Power Ratio (ACPR) with the ITL. Furthermore, the power consumption of ITL is less than 0.5 W, the performance improvement does not increase the cost compared to conventional analog linearization.

Nowadays, Internet of Things (IoT) devices are rapidly proliferating to support a vast number of end-to-end (E2E) services and applications, which require reliable device authentication for E2E data security. However, most low-cost IoT end devices with limited computing resources have difficulties in executing the increasingly complicated cryptographic security protocols, resulting in increased vulnerability of the virtual authentication credentials to malicious cryptanalysis. An attacker possessing compromised credentials could be deemed legitimate by the conventional cryptography-based authentication. Although inherently robust to upper-layer unauthorized cryptanalysis, the device-to-device physical-layer (PHY) authentication is practically difficult to be applied to the E2E IoT scenario and to be integrated with the existing, well-established cryptography primitives without any conflict. This paper proposes an enhanced E2E IoT device authentication that achieves seamless integration of PHY security into traditional asymmetric cryptography-based authentication schemes. Exploiting the collaboration of several intermediate nodes (e.g., edge gateway, access point, and full-function device), multiple radio-frequency features of an IoT device can be estimated, quantized, and used in the proposed PHY identity-based cryptography for key protection. A closed-form expression of the generated PHY entropy is derived for measuring the security enhancement. The evaluation results of our cross-layer authentication demonstrate an elevated resistance to various computation-based impersonation attacks. Furthermore, the proposed method does not impose any extra implementation overhead on resource-constrained IoT devices.