We developed a smartphone‐based on‐site nucleic acid testing (NAT) platform that can image and analyze lateral flow nucleic acid assays at point‐of‐care settings. An inexpensive add‐on was devised to run lateral flow assays while providing homogeneous ambient light for imaging. In addition, an Android app with a user‐friendly interface was developed for the result analysis and management. Linear color calibration is implemented inside the app to minimize the colorimetric reaction difference between smartphones. A relationship function between nucleic acid concentration and colorimetric reaction was established and evaluated by leave‐one‐out cross validation. The predicted concentration and true concentration showed a good agreement with an R‐squared value of 0.96. This smartphone‐based NAT platform can be used to diagnose infectious diseases and monitor disease progression, and assess treatment efficacy, especially for resource‐limited settings.

This paper addresses the waveform design problem of cognitive radar for extended target estimation in the presence of signal-dependent clutter, subject to a peak-to-average power ratio (PAR) constraint. Owing to this kind of constraint and the convolution operation of the waveform in the time domain, the formulated optimization problem for maximizing the mutual information (MI) between the target and the received signal is a complex non-convex problem. To this end, an efficient waveform design method based on minimization–maximization (MM) technique is proposed. First, by using the MM approach, the original non-convex problem is converted to a convex problem concerning the matrix variable. Then a trick is used for replacing the matrix variable with the vector variable by utilizing the properties of the Toeplitz matrix. Based on this, the optimization problem can be solved efficiently combined with the nearest neighbor method. Finally, an acceleration scheme is used to improve the convergence speed of the proposed method. The simulation results illustrate that the proposed method is superior to the existing methods in terms of estimation performance when designing the constrained waveform.

In view of the increasing demand for handwritten digit recognition, a handwritten digit recognition model based on convolutional neural network is proposed. The model includes 1 input layer and 2 convolutional layers (5*5 convolution Core), 2 pooling layers (2*2 pooling core), 1 fully connected layer, 1 output layer, and use the mnist data set for model training and prediction. After a lot of training and participation, the accuracy rate of the training set was finally reached to 100%, and the accuracy rate of 99.25% was also achieved on the test set, which can meet the requirements of recognizing handwritten digits.

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This paper presents a wireless A/D acquisition system based on NRF24L01 and LABVIEW, which can be used to collect A/D data in the harsh environment of industrial production, but the staff can not easily approach or can not persist in the field for a long time. The system mainly by the wireless transmission module NRF24L01 and A/D data acquisition module, which A/D conversion part of the chip using STM32C8T6 own ADC. The computer uses LABVIEW as the host computer for data display and processing. The wireless A/D acquisition system has stable operation, long transmission distance, accurate data transmission, no loss of data, and can be adapted to various industrial control and measurement situations through measurement in different harsh environments of the factory.