Electroencephalography (EEG) is a common and significant tool for aiding in the diagnosis of epilepsy and studying the human brain electrical activity. Previously, the traditional machine learning (ML)-based classifier are used to identify the seizure by extracting features from the EEG signals manually. Although the effectiveness of these contributions have already been proved, they cannot achieve multiple class classification with automatic feature extraction. Meanwhile, the identifiable EEG segment is too long to limit the capability of real-time epileptic seizure detection. In this paper, a novel deep convolutional long short-term memory (C-LSTM) model is proposed for detecting seizure and tumor in human brain and identifying two eyes statuses (open and close). It achieves to predict a result in every 0.006 seconds with a short detection duration (one second). By comparing with other two types deep learning approaches (DCNN and LSTM), the presented deep C-LSTM obtains the best performance for classifying these five classes. All of the obtained total accuracy are over 98.80%.

Low-frequency noise (LFN) is investigated in a set of indium-zinc-oxide thin-film transistors (IZO TFTs) with fixed channel width (W=10 μm) and different channel lengths (L=10, 20, 30 and 40 μm) from sub-threshold, linear to saturation regions. The drain current noise power spectral density is measured as a function of effective gate voltage and drain current. The variation slopes of normalized noise with effective gate voltage are in the range of -1.27 and -1.48, which are close to the prediction of the mobility fluctuation mechanism. According to the N-μ model, the flat-band voltage noise spectral density and coulomb scattering coefficient are extracted. Subsequently, variations of noise with the drain current in the above threshold region are analyzed by considering the band-gap distribution of the tail states. Finally, the BSIM model is also used to model 1/f noise in the IZO TFTs. The noise parameter NOIB is extracted which is inversely proportional to the effective gate voltage. Good agreements are achieved between the simulated and measured results in the linear region.

To overcome the environment susceptibility of flexible amorphous InSnZnO (a-ITZO) thin-film transistors (TFTs), a surface passivation method utilizing ${n}$ -octyltriethoxysilane (OTES) self-assembled monolayers (SAMs) is developed. The electrical characteristics of the developed transistors indicate that device performance can be enhanced upon OTES passivation, exhibiting high mobility (~19.4 cm ² V ⁻¹ s ⁻¹ ), a steep subthreshold slope (~90 mV/dec), near-zero threshold voltage (~0.6 V), and high ON– OFF current ratio ( $\sim 7.9\times 10^{{{9}}}$ ). The passivation layer can effectively improve the stability of flexible ITZO TFTs even under positive bias stress (PBS) and negative bias stress (NBS), and only 0.8/1.3 V threshold voltage shifts are shown after PBS/NBS. In addition, the OTES-passivated transistors exhibit good mechanical reliability and maintain its electrical characteristics during the 10-k bending period, without drastic decline. These results demonstrate that the SAM passivation method is suitable for the fabrication of flexible high-performance metal–oxide electronic devices.

In this paper, a secure plaintext‐related image encryption scheme based on compressive sensing and a tent‐sine system is proposed. First, the discrete wavelet transform (DWT) is used to transform the plain image to get a coefficient matrix. Second, several chaotic sequences generated by the tent‐sine chaotic map are used to scramble the coefficient matrix and construct a measurement matrix. Afterward, compressive sensing is performed on the coefficient matrix to obtain a small‐sized encrypted image. Finally, an image encryption scheme related to plaintext is designed. In particular, the proposed system uses the original image information to participate in the encryption process, ensuring the high sensitivity of the cryptosystem to minor differences in the plain image and good performance on resisting known/selected plaintext attacks. Furthermore, to convey the plaintext‐related parameters to the receiver, the dimension of the ciphertext image is expanded, and the parameters are embedded into the ciphertext image. Simulation results and security analysis show that the proposed image encryption system has strong plaintext sensitivity and robustness for effectively resisting various typical attacks such as brute‐force attacks, statistical attacks, and differential attacks.

Traditional visual simultaneous localization and mapping (SLAM) systems mostly based on small-area static environments. In recent years, some studies focused on combining semantic information with visual SLAM. However, most of them are hard to obtain better performance in the large-scale dynamic environment. And the accuracy, rapidity of the system still needs to strengthen. In this paper, we develop a more efficient semantic SLAM system in the two-wheeled mobile robot by using semantic segmentation to recognize people, chairs, and other objects in every keyframe. With a preliminary understanding of the environment, fusing the RGB-D camera and encoders information, to localization and creating a dense colored octree map without dynamic objects. Besides, for the incomplete identification of movable objects, we used image processing algorithms to enhance the semantic segmentation effect. In the proposed method, enhanced semantic segmentation in keyframes dramatically increases the efficiency of the system. Moreover, fusing the different sensors can highly raise localization accuracy. We conducted experiments on various datasets and in some real environments and compared them with DRE-SLAM, DS-SLAM, to evaluate the performance of the proposed approach. The results suggest we significantly improve the processing efficiency, robustness, and quality of the map.