Spam has been a serious and annoying problem for decades. Even though plenty of solutions have been put forward, there still remains a lot to be promoted in filtering spam emails more efficiently. Nowadays a major problem in spam filtering as well as text classification in natural language processing is the huge size of vector space due to the numerous feature terms, which is usually the cause of extensive calculation and slow classification. Extracting semantic meanings from the content of texts and using these as feature terms to build up the vector space, instead of using words as feature terms in tradition ways, could reduce the dimension of vectors effectively and promote the classification at the same time. In this paper, a novel Chinese spam filtering approach with semantics-based text classification technology was proposed and the related feature terms were selected from the semantic meanings of the text content. Both the extraction of semantic meanings and the selection of feature terms are implemented through attaching annotations on the texts layer-by-layer. This filter performed well when experimented on a public Chinese spam corpus.

The nonlinearity characteristics of memristor have been widely employed in artificial intelligence. However, existing studies do not provide much attention to the analytic analysis of nonlinearity. This paper presents an analytic approach to the nonlinearity of memristor by analyzing the output spectrum based on homotopy analysis method (HAM). The analytic spectrum expressions obtained by this approach are verified by the fingerprints of memristor. It can be simultaneously revealed that one of the potential sources of the nonlinearity is the large variation of state variable. Furthermore, we propose a mapping method based on the obtained total harmonic distortion (THD) and a new parameter—threshold frequency $\omega _{C}$ to investigate the relationship between the memristor transition (i.e., the transition of the memristor between a nonlinear resistor and a linear resistor) and the frequency of input signal. Especially, analytic output spectrum, THD, and threshold frequency $\omega _{C}$ can be solved without complex mathematical operations and, thus, compared with traditional works, the complexity of the proposed approach is reduced.

This paper presents a BJT-based smart CMOS temperature sensor. The analog front-end circuit contains a bias circuit and a bipolar core; the data conversion interface features an incremental delta-sigma analog-to-digital converter. The circuit utilizes the chopping, correlated double sampling, and dynamic element matching techniques to mitigate the effects of process bias and nonideal device characteristics on measurement accuracy. Furthermore, based on the principle of charge conservation, the dynamic range utilization of the ADC increases. We propose a neural network that uses a multilayer convolutional perceptron to calibrate the sensor output results. Using the algorithm, the sensor achieves an inaccuracy of ±0.11 °C (3σ), exceeding the accuracy of ±0.23 °C (3σ) achieved without calibration. We implement the sensor in a 0.18 µm CMOS process, occupying an area of 0.42 mm². It achieves a resolution of 0.01 °C and has a conversion time of 24 ms.

Memristive nonlinear system has drawn much attention in recent years, due to its rich and complex dynamical characteristics. However, there are few studies focus on the analytical analysis of this significant system. In this paper, a novel analytical method for analyzing the chaotic trajectories of memristive circuit is proposed. This method combines Homotopy Analysis Method (HAM) and Multi-objective Optimization (MO), i.e., the convergence control parameter of traditional HAM is divided into lots of subintervals in the time domain and respectively optimized by MO, for accurately solving the Ordinary Differential Equations describing memristive circuits. Hence, this method is named by MO-based multi-interval HAM (MO-MIHAM). By using MO-MIHAM, we accurately tracked the chaotic trajectories of the classical Memristor-Capacitor-Inductor (MCL) circuit and current memristive Band Pass Filter (BPF) chaotic circuit. Furthermore, based on the comparisons of errors between analytical approximate solutions derived from MO-MIHAM and solutions solved by traditional homotopy-based analytical methods and by Runge-Kutta-Fehlberg Method (RKF45) based numerical analysis, we found that, MO-MIHAM is characterized by higher approximation accuracy and computational performance (comprehensively considering the accuracy, computational complexity and execution time by a proposed Quality Factor) among the homotopy-based analytical methods, due to the optimized convergence control parameters in subintervals. Besides this major advantage, MO-MIHAM enables both qualitative and quantitative analyses and high freedom to choose homotopy-related terms for simplicity, and it is insensitive to convergence issues. Therefore, it is a powerful tool for exploratory studies for analytically analyzing chaotic dynamics in memristive circuit.

Conventional sense amplifiers limit the performance of current RRAM computing-in-memory (CIM) macro circuits, resulting in high latency and energy consumption. This paper introduces a multi-bit quantization technology low-latency voltage sense amplifier (MQL-VSA). Firstly, the multi-bit quantization technology enhances circuit quantization efficiency, reducing the number of operational states in conventional VSA. Secondly, by simplifying the sequential logic circuits in conventional VSA, the complexity of sequential control signals is reduced, further diminishing readout latency. Experimental results demonstrate that the MQL-VSA achieves a 1.40-times decrease in readout latency and a 1.28-times reduction in power consumption compared to conventional VSA. Additionally, an 8-bit input, 8-bit weight, 14-bit output macro circuit utilizing MQL-VSA exhibited a 1.11times latency reduction and 1.04-times energy savings.