Molecular communication (MC) leverages molecules as information carriers, offering advantages such as biocompatibility and low energy consumption. Currently, MC’s research focuses on signal detection using chemical sensors, nanoparticles or biomolecules. However, challenges remain in accurately demodulating sequences of bits, particularly due to the influence of system parameters such as channel length, background flow rate, and transmitter-side actuation settings, including injection volume and valve control timing. To address these challenges, this paper introduces a MC testbed based on particle speed detection, which transforms molecular signals into particle speed signals for communication. Using hydrogen peroxide (H2O2) as the information carrier, the signal is demodulated by mixing the solution at the receiving end with specially prepared active particles and detecting the particle movement speed. Sequential transmission experiments were conducted to analyze the effects of various parameters on system performance. Experimental results demonstrate that the system accurately transmits information within a tested range, validating the theoretical model and highlighting its potential for microscopic communication applications.

Recognizing defects in source code through deep learning methods has become an important research subject for improving software quality. Although Transformer-based models such as CodeBERT have demonstrated impressive performance improvement in defect prediction tasks, models relying on single-structured input data, such as sequences, have limited ability to capture the code's structural features. Treating code simply as text overlooks essential information such as control dependencies, data dependencies, and syntactic structures inherent in the code. This paper proposes the Heterogeneous Discrete Code Graph (HDCG), which assigns structural information to code tokens from multiple perspectives. We also introduce an improved transformer model FINEGATE that leverages HDCG to guide self-attention. The experiments demonstrate that FINEGATE can effectively predict source code defects and perform fine-grained defect localization.

This article presents a 0.4-V startup, dead-zone-free, monolithic four-mode synchronous Buck-Boost converter. By introducing constant frequency transition-Buck (T-Buck) mode and transition-Boost (T-Boost) mode in the transition region, the dead-zone problem of the dual-mode Buck-Boost converter and the inefficiency of the single-mode converter can be effectively solved. In addition, this article adopts the adaptive peak and valley current control and designs a reused slope compensation circuit, which reduces the converter’s static power consumption and improves the light load efficiency of the converter. To widen the input voltage range of the converter, a low-voltage startup scheme without off-chip auxiliary components and low-$V_{\mathrm {TH}}$MOSFETs is proposed. The startup voltage is reduced to 0.4 V at a lower cost, and it greatly broadens the input voltage range of the monolithic converter. The proposed converter has been implemented in a standard 0.18-$\mu \text{m}$CMOS process occupying a die area of 1.7 × 2.0 mm. The test results show that the converter with an input range of 0.4–3.3 V and an output voltage of 1.8 V. The peak efficiency can reach 90.8% at 100 mA and the light load efficiency can reach 85% at 10 mA. Besides, the efficiency in the transition region of 1.44–2.25 V is higher than 80%.

This paper presents a fully differential wake-up receiver (WuRX) with ultra-low power and high sensitivity. Implementing the fully differential framework achieves differential signal processing without an off-chip balun, which achieves noise suppression and sensitivity improvement. The low voltage baseband signal processing (LVBSP) is proposed to cut down the power consumption, which is achieved by the low-voltage fast-response differential amplifier (LFDA) and the time-domain signal processing circuit. Moreover, the periodic offset cancellation technique (POCT) in the baseband circuit is proposed to alleviate the offset and low-frequency noise impacts. This WuRX is implemented in the TSMC 65-nm CMOS process occupying an active area of 0.18 mm2. When operating at 434 MHz, the proposed WuRX has a −75-dBm sensitivity at 250 bps while consuming 6.4 nW from 0.4 V supply voltage.

Recognizing and repairing defects to enhance quality in software life circle has become a critical research topic. Unfortunately, it is difficult to guarantee the validity of the defect prediction method based on manually designed features proposed in previous studies. Numerous scholars have endeavored to use a single model to obtain prediction results for different types of fault, but this is difficult to perform. This article improves the defect representation and prediction model in software defect prediction, proposing Augmented-Code Property Graph (CPG) based defect prediction method (ACGDP). Augmented-CPG is a novel encoding graph format introduced in this article. Based on Augmented-CPG, we suggested defect region candidate extraction approach linked to the defect category. Graph neural networks are used for obtaining defect characteristics. Experiments on three distinct types of defects indicate that ACGDP can predict certain classed of defects effectively.

To optimize the process of software testing and to improve software quality and reliability, many attempts have been made to develop more effective methods for predicting software defects. Previous work on defect prediction has used machine learning and artificial software metrics. Unfortunately, artificial metrics are unable to represent the features of syntactic, semantic, and context information of defective modules. In this article, therefore, we propose a practical approach for identifying software defect patterns via the combination of semantics and context information using abstract syntax tree representation learning. Graph neural networks are also leveraged to capture the latent defect information of defective subtrees, which are pruned based on a fix-inducing change. To validate the proposed approach for predicting defects, we define mining rules based on the GitHub workflow and collect 6052 defects from 307 projects. The experiments indicate that the proposed approach performs better than the state-of-the-art approach and five traditional machine learning baselines. An ablation study shows that the information about code concepts leads to a significant increase in accuracy.

This paper is concerned with investigating super-resolution algorithms and solutions for handling electron microscopic images. We note two main aspects differentiating the problem discussed here from those considered in the literature. The first difference is that in the electron imaging setting. We have a pair of physical high-resolution and low-resolution images, rather than a physical image with its downsampled counterpart. The high-resolution image covers about 25% of the view field of the low-resolution image, and the objective is to enhance the area of the low-resolution image where there is no high-resolution counterpart. The second difference is that the physics behind electron imaging is different from that of optical (visible light) photos. The implication is that super-resolution models trained by optical photos are not effective when applied to electron images. Focusing on the unique properties, we devise a global and local registration method to match the high- and low-resolution image patches and explore training strategies for applying deep learning super-resolution methods to the paired electron images. We also present a simple, non-local-mean approach as an alternative. This alternative performs as a close runner-up to the deep learning approaches, but it takes less time to train and entertains a simpler model structure.

Combinatorial testing (CT) seeks to detect potential faults caused by various interactions of factors that can influence the software systems. When applying CT, it is a common practice to first generate a set of test cases to cover each possible interaction and then to identify the failure-inducing interaction after a failure is detected. Although this conventional procedure is simple and forthright, we conjecture that it is not the ideal choice in practice. This is because 1) testers desire to identify the root cause of failures before all the needed test cases are generated and executed 2) the early identified failure-inducing interactions can guide the remaining test case generation so that many unnecessary and invalid test cases can be avoided. For these reasons, we propose a novel CT framework that allows both generation and identification process to interact with each other. As a result, both generation and identification stages will be done more effectively and efficiently. We conducted a series of empirical studies on several open-source software, the results of which show that our framework can identify the failure-inducing interactions more quickly than traditional approaches while requiring fewer test cases.