IV Characteristics of a Stabilized Resonant Tunnelling Diodes

The presence of parasitic oscillations found in the negative differential region (NDR), which can distort the current-voltage (I-V) characteristics of the device is one of the main problems when designing resonant tunnelling diode (RTD) circuits. A new method for RTD stabilization is proposed based on work done previously on tunnel diodes and results show that there is a significant difference between the I-V characteristics of a tunnel diode and that of an RTD. This work shows promising potential for further increasing the RTD’s output power, DC-RF conversion efficiency and provides the basis for an accurate model of the NDR regio

Loading Effect of W-band Resonant Tunneling Diode Oscillator by Using Load-Pull Measurement

Resonant tunneling diode (RTD) is the fastest solid-state electronic device with the highest reported frequency at 1.92 THz [1]. RTD-based THz sources have many promising applications such as ultrafast wireless communications, THz imaging, etc. To date, the main limitation of RTD technology is the low output power. Many efforts had been made to increase the power level by such as optimizing the layer structure [2], employing more devices in an array [3], matching impedance by displacing the device in circuit [3], etc. Here we report the loading effect by using E/H impedance tuner. We found that the maximum power is over 20dB higher than the worst impedance matching and the frequency shift is within 14% range of the central frequency. The load-pull measurement provides a convenient way to investigate the power/frequency variation versus the impedance change. Further work will benefit from the measurement results to design corresponding impedance matching network. The power level of RTD oscillator will be increased

Beyond vanilla: improved autoencoder-based ensemble in-vehicle intrusion detection system.

Modern automobiles are equipped with a large number of electronic control units (ECUs) to provide safe driver assistance and comfortable services. The controller area network (CAN) provides near real-time data transmission between ECUs with adequate reliability for in-vehicle communication. However, the lack of security measures such as authentication and encryption makes the CAN bus vulnerable to cyberattacks, which affect the safety of passengers and the surrounding environment. Detecting attacks on the CAN bus, particularly masquerade attacks, presents significant challenges. It necessitates an intrusion detection system (IDS) that effectively utilizes both CAN ID and payload data to ensure thorough detection and protection against a wide range of attacks, all while operating within the constraints of limited computing resources. This paper introduces an ensemble IDS that combines a gated recurrent unit (GRU) network and a novel autoencoder (AE) model to identify cyberattacks on the CAN bus. AEs are expected to produce higher reconstruction errors for anomalous inputs, making them suitable for anomaly detection. However, vanilla AE models often suffer from overgeneralization, reconstructing anomalies without significant errors, resulting in many false negatives. To address this issue, this paper proposes a novel AE called Latent AE, which incorporates a shallow AE into the latent space. The Latent AE model utilizes Cramér's statistic-based feature selection technique and a transformed CAN payload data structure to enhance its efficiency. The proposed ensemble IDS enhances attack detection capabilities by leveraging the best capabilities of independent GRU and Latent AE models, while mitigating the weaknesses associated with each individual model. The evaluation of the IDS on two public datasets, encompassing 13 different attacks, including sophisticated masquerade attacks, demonstrates its superiority over baseline models with near real-time detection latency of 25ms

Axial vibration mode of coupled liquid-structure-gas system in a rigid cylindrical container

This paper describes the axial vibration analysis of a closed ends rigid cylindrical container containing liquid and gas which separated by a thin circular plate at their interface. The liquid depths inside the container were varied and then the mode of vibration and the natural frequencies were analyzed. The natural frequencies obtained experimentally were compared favorably with those of commercial finite element analysis software, ANSYS. The vibration mode of the liquid-structure interaction of the tank system can be visualized from the software post processing animation/plot. The visualized modes are also consistent with the measurement by the respective experimental transducers. It was found that strong coupling predominantly occur between liquid and structure. In weak coupling conditions, the modes are predominantly gas mod

Defendroid: real-time Android code vulnerability detection via blockchain federated neural network with XAI.

Ensuring strict adherence to security during the phases of Android app development is essential, primarily due to the prevalent issue of apps being released without adequate security measures in place. While a few automated tools are employed to reduce potential vulnerabilities during development, their effectiveness in detecting vulnerabilities may fall short. To address this, "Defendroid", a blockchain-based federated neural network enhanced with Explainable Artificial Intelligence (XAI) is introduced in this work. Trained on the LVDAndro dataset, the vanilla neural network model achieves a 96% accuracy and 0.96 F1-Score in binary classification for vulnerability detection. Additionally, in multi-class classification, the model accurately identifies Common Weakness Enumeration (CWE) categories with a 93% accuracy and 0.91 F1-Score. In a move to foster collaboration and model improvement, the model has been deployed within a blockchain-based federated environment. This environment enables community-driven collaborative training and enhancements in partnership with other clients. The extended model demonstrates improved accuracy of 96% and F1-Score of 0.96 in both binary and multi-class classifications. The use of XAI plays a pivotal role in presenting vulnerability detection results to developers, offering prediction probabilities for each word within the code. This model has been integrated into an Application Programming Interface (API) as the backend and further incorporated into Android Studio as a plugin, facilitating real-time vulnerability detection. Notably, Defendroid exhibits high efficiency, delivering prediction probabilities for a single code line in an average processing time of a mere 300 ms. The weight-sharing transparency in the blockchain-driven federated model enhances trust and traceability, fostering community engagement while preserving source code privacy and contributing to accuracy improvement

Aflatoxin, fumonisin, ochratoxin, zearalenone and deoxynivalenol biomarkers in human biological fluids:A systematic literature review, 2001-2018

Human exposure to mycotoxins occurs mostly through dietary intake, although exposure through dermal and inhalation routes has also been shown. Depending on the type of mycotoxins, the applied dose and duration of exposure, a particular toxin can cause either chronic or acute illnesses such as kidney failure and cancer. Thus, understanding the biotransformation of mycotoxins and identification of reliable biomarkers in the human body is important for accurate risk assessment of mycotoxin exposure. This review provides a comprehensive overview of worldwide aflatoxins, fumonisins, ochratoxin, zearalenone and deoxynivalenol mycotoxin biomonitoring studies reported in the last 18 years. The studies performed in Africa, Europe, Asia and America are based on the measurement of a limited number of mycotoxin biomarkers and do not provide a comprehensive risk assessment of the mycotoxin exposure. Although the findings represent a small segment of a much larger health risk of mycotoxins exposure, it is acknowledged that a multianalyte approach covering bioconjugated and other metabolites of most often occurring mycotoxins would better reflect the extent of the global exposure problems with these highly toxic compounds.</p

Labelled Vulnerability Dataset on Android source code (LVDAndro) to develop AI-based code vulnerability detection models.

Ensuring the security of Android applications is a vital and intricate aspect requiring careful consideration during development. Unfortunately, many apps are published without sufficient security measures, possibly due to a lack of early vulnerability identification. One possible solution is to employ machine learning models trained on a labelled dataset, but currently, available datasets are suboptimal. This study creates a sequence of datasets of Android source code vulnerabilities, named LVDAndro, labelled based on Common Weakness Enumeration (CWE). Three datasets were generated through app scanning by altering the number of apps and their sources. The LVDAndro, includes over 2,000,000 unique code samples, obtained by scanning over 15,000 apps. The AutoML technique was then applied to each dataset, as a proof of concept to evaluate the applicability of LVDAndro, in detecting vulnerable source code using machine learning. The AutoML model, trained on the dataset, achieved accuracy of 94% and F1-Score of 0.94 in binary classification, and accuracy of 94% and F1-Score of 0.93 in CWE-based multi-class classification. The LVDAndro dataset is publicly available, and continues to expand as more apps are scanned and added to the dataset regularly. The LVDAndro GitHub Repository also includes the source code for dataset generation, and model training