This paper introduces a computer-aided low-power design method for tapered buffers that address given load capacitances, output transition times, and source impedances. Cross-voltage-domain tapered buffers involving a low-voltage domain in the frontier stages and a high-voltage domain in the posterior stages are further discussed which breaks the trade-off between the energy dissipation and the driving capability in conventional designs. As an essential circuit block, a dedicated analytical model for the level-shifter is proposed. The energy-optimized tapered buffer design is verified for different source and load conditions in a 180-nm CMOS process. The single-VDD buffer model achieves an average inaccuracy of 8.65% on the transition loss compared with Spice simulation results. Cross-voltage tapered buffers can be optimized to further remarkably reduce the energy consumption. The study finds wide applications in energy-efficient switching-mode analog applications.

In this study, we use a vision-based driving monitoring sensor to track drivers’ visual scanning behavior, a key factor for preventing traffic accidents. Our system evaluates driver’s behaviors by referencing the safety knowledge of professional driving instructors, and provides real-time voice-guided safety advice to encourage safer driving. Our system’s evaluation of safe driving behaviors matched the instructor’s evaluation with accuracy over 80%.

Model-based sensorless control of permanent magnet synchronous motor (PMSM) is promising for high-speed operation to estimate motor state, which is the speed and the position of the rotor, via electric signals of the stator, beside the inevitable fact that estimation accuracy is degraded by electromagnet interference (EMI) from switching devices of the converter. In this paper, the simulation system based on Luenberger observer and phase-locked loop (PLL) has been established, analyzing impacts of EMI on motor state estimations theoretically, exploring influences of EMI with different cutoff frequency, rated speeds, frequencies and amplitudes. The results show that Luenberger observer and PLL have strong immunity, which enable PMSM can still operate stably even under certain degrees of interference. EMI produces sideband harmonics that enlarge pulsation errors of speed and position estimations. Additionally, estimation errors are positively correlated with cutoff frequency of low-pass filter and the amplitude of EMI, and negatively correlated with rated speed of the motor and the frequency of EMI.  When the frequency is too high, its effects on motor state estimations are negligible. This work contributes to the comprehensive understanding of how EMI affects motor state estimations, which further enhances practical application of sensorless PMSM.

When a mathematical model is not available for a dynamical system, it is reasonable to use a data-driven approach for analysis and control of the system. With this motivation, the authors have recently developed a data-driven solution to Lyapunov equations, which uses not the model but the data of several state trajectories of the system. However, the number of state trajectories to uniquely determine the solution is O(n2) for the dimension n of the system. This prevents us from applying the method to a case with a large n. Thus, this paper proposes a novel class of data-driven Lyapunov equations, which requires a smaller amount of data. Although the previous method constructs one scalar equation from one state trajectory, the proposed method constructs three scalar equations from any combination of two state trajectories. Based on this idea, we derive data-driven Lyapunov equations such that the number of state trajectories to uniquely determine the solution is O(n).

Driver fatigue detection has become crucial in vehicle safety technology. Achieving high accuracy and real-time performance in detecting driver fatigue is paramount. In this paper, we propose a novel driver fatigue detection algorithm based on dynamic tracking of Facial Eyes and Yawning using YOLOv7, named FEY-YOLOv7. The Coordinate Attention module is inserted into YOLOv7 to enhance its dynamic tracking accuracy by focusing on coordinate information. Additionally, a small target detection head is incorporated into the network architecture to promote the feature extraction ability of small facial targets such as eyes and mouth. In terms of compution, the YOLOv7 network architecture is significantly simplified to achieve high detection speed. Using the proposed PERYAWN algorithm, driver status is labeled and detected by four classes: open_eye, closed_eye, open_mouth, and closed_mouth. Furthermore, the Guided Image Filtering algorithm is employed to enhance image details. The proposed FEY-YOLOv7 is trained and validated on RGB-infrared datasets. The results show that FEY-YOLOv7 has achieved mAP of 0.983 and FPS of 101. This indicates that FEY-YOLOv7 is superior to state-of-the-art methods in accuracy and speed, providing an effective and practical solution for image-based driver fatigue detection.

This paper proposes the new series highly integrated intelligent power module (IPM), which is developed to provide a ultra-compact, high performance and reliable motor drive system. Details of the key design technologies of the IPM is given and practical application issues such as electrical characteristics, system operation performance and power dissipation are discussed. Layout placement and routing have been optimized in order to reduce and balance the parasitic impedances. By implementing an innovative direct bonding copper (DBC) ceramic substrate, which can effectively dissipate heat, the IPM delivers a fully integrated power stages including two three-phase inverters, power factor correction (PFC) and rectifier in an ultra-compact 75.5mm × 30mm package, offering up to a 17.3 percent smaller space than traditional motor drive scheme.

Fully homomorphic encryption (FHE) enables secret computations. Users can perform computation using data encrypted with FHE without decryption. Uploading private data without encryption to a public cloud has the risk of data leakage, which makes many users hesitant to utilize a public cloud. Uploading data encrypted with FHE avoids this risk, while still providing the computing power of the public cloud. In many cases, data are stored in HDDs because the data size increases significantly when FHE is used. One important data analysis is Apriori data mining. In this application, two files are accessed alternately, and this causes long-distance seeking on its HDD and low performance. In this paper, we propose a new striping layout with reservations for write areas. This method intentionally fragments files and arranges blocks to reduce the distance between blocks in a file and another file. It reserves the area for intermediate files of FHE Apriori. The performance of the proposed method was evaluated based on the I/O processing of a large FHE Apriori, and the results showed that the proposed method could improve performance by up to approximately 28%.

This paper addresses an observer-design method only using data. Usually, the observer requires a mathematical model of a system for state prediction and observer gain calculation. As an alternative to the model-based prediction, the proposed predictor calculates the states using a linear combination of the given data. To design the observer gain, the data which represent dual systems are derived from the data which represent the original system. Linear matrix inequalities that depend on data of the dual system provides the observer gains.

In recent years, driver's visual attention has been actively studied for driving automation technology. However, the number of models is few to perceive an insight understanding of driver's attention in various moments. All attention models process multi-level image representations by a two-stream/multi-stream network, increasing the computational cost due to an increment of model parameters. However, multi-level image representation such as optical flow plays a vital role in tasks involving videos. Therefore, to reduce the computational cost of a two-stream network and use multi-level image representation, this work proposes a single stream driver's visual attention model for a critical situation. The experiment was conducted using a publicly available critical driving dataset named BDD-A. Qualitative results confirm the effectiveness of the proposed model. Moreover, quantitative results highlight that the proposed model outperforms state-of-the-art visual attention models according to CC and SIM. Extensive ablation studies verify the presence of optical flow in the model, the position of optical flow in the spatial network, the convolution layers to process optical flow, and the computational cost compared to a two-stream model.

RESTful web APIs have become ubiquitous with most modern web applications embracing the micro-service architecture. A RESTful API provides data over the network using HTTP probably interacting with databases and other services and must preserve its security properties. However, REST is not a protocol but rather a set of guidelines on how to design resources accessed over HTTP endpoints. There are guidelines on how related resources should be structured with hierarchical URIs as well as how the different HTTP verbs should be used to represent well-defined actions on those resources. Whereas security has always been critical in the design of RESTful APIs, there are few or no clear model driven engineering techniques utilizing a secure-by-design approach that interweaves both the functional and security requirements. We therefore propose an approach to specifying APIs functional and security requirements with the practical Structured-Object-oriented Formal Language (SOFL). Our proposed approach provides a generic methodology for designing security aware APIs by utilizing concepts of domain models, domain primitives, Ecore metamodel and SOFL. We also describe a case study to evaluate the effectiveness of our approach and discuss important issues in relation to the practical applicability of our method.

This research develops a new automatic path following control method for a car model based on just-in-time modeling. The purpose is that a lot of basic driving data for various situations are accumulated into a database, and we realize automatic path following for unknown roads by using only data in the database. Especially, just-in-time modeling is repeatedly utilized in order to follow the desired points on the given road. From the results of a numerical simulation, it turns out that the proposed new method can make the car follow the desired points on the given road with small error, and it shows high computational efficiency.

This study develops a new automatic hovering control method based on just-in-time modeling for a multicopter. Especially, the main aim is to compute gains of a feedback control law such that the multicopter hovers at a desired height and at a desired time without overshoot/undershoot. First, a database that contains various hovering data is constructed, and then the proposed method computes gains for a query input from the database. From simulation results, it turns out that the multicopter achieves control purposes, and hence the new method is effective.

Soil moisture sensor calibration based on the Multivariate Adaptive Regression Splines (MARSplines) model is studied in this paper. Different from the generic polynomial fitting methods, the MARSplines model is a non-parametric model, and it is able to model the complex relationship between the actual and measured soil moisture. Rao-1 algorithm is employed to tune the hyper-parameters of the calibration model and thus the performance of the proposed method is further improved. Data collected from four commercial soil moisture sensors is utilized to verify the effectiveness of the proposed method. To assess the calibration performance, the proposed model is compared with the model without using the temperature information. The numeric studies prove that it is promising to apply the proposed model for real applications.

This paper proposes a low-power 100Gb/s four-level pulse amplitude modulation driver (PAM-4 Driver) based on linear distortion compensation structure for thin-film Lithium Niobate (LiNbO3) modulators, which manages to achieve high linearity in the output. The inductive peaking technology and open drain structure enable the overall circuit to achieve a 31-GHz bandwidth. With an area of 0.292 mm2, the proposed PAM-4 driver chip is designed in a 65-nm process to achieve power consumption of 37.7 mW. Post-layout simulation results show that the power efficiency is 0.37 mW/Gb/s, RLM is more than 96%, and the FOM value is 8.84.

SiC-MOSFETs are being increasingly implemented in power electronics systems as low-loss, fast switching devices. Despite the advantages of an SiC-MOSFET, its large dv/dt or di/dt has fear of electromagnetic interference (EMI) noise. This paper proposes and demonstrates a simple and robust gate driver that can suppress ringing oscillation and surge voltage induced by the turn-off of the SiC-MOSFET body diode. The proposed gate driver utilizes the channel leakage current methodology (CLC) to enhance the damping effect by elevating the gate-source voltage (VGS) and inducing the channel leakage current in the device. The gate driver can self-adjust the timing of initiating CLC operation, which avoids an increase in switching loss. Additionally, the output voltage of the VGS elevation circuit does not need to be actively controlled in accordance with the operating conditions. Thus, the circuit topology is simple, and ringing oscillation can be easily attenuated with fixed circuit parameters regardless of operating conditions, minimizing the increase in switching loss. The effectiveness and versatility of proposed gate driver were experimentally validated for a wide range of operating conditions by double and single pulse switching tests.

The adiabatic quantum flux parametron (AQFP) is an energy-efficient, high-speed superconducting logic device. To observe the tiny output currents from the AQFP in experiments, high-speed voltage drivers are indispensable. In the present study, we develop a compact voltage driver for AQFP logic based on a Josephson latching driver (JLD), which has been used as a high-speed driver for rapid single-flux-quantum (RSFQ) logic. In the JLD-based voltage driver, the signal currents of AQFP gates are converted into gap-voltage-level signals via an AQFP/RSFQ interface and a four-junction logic gate. Furthermore, this voltage driver includes only 15 Josephson junctions, which is much fewer than in the case for the previously designed driver based on dc superconducting quantum interference devices (60 junctions). In measurement, we successfully operate the JLD-based voltage driver up to 4 GHz. We also evaluate the bit error rate (BER) of the driver and find that the BER is 7.92×10-10 and 2.67×10-3 at 1GHz and 4GHz, respectively.

In response to fast charging systems, Silicon Carbide (SiC) power semiconductor devices are of great interest of the automotive power electronics applications as the next generation of fast charging systems require high voltage batteries. For high voltage battery EVs (Electric Vehicles) over 800V, SiC power semiconductor devices are suitable for 3-phase inverters, battery chargers, and isolated DC-DC converters due to their high voltage rating and high efficiency performance. However, SiC-MOSFETs have two characteristics that interfere with high-speed switching and high efficiency performance operations for SiC MOS-FET applications in automotive power electronics systems. One characteristic is the low voltage rating of the gate-source terminal, and the other is the large internal gate-resistance of SiC MOS-FET. The purpose of this work was to evaluate a proposed hybrid gate drive circuit that could ignore the internal gate-resistance and maintain the gate-source terminal stability of the SiC-MOSFET applications. It has been found that the proposed hybrid gate drive circuit can achieve faster and lower loss switching performance than conventional gate drive circuits by using the current source gate drive characteristics. In addition, the proposed gate drive circuit can use the voltage source gate drive characteristics to protect the gate-source terminals despite the low voltage rating of the SiC MOS-FET gate-source terminals.

Hybrid storage techniques are useful methods to improve the cost performance for input-output (IO) intensive workloads. These techniques choose areas of concentrated IO accesses and migrate them to an upper tier to extract as much performance as possible through greater use of upper tier areas. Automated tiered storage with fast memory and slow flash storage (ATSMF) is a hybrid storage system situated between non-volatile memories (NVMs) and solid-state drives (SSDs). ATSMF aims to reduce the average response time for IO accesses by migrating areas of concentrated IO access from an SSD to an NVM. When a concentrated IO access finishes, the system migrates these areas from the NVM back to the SSD. Unfortunately, the published ATSMF implementation temporarily consumes much NVM capacity upon migrating concentrated IO access areas to NVM, because its algorithm executes NVM migration with high priority. As a result, it often delays evicting areas in which IO concentrations have ended to the SSD. Therefore, to reduce the consumption of NVM while maintaining the average response time, we developed new techniques for making ATSMF more practical. The first is a queue handling technique based on the number of IO accesses for NVM migration and eviction. The second is an eviction method that selects only write-accessed partial regions in finished areas. The third is a technique for variable eviction timing to balance the NVM consumption and average response time. Experimental results indicate that the average response times of the proposed ATSMF are almost the same as those of the published ATSMF, while the NVM consumption is three times lower in best case.

With the commercialization of 5G mobile phones, Android drivers are increasing rapidly to utilize a large quantity of newly emerging feature-rich hardware. Most of these drivers are developed by third-party vendors and lack proper vulnerabilities review, posing a number of new potential risks to security and privacy. However, the complexity and diversity of Android drivers make the traditional analysis methods inefficient. For example, the driver-specific argument formats make traditional syscall fuzzers difficult to generate valid inputs, the pointer-heavy code makes static analysis results incomplete, and pointer casting hides the actual type. Triggering code deep in Android drivers remains challenging. We present CoLaFUZE, a coverage-guided and layout-aware fuzzing tool for automatically generating valid inputs and exploring the driver code. CoLaFUZE employs a kernel module to capture the data copy operation and redirect it to the fuzzing engine, ensuring that the correct size of the required data is transferred to the driver. CoLaFUZE leverages dynamic analysis and symbolic execution to recover the driver interfaces and generates valid inputs for the interfaces. Furthermore, the seed mutation module of CoLaFUZE leverages coverage information to achieve better seed quality and expose bugs deep in the driver. We evaluate CoLaFUZE on 5 modern Android mobile phones from the top vendors, including Google, Xiaomi, Samsung, Sony, and Huawei. The results show that CoLaFUZE can explore more code coverage compared with the state-of-the-art fuzzer, and CoLaFUZE successfully found 11 vulnerabilities in the testing devices.

Autonomous vehicles and advanced driver assistant systems (ADAS) are receiving notable attention as research fields in both academia and private industry. Some decision-making systems use sets of logical rules to map knowledge of the ego-vehicle and its environment into actions the ego-vehicle should take. However, such rulesets can be difficult to create — for example by manually writing them — due to the complexity of traffic as an operating environment. Furthermore, the building blocks of the rules must be defined. One common solution to this is using an ontology specifically aimed at describing traffic concepts and their hierarchy. These ontologies must have a certain expressive power to enable construction of useful rules. We propose a process of generating sets of explanatory rules for ADAS applications from data using ontology as a base vocabulary and present a ruleset generated as a result of our experiments that is correct for the scope of the experiment.