Miracle-dz/UniFAR-FTUs huggingface dataset:
A Fully Synthesizable Ultra- ${N}$ Audio Frequency Multiplier for HDMI Applications

This brief develops a highly programmable and fully synthesizable large-N frequency multiplier. It is based on Time-Average-Frequency Direct Period Synthesis (TAF-DPS) and its purpose is to generate audio frequencies in HDMI (high definition multimedia interface) applications in which frequency multiplication ratio is extremely large. The proposed multiplier is a full digital feed-forward frequency control loop. The output frequency is produced by performing a fractional frequency division of the oscillator clock with TAF-DPS. The source of electrical oscillation in the TAF-DPS is a free-running inverter ring that consists of inverters. The novel structure is implemented in a TSMC 0.18 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> 1P6M CMOS process. The implementation is fully digital HDL coding <inline-formula> <tex-math notation="LaTeX">$\to $ </tex-math></inline-formula> simulation <inline-formula> <tex-math notation="LaTeX">$\to $ </tex-math></inline-formula> synthesis <inline-formula> <tex-math notation="LaTeX">$\to $ </tex-math></inline-formula> place & route without any need for analog simulation. The hardware cost is equivalent to 3150 two-input NAND gates. The proposed structure can achieve fast frequency switching within two input reference clock cycles. The multiplication ratio is evaluated experimentally to be as high as 10<sup>6</sup> with an input signal of 50 Hz; this is the largest ratio and lowest input frequency to be reported to the best of our knowledge. Besides generating an audio clock for HDMI applications, this frequency multiplier is very suitable for frequency measurements and system-on-a-chip applications.
Assist-as-needed control strategy for upper-limb rehabilitation based on subject’s functional ability

The assist-as-needed technique in robotic rehabilitation is a popular technique that encourages patients’ active participation to promote motor recovery. It has been proven beneficial for patients with functional motor disability. However, its application in robotic therapy has been hindered by a poor estimation method of subjects’ functional or movement ability which is required for setting the appropriate robotic assistance. Moreover, there is also the need for consistency and repeatability of the functional ability estimation in line with the clinical procedure to facilitate a wider clinical adoption. In this study, we propose a new technique of estimation of subject’s functional ability based on the Wolf Motor Function Test. We called this estimation the functional ability index. The functional ability index enables the modulation of robotic assistance and gives a more consistent indication of subjects’ upper-limb movement ability during therapy session. Our baseline controller is an adaptive inertia-related controller, which is integrated with the functional ability index algorithm to provide movement assistance as when needed. Experimental studies are conducted on three hemiplegic patients with different levels of upper-limb impairments. Each patient is requested to perform reaching task of lifting a can from table-to-mouth according to the guidelines stipulated in the Wolf Motor Function Test. Data were collected using two inertial measurement unit sensors installed at the flexion/extension joints, and the functional ability score of each patient was rated by an experienced therapist. Results showed that the proposed functional ability index algorithm can estimate patients’ functional ability level consistently with clinical procedure and can modify generated robotic assistance in accordance with patients’ functional movement ability.
Back-to-Back Competitive Learning Mechanism for Fuzzy Logic Based Supervisory Control System of Hybrid Electric Vehicles

This article proposes a novel back-to-back competitive learning mechanism (BCLM) for a fuzzy logic (FL) supervisory control system of hybrid electric vehicles (HEVs). This mechanism allows continuous competition between two fuzzy logic controllers during real-world driving. The leading controller will have the regulatory function of the supervisory control system. First, the configuration of the HEV model and its FL-based control system are analyzed. Second, the algorithm of chaos-enhanced accelerated particle swarm optimization (CAPSO) is developed for back-to-back learning of the membership function. Third, based on fuel-prioritized cost functions, the regulation of competitive assessment is designed to select a controller with a better fuel economy. Finally, the competitive performance of using the CAPSO algorithm is contrasted with other swarm-based methods and the BCLM-driven control system is validated by a hardware-in-the-loop test. The results demonstrate that the BCLM control system significantly reduces fuel consumption, at least 9% from charge sustaining and charge depleting based, and at least 7% from conventional FL-based systems.
Economic and Spatial Determinants of Energy Consumption in the European Union

In recent decades, various policies have been implemented to reduce energy consumption, as well as a considerable increase in research that analyzes the determinants of energy consumption, both with a standard feature, mitigating environmental degradation. However, few studies investigate the economic and spatial determinants of energy consumption in the European Union (EU). Therefore, to cover the knowledge gap, based on the review of the existing literature, this document is designed to analyze the determinants and energy policy in energy consumption in the EU. For this reason, the objective of this research is to analyze the effect of human capital, the price of oil, and Kyoto Protocol policy on energy consumption. In addition, the effect of contiguity and spatial concentration on energy consumption is studied for 34 European countries, of which 26 belong to the EU, for the period 2000–2016. For this, data collected from the World Bank (2017) and the World Energy Statistical Review (2017) were used. The work methodology is based on the application of econometric techniques for panel data, and spatial econometrics, based on the application of a Spatial Durbin Model (SDM), through which the existence of “spillovers” was determined in the implementation of the energy policy. The results of the estimates show a negative effect of the price of oil and human capital concerning energy consumption, and the variable "Policy" reflects a reduction in the energy consumption of the EU countries in the period analyzed. Additionally, the space panel confirms that the behavior of a country's energy consumption depends on its past values and the level of consumption presented by its neighbors, corroborating the importance of territorial contiguity in the success of environmental policy in the case of the European Union.
Recognition method of voltage sag causes based on two‐dimensional transform and deep learning hybrid model

The voltage sags' caused recognition is the basis for formulating governance plans and clarifying liabilities for accidents. The diversification of smart grid equipment, the grid-connected power generation of new energy sources and the regional differentiation of power consumption modes pose new challenges to the traditional methods. In this study, a method based on deep learning hybrid model is proposed. The convolutional neural network is used to flexibly receive the voltage after two-dimensional transformation, so as to automatically obtain the time series and spatial characteristics of the voltage sag signals. The deep belief network is used to replace the fully connected layers in convolutional neural network, thereby enhancing the multi-label classification ability of the model. The parameters obtained by the unsupervised training of the stacked sparse denoising auto-encoder are used to initialise the weight of deep belief network, thereby improving the convergence speed and the anti-noise performance of the model. Iterative training and repeated testing of the network using pre-processed simulation data and actual recorded data verify the high recognition accuracy and strong anti-noise performance of the hybrid model. Compared with the traditional methods, the hybrid model also has good generalisation ability and can be effectively applied in practical engineering.
The Role of Laser Irradiance, Pulse Repetition Rate, and Liquid Media in the Synthesis of Gold Nanoparticles by the Laser Ablation Method using an Nd:YAG Laser 1064 nm at Low Energy

The synthesis of gold nanoparticles with high-purity and narrow size distribution is necessary for applications in the medical field. However, it is difficult to achieve this using chemical methods. In this study, the pulse laser ablation method using an Nd:YAG laser operated at a low-energy of 30 mJ has been successfully employed to produce gold nanoparticles with the required high purity and narrow size distribution. The role of laser irradiance, laser pulse repetition rate, and liquid media in the characteristics of the nanoparticles produced, such as shape and size distribution, were examined. In the experiment, an Nd:YAG laser beam (1064 nm, 7 ns) with a low energy of 30 mJ was irradiated on a high-purity gold plate (99.95%) immersed in a liquid medium. The results demonstrate that the average particle diameter and size distribution depended on certain parameters of the laser irradiance, the pulse repetition rate and the liquid medium used in the synthesis process. The diameters of the GNPs increased from 6.5 to 12.3 nm when the laser irradiance was increased from 12 to 20 GW/cm 2 . They also increased from 12.3 to 20.7 nm when the pulse repetition rate was increased from 10 to 15 Hz. In addition, the particle diameters changed in line with different liquid media used; they were much smaller for purified water (diameter of 12.3 nm) compared to ethanol (diameter of 15.0 nm). However, the shape of the GNPs was the same for these parameters; the GNPs produced by the laser ablation method were spherical. By understanding the effects of these parameters on the characteristics of the GNPs produced by the laser ablation method using a low-energy Nd:YAG laser, GNPs with specific characteristics, namely high purity and narrow size distribution, can be synthesized for specific applications in the medical field. gold nanoparticles in liquid media. The effects of laser pulse repetition rate, laser irradiance, and liquid media in the synthesis of colloidal GNPs using a quiet low-energy Nd:YAG were examined to produce nanoparticles with high and narrow distribution. The experimental results show that the gold nanoparticles produced had much narrower distribution compared to those produced by the chemical method. Ablation at pulse repetition from to an in
Philosophies and pedagogies that shape an integrated engineering programme

ABSTRACT Accredited engineering degrees call upon students to develop a wide range of knowledge and skills. These range from technical, scientific and mathematical knowledge, through to transferable skills such as communications, teamwork, business acumen and critical analysis. Through a faculty-wide curriculum development programme we have sought to implement cross-department teaching framework whereby a range of pedagogies are employed to deliver against core philosophies for a new way of teaching aimed at developing students’ knowledge, skills and attitudes while meeting a diverse range of learning outcomes. We argue that is it vital that learning takes place in the context of authentic engineering problems and processes. In this paper, we look at the philosophies, pedagogies and outcomes of an educational-based project which creates a connected curriculum that joins distinct disciplines at key points during the students’ education to provide preparation for, and experience of, professional engineering. It describes the motivation for change and described the implementation and impact of these approaches.
Improving robustness of the MAV yaw angle estimation for low‐cost INS/GPS integration aided with tri‐axial magnetometer calibrated by rotating the ellipsoid model

This study designs a method to improve the robustness of the micro air vehicle (MAV) yaw angle estimation for low-cost inertial navigation system/global positioning system (INS/GPS) integration by using a tri-axial magnetometer. Observability properties of the integration navigation system are first analysed, indicating that all attitude angles have great observability with the aid of the heading measurement by a magnetometer, while the observability of yaw angle is low in traditional INS/GPS. To improve the robustness of yaw angle estimation, the authors calibrate the tri-axial magnetometer according to the rotating ellipsoid model and establish a fault detection and isolation mechanism to adjust the weight of magnetic-measurement adaptively and dynamically in integration navigation. The simulation and experiment results show that the proposed method can calibrate the tri-axial magnetometer properly and screen out the abnormal magnetic measurement for the robustness of yaw angle estimation, suggesting that this technique is a viable candidate for MAV navigation and control applications in complex magnetic fields.
Deadbeat Direct Torque and Flux Control for Permanent Magnet Synchronous Motor Based on Stator Flux Oriented

Direct torque control (DTC) has become a widely acceptable alternative to field-oriented control in permanent magnet synchronous motor (PMSM) drives. Deadbeat direct torque and flux control (DB-DTFC) is a significant improvement over the classical DTC. However, there are some aspects that can be improved, such as insufficiently small electromagnetic torque ripple, unsmooth dynamic response, heavy computation burden, and relatively weak robustness to motor parameters variation. To improve the operation performance, an advanced DB-DTFC strategy is proposed in this article. First, the algorithm model based on stator flux oriented coordinate system is deduced. On this basis, the basic principle of proposed DB-DTFC is presented. Second, a novel graphical analysis method is proposed to help illustrate the proposed strategy. Third, a general solution method is proposed to solve the reference stator voltage, which is a key variable for the DB-DTFC. Finally, the stability of system is analyzed. Theoretical and experimental comparisons between the proposed DB-DTFC and the conventional DB-DTFC are done. The results show that the performance in the aspects of electromagnetic torque and stator current ripples reduction, dynamic response, computation burden, and robustness to motor parameters variation are all improved in the proposed DB-DTFC.
Modeling of Dry Band Formation and Arcing Processes on the Polluted Composite Insulator Surface

This paper modeled the dry band formation and arcing processes on the composite insulator surface to investigate the mechanism of dry band arcing and optimize the insulator geometry. The model calculates the instantaneous electric and thermal fields before and after arc initialization by a generalized finite difference time domain (GFDTD) method. This method improves the field calculation accuracy at a high precision requirement area and reduces the computational complexity at a low precision requirement area. Heat transfer on the insulator surface is evaluated by a thermal energy balance equation to simulate a dry band formation process. Flashover experiments were conducted under contaminated conditions to verify the theoretical model. Both simulation and experiments results show that dry bands were initially formed close to high voltage (HV) and ground electrodes because the electric field and leakage current density around electrode are higher when compared to other locations along the insulator creepage distance. Three geometry factors (creepage factor, shed angle, and alternative shed ratio) were optimized when the insulator creepage distances remained the same. Fifty percent flashover voltage and average duration time from dry band generation moment to flashover were calculated to evaluate the insulator performance under contaminated conditions. This model analyzes the dry band arcing process on the insulator surface and provides detailed information for engineers in composite insulator design.
A Wideband Single End Fault Location Scheme for Active Untransposed Distribution Systems

This paper presents a single end fault location scheme for distribution networks with distributed generation (DG). The high frequency transients generated when a fault occurs are used to locate the fault point. An equivalent circuit at high frequencies is derived for both inverter based and synchronous DGs. The voltages and currents measured only at the main substation are used in conjunction with the proposed DG model to create the fault location scheme. The distributed parameter line model is employed in the derivation to account for the line characteristics, e.g., inductive and capacitive mutual coupling and untransposed lines. The inverter based DG model is validated experimentally on a 400 V, three-phase system. The IEEE 34-bus feeder is simulated and used to evaluate the proposed scheme for different operating conditions, e.g., fault resistance and DG conditions. The extensive simulation studies show the accuracy of the proposed scheme with 95.3% of the test cases having an absolute error in estimation of less than 200 m.
Multiobjective Scheduling of Dual-Blade Robotic Cells in Wafer Fabrication

As a kind of robotic cells, cluster tools are widely used for semiconductor wafer fabrication processes since they provide a reconfigurable and efficient environment. With recent advances in new semiconductor materials, the circuit line width has continuously being shrunk down, which brings new challenges for manufacturers. They require that a wafer should be moved away from a processing chamber as soon as possible after its processing is finished. To ensure high-quality integrated circuits in a wafer, its post-processing residency time must be minimized. It is also highly desirable to maximize the throughput of robotic cluster tools. This article aims at scheduling such tools with multiple objectives subject to wafer residency time constraints. To do so, new algorithms are proposed to calculate robot waiting time delicately upon the analysis of particular events of robot waiting for dual-blade robotic tools and optimally schedule such tools. The numerical results of industrial examples show that the proposed algorithms can provide an effective method to find schedules for dual-blade cluster tools such that multiple objectives are optimized.
MIGHT: Statistical Methodology for Missing-Data Imputation in Food Composition Databases

This paper addresses the problem of missing data in food composition databases (FCDBs). The missing data can be either for selected foods or for specific components only. Most often, the problem is solved by human experts subjectively borrowing data from other FCDBs, for data estimation or imputation. Such an approach is not only time-consuming but may also lead to wrong decisions as the value of certain components in certain foods may vary from database to database due to differences in analytical methods. To ease missing-data borrowing and increase the quality of missing-data selection, we propose a new computer-based methodology, named MIGHT - Missing Nutrient Value Imputation UsinG Null Hypothesis Testing, that enables optimal selection of missing data from different FCDBs. The evaluation on a subset of European FCDBs, available through EuroFIR and complied with the Food data structure and format standard BS EN 16104 published in 2012, proves that, in more than 80% of selected cases, MIGHT gives more accurate results than techniques currently applied for missing value imputation in FCDBs. MIGHT deals with missing data in FCDBs by introducing rules for missing data imputation based on the idea that proper statistical analysis can decrease the error of data borrowing.
Sampling‐based hierarchical motion planning for a reconfigurable wheel‐on‐leg planetary analogue exploration rover

Reconfigurable mobile planetary rovers are versatile platforms that may safely traverse cluttered environments by morphing their physical geometry. Planning paths for these adaptive robots is challenging due to their many degrees of freedom, and the need to consider potentially continuous platform reconfiguration along the length of the path. We propose a novel hierarchical structure for asymptotically optimal (AO) sampling‐based planners and specifically apply it to the state‐of‐the‐art Fast Marching Tree (FMT*) AO planner. Our algorithm assumes a decomposition of the full configuration space into multiple subspaces, and begins by rapidly finding a set of paths through one such subspace. This set of solutions is used to generate a biased sampling distribution, which is then explored to find a solution in the full configuration space. This technique provides a novel way to incorporate prior knowledge of subspaces to efficiently bias search within existing AO sampling‐based planners. Importantly, probabilistic completeness and asymptotic optimality are preserved. Experimental results in simulation are provided that benchmark the algorithm against state‐of‐the‐art sampling‐based planners without the hierarchical variation. Additional experimental results performed with a physical wheel‐on‐leg platform demonstrate application to planetary rover mobility and showcase how constraints such as actuator failures and sensor pointing may be easily incorporated into the planning problem. In minimizing an energy objective that combines an approximation of the mechanical work required for platform locomotion with that required for reconfiguration, the planner produces intuitive behaviors where the robot dynamically adjusts its footprint, varies its height, and clambers over obstacles using legged locomotion. These results illustrate the generality of the planner in exploiting the platform's mechanical ability to fluidly transition between various physical geometric configurations, and wheeled/legged locomotion modes, without the need for predefined configurations.
Clinical Implications of Cancer Stem Cell Properties in Oral Squamous Cell Carcinoma.

Epithelial-to-mesenchymal transition (EMT) is an important driver of tumor invasion and metastasis, which causes many cancer deaths. Cancer stem cells (CSC) that maintain and initiate tumors have also been implicated in invasion and metastasis, but whether EMT is an important contributor to CSC function is unclear. In this study, we investigated whether a population of CSCs that have undergone EMT (EMT CSCs) exists in squamous cell carcinoma (SCC). We also determined whether a separate population of CSCs that retain epithelial characteristics (non-EMT CSCs) is also present. Our studies revealed that self-renewing CSCs in SCC include two biologically-distinct phenotypes. One phenotype, termed CD44ESA, was proliferative and retained epithelial characteristics (non-EMT CSCs), whereas the other phenotype, termed CD44ESA, was migratory and had mesenchymal traits characteristic of EMT CSCs. We found that non-EMT and EMT CSCs could switch their epithelial or mesenchymal traits to reconstitute the cellular heterogeneity which was characteristic of CSCs. However, the ability of EMT CSCs to switch to non-EMT character was restricted to cells that were also ALDH1þ, implying that only ALDH1þ EMT cells had the ability to seed a new epithelial tumor. Taken together, our findings highlight the identification of two distinct CSC phenotypes and suggest a need to define therapeutic targets that can eradicate both of these variants to achieve effective SCC treatment. Cancer Res; 71(15); 5317–26. 2011 AACR.