Miracle-dz/UniFAR-FTUs huggingface dataset:
Optical Network Resource Management Supporting Physical Layer Reconfiguration

To cope with the capacity increase and diversification of information services, enhancing efficiency, flexibility, and agility in optical transport networks is becoming increasingly important. Quick reconfiguration of the optical physical layer, such as changing optical node architectures or optical physical network topologies, provides additional adaptability to requirement or environment changes. This paper proposes a new topology description scheme to support optical physical layer resource management at the granularity of optical functionality blocks in a machine-processible manner. The proposed scheme specifies intra-node structures as well as inter-node fiber connections (links). Furthermore, different switching functionalities of individual optical components are specified in a common format using integer linear programming (ILP) formulas. The ILP formulas are described in a machine-readable GNU MathProg modeling language so as to be directly introduced to the path computation mechanisms. Based on the proposed topology description scheme, a path computation engine named PathFinder is prototyped and demonstrated for optical networks consisting of various optical components with different switching functionalities. The computational feasibility of PathFinder is evaluated through numerical experiments in terms of elapsed time for path computation and the proposed scheme is successful while retaining reasonable time ranges. Operating topology and node architecture changes based on the proposed scheme are also successfully demonstrated over a multi-granular hierarchical optical network testbed with real hardware.
Space Vector Modulation for SiC and Si Hybrid ANPC Converter in Medium-Voltage High-Speed Drive System

Medium-voltage high-speed drive is promising in applications, such as the centrifugal compressors and electrified transportation. However, it raises lots of design challenges for the converters, which drive the motors. A SiC and Si hybrid ANPC converter is developed to address these challenges. In this article, the essence of this hybrid structure is investigated to clearly show the derivation process of the hybrid active neutral-point-clamped (ANPC) converter and inspire future research on these hybrid topologies. A dedicated space vector modulation (SVM) scheme is proposed for the hybrid ANPC converter, which enables the three-phase hybrid ANPC converter operating with SiC mosfets in high frequency and Si IGBTs in fundamental frequency. A neutral point (NP) voltage balancing scheme is added to the SVM scheme to balance the NP voltage imbalance under low-frequency modulation index. All these efforts make the hybrid ANPC converter become a high power density and fairly low-cost solution. A 400 Hz fundamental frequency, 3.6 kHz switching frequency, medium-voltage high-speed drive system is assumed and tested by the scaled-down experimental results. The effectiveness of the proposed SVM and NP voltage balancing method have both been verified.
Predictive Cruise Control Using Radial Basis Function Network-Based Vehicle Motion Prediction and Chance Constrained Model Predictive Control

Predicting future motions of surrounding vehicles and driver’s intentions are essential to avoid future potential risks. The predicting future motions, however, is very challenging because the future cannot be deterministically known a priori and there are infinitely many possible future trajectories. Prediction becomes far more challenging when trying to foresee distant future. This paper proposes a probabilistic motion prediction algorithm that can accurately compute the likelihood of multiple target lanes and trajectories of surrounding vehicles by using the artificial neural network; more specifically radial base function network (RBFN). The RBFN prediction algorithm estimates the likelihood of each lane being the driver’s target lane in categorical distributions and the corresponding future trajectories in parallel. In order to demonstrate the effectiveness of the proposed prediction algorithm, it is applied for the predictive cruise control problem. Chance-constrained model predictive control (CCMPC) is utilized because the chance constraints in CCMPC can handle collision uncertainties associated with future uncertainties from the proposed prediction algorithm. The RBFN-based CCMPC simulation is conducted for several risky cut-in scenarios and compared with the state-of-the-art Interactive Multiple Model (IMM)-based prediction algorithm. The simulation results show that the RBFN-based CCMPC achieves higher collision avoidance success rate than that of the IMM-based CCMPC while using smaller actuator inputs and providing higher passenger comforts. Furthermore, the RBFN-based CCMPC showed high robustness to false braking during near lane-change (lane-keeping) scenarios.
Research on Identification Model of Financial Fraud of Listed Company Based on Data Mining Technology

Corporate financial information fraud is a difficult problem and a great hazard since the birth of the listed company system. The classic method of financial fraud detection is of high accuracy, but it requires enormous manpower and material resources, which also needs a relatively long time for investigation. With the development of computer technology, data mining technology can be used as a very important tool to identify the distortion of enterprise financial accounting information. Through the modeling work of different classification and clustering algorithms, a set of identification models can be constructed with low cost and high efficiency. This paper uses the popular support vector machine as a data mining tool to identify financial fraud and achieves good results.
Are We Leaving Children with Chronic Illness Behind

Novel treatments are improving the prognosis for many illnesses, making it possible to survive diseases that were once considered fatal. With these advancements comes great responsibility to ensure quality of life for those living with chronic illness. Educators are among the group of professionals accountable for ensuring quality of life experiences for students living with chronic conditions. Unfortunately, typical educational systems and policies are not designed to lend support to students battling chronic illness. With limited resources to draw from, well-intended teachers and administrators respond to acute health episodes with impromptu plans, inadvertently creating educational barriers, eliminating the possibility of an equitable educational experience for students with an illness. Looking through the lens of these students, violations of best practice in education are plentiful. Analyzing existing research and laws to understand the path to success achieved for other vulnerable populations will likely guide future research initiatives that may lead to the solution so desperately needed for these children. ARE WE LEAVING CHILDREN WITH CHRONIC ILLNESS BEHIND? The advancement of medical research is sustaining life in ways that were not possible years ago. Novel treatments are improving the prognosis for many illnesses, making it possible to survive diseases that were once considered fatal. 67 Irwin Elam_pp67-80.qxd 7/13/11 7:05 PM Page 67
Tightly Coupled Integration of GNSS and Vision SLAM Using 10-DoF Optimization on Manifold

Vision navigation technique, especially the vision-based simultaneous localization and mapping (V-SLAM), plays a critical role in robotic navigation. As a relative positioning technique, V-SLAM often suffers from drift and scale uncertainty problems which incur bias increasing over time. To overcome these drawbacks and to improve the robustness and accuracy of localization, an effective way is to fuse global navigation satellite system (GNSS) with V-SLAM. In this paper, we propose a novel GNSS and SLAM fusion algorithm, which provides ego-motion estimation through tightly coupling GNSS pseudo-range measurements and camera feature points. It first decomposes the pose state into basic motion vectors, based on which an asynchronous tracking is performed. Then, a 10-DoF joint-optimization formulation on manifold is proposed to achieve tight fusion of the raw measurement from camera and GNSS. Finally, this formulation is solved to calculate the ego-motion state. The proposed algorithm is verified on an autonomous ground vehicle in two typical environments. The results demonstrated that, the new algorithm can amend the bias in vision SLAM and constrain the GNSS solution, which achieves a better localization result than the traditional methods.
Organic Photovoltaics: Toward Self-Powered Wearable Electronics

In the past several years, research in soft and flexible electronics has promised to yield developments in wearable technology, including health and fitness monitoring tools, independence and mobility aids for the disabled, and human–computer interfaces for virtual and augmented reality. However, as these devices become thinner and lighter, they demand efficient power supplies with similar mechanical properties. Organic photovoltaics (OPVs)—–known for their high flexibility, lightweight, and scalable fabrication methods—have recently attained power conversion efficiencies of over 17% and are excellent candidates to power these next-generation wearable devices. In this paper, we discuss recent developments in flexible OPVs, including advances in materials, structure, and integration with additional wearable components, such as sensors and displays. In addition, we describe our recent work in developing a self-powered actuator for a tactile feedback system.
A bias-variance trade-off in the prediction error estimation behavior in bootstrap methods for microarray leukemia classification

Received 22.12. 2017 Revised 03.01. 2018 Accepted 16.03. 2018 Background & Aim: The bootstrap is a method that resample from the original data set. There are the wide ranges of bootstrap application for estimating the prediction error rate. We compare some bootstrap methods for estimating prediction error in classification and choose the best method for the microarray leukemia classification. Methods & Materials: The sample consist of n=38 patients with acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) with p=4120 genes that n<<p from an existing database. We carried out following steps. (1) Resample from the original sample. (2) Divide the sample to two sets, learning set and test set by 3-fold cross validation. (3) Train 1NN, CART and DLDA classifiers and compute its misclassification error by comparing the predicted class of the remaining samples with the true class. (4) Average the errors on B bootstrap samples. Results: Standard deviation, bias and MSE for comparing four bootstrap methods by three classifiers were computed. For choosing the best method, we assess a bias-variance tradeoff in the behavior of prediction error estimates. The 0.632+ BT is approximately un-bias and has small variability. However, the LOOBT procedure has big variability and is biased. Also we provide a table and some figures in the section 4. Conclusion: The bias and variance of the prediction error rates have high variability in various bootstrap methods. Although the 0.632+ BT is approximately un-bias and has small variability, other resampling methods maybe are useful for the microarray classification in the different situations.
A low-PDAP and high-PSNR approximate 4:2 compressor cell in CNFET technology

Purpose This paper aims to present an inexact 4:2 compressor cell using carbon nanotube filed effect transistors (CNFETs). Design/methodology/approach To design this cell, the capacitive threshold logic (CTL) has been used. Findings To evaluate the proposed cell, comprehensive simulations are carried out at two levels of the circuit and image processing. At the circuit level, the HSPICE software has been used and the power consumption, delay, and power-delay product are calculated. Also, the power-delaytransistor count product (PDAP) is used to make a compromise between all metrics. On the other hand, the Monte Carlo analysis has been used to scrutinize the robustness of the proposed cell against the variations in the manufacturing process. The results of simulations at this level of abstraction indicate the superiority of the proposed cell to other circuits. At the application level, the MATLAB software is also used to evaluate the peak signal-to-noise ratio (PSNR) figure of merit. At this level, the two primary images are multiplied by a multiplier circuit consisting of 4:2 compressors. The results of this simulation also show the superiority of the proposed cell to others. Originality/value This cell significantly reduces the number of transistors and only consists of NOT gates.
Composite DC Power Flow Controller

The utilization of a dc power flow controller in multiterminal HVDC (MTDC) can increase the control dimension of dc power flow, by which both the active power distribution capability and coordination control performance can be enhanced in MTDC. In this article, a novel concept of “composite dc power flow controller (CDCPFC)” has been proposed, and the general construction principles of technical frame have been given for function description. A specific CDCPFC topology has been implemented, which is featured with a straightforward dc–dc power conversion path via a transformerless structure. The operation principle, circuit characteristics, and control strategy of the proposed CDCPFC are analyzed in detail. Both simulation and experimental results proved that the proposed topology could realize power flow control function of two individual lines under various conditions with a good application prospect.
Nonlinear Hybrid Precoding for Coordinated Multi-Cell Massive MIMO Systems

This paper examines nonlinear hybrid precoding with minimum mean square error (MMSE)-vector perturbation (VP) for multi-cell massive multiple-input multiple-output (MIMO) systems. Two-timescale channel state information (CSI) is assumed, which consists of short-term noisy observations of the RF-precoded MIMO channel, and perfect knowledge of the long-term channel temporal and spatial correlation. By exploiting the low-dimensional effective CSI, we propose to estimate the instantaneous realization of the high-dimensional CSI via Kalman filtering. The CSI estimate is then utilized for RF precoding in consideration of centralized and distributed MMSE-VP at baseband. By abstracting the effect of nonlinear baseband precoding, RF precoding is separately formulated as a solution to balance the error performance of signal detection with the accuracy of channel tracking. To solve such nonconvex problems, we develop Cayley transformation-based gradient descent algorithms. Numerical results demonstrate the benefits of incorporating CSI tracking into hybrid precoding from its superior bit error rate to other transmit spatial correlation-based baselines, and its improved resilience to the channel estimation errors over the fully digital counterpart.
Highly Sensitive Signal Processing Devices for Capacitive Transducers of Micromechanical Accelerometers

In this paper, the principles of the open-loop frequency-based signal processing devices for capacitive MEMS accelerometers are used to develop three CMOS IP-core (Intellectual Property core) projects of highly sensitive signal processing devices with frequency output. Signal processing devices designed in accordance with the considered method form an output of rectangular pulses whose frequencies equal a difference of signal frequencies from two identical generators with micromechanical accelerometer capacitive transducers in their frequency control circuits. First, the analog project scheme uses two harmonic LC oscillators and an analog mixer to form an output rectangular-shape differential-frequency signal, the frequency of which is dependent on the measured acceleration. Second, the digital project is fully scalable for various CMOS-technologies due to oscillators of rectangular pulses and a digital mixer. Third, the mixed-signal project combines the advantages of the analog and digital projects. The signal processing device projects were developed, modeled and compared to comprehensively solve the problems of increasing sensitivity, dynamic range, noise immunity and resistance to destabilizing factors (e.g., to temperature changes).
Robust Joint Optimization of Transmit Power and Decoding Order in Uplink NOMA Systems

We consider a robust design problem for achieving max-min fairness amongst users in an uplink non-orthogonal multiple access system under imperfect channel state information. Contrary to the conventional approach adopted in the literature, we propose an optimal decoding order-based successive interference cancellation technique by introducing new binary variables, which results in a difficult class of mixed-integer nonconvex optimization problem. For a practical application, we devise an efficient suboptimal solution based on the inner convex approximation framework, which solves a second-order-cone program at each iteration. Simulation results are provided to demonstrate its performance gain over state-of-the-art designs. The proposed design also yields data rates close to those obtained by an exhaustive search method.
Vibration‐Energy‐Harvesting System: Transduction Mechanisms, Frequency Tuning Techniques, and Biomechanical Applications

Vibration‐based energy‐harvesting technology, as an alternative power source, represents one of the most promising solutions to the problem of battery capacity limitations in wearable and implantable electronics, in particular implantable biomedical devices. Four primary energy transduction mechanisms are reviewed, namely piezoelectric, electromagnetic, electrostatic, and triboelectric mechanisms for vibration‐based energy harvesters. Through generic modeling and analyses, it is shown that various approaches can be used to tune the operation bandwidth to collect appreciable power. Recent progress in biomechanical energy harvesters is also shown by utilizing various types of motion from bodies and organs of humans and animals. To conclude, perspectives on next‐generation energy‐harvesting systems are given, whereby the ultimate intelligent, autonomous, and tunable energy harvesters will provide a new energy platform for electronics and wearable and implantable medical devices.
Enhanced soft‐switching strategy for flyback‐based microinverter in PV power systems

Flyback topology has been widely used to construct modular power conversion for solar photovoltaic (PV) grid-tied systems, which creates a parallel interconnection infrastructure and is considered as the most straightforward way to cope with the harmful mismatch effects among PV modules. The challenge becomes the conversion efficiency and system reliability to be competitive with traditional system configurations. This study proposes a comprehensive solution to overcome the present problem, which optimally integrates the technology of resonant circuit, adaptive modulation scheme, and active clamping to enhance soft-switching capability and system efficiency. The effectiveness of theoretical analysis, circuit design, and modulation scheme is analysed by simulation and verified by experimental evaluation. The California Energy Commission efficiency is tested and shown as 97.4%.
How and where to use technology of laser scanning and aerial photogrammetry for building purposes – case study

Laser scanning is a method of high – accuracy mapping or reality capture that uses laser beams to quickly capture complete detail of the entire building construction project in a short time. Laser scanners are a great tool for measuring existing buildings but not always because they have the same problem as visual surveys-if you can’t see it, you can’t scan it, i.e. the laser beam from the scanner can’t measure a certain part of the surface of the structure if this part isn’t in the scanner’s field of view. In particular, this limitation applies to the surfaces of the roof structures and certain parts of the facades. In these cases, the resulting point cloud may contain no information from the building surface what may be a problem for the final results. A possible solution is a combination of two technologies, terrestrial laser scanning and aerial photogrammetry. Aerial photogrammetry (or airborne imagery) means taking of photographs from an unmanned aerial vehicle (i.e. “drones”). The aim of this paper is to describe the use of these two technologies, it means, when it is better to use these technologies separately and when it is better to make their integration. As a reference building the Faculty of Civil Engineering, located in Kosice, Slovakia was chosen
Design, Deployment and Evolution of Heterogeneous Smart Public Lighting Systems

Street lighting characterizes many smart city initiatives around the world. In fact, significant savings can be achieved by not only replacing traditional luminaires with low-power LEDs, but also providing streetlights with smart light controllers and network connectivity, allowing the introduction of a sensible light intensity management and reduction of maintenance costs. Moreover, if designed with a far-looking view, smart lighting infrastructure could also support city-wide Internet of Things services, becoming key enablers of the smart city revolution, also in the 5G perspective. In this paper, we provide a thorough discussion on network architectures and communication technologies that could be adopted for smart public lighting applications, showing their benefits and downsides. Starting with significant activity on research, implementation and in-field testing, we also outline the steps required for the deployment of a smart public lighting infrastructure, each discussed in accordance with the network topology considered. Finally, we introduce some additional services that a smart public lighting infrastructure could support and discuss the benefits that would arise from integration with the upcoming 5G cellular network.
Calculation and evaluation of load performance of magnetic levitation system in medium-low speed maglev train

The medium-low speed maglev train is an entirely new way of transportation without wheels, and it makes the best of the attraction between the electromagnet and the F-type track to achieve levitation. The magnetic levitation system is considered as the wheels of medium-low speed maglev train, which plays a major role in bearing the weight of vehicle. The theoretical calculation and finite element numerical analysis are performed to calculate the load performance of the magnetic levitation system under different load conditions. The difference of calculation results between the two methods is analyzed and discussed. In addition, a series of load-carrying tests in vehicles operating on Changsha maglev express is carried out. The experimental results have revealed that the calculation results based on FEM are basically matched with that of actual well conditions. Therefore, the load performance of the magnetic levitation system in medium-low speed maglev train is calculated and evaluated successfully.