Miracle-dz/UniFAR-FTUs huggingface dataset:
Target Detection Algorithm with Information Geometry under Cooperative Position

Due to the display of the advantages and advantages of “stealth” weapons and equipment in modern warfare, the trend of invisibility and miniaturization of new weapons and equipment is becoming more and more obvious. Therefore, exploring and researching the detection methods for these weapons and equipments has a strong military application value. Based on electromagnetic field theory and information geometry theory, this paper obtains electromagnetic wave excitation potential in electromagnetic space, and then acquires multi-point electromagnetic wave state potential changes through passive receivers in space, and constructs Riemann geometric statistics by using electromagnetic space situation big data. Manifolds, perceptions and discoveries may enter targets in the electromagnetic space region and their positional orientation. Finally, the electromagnetic potential data of the cyberspace is used to quickly find and identify the target, and the rapid discovery and effective identification of the low, slow, low and fast targets are realized. Finally, the performance of the algorithm is analyzed by experimental simulation from the aspects of target scattering intensity, time base and position reference.
Long Short Term Memory Based Status Prediction for Diesel Generator Set

The diesel generator set is a kind of equipment which uses the diesel engine as the prime mover to drive the synchronous generator to generate electricity. Diesel generator sets play an important role in production and emergency power supply. To detect and supervise the operation status and running states of diesel generating sets, the prediction of the operation status of diesel generating sets is becoming more and more important. Long short term memory (LSTM) neural network can control the memory and forgetting degree of the previous data and the current data in the time series data by forgetting gates, input gates, output gates and so on, so that the neural network has the function of long-term memory. LSTM model and prediction method for diesel generator set is proposed. Oil inlet temperature data are collected with sensor network from diesel generator set, and the data are used for status prediction. Compared with support vector regression, RNN and gated recurrent unit, proposed LSTM model higher prediction accuracy, and it can be used for status prediction for diesel generator set.
Millimeter Wave SOI-CMOS Power Amplifier With Enhanced AM-PM Characteristic

This paper proposes a novel inter-stage load-pull characterization method to enhance the linearity of millimeter wave integrated power amplifiers (PAs) by minimizing their amplitude-to-phase (AM-PM) distortion without worsening their AM-AM or efficiency performances. The proposed method identifies the optimal solution for the inter-stage matching network which enables the synthesis of a driver stage AM-PM characteristic that is complementary to that of the power stage; consequently, reducing the overall AM-PM distortion of the PA. The proposed technique is applied to design a proof-of-concept 28–31 GHz PA demonstrator using 45 nm silicon-on-insulator CMOS technology. The measurement results obtained under continuous wave excitation at 29 GHz demonstrate an excellent AM-PM characteristic, with phase distortions as low as 0.2° at the 1-dB compression power level of 13.9 dBm and less than 1° at an output power level of up to 16 dBm (very close to the saturation power of 16.6 dBm). This enhanced AM-PM linearity improves the linearizability of the PA. This was confirmed by testing the PA with a 64 quadrature amplitude modulated test signal with an instantaneous bandwidth of 800 MHz. Without applying any digital pre-distortion (DPD) technique, the PA delivers a power added efficiency (PAE) of 8.7% at an average output power ( $P_{avg}$ ) of 9.4 dBm while maintaining an error vector magnitude (EVM) of −25 dB. However, after applying a very simple memoryless DPD function with only four coefficients, the PA can operate at a higher $P_{avg}$ of 11.1 dBm with a much better PAE of 12.2% while still maintaining an acceptable EVM of −25.2 dB. Thanks to the proposed technique, the PAE of the proposed PA can be improved by 40% with a very simple application of a low cost and low complexity DPD technique.
Predictive Torque Control With Simple Duty-Ratio Regulator of PMSM for Minimizing Torque and Flux Ripples

In this paper, a new method for reducing the ripples of torque and flux is proposed for the predictive torque control (PTC) of permanent magnet synchronous motors. The conventional PTC uses equations to analyze the relationship between the electrical torque and voltage with a fixed magnitude of reference voltage. Consequently, flux and torque ripples increase significantly at low-speed operation. This paper proposes an improved PTC algorithm using a simple duty-ratio regulator. The proposed method will minimize the torque error by calculating an appropriate torque minimization function to mitigate the induced torque and flux ripple at low speed operation. This torque minimization function is synthesized into the space vector pulse-width modulation block to reduce the torque ripple induced owing to the fixed time duration. The proposed scheme considerably reduces the torque and flux ripples and establishes a fast-dynamic response of the torque in the transient state. Simulation and experimental results show that the proposed method achieves a more efficient steady-state performance and a faster step response compared with the conventional PTC.
Event-Triggered Average Consensus of Multiagent Systems with Switching Topologies

Event-triggered average consensus of multiagent systems with switching topologies is studied in this paper. A distributed protocol based on event-triggered time sequences and switching time sequences is designed. Based on the inequality technique and stability theory of differential equations, a sufficient condition for achieving average consensus is obtained under the assumption that switching signal is ergodic and the total period over which connected topologies is sufficiently large. A numerical simulation is presented to show the effectiveness of the theoretical results.
Learning-Based Falsification for Model Families of Cyber-Physical Systems

Verification of cyber-physical systems is challenging with the continuous dynamics of increasing complexity. Falsification has been considered as a promising pragmatic approach by using optimization techniques to search for input signals that lead to violation of a quantified formal specification. However, current falsification methods run a search every time from scratch given any variation in the target model. We propose a learning-based method that builds a falsifier for a family of models with a preliminary learning process. We adapt reinforcement learning techniques for this purpose. We evaluated the performance of the proposed method with a major falsification tool.
Targeted Partial-Shift For Mitigating Shift Switching Activity Hot-Spots During Scan Test

Shifting scan chains during testing causes high switching activity in the combinational logic. Excessive shift switching activity can give rise to severe, localized IR-drop that may invalidate the test by corrupting the contents of scan flip-flops or inducing excessive shift clock skew. In this work, we propose new methods to (1) quickly analyze all shift cycles of a given scan design and a test set for potential shift switching activity hot-spots and to (2) avoid them by targeted partial shifting of the scan chains. The results on ITC'99 benchmark circuits show the computational feasibility of the analysis and demonstrate the effectiveness of targeted partial-shift for mitigating test data corruption risk with minimal impact on test time.
Neutral Voltage Balancing Of Three Level Neutral-Clamped Inverter

Multilevel inverter’s most commonly used configuration of neutral point clamped inverter have vast applications with high power areas, due to its various benefits such as, small dv/dt and total harmonic distortion. However neutral potential balancing problems can be seen in NPC inverters. Various balancing techniques are discussed in past for balancing, but they have vulnerable balancing ability at low PFs and high modulation index. In proposed strategy, the switching vectors that vary the potential at neutral point are changed with N and P type small vectors that do not have effect on neutral point. And for balancing potential at neutral point, the zero and medium vectors are used.
A Unity-Gain FDNR Approach to a Six-Component Sinusoidal Oscillator Based on a Single Op-Amp

A new six-component single-op-amp-based sinusoidal oscillator is proposed through the use of a frequency-dependent negative resistance (FDNR). The proposed oscillator forms such a simple design with only one unity-gain amplifier and five passive components to generate sinusoidal oscillations. Thus, it offers among the lowest component counts with respect to some well-known previously published works. Simulations and experiments are performed to check the validity of our theory, and they are found to be agreeable. Good-quality sinusoidal output waveform is demonstrated. Frequency of oscillation is tunable via a resistor and a capacitor in the circuit.
Fast Marching-Based Path Generating Algorithm in Anisotropic Environment With Perturbations

Fast Marching is a widely used method in path planning, especially continuity is demanded due to kinodynamic constraints of automatic vehicles. However, its application in real environment with obstacles and perturbations requires simplifications to be made, resulting in non-optimal or even unfeasible path. We introduce a risk function $\zeta $ to describe the effect of obstacles to the safety of a generated path. Combined with the physical law based description of external flow perturbation, we formulate the path planning problem in realistic environment as a wave front propagation in terms of an extended Eikonal equation. Inspired by the underlying mechanism of the numerical recipe of Fast Marching algorithm, we transform the equation into its canonical form based on the physical rules behind. This enables solutions of the equation without any simplification nor introduction of additional complexity. Comparing to the classical fast marching based method in anisotropic environment, numerical simulations show that the proposed method is more efficient and robust to external flow perturbations, especially in the case of weak or strong external flow environment.
Class Embodiment Autoencoder (CEAE) for classifying the botanical origins of honey

Autoencoders have shown to be very useful when applied to preprocessing, and pretraining for neural networks. They have also been applied to feature extraction, and data compression although they are not widely used in these applications. For feature extraction, a common problem with autoencoders is that they learn features that can reconstruct the data. However, these features are not necessarily sufficient when it comes to classifying the data. Recently Autoencoders have been adapted for use in semi supervised tasks by introducing a new layer as a classification output during the training process. This structure aims to train features that are good at both data classification and reconstruction and are called supervised autoencoders. In this paper, we apply a new variant of this technique called the Class Embodiment Autoencoder to hyperspectral imaging data of honey samples where we aim to classify the botanical origins of New Zealand honey.
Implementation of Mobile-Based Real-Time Heart Rate Variability Detection for Personalized Healthcare

The ubiquity of wearable devices together with areas like internet of things, big data and machine learning have promoted the development of solutions for personalized healthcare that use digital sensors. However, there is a lack of an implemented framework that is technically feasible, easily scalable and that provides meaningful variables to be used in applications for translational medicine. This paper describes the implementation and early evaluation of a physiological sensing tool that collects and processes photoplethysmography data from a wearable smartwatch to calculate heart rate variability in real-time. A technical open-source framework is outlined, involving mobile devices for collection of heart rate data, feature extraction and execution of data mining or machine learning algorithms that ultimately deliver mobile health interventions tailored to the users. Eleven volunteers participated in the empirical evaluation that was carried out using an existing mobile virtual reality application for mental health and under controlled slow-paced breathing exercises. The results validated the feasibility of implementation of the proposed framework in the stages of signal acquisition and real-time calculation of heart rate variability (HRV). The analysis of data regarding packet loss, peak detection and overall system performance provided considerations to enhance the real-time calculation of HRV features. Further studies are planned to validate all the stages of the proposed framework.
Tracking Topics of Influential Tweets on Fukushima Disaster Over Long Periods of Time

Social media has been extensively and effectively deployed to share information and communicate during emergencies, such as the 2011 Fukushima Daiichi nuclear power plant accident in Japan. It is important to provide information during crises on social media and find the most effective way to transmit information in such situations. It is necessary to carefully preprocess Twitter data because it includes a considerable amount of noise. However, compared to other resources, such as government statistics and newspapers, Twitter provides varied information and is distinguished by its immediacy. We can also regard Twitter data as data that reflect human behaviors, thoughts, and intentions across different domains by characteristics of the platform. We propose an expansion model of Topic Dynamics for tracking the trend and detecting the moments of the occurrences of influential tweets on the Fukushima disaster. Using this method, we obtained the list of bursting words at different periods over a long duration following the Fukushima disaster.
A Comparison Between Novel FPGA-Based Pad Monitoring System Using Ballistocardiography and the Conventional Systems for Synchronization and Gating of CMRI at 3 Tesla: A Pilot Study

This pilot pre-clinical study demonstrates the applicability of a new type of pneumatic cardiac triggering (PCT) for cardiac imaging. The pilot research compares the novel FPGA-based pad monitoring system for cardiac triggering using ballistocardiography (BCG) with conventional systems based on electrocardiography (ECG) and photoplethysmography (PPG). The implemented system enables cardiac triggering without the need to fix the sensors to the patient’s body. This unique approach has the potential to reduce the preparation time for examination and the examination itself and to increase patient’s comfort. The pilot pre-clinical study was conducted on 10 subjects at the Siemens Prisma 3T MRI Scanner within the CEITEC Multimodal and Functional Imaging Laboratory - Central European Institute of Technology, Masaryk University, upon the approval of the Ethics Committee. In total, 748 peaks (heart beats) were detected, with 7.347 correctly identified as true positive peaks, 140 incorrectly detected as false positive peaks, and 106 missed peaks (false negative). For all subjects, the total accuracy reached 96.31% and F1 score reached 98.18%. The applicability of the proposed BCG system was also analyzed in terms of objective (BRISQUE, NIQE, PIQE) and subjective evaluation of the images by 10 experts. The study compares images from two basic cardiac sequences - TRUE FISP (Free Induction Decay Steady-State Precession) and PSIR (Phase Sensitive Inversion Recovery) sequences. The BCG system achieves comparable results with the most frequently used and most accurate clinical ECG system used as gold standard. The results prove that the BCG signal captured by our new sensor can be used as a substitute for ECG signal during MRI exam with reliability of 97%.
Computational Glasses: Vision Augmentations Using Computational Near-Eye Optics and Displays

Wearable computing devices are small enough that they can be worn on the body and are a constant companion to the user. While many wearable devices have been associated with monitoring health or managing diseases, head-mounted displays are traditionally linked to Augmented and Virtual Reality, and generally overlay 3D information that supports professionals or for edutainment. This is surprising as prescription glasses, their traditional siblings, are widely accepted as a standard device for managing focusing errors of the human eye. In this work, we want to make the case for Computational Glasses that utilise technologies from optical see-through head-mounted displays or computational optics to compensate visual impairments. We will introduce some of the seminal works in the field as well as introduce our own work in the field. We will also include some of the challenges for doing research on Computational Glasses as well as give an outlook for future developments.
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Objective: This paper proposes a multiclass deep learning method for the classiﬁcation of dementia using an informant-based questionnaire. Methods: A deep neural network classiﬁcation model based on Keras framework is proposed in this paper. To evaluate the advantages of our proposed method, we compared the performance of our model with industry-standard machine learning approaches. We enrolled 6,701 individuals, which were randomly divided into training data sets (6030 participants) and test data sets (671 participants). We evaluated each diagnostic model in the test set using accuracy, precision, recall, and F1-Score. Results: Compared with the seven conventional machine learning algorithms, the DNN showed higher stability and achieved the best accuracy with 0.88, which also showed good results for identifying normal (F1-score = 0.88), mild cognitive impairment (MCI) (F1-score = 0.87), very mild dementia (VMD) (F1-score = 0.77) and Severe dementia (F1-score = 0.94). Conclusion: The deep neural network (DNN) classiﬁcation model can effectively help doctors accurately screen patients who have normal cognitive function, mild cognitive impairment (MCI), very mild dementia (VMD), mild dementia (Mild), moderate dementia (Moderate), and severe dementia (Severe).
Hybrid Deep Framework for Group Event Recommendation

Group recommender systems suggesting items for a group of users have received many attentions recently. Some aggregation-based and model-based group recommendation methods have been proposed. However, the cold-start problem in group recommendation has not been well studied, which limits the application of group recommendation in many important domains, such as recommending offline events for a group of users. In this paper, we propose a new hybrid deep framework to solve cold-start problem of group event recommendation. Our framework incorporates multiple Restricted Boltzmann Machines (RBM) and conditional RBM. The former extracts high latent group preference from user feedback and group feedback. The latter obtains latent event features based on contextual information, such as location and organizer of events. Thus, the hybrid deep framework can utilize user feedback and contextual information of events to overcome cold-start problem. We conduct exhaustive experiments on two real-world datasets and the results show that our proposed framework outperforms the baseline group recommendation methods and alleviates the cold-start problem of group event recommendation effectively.
Alignment-free Phylogenetic Tree Estimation

Alignment free approaches for constructing phylogenetic tree have been developed and used widely in recent times. The construction of phylogenetic tree based on multiple sequence alignment or pairwise alignment is likely to consume more time and is less accurate than alignment-free methods. For this convenience, a large number of techniques have been implemented regarding alignment-free approaches. Accordingly, a clear concept of previous methods of alignment-free approach can pave the way for further development. In this paper, a detail analysis has been performed on three popular alignment-free methods based on the improvement of average common substring (ACS) which are kmacs, ALFRED-G, Spaced Seed. Evaluating the performances of these methods for various datasets, the pros and cons of each of these have been identified and a new method based on k-mer frequency has been proposed in this paper. Performance evaluation using real sequence datasets shows that in most of the cases our method is more accurate than Kmacs and Spaced Seed and in addition takes less time than all three algorithms- ALFRED-G, kmacs and Spaced Seed.
A Predicting Initial Layout of Components Method Using Machine Learning

In the structural topology optimization approaches, the Moving Morphable Components (MMC) is a new method to obtain the optimized structural topologies by optimizing shapes, sizes, and locations of components. However, the initial layout of components has a strong influence on the rate of convergence. In this paper, a predicting the initial layout of components method using machine learning is developed. In this method, the training set is generated under the MMC framework and supported vector regression (SVR) is employed to establish the mapping between the design parameters characterizing the initial layout and the number of iterations. How to combine machine learning (ML) with the MMC to predict the tilt angle initial layout of components that satisfy a given number of iterations is discussed. Finally, the cantilever beam example is sued to demonstrate the effectiveness of the proposed method.
Robust nonnegative mixed-norm algorithm with weighted $l_{1}$-norm regularization

Nonnegativity as a constraint has attracted much attention in recent years. The recently proposed sign-sign nonnegative least-mean-square (NNLMS) algorithm is robust against impulsive noise, but it suffers from slow convergence speed. In this paper we propose a new nonnegative mixed-norm algorithm with faster convergence speed than sign-sign NNLMS. To further improve the convergence speed for estimating sparse systems with nonnegativity constrains, we apply the weighted $l_{1}$-norm of the adaptive filter weight vector as a regularization term to the cost function of the estimation error. Simulation results are provided to illustrate the superior performance.
CocoQa: Question Answering for Coding Conventions Over Knowledge Graphs

Coding convention plays an important role in guaranteeing software quality. However, coding conventions are usually informally presented and inconvenient for programmers to use. In this paper, we present CocoQa, a system that answers programmer's questions about coding conventions. CocoQa answers questions by querying a knowledge graph for coding conventions. It employs 1) a subgraph matching algorithm that parses the question into a SPARQL query, and 2) a machine comprehension algorithm that uses an end-to-end neural network to detect answers from searched paragraphs. We have implemented CocoQa, and evaluated it on a coding convention QA dataset. The results show that CocoQa can answer questions about coding conventions precisely. In particular, CocoQa can achieve a precision of 82.92% and a recall of 91.10%. Repository: https://github.com/14dtj/CocoQa/ Video: https://youtu.be/VQaXi1WydAU