Miracle-dz/UniFAR-FTUs huggingface dataset:
Angular Glint Error Simulation Using Attributed Scattering Center Models

Angular glints are induced essentially by the superposition of scattering responses from multiple scattering centers and usually fluctuate quickly and widely with the aspect angles for complex targets change. This paper presents angular glint simulation of complex conducting targets using attributed scattering centers. The formulae for linear glint errors (LGEs) are derived based on the parametric models of attributed scattering centers and can be applied for real-time LGE simulation. Our analyses and numerical experiments show that the LGE simulation requires more precise scattering center modeling than the radar imaging simulation does. To ensure the accuracy of the LGE simulation, a forward approach that uses induced currents on conducting targets is used to precisely extract the scattering centers. The numerical results show that the LGE simulation based on the developed scattering center model has high accuracy. More importantly, the scattering center model developed for sparse aspect angles can be used to simulate LGEs for dense aspect angles, as required for complex targets, which leads to high efficiency in simulating LGEs.
RAN Runtime Slicing System for Flexible and Dynamic Service Execution Environment

Network slicing is one key enabler to provide the required flexibility and to realize the service-oriented 5G vision. Unlike the core network slicing, radio access network (RAN) slicing is still at its infancy and several works just start to investigate the challenges and potentials to enable the multi-service RAN, toward a serviced-oriented RAN architecture. One of the major concerns in the RAN slicing is to provide different levels of isolation and sharing as per slice requirement. Moreover, both control and user plane processing may be customized allowing a slice owner to flexibly control its service. Enabling dynamic RAN composition with flexible functional split for disaggregated RAN deployments is another challenge. In this paper, we propose a RAN runtime slicing system through which the operation and behavior of the underlying RAN could be customized and controlled to meet slice requirements. We present a proof-of-concept prototype of the proposed RAN runtime slicing system for LTE, assess its feasibility and potentials, and demonstrate the isolation, sharing, and customization capabilities with three representative use cases.
Research of Personalized Recommendation Algorithm Based on Trust and User's Interest

Most traditional recommendation algorithms only consider the binary relationship between users and projects, these can basically be converted into score prediction problems. But most of these algorithms ignore the users's interests, potential work factors or the other social factors of the recommending products. In this paper, based on the existing trustworthyness model and similarity measure, we puts forward the concept of trust similarity and design a joint interest-content recommendation framework to suggest users which videos to watch in the online video site. In this framework, we first analyze the user's viewing history records, tags and establish the user's interest characteristic vector. Then, based on the updated vector, users should be clustered by sparse subspace clust algorithm, which can improve the efficiency of the algorithm. We certainly improve the calculation of similarity to help users find better neighbors. Finally we conduct experiments using real traces from Tencent Weibo and Youku to verify our method and evaluate its performance. The results demonstrate the effectiveness of our approach and show that our approach can substantially improve the recommendation accuracy.
Formal Specification and Verification of Self-Adaptive Concurrent Systems

The assurance of required quality properties is one of the major challenges in self-adaptive systems (SASs). SASs have the capability to adapt their dynamic behavior autonomously at runtime due to uncertain changes in the environment. In general, an SAS is much difficult to specify and verify, because of its highly complex internal behavior and especially when time constraints are involved. In this research, we use modal $\mu $ -calculus ( $\mathcal {M_{\mu }}$ ) for the specification and verification of colored Petri nets-based self-adaptive concurrent systems. We propose self-adaptive multi-agent concurrent system (SMACS) framework that is specifically designed for complex architectures. The internal structure of SMACS framework is based on MAPE-K feedback loop. Each phase of the feedback loop works as an internal agent (Int-Agent) known as Monitor Int-Agent, Analyzer Int-Agent, Planer Int-Agent, and Executer Int-Agent. The decentralized approach is being used in this research, and due to this approach, all agents intelligently adapt their behavior in the environment and send updates to other agents. For verification of internal properties like liveness, safeness, and deadlock-freedom of each agent, the TAPA model checker is being used. For the implementation of SMACS framework, traffic monitoring system is chosen as a case study.
Runtime Energy Estimation and Route Optimization for Autonomous Underwater Vehicles

This paper is focused on improving the self-awareness of autonomous underwater vehicles (AUVs) operating in unknown environments. A runtime estimation framework is introduced to derive energy usage and navigation performance metrics in the presence of external disturbances, such as slowly varying sea currents. These are calculated by a state-of-the-art nonlinear regression algorithm (LWPR) using measurements commonly available on-board modern AUVs without relying on external sensors or a priori knowledge about the environment. The proposed framework is validated on two vehicles, an IVER3 AUV and a Nessie VII AUV, in the context of real sea trials with no modification required for the AUVs or their missions. Derived metrics are used to estimate the feasibility of underwater missions employing the concept of probability of mission completion (PoMC). If environmental effects modify the vehicle's effectiveness, a mission plan update is performed. This is based on an energy-aware route optimization algorithm that is also introduced in the paper. This algorithm, known as energy-aware orienteering problem (EA-OP), shows a practical usage for the runtime metrics. It allows an AUV to optimize its navigation and to maximize its mission's outcome according to measured performances. Simulation results are also presented for inspection scenarios. These show average improvements of 5%–20% for the mission's outcome when using the proposed strategy in the presence of environmental disturbances.
Heuristic for Page-Based Incremental Reprogramming of Wireless Sensor Nodes

Wireless sensor nodes may need code updates for a variety of reasons. These updates are costly in terms of energy and can lead to early battery failure. Incremental reprogramming can save energy by reusing the existing code image on the node and transmitting only a delta file. It can not be assumed that the flash memory is randomly erasable and programmable. When updating the flash page by page, usable data could be unintentionally overwritten, which increases the size of the delta. This paper proposes heuristics for page-based incremental reprogramming of wireless sensor nodes, to find a good sequence for pages in the flash memory to be updated that lead to an overall smaller delta file. A graph model for page dependencies is presented, graph-based algorithms are proposed and page dependency weights are estimated. Results show that the page update sequence can have a significant impact on the size of the delta file.
Optimal Caching in 5G Networks With Opportunistic Spectrum Access

Cache-enabled small base station (SBS) densification is foreseen as a key component of 5G cellular networks. This architecture enables storing popular files at the network edge (i.e., SBS caches), which empowers local communication and alleviates traffic congestion at the core/backhaul network. This paper develops a mathematical framework, based on stochastic geometry, to characterize the hit probability in multi-channel cache-enabled 5G networks with both unicast/multicast capabilities and opportunistic spectrum access. To this end, we first derive the hit probability by characterizing the opportunistic spectrum access success probabilities, service distance distributions, and coverage probabilities. An optimization framework for file caching is then developed to maximize the hit probability. To this end, a simple concave approximation for the hit probability is proposed, which highly reduces the optimization complexity and leads to a closed-form solution. The sub-optimal solution is benchmarked against two widely employed caching distribution schemes, namely, uniform and Zipf caching, through numerical results and extensive simulations. It is shown that the caching strategy should be adapted to the network parameters and capabilities. For instance, diversifying file caching according to the Zipf distribution is better in multicast systems with large number of channels. However, when the number of channels is low and/or the network is restricted to unicast transmissions, it is better to confine caching to the most popular files only.
Real-Time Robust Noncontact Heart Rate Monitoring With a Camera

Noncontact heart rate monitoring that enables the detection of cardiac activity cycles with a normal camera without cutaneous contact has broad application prospects. However, the state-of-the-art approaches lack robustness to the lower illuminance and the varying illuminance. This paper builds an optical noncontact heart rate monitoring framework (lower/vary illumiance PPG, LiPPG) with the aim of improving the robustness of these illuminance situations. In LiPPG, we use the YIQ color space to represent the PPG signal. By applying the Savitzky–Golay filter, we propose a mathematical model, named, multiscale variable-weight Savitsky–Golay combination, to extract signals in the time domain and reduce the disturbance from noise. We test the LiPPG on three scenarios: stationary, facial motion, and varying illuminance, with 18 video clips from 6 participants. The experimental results show that the noncontact heart rate estimation using the LiPPG reduced the errors compared with the gold-standard method of recording from a finger using a pulse oximeter and that it is superior to existing methods.
Development of a wireless CPS for gait parameters measurement and analysis

This work presents a review of recent research related to gait parameters measurement and analysis. Then, a wireless CPS (Cyber-Physical System) setup is described with the goal of monitoring, visualizing, and recording detailed gait data over a conventional IEEE 802.11 WiFi. A detailed description of the design and implementation of the physical part of the CPS is provided in the form of a shoe insole with 12 embedded FSR (Force-Sensitive Resistor) sensors and an IMU (Inertial Measurement Unit) sensor. The circuit is printed on a 2-layer flexible PCB to enhance durability and quality. Multiple insoles were designed and tested for wireless transmission and real-time visualization of sensory data. A discussion of test results shows the use of collected data in identifying gait phases, stride-to-stride durations, and walking direction, in a natural environment.
Ensemble Learning for Overall Power Conversion Efficiency of the All-Organic Dye-Sensitized Solar Cells

Ensemble learning breaks the bottleneck of weak learners and is usually significantly more accurate than base learners. The overall power conversion efficiency of all-organic dye-sensitized solar cells is difficult to obtain by either calculations or experiments. To achieve high-accuracy models, various ensemble learning methods are investigated. Three types of global ensemble models, including homogeneous and heterogeneous ensembles, are constructed, which outperformed the best single base learner, a support vector machine model (MAE: 0.52; <inline-formula> <tex-math notation="LaTeX">$Q^{2}$ </tex-math></inline-formula>: 0.76); in particular, a novel local heterogeneous ensemble model (MAE: 0.34 and <inline-formula> <tex-math notation="LaTeX">$Q^{2}$ </tex-math></inline-formula>: 0.91) achieved high accuracy and generalization. This paper shows ensemble learning model is capable of exploring complicated quantitative structure activity relationship, where the features are distant from targets.
Flip-Flop SEUs Mitigation through Partial Hardening of Internal Latch and Adjustment of Clock Duty Cycle

A radiation-hardness-by-design (RHBD) method for flip-flop single-event upsets (SEUs) mitigation is studied in this paper. This method applies a certain radiation hardened structure, e.g., the dual-interlocked storage cell (DICE), to implement one stage latch of a flip-flop while the SEUs protection for the other stage is realized by adjusting the clock duty cycle to shorten its hold state duration. Since the radiation hardening technique is used for only one stage latch, the overall area and power costs can be lowered. This technique is compatible with the automatic digital design flow and was implemented for an asynchronous first-in-first-out (FIFO) circuit as a case study in this paper.
Real-time flow monitoring for a roller-based peristaltic mini-pump

Micromachined flow sensors have appropriate features that allow the use for microfluidic systems in biosensors. Among these flow sensors, those based on the principle of thermal transfer are the most recently investigated. Here, a miniaturized sensor, which is based on the thermal anemometry, has been used to measure the flow velocity produced by a roller-based peristaltic mini-pump. The detection element consists of a heating element and a temperature monitoring element. Temperature compensation is achieved, and the instantaneous flow velocity is obtained from the sensor output voltage measurement. The sensor has been characterized and positioned in the normal direction to the flow. Eventually, the characteristic curve has been evaluated to provide an adequate control mechanism.
Metabolic Fingerprinting of Seminal Plasma from Non-obstructive Azoospermia Patients: Positive Versus Negative Sperm Retrieval

Background: Non-obstructive azoospermia (NOA) occurs in approximately 10% of infertile men. Retrieval of the spermatozoa from the testicle of NOA patients is an invasive approach. Seminal plasma is an excellent source for exploring to find the biomarkers for presence of spermatozoa in testicular tissue. The present discovery phase study aimed to use metabolic fingerprinting to detect spermatogenesis from seminal plasma in NOA patients as a non-invasive method. Methods: In this study, 20 men with NOA were identified based on histological analysis who had their first testicular biopsy in 2015 at Avicenna Fertility Center, Tehran, Iran. They were divided into two groups, a positive testicular sperm extraction (TESE(+)) and a negative testicular sperm extraction (TESE(−)). Seminal plasma of NOA patients was collected before they underwent testicular sperm extraction (TESE) operation. The metabolomic fingerprinting was evaluated by Raman spectrometer. Principal component analysis (PCA) and an unsupervised statistical method, was used to detect outliers and find the structure of the data. The PCA was analyzed by MATLAB software. Results: Metabolic fingerprinting of seminal plasma from NOA showed that TESE (+) versus TESE(−) patients were classified by PCA. Furthermore, a possible subdivision of TESE(−) group was observed. Additionally, TESE(−) patients were in extreme oxidative imbalance compared to TESE(+) patients. Conclusion: Metabolic fingerprinting of seminal plasma can be considered as a breakthrough, an easy and cheap method for prediction presence of spermatogenesis in NOA.
Study on measurement technology of thermal gas flow based on effective velocity

At low gas velocity, the effect of natural convection on the measurement of thermal gas flow is studied by theoretical-experimental method. In this paper, taking the horizontal thermal probe as the study object, natural convection heat transfer from the thermal probe is analyzed and the velocity corresponding to natural convection (VCNC) is solved. According to the method of vector synthesis, the effective velocity corresponding to forced convection is obtained. A new correction formula based on the effective velocity, for the measurement of the gas mass flow, is presented. Experimental investigation is carried out in the medium pressure closed-loop gas flow device in Tianjin University. The gas velocity is varied from 0.1 to 3.5m/s, resulting in VCNC in the ranges of 1.755m/s to 2.372m/s. The reference error of gas mass flow is controlled within 2.49%.
Near-field millimeter-wave imaging using a fast matrix-free sparse Bayesian learning approach

We present a computationally efficient method, named matrix-free sparse Bayesian learning (SBL), for the 2-D millimeter-wave imaging from compressive measurement. This method is developed within a hierarchical Bayesian framework, using variational inference to approximate the posterior distribution of the underlying signal. To circumvent the difficulty of matrix computation in the variational inference, we replace the original evidence lower bound by its smooth relaxation. Besides, we illustrate that, in the forward transform which converts the scattered field measurement data to the corresponding image, can be expressed as an orthogonal linear operator, and implemented using the 2-D FFT and fast 2-D wavelet filtering. Therefore, no large-scale sensing matrix needs to be explicitly constructed, bringing efficient sparse Bayesian learning within reach. The proposed method is testified by real world experiment, on par with well established fast marginal likelihood method, as well as L1-FISTA based method.
Efficient Adaptive Turbo Equalization for Multiple-Input–Multiple-Output Underwater Acoustic Communications

An efficient adaptive turbo equalization (ATEQ) scheme is proposed for multiple-input–multiple-output underwater acoustic (UWA) communications. The proposed ATEQ scheme utilizes two layers of iterative processing: The inner-layer iteration is the soft-decision-based equalizer parameters adaptation and filtering of received signals in the equalizer, and the outer-layer iteration is the Turbo exchange of extrinsic log-likelihood ratio between the equalizer and decoder. In contrast, the existing ATEQ schemes use hard-decision symbols for filter adaptation but soft symbols for filtering and Turbo iteration. When the adaptive filters are designed and updated via the normalized least mean squares (NLMS) or the improved proportionate NLMS algorithms for low computational complexity and good channel tracking, the soft symbols utilized in both the pilot-assisted and the decision-directed modes of the proposed ATEQ scheme achieve fast convergence with short training sequences, thus achieving high spectrum efficiency. The proposed scheme is evaluated by the field trail data collected in the 2008 Surface Processes and Acoustic Communications Experiment. The results demonstrate that the proposed ATEQ scheme is robust against the severe triply-selective UWA channels and mitigate slow-convergence problem commonly suffered by direct-adaptation equalizers.
A resonant cavity based on birdcage coil for overhauser geomagnetic sensor

The resonant cavity is an important component of Overhauser magnetometer sensor. Its function is to make the working substance generate dynamic nuclear polarization effect in the sensor. An alternative design of resonant cavity based on the birdcage coil structure was proposed to make the resonant frequency in accordance with the polarization frequency of the working substance. The structure and equivalent circuit of resonant cavity were analyzed. The model of resonant cavity was established and S-parameter is calculated to verify the frequency consistency of the resonant cavity and working substance. In addition, output signal experiment and simultaneous comparison testing of sensor prototype were conducted. Overall, this design has the advantages of high performance and strong anti-interference.
Nonintrusive Speech Intelligibility Prediction Using Convolutional Neural Networks

Speech Intelligibility Prediction (SIP) algorithms are becoming popular tools within the development and operation of speech processing devices and algorithms. However, many SIP algorithms require knowledge of the underlying clean speech; a signal that is often not available in real-world applications. This has led to increased interest in nonintrusive SIP algorithms, which do not require clean speech to make predictions. In this paper, we investigate the use of Convolutional Neural Networks (CNNs) for nonintrusive SIP. To do so, we utilize a CNN architecture that shows similarities to existing SIP algorithms, in terms of computational structure, and which allows for easy and meaningful visualization and interpretation of trained weights. We evaluate this architecture using a large dataset obtained by combining datasets from the literature. The proposed method shows high prediction performance when compared with four existing intrusive and nonintrusive SIP algorithms. This demonstrates the potential of deep learning for speech intelligibility prediction.
A Test Platform for the Noise Characterization of SiGe Microbolometer ROICs

This paper introduces an in-circuit performance evaluation system for SiGe microbolometer readout integrated circuits (ROICs) that can characterize the overall system noise performance by emulating microbolometers with MOSFETs biased in the triode region. Specifically, the proposed test platform is designed for the testing of imagers with high resistance SiGe microbolometers. The architecture of the ROIC is based on a bridge with active and reference bolometer pixels with a capacitive transimpedance amplifier input stage and column parallel integration with serial readout. Noise measurements along with simulated resistance curves of the dummy detectors are reported. The prototype with 17-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> pixel pitch has been designed and fabricated in a 0.25-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> SiGe BiCMOS process.
A Personalized Group-Based Recommendation Approach for Web Search in E-Learning

The unprecedented growth of the Internet, its pervasive accessibility, and ease of use have increased students’ dependencies on the Web for quick search and retrieval of learning resources. However, current search engines tend to rely on the correct keywords. This excludes other characteristics, such as the individual’s learning capability and readiness for specific learning materials. As a result, the same set of search-keywords delivers the same search results. This situation hinders the optimization of the Web search engines in supporting the heterogeneity of its users in their learning endeavors. This paper aims to address the issue. It attempts to augment Web search engines with personalized recommendations of search results which match students’ learning competencies and behaviors. The results drawn from our experiments suggest that our novel approach can provide a notable improvement in terms of performance and satisfaction for the students.