Miracle-dz/UniFAR-FTUs huggingface dataset:
Spinal Codes over BSC: Error Probability Analysis and the Puncturing Design

As a newly invented type of rateless codes, Spinal codes can be capacity-achieving with short message length and thus hold great prospects for the design of Ultra-Reliable Low-Latency Communication (URLLC) systems. However, the error probability of Spinal codes over Binary Symmetric Channel (BSC) in the finite-length regime lacks explicit analysis in the literature, which in turn hinders efforts to the analytical design of high-efficiency associated techniques, such as the puncturing strategy. In this paper, with the bound on the number of erroneous bits in the Maximum Likelihood (ML) decoding result, we derive the asymptotically tight bound on the Bit Error Rate (BER) of Spinal codes over BSC. Based on this result, we then design the optimal puncturing strategy for Spinal codes over BSC by formulating a rate maximization problem under the constraint of low error probability. In addition, we carry out extensive simulations to verify the correctness of the error probability analysis and the effectiveness of the puncturing strategy design.
A versatile and high-yielding protocol for the preparation of genomic DNA from Capsicum spp. (pepper)

The isolation of genomic DNA from Capsicum spp. (pepper) of the quality and quantity required for analyses based on nucleic acid hybridization and/or the polymerase chain reaction (PCR) has proven difficult. We report here a procedure that reliably yields sufficient quantities of good-quality pepper genomic DNA from a variety of Capsicum genotypes.
Sedimentation monitoring of the active biomass in bioreactors by electrical impedance spectroscopy

Sequencing batch reactors are used in wastewater treatment to produce biogas (methane). Currently, when biogas production ceases the active sludge sedimentation process is not monitored but operated on a fixed time basis. One step towards gas production optimization would be to detect sedimentation and no expend time unnecessarily. In this work the use of electrical impedance spectroscopy is explored as a tool for sedimentation monitoring. Two electrodes were placed vertically on the reactor wall and the impedance measurements were used to compute the impedance estimator E2. Changes in E2 have proven to be reliable enough to detect whenever the active sludge fell below the lower electrode position hence enabling the development of impedance probes to monitor sedimentation for this kind of reactors.
Cellular Network Performance using Machine Learning based Quantitative Association Rule Mining Method

Cellular network performance is often evaluated by key performance indicator (KPI) and key quality indicator (KQI). The association between KQI and KPI is the most critical step to optimize the performance of cellular networks. Traditional association methods between KPI and KQI are based on the end-to-end evaluation. However, these methods require professional engineers to evaluate cellular network performance, and the man-manned evaluation is often inaccurate and labor-consuming, which cannot find out the main caused factor of deterioration networks. In order to solve the problem, we propose a machine learning-based quantitative association rule mining (QARM) method called SWP to associate KPI with KQI. Specifically, we mainly discretize continuous attributes into boolean values and then the association rules of these boolean values are mined by QARM algorithms, such as the Apriori algorithm. Finally, we select the warning intervals and the warning points obtained by SWP method as an optimal output solution. Experiments are conducted over the actual data from telecom operators and the results confirm the feasibility and accuracy of the proposed method
Assessment of Frying Oil Degradation using Laser Direct Writing Interdigital Sensors

The repeated usage of frying oil is proven to be unhealthy because the chemical reactions that occurs in the oil degrades the oil quality and creates high molecular weight and complicated structure saturated compounds which are harmful to human health. A series of interdigital sensors with different electrode width and interelectrode space are designed and produced by the laser direct writing ablation technique to evaluate the vegetable oil quality in regards to usage days. The sensor with 100 pm electrode width and interelectrode space could detect the change of frying oil over time using the LCR meter with scanning ranges of 20 Hz to 10 MHz. The LCR meter can monitor the absolute value of impedance and phase angle. The higher applied voltage for the sensor, the larger the response impedance signals. However, sensors with 300 pm and 500 pm electrode width and interelectrode space did not receive significant signals and functioned as a regular capacitance gauge because of the small magnitude changed signals embedded in the large magnitude of impedance values.
A Digital Twin for Operational Evaluation of Vertical Transportation Systems

The Digital Twin (DT) is a promising concept which aims at creating a virtual replica of an individual system, that can provide information otherwise not available. This paper presents a DT of a vertical transportation system, focusing on how to model this DT, and using it to evaluate the system condition and potential corrective solutions. Exploiting the advantages of object-oriented modeling, this DT has been built ensuring that it may be dynamically adapted to different monitoring scenarios. Moreover, its adaptability is tested showing that reduced models behave similarly. The resulting reduced models are used to estimate installation parameters in a bottom-up way, using the measurements of a scaled test bench. The estimated parameters are used to update a high-fidelity model and simulate the effect of corrective actions.
Modelling Multi-Exit Large-Venue Pedestrian Evacuation With Dual-Strategy Adaptive Particle Swarm Optimization

Modeling individual and crowd behaviors is the basis for evacuation research, which is essential to reduce casualties under emergency conditions in large venues. This paper focuses on the pedestrian evacuation of large-scale venues with multiple exits, and proposes an improved pedestrian evacuation model which uses a novel dual-strategy adaptive particle swarm optimization algorithm with affinity propagation clustering. Compared with the traditional models, the proposed model is demonstrated to have a better performance in simulating pedestrians’ herding behavior in panic situations, especially when not familiar with the environment, as i) individual heterogeneity is considered and affinity propagation clustering is used in population division to simulate the process of people spontaneously gathering into swarms; ii) A dual-strategy updating scheme is designed to balance the cognition difference between the leaders and the agents in the swarm; and iii) Adaptive control of parameters is used to simulate the human-surrounding interaction and psychological fluctuates when the evacuees are moving towards exits. Numerical examples, which simulated the evacuation of a rectangle venue with multiple exits, demonstrate the effectiveness and practicality of the proposed model. Moreover, the influences of pedestrian velocity, characteristics of exits, and leader movement on evacuation are analyzed. Experimental results show that the movement of leaders is different from other evacuees and the parameters of doors, such as width, quantity, and location all have a great influence on evacuation time.
A Novel Pseudonym Linking Scheme for Privacy Inference in VANETs

The leakage of driving positions or traces poses a serious privacy threat upon the users in the vehicular ad-hoc networks (VANETs). A series of pseudonym changing approaches have been proposed to achieve unlinkability between the users’ identities and their driving information. To investigate the effectiveness of the changing strategies on user anonymity, it is important to stand at the side of an adversary to implement posterior linking between different pseudonyms. In this paper, we remove the assumption of motion models commonly used in existing works, and propose a novel pseudonym linking scheme by focusing on the prediction of acceleration and direction angle. Further, we plug several side information (e.g., road structure, traffic signal) into the proposed scheme to improve the linking performance. Finally, based on five representative pseudonym changing strategies, extensive experiments are conducted to evaluate the performance of the proposed linking scheme. The experimental results show that the side-information assisted pseudonym linking scheme achieves success rates of over 74%.
Construction Of A Temporary Message Collection System Using A Drone For Refugees In A Large-Scale Disaster

When a large-scale disaster happens, which causes catastrophic damage to terrestrial information/road traffic networks and forces a huge number of people to live as refugees in shelters, it is necessary to quickly check the safety of victims and damage state in the disaster-hit area. Unmanned Aerial Vehicles (UAVs) including drones are promised to play an essential role in such a disaster recovery process because of its infrastructureless workability.In this paper, we propose a message collection system using a drone for refugees in a disaster-hit area. In order to quickly and efficiently collect messages for all refugees dispersed in shelters, we try to solve a problem to create a flight plan in the form of the Vehicle Routing Problem (VRP), taking into consideration two major drawbacks of drone such as short time to fly and vulnerability to wind. Our numerical results for an area of Osaka, Japan, show that off-line pre-creation of drone routes assuming no wind is risky, because the drone cannot safely return to a depot, if it is used under some windy conditions.
Modified Multisine Fitting Algorithm for the Estimation of Time-Varying Carrier Frequency Signal Parameters

In this paper, a procedure for the estimation of the harmonic signal components of a signal with continuous time-varying carrier frequency it is proposed. The procedure in a first phase estimates the coarse trend of the time-varying carrier frequency and then the coarse values of the harmonic components. In particular, the harmonic components are estimated by using the modified three parameters MultiSine Fitting Algorithm (MSFA), here proposed. To take into consideration the frequency offset that can be introduced in the frequency estimation phase, an iterative procedure is also proposed to minimize the difference between the estimated parameters and those of the original signal. The iterative procedure is based on a modified four parameters MSFA. Both the three and four parameters MSFA are modified to operate with the time varying frequency. The main advantages of the proposed procedure are: (i) a reduced sampling frequency is required, and (ii) the spectral leakage is avoided.
Parameter Identification for Photovoltaic Models Using an Improved Learning Search Algorithm

As a renewable energy resource, solar photovoltaics (PV’s) possess a promising future. Thus, it is important to simulate, evaluate and control the PV systems. To identify the parameters for the photovoltaic (PV) models, an improved learning search optimization algorithm (ILSA) is proposed in this paper. The proposed ILSA has the following three key features: (i) the constant self-adjustment rate changes with the iteration. (ii) a new part with a self-adaptive weighting of the current best and worst solution is introduced to learning patterns to guide the iterative direction. (iii) a perturbation method is added to avoid the algorithm falling into local optimum. In order to assess the effectiveness of ILSA relative to other state-of-the-art algorithms, single diode, double diode, and PV model are used for test. Our experimental results reveal that the ILSA performs well in terms of the accuracy of optimization solutions and effectiveness.
Interval Job Scheduling With Machine Launch Cost

We study an interval job scheduling problem in distributed systems. We are given a set of interval jobs, with each job specified by a size, an arrival time and a processing length. Once a job arrives, it must be placed on a machine immediately and run for a period of its processing length without interruption. The homogeneous machines to run jobs have the same capacity limits such that at any time, the total size of the jobs running on any machine cannot exceed its capacity. Launching each machine incurs a fixed cost. After launch, a machine is charged a constant cost per time unit until it is terminated. The problem targets to minimize the total cost incurred by the machines for processing the given set of interval jobs. We focus on the algorithmic aspects of the problem in this article. For the special case where all the jobs have a unit size equal to the machine capacity, we propose an optimal offline algorithm and an optimal 2-competitive online algorithm. For the general case where jobs can have arbitrary sizes, we establish a non-trivial lower bound on the optimal solution. Based on this lower bound, we propose a 5-approximation algorithm in the offline setting. In the non-clairvoyant online setting, we design a <inline-formula><tex-math notation="LaTeX">$O(\mu)$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:mi>μ</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="ren-ieq1-3002786.gif"/></alternatives></inline-formula>-competitive Modified First-Fit algorithm which is near optimal (<inline-formula><tex-math notation="LaTeX">$\mu$</tex-math><alternatives><mml:math><mml:mi>μ</mml:mi></mml:math><inline-graphic xlink:href="ren-ieq2-3002786.gif"/></alternatives></inline-formula> is the max/min job processing length ratio). In the clairvoyant online setting, we propose an asymptotically optimal <inline-formula><tex-math notation="LaTeX">$O(\sqrt{\log \mu })$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:msqrt><mml:mrow><mml:mo form="prefix">log</mml:mo><mml:mi>μ</mml:mi></mml:mrow></mml:msqrt><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="ren-ieq3-3002786.gif"/></alternatives></inline-formula>-competitive algorithm based on our Modified First-Fit strategy.
Pathological Image Classification Based on Hard Example Guided CNN

The diagnosis of biopsy tissue with hematoxylin and eosin (H&E) stained images has been widely used by pathologists to detect the lesions and assess the malignancy. Nevertheless, the diagnostic result relies on the visual observation of pathologists which may vary from person to person under different circumstances. With the advantage of automatically and adaptively learning features at multiple levels of abstraction, Convolutional Neural Networks (CNNs) have rapidly become promising alternatives for pathological image analysis. Therefore, in this paper, we propose an effective method for tumor classification called Hard Example Guided CNN. Our contribution is twofold: firstly, to optimize image representation, we design the CNN architecture as dual-branch, used for extracting global features and local features simultaneously. Secondly, we propose a re-weight training algorithm, which improves learning accuracy and accelerates the convergence by increasing the weight of hard examples. Extensive experiments on multiple datasets demonstrate the superiority of our proposed classification method.
Scalable Modeling Approach and Robust Hardware-in-the-Loop Testing of an Optimized Interleaved Bidirectional HV DC/DC Converter for Electric Vehicle Drivetrains

Automotive Original Equipment Manufacturers (OEMs) require varying levels of functionalities and model details at different phases of the electric vehicles (EV) development process, with a trade-off between accuracy and execution time. This article proposes a scalable modelling approach depending on the multi-objective targets between model functionalities, accuracy and execution time. In this article, four different fidelity levels of modelling approaches are described based on the model functionalities, accuracy and execution time. The highest error observed between the low fidelity (LoFi) map-based model and the high fidelity (HiFi) physics-based model is 5.04%; while, the simulation time of the LoFi model is ~104 times faster than corresponding one of the HiFi model. A detailed comparison of all characteristics between multi-fidelity models is demonstrated in this paper. Furthermore, a dSPACE SCALEXIO Hardware-in-the-Loop (HiL) testbench, equipped with a minimal latency of $18\mu $ sec, is used for real-time (RT) model implementation of the EV’s HV DC/DC converter. The performance of the entire HiL setup is compared with the Model-in-the-Loop (MiL) setup and the highest RMSE is limited to 0.54 among the HiL and MiL results. Moreover, the accuracy (95.7%) of the passive component loss estimation is verified through the Finite Element Method (FEM) software model. Finally, the experimental results of a full-scale 30-kW SiC DC/DC converter prototype are presented to validate the accuracy and correlation between multi-fidelity models. It has been observed that the efficiency deviation between the hardware prototype and multi-fidelity models is less than 1.25% at full load. Furthermore, the SiC Interleaved Bidirectional Converter (IBC) prototype achieves a high efficiency of 98.4% at rated load condition.
Multi-Armed Bandit Based Task Offloading By Probabilistic V2X Communication in Vehicle Edge Cloud System

Compared with traditional cloud computing, service providers in edge computing are closer to the requester of the demand, and can better meet the requirements of communication transmission delay. The Internet of Vehicles is one of the important application scenarios of 5G, the vehicle edge nodes of it have a large number of free resources, but its highly dynamic characteristics will lead to environmental uncertainty, which will affect task offloading decisions. In this article, in the vehicle edge cloud computing system, a V2V or V2I communication method is selected based on probability to achieve low-latency and high-reliability commu ication requirements, and the Multi-Armed Bandit (MAB) method is used to learn the surrounding environment of the vehicle so that it can adapt dynamically changing environments to make task offloading decisions. After a large number of simulations, our solution can adaptively offload tasks to minimize the expected delay and obtain a sub-linearly increasing learning regret curve in a high-speed mobile vehicle networking scenario.
Correlation of NDVI Obtained by Different Methodologies of Spectral Data Collection in a Commercial Crop of Quinoa (Chenopodium Quinoa) in Central Ecuador

The current study relates the normalized difference vegetation index (NDVI) of a variety of methodologies of quinoa in a controlled experiment and a commercial crop, with airborne multispectral sensor and a spectroradiometer. Hereby, an unmanned aerial vehicles (UAV) flight has been realized, obtaining the NDVI indexes values as well as spectral signatures. Correlation has been performed between the NDVI values obtained with the spectroradiometer (NDVIE), from the automatically generated raster (NDVI1) and from the Red and NIR ortho-mosaics (NDVI2). Hereby, the value of R2 of NDVIE vs. NDVI1 indicates a negative correlation in the data. The data yielded, that this is due to the fact that the NDVI1 map did not perform an orthorectification, due to the difficulty in identifying control points. In the case of the NDVIE vs. correlation of NDVI2, the value of R2 has been very low, allowing only a weak correlation. Preliminarily, the use of NDVI in plants that do not reach a high plant density is not feasible, due to the influence of the soil, what is reflected in the given values.
Doping-Induced Schottky-Barrier Realignment for Unipolar and High Hole Current WSe2 Devices With >108 On/Off Ratio

Direct evidence of O2-plasma induced contact metal Fermi-level realignment is observed in WSe2 based SB field-effect transistors (FETs), leading to high hole currents with on/off ratios >108. The formation of tungsten oxide (WOx) converted from top WSe2 layers not only serves as an effective p-type dopant, but also unpins the Fermi-level of the metal contacts. The phenomena are corroborated by significant threshold voltage shifts, improved hole currents and suppression of the electron branch, observed from our statistical device characteristics. In addition, channel thickness dependence is investigated with the observation of complete suppression of the electron branch in thin WSe2 (< 4.9nm) devices after the O2-plasma treatment, which is interpreted as more substantial increase of SB height to the conduction band of larger bandgaps. Excellent air-stability is also concluded from statistical device results, suggesting a robust p-doping approach for WSe2 through O2-plasma treatment.
Multichannel Speech Dereverberation using Generalized Sidelobe Canceller and Post Filter

Speech signal captured by distant microphones in room includes reverberation as well as noise. It worsens the received signal quality. Therefore to recover the original signal, it is necessary to eliminate reverberation and noise. By dereverberation technique reverberation can be removed and target speech signal can be recovered from received reverberant signal. In this paper reverberation as well as noise is removed by incorporating Generalized Sidelobe Canceller (GSC) along with post filter implemented using Wiener filter. It is experimentally verified that, the intended approach improves speech quality in reverberant, noisy environment for variations in reverberation time, signal to noise ratio (SNR) and distance between source to microphone array.
Design and Implementation of FC-AE Fault Simulation Instrument

With the development of integrated avionics technology, higher requirements are put forward for the transmission speed and bandwidth of the interconnection bus. To tackle with these problems, FC-AE protocol is proposed, which has the advantages of high data bandwidth and is also suitable for long-distance transmission. In order to accelerate the process of designing and developing FC-AE (Fiber Channel Aerospace) devices and building FC-AE systems, we developed a portable FC-AE fault simulation instrument. This instrument can simulate the failure in the bus transmission path between two FC-AE instruments at three rates, which can be used to verify the fault tolerance and error handling program of the system. This paper shows the simulation project and development scheme of the fault simulation instrument firstly. After that, this paper introduces the design of logic firmware based on FPGA in detail, especially the logic design of frame/word trigger and fault simulation under four sets of trigger conditions. Finally, in order to verify whether the function of the instrument is normal or not, we built a test platform with different communication rates, and conducted fault simulation tests. The test results show that the FC-AE fault simulation instrument can meet the requirements of the proposed technical performance, and can reconstruct the fault in the process of system maintenance, which is of great significance to test the stability and reliability of an FC-AE system.