Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Network Architecture of Decision-Making for Self-Driving Vehicles Based on Long Short-Term Memory and Grasshopper Optimization Algorithm

Long short-term memory network is one of the most important network architectures of decision-making for self-driving vehicles. Nevertheless, the decision-making accuracy of long short-term memory network is limited, the information of the surrounding vehicles is not taken into consideration, which is critical for the decision-making of the ego vehicle, and the classification capability of long short-term memory network is poor. In this article, a novel network architecture called improved long short-term memory network with support vector machine classifier optimized by grasshopper optimization algorithm (GOA-ImLSTM) is proposed. Three improvements are presented in GOA-ImLSTM. Firstly, to consider the information of the surrounding vehicles, a new network architecture, used to extract vital features for self-driving vehicles, with three parallel long short-term memory network units and a network unit serial connected according to vehicle location is designed. Secondly, to improve classification accuracy, support vector machine with stronger classification capability than softmax is introduced to accomplish the classification task. Thirdly, to promote the classification capability of support vector machine, grasshopper optimization algorithm is employed to optimize the parameters of support vector machine. Moreover, to balance exploration and exploitation ability of grasshopper optimization algorithm, dynamic weights in position movement formula are defined. The experiments indicate that GOA-ImLSTM improves the accuracy of results compared with other decision-making methods for self-driving vehicles on the Next Generation SIMulation.
A Survey of Optical and Electronic Delay Lines with a Case Study on Using Optical Delay Lines in 65 nm CMOS Optical Receivers

This paper presents a survey of electronic delay lines and recent advances in silicon photonics-based optical delay lines targeting data and clock synchronization in the optical receiver. Important considerations such as delay range, delay resolution, tunability, and power consumption are discussed. Measurements of two optical receivers developed in CMOS 65 nm utilizing silicon photonic based delay lines are presented demonstrating the benefits of optical delays. Superior energy efficiency of 156 fJ/bit is achieved at 17 Gb/s.
Multi-exposure Image Fusion with Layering Adaptive Detail Enhancement and Ghosting Removal

To solve the problem of the losses of visual details and ghost artifacts in traditional multi-exposure images, a new multi-exposure image fusion algorithm with detail enhancement and ghosting removal is proposed in this paper. Firstly, the local contrast and exposure brightness are used to measure weight maps of the original multi-exposure image. In addition, the decomposed weighted Gaussian pyramid is done by function mapping in order to preserve the information-rich areas with the maximum weight. Secondly, an adaptive algorithm for image layer detail enhancement based on the contrast of the images is proposed to achieve improvements of Laplacian pyramid fusion framework. Most importantly, once the moving objects are captured in most occasions, the ghost will be produced and is needed to be removed, the weight maps are refined to remove ghosting artifacts based on the image difference and two-dimensional information entropy. The experimental results showed that the proposed algorithm has induced an average increase of 10% in image definition, 9% in spatial frequency, and its efficiency has been identified in objective evaluations.
A Wide Input Range 8/16x Time Amplifier with Gated Ring Oscillator Based Time Registers

In this paper, an 8/16x time amplifier (TA) using ring oscillator (ROSC) is presented. For a wide input range, a time register (TR) based on gated ring oscillator (GRO) is proposed which has a variable capacity and can realize the accumulation and subtraction of time. Together with the proposed double multiplication phase detector (PD), the TA is implemented, which achieves a wide input range of more than ±200ps, and programmable gain without any calibration. The gain error is less than ±4.5% in all gain modes under various process, voltage, and temperature (PVT), while consuming 477uw with a 30MHz sampling frequency. The TA has no areaintensive passive devices and occupies an area of 0.0068mm2 in 130nm CMOS process. In addition, by adjusting the capacity of TR, a higher throughput rate or a wider input range can be selected for TA according to the actual requirement.
On Study of Intelligent Control Methods for a Bionic Dolphin Based on Sensory Feedback CPG Model

Widely applied in motion controllers, the central pattern generator (CPG) model is able to generate multiple swimming patterns by the same CPG network, but is poor in stability and adaptability. Therefore, the studies on methods of closed-loop sensory feedback CPG are insufficient. While in this paper, the author proposes a new artificial intelligence control model for a bionic dolphin based on the closed-loop sensory feedback CPG, which is suitable for self-propelled swimming underwater of multi-joint bionic dolphin. The model is composed of analogous lamprey oscillator and a sensory neuron (SN) one-way link network, and it provides oscillation output with stable rhythm for multi-joint bionic dolphin, and is equipped with a sensory feedback mechanism. Moreover,a decision-making mechanism-fuzzy logic control is designed to make it perform through a finite state machine (FSM) algorithm when autonomously executing a given task. According to the experimental analysis on still waters, the model shows good self-propelled swimming performance and high yaw control accuracy in obstacle areas.
Osteogenic capacity of the sinus membrane following maxillary sinus augmentation procedures: A systematic review.

PURPOSE The evidence pertaining to the contribution of the sinus membrane to new bone formation following maxillary sinus augmentation procedures is equivocal. The purpose of this study was to analyse the evidence currently available on the osteogenic capacity of the sinus membrane following maxillary sinus augmentation procedures, and the effect of local delivery of recombinant human bone morphogenic proteins (rhBMPs) on the bone-forming potential of the sinus membrane. MATERIALS AND METHODS An electronic search was conducted using six different databases to identify controlled trials, prospective and retrospective cohort studies, case series and case reports, as well as preclinical (animal) studies reporting on new bone formation in close proximity with the sinus membrane after maxillary sinus augmentation procedures, assessed through histological and/or histomorphometrical evaluation, on the basis of pre-established eligibility criteria. RESULTS No clinical studies were identified. Twenty-six preclinical studies were included in the review. Nine of them supported the osteogenic potential of the sinus membrane, while eight reported no evidence of osteogenicity from the sinus membrane. The nine remaining studies reported on the local effect of rhBMPs. The majority of these nine studies reported enhanced new bone formation in the sinus membrane region. CONCLUSIONS The sinus membrane contains pluripotent mesenchymal cells with the capacity to differentiate and participate in the process of new bone formation. However, the findings from the studies selected in this systematic review do not consistently support that the sinus membrane significantly contributes to new bone formation following maxillary sinus augmentation procedures.
Chilling Disaster Factors in Maize Reproductive Stage Based on Crop Growth Model

In recent years,temperature in the northeast of China has been significantly increasing with global warming.However,since planting areas of relatively late-maturing varieties of maize are shifting towards north,the frequency and intensity of maize chilling disaster are increased in some areas and some years.The prevention and mitigation of chilling disaster of maize in the Northeast depend on our understanding about chilling disaster factors and the degrees of impacts.In this paper,using North East China Maize Growth Model(NEC_MaGM) and historical chilling disaster data,we analyzed temporal and spatial variations of low temperature during the grain filling period and the first frost day,and discussed the match relations between chilling years based on the two disaster factors and actual chilling years.The results indicated that NEC_MaGM had a satisfied outcome in simulating maize growth and development.The absolute deviation at simulated tasseling and maturity stages calculated by NEC_MaGM was 3.6 d and 5.9 d,respectively.The root mean square error of simulated weight of storage organ(WSO) was 839.5 kg ha-1.The average temperature in grain filling stage of maize in the Northeast was 16.7-23.5℃. However,the rapid decrease of temperature(16℃) that usually occurred in the late period of maize development stage had a great impact on grain filling.The average temperature on the first frost day in the northeast was 7.4-9.2℃,which was below the threshold for maize growth.In addition,the first frost day in the Northeast had a trend of delay with time.However,maturity of new maize variety also delayed with global warming,so the first frost frequently occurred before maturity.When the average daily temperature of five successive days lower than 16℃ occurred before DVS =1.9(development stage),there was a severe impact on WSO.The years with above situation were consistent with those actually occurred in historical record.The partial early first frost occurred mainly in the period of 1.8DVS≤1.9 in the Northeast.The numbers of sites where WSO reduced owing to the first frost were more than 11% in the Northeast for 44% of years.The years that the first frost occurred before maize maturity resulted in halt of WSO accumulation were consistent with historical record.It is concluded that low temperature in the grain filling period and the earlier first frost are the key factors of maize chilling disaster in the Northeast,which could be used as the indicators in earlier warning and prediction of chilling disaster based on the crop growth model output.Meanwhile,the crop growth model can be a powerful tool to analyze agro-meteorological disasters factors.
Speaker Recognition System using Gaussian Mixture Model

The medical field is one of the most important are as that is researched by numerous researchers in order to develop the existing system. Though there have been numerous new and innovative systems developed, there are always some challenges present in the field that needs to be solved. It is important for a healthcare system to make sure that it is secure enough and any leak of the patient data could lead to numerous other risk factors. In this paper, we propose a speaker recognition system that makes use of the Gaussian Mixture Model which identifies a person from characteristics of their voice to authenticate them to access the medical records. The model is able to determine the gender of the person to be authenticated and also recognizes the person. The model uses Cepstral Analysis as the feature extraction technique using the feature Mel Frequency Cepstral Coefficient (MFCC). The proposed work is observed to work well when compared with other existing systems and could be of much use in performing and giving access to the authorized employees in a health care system.
A Robust Systematic Approach for Ensuring Optimal Telecom Service Delivery

Ensuring a high degree of customer satisfaction is critical to the success of telecom companies in maintaining or increasing market share. Using churn prediction as an indicator of customer satisfaction, we present a robust system that allows telecom companies to gauge the perceived quality of service delivery. The system employs a novel approach known as the churn drift detection method (CDDM), which can effectively predict customer churn by simultaneously addressing the interdependent problems of concept drift and class imbalance in raw data. We have applied CDDM to the call detail record (CDR) of a south Asian telecom company for churn prediction. By measuring criteria like prediction error and detection delay, experiments have shown that on average, CDDM gives 3 percent less prediction error, 50 percent less detection delay, and almost 1-2 percent better evaluation time than existing techniques.
A Novel Both-Sided Bitline Driving Technique for Low Latency in High Capacity NAND Flash

High capacity design in NAND Flash has become one of the important issues due to increasing demand for memory capacity expansion. As the number of blocks in NAND Flash increases for high capacity, the driving time of bitline (BL) increases with a large RC, which should be resolved for high capacity NAND Flash. This paper proposes a both-sided BL driving scheme (BSBLDS) for high capacity NAND Flash to achieve a fast driving operation by using an area-optimal NMOS at the far side of the BL. In the conventional one-sided BL driving scheme, the BL driving takes a long time because the current of the BL far node is significantly smaller than that of the BL near node. In the proposed BSBLDS, the both-sided BL overdriving and discharging modes are respectively implemented for fast precharge and discharge operations. The proposed BSBLDS achieves 180% faster precharge speed and 190% faster discharge speed than the conventional driving scheme.
Deterministic and Probabilistic Assessment of Distribution Network Hosting Capacity for Wind-Based Renewable Generation

This paper evaluates deterministic and probabilistic approaches for assessing hosting capacity (HC) of distribution networks for wind-based distributed generation (DG). The presented methodology considers variations of demands and DG power outputs, as well as dynamic thermal ratings (DTR) of network components. Deterministic approaches are based on a limited number of scenarios with minimum and maximum demands and DTR limits, while probabilistic approaches use simultaneous hourly values of all input parameters. The presented methodology has three stages. First, locational HC (LHC) of individual buses is calculated assuming connection of a single DG unit in the considered network. Afterwards, the LHC results are used to calculate network HC (NHC), assuming that DG units are connected at all network buses. Finally, busto-bus LHC-sensitivity factors are used to determine LHC and NHC for any number of DG units connected at arbitrary network buses.
Efficient Hybrid Integration of Long-Wavelength VCSELs on Silicon Photonic Circuits

An efficient integration scheme for a vertical cavity surface emission laser (VCSEL) on a silicon photonic circuit is introduced. A grating coupler fabricated on a tilted silicon membrane is used to couple the vertical emission of the VCSEL to the in-plane silicon waveguide with a high coupling efficiency and a low reflection back to the laser. Single-mode and single-polarization VCSELs at 1310nm wavelength band are flip-chip bonded on the silicon photonic chip. A coupling efficiency of -5.8dB is measured at the laser wavelength and a high optical power of 0.66mW is coupled to the silicon waveguide. Data transmission up to 10Gb/s with direct current modulation is realized using the integrated VCSEL. All the structures in the present coupling scheme are massively producible, and the hybrid integration of VCSELs relies on a standard high-accuracy flip-chip process with passive alignment.
A Fractional-Order Analytical Redundancy Approach for Fault Detection on a Hovering Helicopter

This paper presents an analytical redundancy approach for fault detection on a simulated hovering helicopter. For this purpose, a standard state-observer fault-detection technique is extended to adopt a commensurate fractional-order model. The results are favorably compared with those of an integer-order state observer, in terms of after-fault residue amplification and area under the receiver operating characteristic (ROC) curve.
Modelling and Risk Assessment of Special Protection Schemes in Transmission Systems

According to the N-1 criterion, system security in the event of a failure of a network element (N-1 situation) must still be guaranteed. In transmission systems, this is traditionally realized with different preventive (i. e. pre-fault) actions, e. g. the provision of unexploited transmission capacities and redispatch of power plants. In contrast to this, the curative approach ensures a N-1 security reactively (i. e. post-fault). This approach is based on comprehensive information and communication technologies which allow a highly automated process for identifying critical system states and the determination of subsequent corrective actions, e. g. load shedding or generator rejection. These actions are commonly designated as Special Protection Schemes (SPS). However, with increasing applications of SPS, the reliability of SPS needs to be determined and the impact of SPS on the security of supply needs to be assessed. In this contribution analytical methods are applied to determine the probability of different states (in service, limited operation and outage) for five different SPS. Furthermore, the probability and maximum level of overloads, quantifying risk in situations where curative and preventive actions fail, are compared in certain network areas in the transmission system. Results show that a highly redundant design of SPS (especially communication networks, battery storages) is crucial to reach a similar level of reliability compared to conventional network elements. The risk analysis emphasizes that the probability and level of overloads can be reduced or is on a similar level compared to preventive actions.
HER2‐positive breast cancer brain metastasis: A new and exciting landscape

Brain metastases (BrM) incidence is 25% to 50% in women with advanced human epidermal growth factor receptor 2 (HER2)‐positive breast cancer. Radiation and surgery are currently the main local treatment approaches for central nervous system (CNS) metastases. Systemic anti‐HER2 therapy following a diagnosis of BrM improves outcomes. Previous preclinical data has helped elucidate HER2 brain trophism, the blood‐brain/blood‐tumor barrier(s), and the brain tumor microenvironment, all of which can lead to development of novel therapeutic options.
Non-Repudiation Storage and Access Control Scheme of Insurance Data Based on Blockchain in IPFS

The insurance business plays a quite significant role in people’s lives, but in the process of claim settlement, there are still various frauds such that the insurance companies’ refusal to compensate or customers’ malicious fraud to obtain compensation. Therefore, it is very important to ensure fair and just claims. In this paper, by combining the blockchain technology and the ciphertext-policy attribute-based encryption system, we build a scheme for secure storage and update for insurance records under the InterPlanetary File System (IPFS) storage environment in the insurance system. In this scheme, we use the fog node to outsource encryption of insurance records to improve the efficiency of the staff; In addition, we store encrypted insurance records on IPFS to ensure the security of the storage platform and avoid the single point failure of the centralized mechanism. In addition, we use the immutability of the blockchain to achieve the non-repudiation of both insurance companies and the client. The security proof shows that the proposed scheme can achieve selective security against selected keyword attacks. Our scheme is efficient and feasible under performance analysis and real data set experiments.
Control Strategy for the Pseudo-Driven Wheels of Multi-Wheeled Mobile Robots Based on Dissociation by Degrees-of-Freedom

The tractive capability of Multi-wheeled mobile robots (WMRs) depends heavily on the performance of the driving control of their steerable wheels. The recently proposed concept of a pseudo-driven wheel (PDW) provides a simple, versatile, and effective way to improve the tractive capability. However, its focus is to eliminate the internal forces between the wheels and save energy; therefore, it cannot provide sufficient driving force to the body on rough terrains. This paper proposes a control strategy to adjust the power output of a PDW along the directions of two degrees of freedom, an approach that improves the trajectory tracking control of WMRs, especially on rough terrains. To achieve the desired active following control of the PDW, an active disturbance rejection controller is used to compensate the disturbances arising from the wheel-terrain interaction. The Adams and Simulink are used for a joint simulation of which results show that the proposed control strategy is necessary for accurate trajectory tracking, for which the feasibility and effectiveness were verified by the experimental results. In summary, the trajectory tracking accuracy of the WMR and its tractive capabilities on rough, sandy terrain were improved.
Peak to Average Power Ratio and Bit Error Rate Analysis of MultiCarrier Modulation Techniques

Filter bank multicarrier (FBMC) modulation technique has potential output in the 5G systems.The choice of FBMC over Orthogonal Frequency Division Multiplexing (OFDM) is attributed to lower Peak to Average Power Ratio(PAPR) and reduced Bit Error Rate(BER). FBMC benefits due to its structural simplicity achieved in FIR filter realization. The PolyPhase Networking(PPN) filter banks are used in the implementation of FIR filters in FBMC. This in turn decomposes the wideband signals into a narrowband signal, resulting in lower PAPR of the whole system. Parameters like Signal to Noise Ratio(SNR),BER,PAPR are used as performance metrics to analyze OFDM, SC-FDMA and FBMC. Performance Metrics are compared across various channels like Rayleigh, Rician and Double Selective and their influence on the modulation techniques is discussed.
Creating a Metamodel Based on Machine Learning to Identify the Sentiment of Vaccine and Disease-Related Messages in Twitter: the MAVIS Study

MAVIS was a project that aimed to study the interactions in social networks (Twitter and Instagram) between users regarding the sentiment expressed in their messages when they talked about specific vaccines or diseases. The study was performed during the period 2015-2018 and was initially technically done by using a set of commercial tools to identify the polarity of the messages. With the aim of improving the results provided by such tools, we performed a deep analysis of the results from such tools and provide a machine learning method as a metamodel over the results of the commercial tools. In this paper we explain both the technical process performed together with the main results that were obtained.