In order to improve the frequency band utilization and avoid mutual interference between signals, the BD3 satellite signals adopt Binary Offset Carrier (BOC) modulation. On one hand, BOC modulation has a narrow main peak width and strong anti-interference ability; on the other hand, the phenomenon of false acquisition locking caused by the multi-peak characteristic of BOC modulation itself needs to be resolved. In this context, this paper proposes a new BOC(n,n) unambiguous acquisition algorithm based on segmentation reconstruction. The algorithm is based on splitting the local BOC signal into four parts in each subcarrier period. The branch signal and the received signal are correlated with the received signal to generate four branch correlation signals. After a series of combined reconstructions, the final signal detection function completely eliminates secondary peaks. A simulation shows that the algorithm can completely eliminate the sub-peak interference for the BOC signals modulated by subcarriers with different phase. The characteristics of narrow correlation peak are retained. Experiments show that the proposed algorithm has superior performance in detection probability and peak-to-average ratio.

This study tries to predict date of ear emergence of rice plants, based on cropping records over 25 years. Predicting ear emergence of rice plants is known to be crucial for practicing good harvesting quality, and has long been dependent upon old farmers who acquire skills of intuitive prediction based on their long term experiences. Facing with aging farmers, data driven approach for the prediction have been pursued. Nevertheless, they are not necessarily sufficient in terms of practical use. One of the issue is to adopt weather forecast as the feature so that the predictive performance is varied by the accuracy of the forecast. The other issue is that the performance is varied by region and the regional characteristics have not been used as the features for the prediction. With this background, we propose a feature engineering to quantify hidden regional characteristics as the feature for the prediction. Further the feature is engineered based only on observational data without any forecast. Applying our proposal to the data on the cropping records resulted in sufficient predictive performance, ±2.69days of RMSE.

In ball games, acquiring skills to change the direction becomes necessary. For revealing the mechanism of skill acquisition in terms of the relevant field, it would be necessary to take an approach regarding players' cognition as well as body movements measurable from outside. In the phase of change-of-direction performance that this study focuses on, cognitive factors including the prediction of opposite players' movements and judgements of the situation have significance. The purpose of this study was to reveal cognitive transformation in the skill acquisition process for change-of-direction performance. The survey was conducted for three months from August 29 to November 28, 2020, and those surveyed were seven university freshmen belonging to women's basketball club of M University. The way to analyze verbal reports collected in order to explore the changes in the players' cognition is described in Sect.2. In Sect.3, we made a plot graph showing temporal changes in respective factors based on coding outcomes for verbal reports. Consequently, as cognitive transformation in the skill acquisition process for change-of-direction performance, four items such as (1) goal setting for skill acquisition, (2) experience of change in running direction, (3) experience of speed and acceleration, and (4) experience of the movement of lower extremities such as legs and hip joints were suggested as common cognitive transformation. In addition, cognitive transformation varied by the degree of skill acquisition for change-of-direction performance. It was indicated that paying too much attention to body feelings including the position of and shift in the center of gravity in the body posed an obstacle to the skill acquisition for change-of-direction performance.

A low frequency electric field probe that integrates data acquisition and storage is developed in this paper. An electric small monopole antenna printed on the circuit board is used as the receiving antenna; the rear end of the monopole antenna is connected to the integral circuit to achieve the flat frequency response; the logarithmic detection method is applied to obtain a high measurement dynamic range. In addition, a Microprogrammed Control Unit is set inside to realize data acquisition and storage. The size of the probe developed is not exceeding 20 mm × 20 mm × 30 mm. The field strength 0.2 V/m ~ 261 V/m can be measured in the frequency range of 500 Hz ~ 10 MHz, achieving a dynamic range over 62 dB. It is suitable for low frequency electric field strength measurement and shielding effectiveness test of small shield.

The consistent alignment of point clouds obtained from multiple scanning positions is a crucial step for many 3D modeling systems. This is especially true for environment modeling. In order to observe the full scene, a common approach is to rotate the scanning device around a rotation axis using a turntable. The final alignment of each frame data can be computed from the position and orientation of the rotation axis. However, in practice, the precise mounting of scanning devices is impossible. It is hard to locate the vertical support of the turntable and rotation axis on a common line, particularly for lower cost consumer hardware. Therefore the calibration of the rotation axis of the turntable is an important step for the 3D reconstruction. In this paper we propose a novel calibration method for the rotation axis of the turntable. With the proposed rotation axis calibration method, multiple 3D profiles of the target scene can be aligned precisely. In the experiments, three different evaluation approaches are used to evaluate the calibration accuracy of the rotation axis. The experimental results show that the proposed rotation axis calibration method can achieve a high accuracy.

Automatic game strategy data acquisition is important for the realization of the professional strategy analysis systems by providing evaluation values such as the team status and the efficacy of plays. The key factor that influences the performance of the strategy data acquisition in volleyball game is the unknown player roles. Player role means the position with game meaning of each player in the team formation, such as the setter, attacker and blocker. The unknown player role makes individual player unreliable and loses the contribution of each player in the strategy analysis. This paper proposes a court-divisional team motion feature and a player performance curve to deal with the unknown player roles in strategy data acquisition. Firstly, the court-divisional team motion feature is proposed for the team tactical status detection. This feature reduces the influence of individual player information by summing up the ball relative motion density of all the players in divided court area, which corresponds to the different plays. Secondly, the player performance curves are proposed for the efficacy variables acquisition in attack play. The player roles candidates are detected by three features that represent the entire process of a player starting to rush (or jump) to the ball and hit the ball: the ball relative distance, ball approach motion and the attack motion feature. With the 3D ball trajectories and multiple players' positions tracked from multi-view volleyball game videos, the experimental detection rate of each team status (attack, defense-ready, offense-ready and offense status) are 75.2%, 84.2%, 79.7% and 81.6%. And for the attack efficacy variables acquisition, the average precision of the set zone, the number of available attackers, the attack tempo and the number of blockers are 100%, 100%, 97.8%, and 100%, which achieve 8.3% average improvement compared with manual acquisition.

In order to obtain road information, we propose an information acquisition method using infrared laser radar by detecting 3D reflector code on roadside. The infrared laser radar on vehicle scans the 3D reflector code on guardrail. Through experiments, we show that the proposed method is able to obtain road information by detecting 3D reflector code on guardrail.

Named Entity Translation Equivalents extraction plays a critical role in machine translation (MT) and cross language information retrieval (CLIR). Traditional methods are often based on large-scale parallel or comparable corpora. However, the applicability of these studies is constrained, mainly because of the scarcity of parallel corpora of the required scale, especially for language pairs of Chinese and Japanese. In this paper, we propose a method considering the characteristics of Chinese and Japanese to automatically extract the Chinese-Japanese Named Entity (NE) translation equivalents based on inductive learning (IL) from monolingual corpora. The method adopts the Chinese Hanzi and Japanese Kanji Mapping Table (HKMT) to calculate the similarity of the NE instances between Japanese and Chinese. Then, we use IL to obtain partial translation rules for NEs by extracting the different parts from high similarity NE instances in Chinese and Japanese. In the end, the feedback processing updates the Chinese and Japanese NE entity similarity and rule sets. Experimental results show that our simple, efficient method, which overcomes the insufficiency of the traditional methods, which are severely dependent on bilingual resource. Compared with other methods, our method combines the language features of Chinese and Japanese with IL for automatically extracting NE pairs. Our use of a weak correlation bilingual text sets and minimal additional knowledge to extract NE pairs effectively reduces the cost of building the corpus and the need for additional knowledge. Our method may help to build a large-scale Chinese-Japanese NE translation dictionary using monolingual corpora.

Sensor networks are often used to understand underlying phenomena that are reflected through sensing data. In real world applications, this understanding supports decision makers attempting to access a disaster area or monitor a certain event regularly and thus necessary actions can be triggered in response to the problems. Practitioners designing such systems must overcome difficulties due to the practical limitations of the data and the fidelity of a network condition. This paper explores the design of a network solution for the data acquisition domain with the goal of increasing the efficiency of data gathering efforts. An unmanned aerial vehicle (UAV) is introduced to address various real-world sensor network challenges such as limited resources, lack of real-time representative data, and mobility of a relay station. Towards this goal, we introduce a novel cooperative path selection framework to effectively collect data from multiple sensor sources. The framework consists of six main parts ranging from the system initialization to the UAV data acquisition. The UAV data acquisition is useful to increase situational awareness or used as inputs for data manipulation that support response efforts. We develop a system-based simulation that creates the representative sensor networks and uses the UAV for collecting data packets. Results using our proposed framework are analyzed and compared to existing approaches to show the efficiency of the scheme.

Unsupervised spoken unit discovery or zero-source speech recognition is an emerging research topic which is important for spoken document analysis of languages or dialects with little human annotation. In this paper, we extend our earlier joint training framework for unsupervised learning of discrete density HMM to continuous density HMM (CDHMM) and apply it to spoken unit discovery. In the proposed recipe, we first cluster a group of Gaussians which then act as initializations to the joint training framework of nonnegative matrix factorization and semi-continuous density HMM (SCDHMM). In SCDHMM, all the hidden states share the same group of Gaussians but with different mixture weights. A CDHMM is subsequently constructed by tying the top-N activated Gaussians to each hidden state. Baum-Welch training is finally conducted to update the parameters of the Gaussians, mixture weights and HMM transition probabilities. Experiments were conducted on word discovery from TIDIGITS and phone discovery from TIMIT. For TIDIGITS, units were modeled by 10 states which turn out to be strongly related to words; while for TIMIT, units were modeled by 3 states which are likely to be phonemes.

The key factors in overcoming for weak global navigation satellite systems (GNSS) signal acquisition are sensitivity and dwell time. In the conventional MAX/TC criteria, a preset threshold value is used to determine whether the signal exists. Thus the threshold is calculated carefully to balance the sensitivity and the dwell time. Affected by various environment noise and interference, the acquisition circuit will enter verifying mode frequently to eliminate false alarms, which will extend the mean acquisition time (MAT). Based on the periodicity of spread spectrum code in GNSS, this paper presents an improved double-dwell scheme that uses no threshold in detecting weak GNSS signals. By adopting this method, the acquisition performance of weak signal is significantly improved. Theoretical analysis and numerical simulation are presented detailed. Compared with the conventional MAX/TC criteria, the proposed method achieves improved performance in terms of detection probability and false alarm rate. Furthermore, the MAT decreases 15s when C/N0 is above 20dB-Hz. This can enhance the receiver sensitivity and shorten the time to first fix (TTFF).

The Earth's ionosphere can hinder radio propagation with two serious problems: group delay and phase advance. Ionospheric irregularities are significantly troublesome since they make the amplitude and phase of the radio signals fluctuate rapidly, which is known as ionospheric scintillation. Severe ionospheric scintillation could cause loss of phase lock, which would degrade the positioning accuracy and affect the performance of navigation systems. Based on the phase screen model, this paper presents a novel power spectrum model of phase scintillation and a model of amplitude scintillation. Preliminary results show that, when scintillation intensity increases, the random phase and amplitude fluctuations become stronger, coinciding with the observations. Simulations of the scintillation effects on the acquisition of Beidou signals predict acquisition probability. In addition, acquisition probabilities of GPS and Beidou signals under different scintillation intensities are presented. And by the same SNR the acquisition probability decreases when the scintillation intensity increases. The simulation result shows that scintillation could cause the loss of the acquisition performance of Beidou navigation system. According to the comparison of Beidou and GPS simulations, the code length and code rate of satellite signals have an effect on the acquisition performance of navigation system.

In recent years, there are many collision accidents between vehicles due to human errors. As one of countermeasures against the collision accidents, automotive radar systems have been supporting vehicle drivers. By the automotive radar mounted on the vehicle, it is possible to recognize the situation around the vehicle. The ranging with automotive infrared laser radar is very accurate, and able to understand the object existence in the observation around the vehicle. However, in order to grasp the situation around the vehicle, it is necessary to be aware of the attribute of the detected object. The information obtained by the automotive radar vehicle is only the direction and the distance of the object. Thus, the recognition of the attribute of the detected object is very difficult. In this paper, we propose a novel vehicle information acquisition method by using 2D reflector code. Through experiments, we show that the proposed method is able to detect 2D reflector code and is effective for vehicle information acquisition.

A maximum-likelihood code acquisition scheme is investigated for frequency-selective fading channels with an emphasis on the decision strategies. Using the maximum-likelihood estimation technique, we first derive an optimal decision rule, which is optimal in the viewpoint of probability of detection. Based on the derived optimal decision rule, a practical and simple decision rule is also developed, and its performance is assessed for both single dwell and double dwell acquisition systems. Simulation results demonstrate that the proposed acquisition scheme significantly outperforms the previously proposed schemes in frequency-selective fading channels.

The composite binary offset carrier (CBOC) modulated signal contains multi-peaks in its auto-correlation function, which brings ambiguity to the signal acquisition process of a GNSS receiver. Currently, most traditional ambiguity-removing schemes for CBOC signal acquisition approximate CBOC signal as a BOC signal, which may incur performance degradation. Based on Galileo E1 CBOC signal, this paper proposes a novel adaptive ambiguity-removing acquisition scheme which doesn't adopt the approximation used in traditional schemes. According to the energy ratio of each sub-code of CBOC signal, the proposed scheme can self-adjust its local reference code to achieve unambiguous and precise signal synchronization. Monte Carlo simulation is conducted in this paper to analyze the performance of the proposed scheme and three traditional schemes. Simulation results show that the proposed scheme has higher detection probability and less mean acquisition time than the other three schemes, which verify the superiority of the proposed scheme.

The large Doppler offset that exists in high dynamic environments poses a serious impediment to the acquisition of direct sequence spread spectrum (DSSS) signals. To ensure acceptable detection probabilities, the frequency space has to be finely divided, which leads to complicated acquisition structures and excessively long acquisition time at low SNR. A local frequency folding (LFF) method designed for combined application with established techniques dedicated to PN-code synchronization is proposed in this paper. Through modulating local PN-code block with a fixed waveform obtained by folding all frequency cells together, we eliminate the need for frequency search and ease the workload of acquisition. We also analyze the performance of LFF and find that the detection performance degradation from folding can be compensated by FFT-based coherent integration. The study is complemented with numerical simulations showing that the proposed method has advantages over unfolding methods with respect to detection probability and mean acquisition time, and the advantage becomes obvious but limited if the folded number gets larger.

Introducing inter-satellite ranging and communication links in a Global Navigation Satellite System (GNSS) can improve its performance. In view of the highly dynamic characteristics and the rapid but reliable acquisition requirement of inter-satellite link (ISL) signal of navigation constellation, we utilize navigation data, which is the special resource of navigation satellites, to assist signal acquisition. In this paper, we introduce a method that uses the navigation data for signal acquisition from three aspects: search space, search algorithm, and detector structure. First, an iteration method to calculate the search space is presented. Then the most efficient algorithm is selected by comparing the computation complexity of different search algorithms. Finally, with the navigation data, we also propose a method to guarantee the detecting probability constant by adjusting the non-coherent times. An analysis shows that with the assistance of navigation data, we can reduce the computing cost of ISL signal acquisition significantly, as well effectively enhancing acquisition speed and stabling the detection probability.

A probing front end circuit (PFE) senses and digitizes voltage noises at in-circuit locations on such as power supply wiring and substrate taps in a chip, with the simplest circuit construction only with a source follower or a unity gain buffer, followed by a latch comparator. The PFE with 2.5V I/O transistors in a 65nm CMOS technology node demonstrates 9.0 ENOB and 60.7dB SFDR in equivalent sampling at 1.0Gs/s, for a sinusoidal waveform of 10MHz with 200mV peak-to-peak amplitude. Behavioral modeling of an entire waveform acquisition system using PFEs includes the statistical variations of reference voltage and sampling timing. The simulation quantitatively explains the measured dynamic properties of on-chip noise monitoring, such as the AC response in SNDR and digitizing throughputs, with the clear dependency on the frequency and amplitude of input waveforms.

In the field of Factory Automation (FA), process control, and Supervisory Control and Data Acquisition (SCADA), an analog data acquisition system using isolation transformers is commonly used to measure and record analog signals through isolated inputs. In order to improve the input precision of the acquisition system, circuit techniques and a design method of the analog frontend circuit with the signal transformers are proposed in this paper. A circuit technique to compensate for the droop of the pulse signal due to the characteristics of the signal transformer is employed. Also, a numerical analysis of a non-linear circuit equation, which represents a behavior of the core saturation of the signal transformer, is performed in order to determine the parameters of the circuit. Using a small signal transformer, dedicatedly developed for this acquisition system, the performance of the precision achieved for the linearity error is experimentally confirmed within +0.0204%/-0.0215%.

For real-time services, such as VoIP and videoconferencing supplied through a multi-domain MPLS network, it is vital to guarantee end-to-end QoS of the inter-domain paths. Thus, it is important to allocate an appropriate QoS class to the inter-domain paths in each transit domain. Because each domain has its own policy for QoS class allocation, each domain must then allocate an appropriate QoS class adaptively based on the estimation of the QoS class allocation policies adopted in other domains. This paper proposes an adaptive method for acquiring a QoS class allocation policy through the use of reinforcement learning. This method learns the appropriate policy through experience in the actual QoS class allocation process. Thus, the method can adapt to a complex environment where the arrival of inter-domain path requests does not follow a simple Poisson process and where the various QoS class allocation policies are adopted in other domains. The proposed method updates the allocation policy whenever a QoS class is actually allocated to an inter-domain path. Moreover, some of the allocation policies often utilized in the real operational environment can be updated and refined more frequently. For these reasons, the proposed method is designed to adapt rapidly to variances in the surrounding environment. Simulation results verify that the proposed method can quickly adapt to variations in the arrival process of inter-domain path requests and the QoS class allocation policies in other domains.