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In order to improve the global search ability of K-means algorithm and the clustering effect, a K-means method based on the approximate backbone and the shuffled frog leaping algorithm was proposed. Firstly, the classic iterative formula of the K-means algorithm is replaced by the classic shuffled frog leaping algorithm to obtain better clustering results. Secondly, the K-means algorithm based on the approximate backbone and the shuffled frog leaping algorithm is used for the obtained clustering results. Instead of searching for cluster centers, the cluster division is directly modified. Finally, the experimental results on the UCI dataset show that, the running time of the improved clustering algorithm is shorter than that based on the shuffled frog leaping algorithm only, and clustering results obtained by using the improved clustering algorithm are better than those of other algorithms. In addition, the paper uses the improved clustering algorithm to preprocess medical fundus images to optimize the effect of vascular cutting.

In order to solve the problem of low spraying efficiency and excessive paint waste caused by overspray when the robot automatically sprays irregular plane, a method of spray trajectory planning with boundary-constrained considering spray gun changing posture is proposed. The static spraying model of the spray gun with variable posture, and the dynamic spraying model of the spray gun along the arc path are established separately, an optimization method for the symmetry of coating distribution based on the variable spray angle when the gun spraying along the arc path is proposed. Orthogonal experiment method based on numerical simulation is used to clarify the influence of boundary curve shape and spray gun posture on boundary constraint distance, the prediction of the value range of the boundary constraint distance at the boundary of different curvature radius is realized. Within the range of the value of the boundary constraint, combined with the equal division method of the plane intercept line and the distance search method, a boundary constraint spray path generation algorithm is established by setting a uniform boundary constraint distance value. Finally, a multivariable spraying parameter optimization algorithm based on PSO is established, the spraying velocity, spraying height and spraying angle in the dynamic spraying process are solved, and the coating uniformity is optimized. The effectiveness and feasibility of the proposed spray trajectory planning method is verified by robot spraying experiment for any irregular plane.

With the integration of huge renewable energy sources (RESs) into active distribution networks, how to address the uncertainty outputs of RESs for the day-ahead volt/var control (VVC) is a significant challenge. This paper presents a chance constrained mixed integer second order cone model to handle the nodal power uncertainties and nonlinear branch flow equations. A direct and fast scenario generation method is proposed by employing the group division method and the seven-step probability distribution model of RESs outputs. An efficient and accurate solution method which only uses few larger probability level scenarios instead of all reserved scenarios is also proposed. Numerical simulations on the IEEE 69 standard system show the superiority of the proposed algorithm over traditional Monte Carlo sampling (MCS)-based methods.

This tutorial guides the reader through the open literature on the aspect of Initial Cell Search (ICS) in both Code Division Multiple Access (CDMA) and Orthogonal Frequency Division Multiple Access (OFDMA) systems perspectives with a special focus on the DownLink (DL), where the ICS represents the very first operational stage of commercial mobile station modem’s always-on mobility. There is no comprehensive up-to-date tutorial of the ICSs encompassing 3G, 4G, and 5G systems. Hence, we review the ICS-related operations which have been proposed for Wideband CDMA (W-CDMA), Time Division-Synchronous CDMA (TD-SCDMA), Long Term Evolution Frequency Division Duplex (LTE-FDD), and Time Division LTE (TD-LTE) from the point of view of 3rd Generation Partnership Project (3GPP) standard and implementation. In the case of TD-SCDMA, there is no in-depth literature illustrating the details of ICS. Therefore, this tutorial aims to fill the related gap in the literature. As an evolution from the operational systems, we also shed light on the ICS being employed in New Radio (NR) system operating at sub-6 GHz and provide insight into interesting and challenging technical issues in millimeter-Wave spectrum bands, which is an ultimate foundation to be deployed in the full-scale NR system. Furthermore, we exemplify entire cell search operations over diverse spectrum bands and elucidate intelligent ways of designing multi-mode mobile station modem in a systematic manner. A taxonomy diagram also classifies three main targets constituting ICS procedures in 3G, 4G, and 5G systems. It is expected that this tutorial is beneficial not only for academic researchers but also for practical modem development engineers.

The electronics world is very well described in two distinct but dependent interdisciplinary areas, namely hardware and software. Arithmetic operations are very vital building blocks of an electronic system. An algorithm is a systematic arrangement that helps develop a sophisticated electronic system, including hardware and software aspects. Addition, subtraction, multiplication, and division are critical elements of arithmetic implementation in the electronic system, but fewer efforts have been made to implement division than other arithmetic operations, even though the number of transistors on a chip is increasing beyond the Moore’s law prediction. It is quite complicated to implement arithmetical operations; here, a sophisticated algorithm is essential to successful implementation. Technological upgrades are leading to a new paradigm of applications, where the performance of a division circuit or block is a vital and critical feature of a successful system. The lexicon of algorithms used in the implementation of the division operation in electronics systems is discussed in detail in the present article, which indicates the mathematical formulation, criticality, conversion pattern, hardware requirements, and logic used for conversion. The current report describes the broad classification of dividers into basic classes named digit recurrence, high radix, functional iteration, estimation, a look-up table, and variable latency. It also illustrates that, in practical implementation, many algorithms have been developed that combine one or many classes and are implemented with different hardware architectures. The study indicated the possibility of improving the presently available algorithms or creating a new algorithm to enhance practical implementation.

Distributed coherent aperture radar (DCAR) is an important direction of new generation radar development. It promotes the SNR by N3 times. What’s more, it is capable of achieving high accuracy angle estimation due to its large aperture. However, the grating lobe in its antenna pattern will cause serious ambiguity during the angle estimation process. This study proposed an unambiguous angle estimation method. Firstly, divide the whole distributed array into two parts in three different ways, which have different equivalent phase center spacing, means different accuracy and different ambiguity number. Secondly, phase comparison monopulse is applied to estimate the target angle. Then the high accuracy result can be obtained by utilizing the above results step by step. Moreover, the accuracy of this method and the design criterion on correct angle extraction are also analyzed. Finally, simulation experiments are presented to demonstrate the feasibility and advantages of this method.

With the growth of building energy consumption, determining how to effectively reduce energy waste has become a matter of concern. We believe that by acquiring the basic data for intelligent regulation such as the location and number of personnel, we can achieve the intelligent adjustment of indoor energy-using equipment and ultimately reduce energy consumption. First, this study describes the division of an indoor scene by space coordinate transformation. Second, considering the complex indoor scenes, the main module of the backbone feature network is improved based on YOLOV4-Tiny, making the algorithm more suitable for people detection in complex indoor environments. Finally, the improved algorithm adopted in this study is verified in the indoor real-time monitoring scene. The experimental results show that the improved network's ability to detect people in complex indoor scenes is improved. The overall average accuracy is increased by 18.15%, and the detection speed reaches about 73 Frames Per Second (FPS).

Hybrid modular multilevel converter (Hyb-MMC) consisting of a mix of half bridge (HB) and full bridge (FB) submodules (SMs) has emerged as a topology capable of handling reduced dc voltage conditions without causing large increase in capital cost and losses. However, successful application of Hyb-MMC requires suitable modulation schemes that can establish stable operation. The challenge intensifies in cases of reduced dc voltage operation where the modulation index reaches values much higher than unity. This work proposes a method in which the arm voltage reference is divided between the HB and FB SMs such that the energy balance for both the groups (HB and FB) are maintained independently. The mathematical basis of the method of division of the arm voltage reference has been derived and the upper limit of modulation index for stable operation with the proposed strategy is computed. The method is tested by applying it on a Hyb-MMC simulation in EMTDC/PSCAD platform.

Lung tumor is a complex disease that occurs due to the abnormal growth of lung cells. For efficient treatment planning, earlier detection of tumor is necessary. Computer-Aided Determination (CAD) can be nearly as viable advertisement twofold perusing by giving a moment conclusion to the radiologist and offer assistance in expanding the affectability and exactness of discovery. The mechanized Division with earlier models or utilizing earlier information is troublesome to actualize. The flawless Division of inside structures of the lung is of incredible eagerness to ponder and treat tumors. The proposed Lung Tumor location calculation comprises four stages: Pre-processing, Division, Include extraction and Classification. Major steps in pre-processing are made strides colour based histogram equalization. A novel calculation for lung MRI division utilizing adjusted colour-based K-mean clustering proposed in this work.

We demonstrate optical frequency division of an octave-spanning large repetition rate microcomb to an electronically-detectable frequency in an all-silicon nitride dual microcomb platform

Humanoid robots are robots that resemble humans. ERISA robot is a robot made by EEPIS participating in the KRI robot competition in the humanoid dancing robot division. This robot has a mission to walk and dancing in predetermined game field zones. While dance, the walking-trajectory may drift outside the desired zone since there is no feedback for the robot to correct its trajectory. In this research, a VL53L0X rangefinder sensor and an IMU MPU6050 sensor are deployed in the system to measure the robot position against the fence installed in the game field. The sensor is mounted on the side sole in one of the robot legs. The sensor will give the relative distance from the robot to the fence and its absolute heading angle. The footstep distance will be calculated using odometry by using the robot steps and its angle heading. By using walking-trajectory control the robot can move in the desired trajectory relative to the fence, which is used as the guardrail, while the robot walks to the target zone in the game field.

Variant calculation research revealed the main regularities of the distribution of heat release along the radial and tangential coordinates at the pole division in the active zone of solid gear rotors when starting synchronous machines of various designs. The changes in the values of local specific heat releases in the elements of the surface layer of characteristic cog divisions and, in general, in individual structural areas of the rotors in the slip function are analyzed. Based on numerical modeling, an assessment of design measures aimed at reducing local thermal loads in the rotor elements during asynchronous start of electric motors of compressor stations of main gas pipelines is carried out.

In the intra-frame video coding, an image is divided into small blocks, and the actual coding is performed individually in these blocks. In this paper, the process is considered in the context of the widely used HEVC compression, where the optimum choice of the division is crucial for the ratedistortion performance. Unfortunately, the search for such optimum division needs very many operations, and is done on the basis of “try and check” approach in the classic implementations. The idea of the paper is to replace this complex part of the encoder by a neural network, and some variants of the potential neural networks are studied and compared in the paper. For the chosen network, the complexity of the encoder is vastly reduced at the cost of negligible loss in the rate-distortion performance. These features are demonstrated using an extensive set of frames from many test video sequences.

A method for converting voxel data arrays into a function-based description of surfaces is proposed. When implementing the method, the reverse traversal of the octal tree of division of the object space was used. The transformation takes place through a rigorous mathematical calculation, rather than iterations and approximations that lead to partial loss of information. The method consists in solving a system of equations for each level of subdivision of the object space and minimizing the number of functions. The simulation results confirmed the efficiency and effectiveness of the developed method.

The residue numeral system (RNS) is one of the alternative number representation systems which has a strong potential for using it in computing systems. One of the known problems of the RNS is the complexity of the implementation of some basic arithmetic operations, including comparison and division operations. The paper considers the problem of implementing the comparator for the residue number system with one of the popular moduli set {2n-1, 2n, 2n+1}. Description of two existing methods and its properties are provided. After that, an experimental evaluation of the described methods using modern EDA tools and analysis of results are presented.

Discrete time-varying complex division has perplexed researchers in many fields for many years. How to solve such problem has actually been discussed for a long time. In recent years, lots of researchers have studied the recurrent neural network (RNN) model for solving time-varying problems. As an extended mathematical problem, we introduce the discrete time-varying complex division from the traditional division and reducing disturbance by using the 5-instant discretization formula type advanced discrete time-varying recurrent neural network (5IDF-type advanced DTRNN) model. First, the continuous time-varying complex division is proposed based on the traditional Euclid division. Then, inspired by achievements of previous researches, we equivalently transform the continuous time-varying complex division into a simple matrix-vector equation in the continuous time-varying environment. Accordingly, the discrete time-varying complex division is transformed into the discrete time-varying matrix vector equation. In addition, we also show the 5-instant discretization formula and propose the 5IDF-type advanced DTRNN model. Numerical experiments further prove that validity and superiority of such 5IDF-type advanced DTRNN model.

In this paper, module division was conducted for the pipeline robot system by way of combining axiomatic design method and IDEF functional modeling method, to solve the problem of single function and repeated structure design in the pipeline robot system. This method can extract the general module and customized module in the pipeline robot system via the function-structure mapping and the relation between products and activities. In this way, the compatibility and interchangeability between components can be realized, and resource sharing formed. Different functional modules can be assembled as customized modules according to different task requirements, to realize the multi-functionalization of the pipeline robot and meet market demands.

Chinese authors indirectly portray real social relationships in society by incorporating their own experiences and feelings about politics, economic life, and cultural habits. Social and natural knowledge are mixed and hidden in natural literary language. Therefore, by well defining the type of original text, the pipeline for processing the text and the rules to build the graph, we can extract enough valid and useful information. Based on higher-level information, we can build a literary character graph to promote computers’ comprehension of literature. The identification of opposite characters is a challenging topic because the character relationships are intricate and complex. The cryptic expression in literary works can enhance the readability of the plot, but it is obvious that it increases the cost of understanding. Even for human readers different types of characters are difficult to classify and identify, and it usually needs careful reading for several times to get the subtle differences among the characters. Though extracting knowledge from Chinese literature is a complicated and difficult topic, this paper models the graph of polar literary characters and divides polar communities by extracting polar vertices and polar edges based on the concept of literary sentiment polarity. The results show that using a long-sentence window is a good trade-off. The experiments of modern Chinese polar literature show that the accuracy of the community division method for the integrated graph polarity is obviously better than the method based on the co-occurrence network, and it can automatically match the positive and negative communities as well as build the complete graph structure. It clearly reflects the meaning of literary works from the perspective of polarity. This systematic model describes general standard operation steps for the machine to understand complex Chinese literature. The experiments conducted on seven benchmark Chinese novel datasets demonstrate that the method based on emotional polarity shows a significant improvement compared to baseline performance. Though we use Chinese datasets in this paper, the model and methods are important references for literature analysis and graph extraction in other languages.

In view of the lack of effective energy station site optimization method in the existing integrated energy system (IES) planning, and the failure to consider the load characteristics in the division process of the energy supply area of energy stations, a pipeline network layout method of integrated energy system is proposed based on energy station site selection and load complementary characteristics. Firstly, based on the analysis of the interaction between the station and network planning of IES, an alternated optimal method framework of energy station site, energy supply area division and pipe network layout are proposed. Secondly, based on the energy supply radius of the energy station, this paper proposes a method to determine the number of new energy stations, and based on the topological characteristics of the cellular networks, a method of determine the initial energy station site is proposed. Thirdly, considering the complementary load characteristics, the evaluation index and division method of energy supply area of integrated energy station are proposed. Fourthly, aiming at the problem of pipe network layout on the road, a pipe network optimization method based on Kruskal algorithm is proposed, and optimize the direction of load access to the pipe network by the linear optimization model. Fifthly, the calculation method of equivalent energy distance of load and the optimization method of energy station site based on equivalent energy distance are proposed. Finally, the rationality and effectiveness of this method are verified by an example.

Applying circular economy into agriculture is one of the most efficient methods to implement agricultural sustainable development. Designing different circular economy modes according to local characteristics of various regions can be more efficient. This paper focuses on agricultural circular economy region division in Heilongjiang province in China. First, the specific index system has been constructed. Then, according to these indexes to divide the regions via a novel but more efficient clustering method called Improved Fuzzy min-max neural network with fuzzy lattice inclusion measure (FL-IFMM) proposed in this paper. Heilongjiang province was divided into four agricultural circular economy regions which are respectively Farming(based on grain crops)-animal husbandry dominant region, Farming(based on rice) - animal husbandry dominant region, Vegetable and edible fungi - melons and fruits - animal husbandry dominant region, and Farming - Forestry - Animal husbandry dominant region. Finally, the circular economy modes fitting each region and some detailed policy suggestions have been proposed to help promote agricultural sustainable development in Heilongjiang province.

In this paper, an area decomposition algorithm is presented that is suitable for multiple vessels and multiple aircraft participating in maritime searches over a large region. The algorithm can decompose the entire sea region to be searched (deemed as polygonal area) into nonoverlapping subpolygons (search subareas) according to the sizes of the areas covered by various search facilities while considering their search capabilities as well as their corresponding commence search points. The algorithm draws on the concept of a polygon division algorithm in computational geometry. The main novelty in this study is the optimization of the classic polygon division algorithm by introducing a “maximizing-minimum-angle” strategy, which can effectively compensate for the deficiency of the traditional algorithm, as reflected in the area decomposition result. This improved algorithm can produce rectangle-like subareas, especially for a rectangular search region, which is commonly used in maritime search operations. For nonrectangular search regions, a right-angle division can be achieved so that the shapes of the search subareas are more conducive to planning specific search routes for search facilities. Fast maritime search coverage over a large region can be achieved. The effectiveness of the algorithm is validated by comparing decomposition results before and after the improvement.

Low altitude small target group has the special motion characteristics of orderly as a whole and flexibly as individuals. The performances of traditional target tracking algorithms (such as centroid tracking, geometric centre tracking, etc.) decrease obviously when the group outline changes. Hence in this paper, a robust tracking algorithm for target group with dynamic outline is studied. Aiming at the two key issues of group division and equivalent tracking point estimation, a robust group division algorithm, which can maintain the stability of group division results under any reference point selection, is firstly introduced. Then, on this basis, a new optimal tracking point estimation method is proposed by introducing the group structure into the likelihood function. The simulation results show that this proposed method can effectively improve the robustness of low altitude small target group tracking in the scene of group merging and group splitting.

In the past years, the energy constraint problem is known as a design issue of Wireless Sensor Networks (WSNs) due to equipping the sensor nodes with limited power supplies. During the last few years, Wireless Rechargeable Sensor Networks (WRSNs) have gained researchers attention. In a WRSN, the sensor nodes are equipped with RF circuits, which enables them to receive energy from Wireless Mobile Chargers (WMC). However, most of the existing wireless charging algorithms consider the unlimited power budget for WMCs, which is the opposite of feasibility of a real network environment. Likewise, most of the previous works fail to take full advantage of WMC as it starts recharging the nodes when their energy level reaches a threshold, which leads to increasing the inactive time of WMC. Moreover, although previous works employed WMCs, the network lifetime is limited. However, optimal division of the energy of WMC among nodes can guarantee the perpetual network operation. Therefore, proposing an efficient method that jointly solves these challenges is required. In this paper, a new Fair Energy Division Scheme (FEDS) is presented, which undertakes the permanent network operation by optimizing the energy division at the beginning of each cycle. Simulation results exhibit that FEDS achieves perpetual network lifetime. In addition, the proposed scheme improves energy efficiency, (25%) compared to Uneven Cluster-based Mobile Charging (UCMC) and (75%) compared to Nearest-Job-Next Preemption NJNP; travelling time of WMC, (50%) compared to UCMC and (75%) compared to NJNP. In conclusion, the proposed protocol significantly improves network.

The high-speed railway (HSR) wireless channel models based on field measurements have poor universality and low modeling accuracy due to the limitations of the experimental methods and the terrain conditions. To overcome this problem, this paper considers the wireless channels in various HSR scenarios (such as tunnels, mountains, viaducts, cuttings and plains) as the research objects and establishes a novel finite-state Markov chain (FSMC) optimization simulation model based on the signal-to-noise ratio (SNR) threshold, the channel states and the state transition probability matrix, by using the nonuniform space division SNR quantization strategy (hereinafter referred to as Strategy 1) and the equal-area space division SNR quantization strategy (hereinafter referred to as Strategy 2). The SNR curves that are obtained via simulation closely fit the experimental results; therefore, the proposed simulation model can accurately characterize the channel state in a variety of HSR scenarios. Furthermore, the simulation results demonstrate that in the tunnel scenario, Strategy 1 realizes a smaller mean square error (MSE) and a higher modeling accuracy than Strategy 2. The MSE values of the two strategies are similar in the plain scenario. Strategy 2 realizes a smaller MSE and a higher modeling accuracy in the mountain, viaduct and cutting scenarios.

Short-term traffic flow prediction is very important and provides the basic data for traffic management and route guidance. The rules of traffic flow data during different periods in a day are different. Thus, this article proposes a membership degree-based Markov (MM) model and two period division-based Markov (PM and PW) models. The MM model introduces the membership degree to determine the state of traffic flow. The PM and PW models introduce the Fisher optimal division method to divide one day into several periods based on traffic flow data. Then, the period division-based Markov models integrate the Markov (CM) or weighted Markov (WM) model with the MM model to predict traffic volumes during different periods. The impacts of vehicle type on traffic flow prediction are also discussed. The proposed models are verified using the field data. The results show that: (1) the PM and PW models both perform better than the CM, WM, state membership degree-based Markov and weighted state membership degree-based Markov models; (2) the PW model sometimes performs better than the backward propagation (BP) neural network; (3) when traffic flow data are distinguished by vehicle type, the performance of the PM and PW models can be improved. It is suggested to adopt the proposed period division-based Markov models to predict traffic flow with the concern of vehicle type, so that more accurate traffic flow information can be provided for traffic management and route guidance.

Artificial intelligence algorithms have a leading role in the field of cybersecurity and attack detection, being able to present better results in some scenarios than classic intrusion detection systems such as Snort or Suricata. In this sense, this research focuses on the evaluation of characteristics for different well-established Machine Leaning algorithms commonly applied to IDS scenarios. To do this, a categorization for cybersecurity data sets that groups its records into several groups is first considered. Making use of this division, this work seeks to determine which neural network model (multilayer or recurrent), activation function, and learning algorithm yield higher accuracy values, depending on the group of data. Finally, the results are used to determine which group of data from a cybersecurity data set are more relevant and representative for the intrusion detection, and the most suitable configuration of Machine Learning algorithm to decrease the computational load of the system.

Decomposition-based evolutionary algorithms, especially the branch based on objective space division using a set of uniformly distributed reference vectors, are widely envisioned as a promising technique to solve many-objective optimization problems. Nevertheless, their performance deteriorates severely when solving problems with irregular Pareto fronts (PFs), such as inverted, degenerated, and discontinuous PF shapes. So far, there are some works trying to design reference vector adjustment approaches to make up for such deficiencies of decomposition-based evolutionary algorithms. Unfortunately, the task of designing effective reference vector adjustment approaches adapting to irregular PFs remains challenging. To tackle this challenge, we propose a Two-Stage Adjustment Strategy, namely TSAS, for space division based many-objective evolutionary optimization to deal with the irregular PFs. To be specific, the first stage attempts to approach the boundaries of objective spaces having solutions by inserting new reference vectors between obtained solutions and reference vectors; for achieving better diversity, the second stage adjusts the reference vectors on the basis of both the reference vectors having solutions and the objective vectors of solutions with better diversity, and also adds some reference vectors to explore the sparse sub-spaces. To verify the effectiveness of the proposed TSAS, extensive experiments on benchmarks are carried out to compare it with five recent representative algorithms using three widely-used metrics. The compared results demonstrate the superior performance of our proposal, especially it significantly outperforms all the five algorithms in 45 out of 65 test instances with respect to Inverted Generational Distance (IGD) metric. Furthermore, to test the performance of TSAS in solving real-world problems, 6 test instances from agile satellite task planning are used to compare its performance with five other algorithms. The experimental results show that the TSAS has the best performance on 5 out of 6 test instances.

Crowd formation of aesthetic transformation is considered to have extremely high artistic value and is widely applied in large-scale performances. In this paper, a spatio-temporal hierarchical model that parts the crowd formation transform into multiple granularities is proposed. Its core idea is to add spatio-temporal constraints created by directors into transformation process after multi-level division. In this model, average hash value and energy optimization are used to achieve reasonable crowd formation arrangement, while smooth and collision-free formation transformations are presented by constrained region growth and Kuhn-Munkres algorithm. We have also proposed a framework to achieve the generation of visually pleasing crowd formation transform performance based on the constraints. Besides, a virtual crowd formation transformation simulation was built to verify the effect of the proposed model. Through simulation experiments and comparisons, it was demonstrated that this hierarchical model can generate aesthetic crowd formation transformation with a satisfactory process.

As a general 3D model data format, triangular mesh model has been widely used in mechanical engineering to realize the function of transmitting data between different CAD software, but during the transmission, a large amount of geometric and topological data from mechanical parts has been lost. In order to solve this problem while using triangular mesh model in On-Machine Inspection (OMI), in this paper, a surface division method of parts mesh model for OMI is proposed, intending to solve the problem about mesh model that divided into triangular pieces cannot be operated during OMI. The proposed method contains two processes: boundary line extraction and mesh model division. During the process of extracting boundary line of model surface, a method of extracting coordinate points on the model boundary is proposed by analyzing the Gauss curvature between triangular surfaces, and then using the maximum entropy threshold theory to analyze the correlation between coordinate points to screen out the qualified model boundary line. In the process of mesh model division, based on the extracted model boundary, a mesh segmentation method based on region - growing algorithm is studied to obtain the triangular mesh within the boundary. At last, the validity of the descripted method is verified by some example.

The online monitoring of pointing error (PE) caused by the main reflector (MR) deformation under wind disturbance has become a knotty problem for large reflector antennas. This paper presents a real-time estimation algorithm for monitoring wind-induced PE by optimized acceleration measurement points. First, based on an unbiased minimum-variance input-state estimation theory via accelerations measurement, we set up the PE observation model which illustrates the relation between the structural vibration state and PE. Moreover, compared with the frequency domain integration approach, the non-integral observation model without introducing a low-frequency-cut filter can sense pseudo-static and low-frequency PE. Second, a sensor placement method considering sensor constraint spacing (SPMCSCS) is proposed to achieve a high-quality monitoring effect with low information redundancy. In the method, the constraint spacing between sensors is determined by a novel scan recognition algorithm in which regional division, and regional correlation analysis of the modal shape are carried out successively. Finally, the simulation implementation of a 7.3-m antenna under a wind disturbance is used to illustrate the algorithm.

The strip floating height is a key factor affecting production efficiency and quality of the air cushion furnace. At present, the air cushion furnace with hybrid nozzles is the most typical heating and drying equipment. In order to predict the floating height of the strip in this equipment, a hard-division and multi-model based floating height prediction method is proposed. In hard division method, the process is divided into several stable and vibration stages. On the one hand, time interval based novel density clustering algorithm is proposed so as to avoid the wrong division caused by noise at hard division stage. On the other hand, covariance matrices based local density is presented to better capture dynamic characteristics of the process. In the multi-model stage, a hybrid prediction model under stable state and XGBoost-based model under vibration state are established to predict the strip floating height. The hybrid prediction model is composed of mechanism model and data driven model. The mechanism model is proposed by combining the force balance equation and inviscid theory which can predict the major information of the floating height and the data driven model compensates the error of the mechanism model. A mount of experiments have been carried out on the existing air cushion experimental platform and show desirable prediction effect.

In this paper, an accurate off-line map-matching (OM2) system is designed for complex trajectory networks. It is difficult to complex trajectories input into the hidden Markov model (HMM) directly. OM2 includes three key modules that are pre-processing, map-matching based on weight adaptation HMM (WA-HMM), and post-processing. The pre-processing module divides complex multi-trajectory into single-trajectory sets based on the self-defined trajectory division model (TDM) of crossroads. Another core module is the WA-HMM based on Boxplot, which is used to balance efficiency and accuracy of off-line map-matching. The post-processing is used to map the points of the crossroads so as to further improve the map-matching accuracy. OM2 employs the actual GPS trajectories of the internet company and road map of Sichuan province police GIS (PGIS). Our evaluation results show that the accuracy is about 98%, which is suitable for off-line map-matching and solves the problem of complex trajectory network matching being difficult and time-consuming.

The paper presents algorithms for the generation of Residue Number System (RNS) triples with $SQ=2^{k}-1$ and quadruples with $SQ=2^{k}$ for some k. Triples and quadruples allow us to design efficient hardware implementations of non-modular operations in RNS such as division, sign detection, comparison of numbers, reverse conversion with using of a diagonal function from requiring division with the remainder by the diagonal module SQ. Division with a remainder in the general case is the most complex arithmetic operation in computer technology. However, the consideration of special cases can significantly simplify this operation and increase the efficiency of hardware implementation. We show that there are 5573 good RNS triples (2301 even and 2372 odd) with elements less than 10 000, as the values of SQ vary from $2^{5}-1$ to $2^{27}-1$ . In contrast, RNS quadruples with $SQ=2^{k}$ seem to be quite rare. Restricting our search to sums of the elements in a quadruple less than 4000 we find that exactly 31 such quadruples exist. Their values of SQ vary between 220 and 230 with always even exponent. We suggest the measure of RNS balance and find perfectly balanced RNS among triples according to this measure. We demonstrate the advantages of more balanced quadruples by means of hardware implementation.

Stochastic computing (SC) promises extremely low cost and energy efficiency for error-tolerant arithmetic operations in many emerging applications such as image processing and deep neural networks. Existing SC-based nonlinear functions like division, however, require highly correlated bit-streams, which does not fit well with the existing SC computing framework in which randomness is required for accuracy. In this paper, we propose a novel SC-based divider design based on recently proposed counting-based stochastic computing scheme, which is much more accurate and faster than traditional SC, and does not depend on randomness of bit-streams for accuracy. We show how such counting-based SC can be applied to nonlinear functions like division. The new divider, called counting-based divider, or CBDIV, exploits both the correlation requirement of existing SC-based division methods and high efficiency of counting-based SC scheme. It essentially combines the best of two worlds in SC and the resulting division operation can be performed as a more efficient partial counting process. Experimental results show that the proposed CBDIV implemented in a 32nm technology node outperforms state of art works by 77.8% in accuracy, 37.1% in delay, 21.5% in area, 50.6% in ADP (area delay product) and 25.9% in power. CBDIV also saves 31.9% in energy consumption when compared to the fixed-point division baseline, and is much more energy efficient than existing SC-based dividers for binary inputs and outputs required in efficient image process implementations. Furthermore, CBDIV with 5-bit precision can even outperform state of art works with 7-bit precision in accuracy by 15.4%. Finally, we compare CBDIV with other state of art SC dividers in contrast stretch application and show that CBDIV can improve the accuracy with 20.6dB in average, which is a huge improvement.

Novel parallel algorithm is introduced for electromagnetic transient analysis in order to design plasmonic devices. We have established time-division parallel computation for the finite-difference time-domain (FDTD) method. This completely parallel technique is extremely useful, since the computational task can be equally distributed to many processors and there is no data communication during a computation. The key idea of this parallel algorithm is as follows: (i) The coarse values at temporal sampling points can be independently obtained using a finite-difference complex-frequency-domain with a fast inverse Laplace transform. (ii) These values are transferred to the initial responses of the FDTD frames in many computational processors. (iii) Conventional FDTD computation is simply performed in completely parallel. The computational time for sequential part can be reduced to a fraction of the number of processors. We will apply the proposed technique to designing a plasmonic antenna for all optical magnetic recording purpose.

Point cloud precision registration is the most time-consuming link in registration, and the main factor that hinders point cloud registration from meeting the real-time requirement. In this paper we propose a fast point cloud precision registration method based on spatial grid division. By virtue of the spatial grid, the scattered point cloud is transformed into the spatial grid sequence. During the applicaton of the Iterative Closest Point (ICP) algorithm, we can get the neighborhood information of any point with the aid of the neighborhood information of the grid sequence to efficiently determine the nearest neighbor points. When calculating the nearest neighbor points, the grids don’t interfere with each other. Hence, we can use GPU parallel processing to accelerate the search of the nearest neighbor points. Spatial grid division and parallel processing greatly improve registration efficiency.

High Speed DSP applications are influenced by the efficiency of the arithmetic modules used. Division is a bulky and computational complex operation with high latency than the other arithmetic operations. Vedic Mathematics provides a better method of computation as they are based on the principles on which the human mind works. In this paper, a new scalable architecture for Paravartya division have been proposed in which 8 partial quotient terms are generated parallelly in each stage and produces a quotient that is accurate upto one decimal place. A 16-bit by 16-bit divider based on this architecture is found to be 4.5ns faster than the conventional serial implementation of Paravartya sutra. It is also found to have 62 percent reduction in delay when compared with the existing dividers like SRT, Newton-Raphson and Nikhilam dividers that produces quotient and remainder.

Decision tree is a kind of machine learning method which can decide a corresponding result according to the probability of different eigenvalues, The effective decision number constructed can provide help for our data analysis. The generation of decision tree is a recursive process, It mainly uses the optimal partition attribute as the corresponding tree node, and then uses various values of the attribute to construct branches. In this way, until the data reaches a certain purity, the leaf nodes are obtained, and a decision tree in accordance with the rules is constructed. Among the traditional decision tree algorithms, C4.5 algorithm has a gain rate because of its attribute division. This leads to another obvious disadvantage, that is, it has a preference for the attributes with a small number of values, so that the accuracy of the decision tree is often not particularly ideal. In view of this, this paper proposes an improved E-C4.5 algorithm, which combines information gain and information gain rate to generate a new attribute partition criterion. The attribute partition method greatly eliminates the shortcoming of C4.5 algorithm which has a preference for the attributes with a small number of values, and further improves the decision accuracy of decision tree generation. In this paper, the actual data sets are used to verify the accuracy of the decision tree generated by the improved algorithm compared with the traditional C4.5 algorithm.

Performance measurement system is essential for any organization to guide them optimizing their activities. For a small and medium size entities, the performance measurement system mostly exists but not integrated. The study is a case study in a medium size metal manufacturer producing metal-based parts and component for various industries. The research captured the performance measurement requirement from each division with interviews, studying company’s records, and participative observation. The result was visualized into inter-connected IDEF0 diagrams to ensure performance integration. The performance measurement system was then constructed from their existing excel worksheets into several visualized dashboards using Power BI.

In recent years, dynamic frequency sharing using radio sensors to observe radio wave usage has been attracting attention in response to the shortage of frequency resources. In this paper, we propose an event-driven access control system that notifies the user when the Received Signal Strength Indicator (RSSI) exceeds a certain level, in order to detect the radio wave usage of the existing system. In this paper, we propose a new method of access control based on cluster partitioning. In this paper, we studied an exploratory data aggregation method for each sensor group using cluster division. In this paper, we propose an exploratory data aggregation method for each sensor group using cluster division.

Street surface weakening, for example, potholes, has caused drivers substantial money-related harm each year. Notwithstanding, viable street condition observing has been a proceeding with a challenge to street proprietors. Profundity cameras have a small field of view and can be effectively influenced by vehicle bobbing. Customary picture handling strategies are dependent on calculations. For example, the division can't adjust to shifting ecological and camera situations. In this paper, the object detection API for pothole detection is used to test the set of images and videos and give the output results of the tested images and videos. By evaluating the R-CNN algorithm and SSD mobile net algorithm, the results of the test showed successful results in getting potholes from test images with a maximum confidence level of 93%.

In the past years, the demand for high-precision arithmetic has significantly increased, which raised the need for devices that can handle high-precision computations. Therefore, in this paper, we propose a hardware for high-precision division. The proposed hardware is implemented for 1024 bits, and it can be easily extended to larger bits. The hardware performs division on integer numbers represented in sign and magnitude, and produces quotient and remainder. A priority encoder is used to improve the convergence rate, and to make the dividend and divisor lengths not required as inputs. To reduce complexity, most of the components were implemented in two levels. We provide an upper limit and a closed-form expression for the convergence rate. We synthesized the hardware on a Field-Programmable Gate Array (FPGA) device. We simulated the hardware at the bit level in Matlab using Monte Carlo simulations. We also simulated the hardware in ModelSim. Compared to the existing approaches, the results show that for 64-bit division, our hardware reduces the number of LUTs and slices by 18.4% and 67.5%, respectively, and improves delay by 50.3%. As for 256-bit division, our hardware reduces the number of slices by 60.7% and improves delay by 33.25%, however, it increases the number of LUTs by 11.32%.

In this paper, a proof-of-concept implementation of hyperbolic tanh(ax) and sigmoid(2ax) functions for high-precision as well as compact computational hardware based on stochastic logic is presented. Nonlinear activation introducing the non-linearity in the learning process is one of the critical building blocks of artificial neural networks. Hyperbolic tangent and sigmoid are the most commonly used nonlinear activation functions in machine-learning system such as neural networks. This work demonstrates the stochastic computation of tanh(ax) and sigmoid(2ax) functions-based Bernstein polynomial using a bipolar format. The format conversion from bipolar to unipolar format is involved in our implementation. One achievement is that our proposed implementation is more accurate than the state-of-the-arts including FSM based method, JK-FF and general unipolar division. On average, 90% of improvement of this work in terms of mean square error (MAE) has been achieved while the hardware cost and power consumption are comparable to the previous approaches.

At the heart of each frequency modulated continuous wave (FMCW) radar system lies the synthesizer. Its task is the generation of the waveform for distance and/ or velocity measurements with the system. The performance characteristics of this component have a direct impact on key performance criteria of the radar system, such as measurement speed and accuracy. A number of different synthesizer concepts exist in the literature [1] . Nowadays the most popular concepts rely on a phased-locked loop (PLL) for the generation of the radar chirp. Two different PLL-based synthesizer solutions exist today. One is based on an Integer-N PLL and a direct digital synthesizer (DDS). In this concept the DDS acts as a reference oscillator for the PLL and the chirp is generated at the reference frequency. The task of the PLL is then to multiply the frequency by its division factor N to the high output frequency. The output frequency is a fixed multiple of the reference frequency. The other solution is the usage of a fractional-N PLL. In this concept the reference frequency is fixed and the division value of the frequency divider is variable. Normally the step size of the output frequency would be limited to the reference frequency. However with a continuous switching of the divider value any fractional value can be generated for the division ratio. Early fractional-N PLL used a regular pattern of divider values generated with a fractional accumulator. This however leads to additional spurs in the output spectrum. An improved version of a fractional-N PLL uses a ΔΣ modulator to mitigate this effect. The fractional-N PLL is superior to the integer-N approach. On the one hand the power consumption is significantly reduced. Instead of a power-hungry DDS, often using more than 100mW, only a ΔΣ modulator with a power consumption of around 1mW is necessary. On the other hand also the chip area and thus the costs for the system are reduced.

This paper describes details of floating-point square root calculation algorithms using Taylor-series expansion and mantissa region division in the viewpoint of the efficient dedicated hardware design. Taylor-series expansions of the square root are examined at the center points of divided mantissa regions, and based on these, square root calculation hardware design balances among accuracy, numbers of basic floating point arithmetic operations (multiplications and additions/ subtractions) as well as the required look-up table size are clarified quantitatively. These results lead to obtaining the suitable algorithm for floating-point square root calculation, and building its corresponding hardware architecture for the digital processor designer.

This paper presents 4-layer(4L) Layered Division Multiplexing(LDM) using ATSC 3.0. The studies on 4L LDM with PN sequences and on 4L LDM with QPSK are conducted by applying PN sequences and QPSK as the 4th layer in 3-layer(3L) LDM with amplitude shift keying respectively. In the previous researches, 4L LDM with PN sequences using DVB-T2 was conducted and its bit error rate(BER) analysis was performed. We conduct studies on 4L LDM with PN sequences using ATSC 3.0 and present its BER analyses as subsequent researches of 4L DLM with PN sequences using DVB-T2. Additionally, we conduct researches on 4L LDM with QPSK as the 4th layer and present its BER analyses.