CALCULUS ON FRACTALS BASED UPON LOCAL FIELDS─In memory of Founding Editor Professor M.T.Cheng with great respect and deep sorrow

The main contents in this note are: 1. introduction; 2. locally compact groups and local fields; 3. calculus on fractals based upon local fields; 4. fractional calculus and fractals; 5. fractal function spaces and PDE on fractals.

Secure Localization Based Authentication (SLA) Strategy for Data Integrity in WNS

Wireless Sensor Networks(WSN)has been extensively utilized as a communication model in Internet of Things(IoT).As well,to offer service,numerous IoT based applications need effective transmission over unstable locations.To ensure reliability,prevailing investigations exploit multiple candidate forwarders over geographic opportunistic routing in WSNs.Moreover,these models are affected by crucial denial of service(DoS)attacks,where huge amount of invalid data are delivered intentionally to the receivers to disturb the functionality of WSNs.Here,secure localization based authentication(SLA)is presented to fight against DoS attack,and to fulfil the need of reliability and authentication.By examining state information,SLA projects a trust model to enhance efficacy of data delivery.Indeed,of the prevailing opportunistic protocols,SLA guarantees data integrity by modelling a trust based authentication,providing protection against DoS attackers and diminishing computational costs.Specifically,this model acts as a verification strategy to accelerate?attackers and to handle isolation.This strategy helps SLA in eliminating duplicate transmission and by continuous verification that results from conventional opportunistic routing.Simulation is performed in a MATLAB environment that offers authentic and reliable delivery by consuming approximately 50%of the cost in contrast to other approaches.The anticipated model shows better trade off in comparison to the prevailing ones.

RSSI-based Algorithm for Indoor Localization

Wireless node localization is one of the key technologies for wireless sensor networks. Outdoor localization can use GPS, AGPS (Assisted Global Positioning System) [6], but in buildings like supermarkets and underground parking, the accuracy of GPS and even AGPS will be greatly reduced. Since Indoor localization requests higher accuracy, using GPS or AGPS for indoor localization is not feasible in the current view. RSSI-based trilateral localization algorithm, due to its low cost, no additional hardware support, and easy-understanding, it becomes the mainstream localization algorithm in wireless sensor networks. With the development of wireless sensor networks and smart devices, the number of WIFI access point in these buildings is increasing, as long as a mobile smart device can detect three or three more known WIFI hotspots’ positions, it would be relatively easy to realize self-localization (Usually WIFI access points locations are fixed). The key problem is that the RSSI value is relatively vulnerable to the influence of the physical environment, causing large calculation error in RSSI-based localization algorithm. The paper proposes an improved RSSI-based algorithm, the experimental results show that compared with original RSSI-based localization algorithms the algorithm improves the localization accuracy and reduces the deviation.

Comparison of Indoor Localization Systems Based on Wireless Communications

Localization using a Wireless Sensor Network (WSN) has become a field of interest for researchers in the past years. This information is expected to aid in routing, systems maintenance and health monitoring. For example, many projects aiming to monitor the elderly at home include a personal area network (PAN) which can provide current location of the patient to the medical staff. This article presents an overview of the current trends in this domain. We introduce the mathematical tools used to determine position then we introduce a selection of range-free and range-based proposals. Finally, we provide a comparison of these techniques and suggest possible areas of improvement.

Synthesis and Characterisation of a Biolubricant from Cameroon Palm Kernel Seed Oil Using a Locally Produced Base Catalyst from Plantain Peelings

Biolubricant was synthesized from Cameroon palm kernel oil (PKO) by double transesterification, producing methyl esters in the first stage which were then transesterified with trimethylolpropane (TMP) to give the PKO biolubricant in the presence of a base catalyst obtained from plantain peelings (municipal waste). The yields from both catalysts were significantly similar (48% for the locally produced and 51% for the conventional) showing that the locally produced catalyst could be valorized. The synthesized biolubricant was characterized by measuring its physical and chemical properties. The specific gravity of 1.2, ASTM color of 1.5, cloud point of 0°C, pour point of -9°C, viscosities at 40°C of 509.80 cSt and at 100°C of 30.80 cSt, viscosity index of 120, flash point greater than 210°C and a fire point greater than 220°C were obtained. This synthesized biolubricant was found to be comparable to commercial T-46 petroleum lubricant sample produced industrially from mineral sources. We have therefore used local materials to produce a biolubricant using a cheap base catalyst produced from municipal waste.

A MAP Approach for Vision-based Self-localization of Mobile Robot

一当场，自我本地化系统为在有深入的 3D 里程碑的 3D 环境起作用的活动机器人被开发。机器人通过合并从 odometry 和单向性的照相机收集的信息的一个地图评估者递归地估计它的姿势。我们为这二个传感器造非线性的模型并且坚持说机器人运动和不精密的传感器大小的无常操作应该全部被嵌入并且追踪我们的系统。我们在一个概率的几何学观点和使用 unscented 变换描述无常框架宣传无常，它经历给定的非线性的功能。就我们的机器人的处理力量而言，图象特征在相应投射特征的附近被提取。另外，数据协会被统计距离评估。最后，一系列系统的实验被进行证明我们的系统的可靠、精确的性能。

EDGE-DETECTION BASED ON THE LOCAL VARIANCE IN ANGIOGRAPHIC IMAGES

This paper presents a new method for detection of edges in digital angiographic images. It is found that variances of local regions across edges of images are statistically different from that of those where no edge is crossed. This difference can be utilized for the detection of edges of angiographic images. An algorithm based on local variance is proposed. As a result, the edge-detection algorithm is not sensitive to noise and low-level textures of images. A computer program based on the new algorithm has been developed and used by several hospitals.

Protein Based Localized Surface Plasmon Resonance Gas Sensing

We apply the localized surface plasrnon resonance （LSPR） of gold nanoparticles （GNPs） covalently coupled with cytochrorne c （cyt c） to create a nanobiosensor for detecting hydrogen sulfide （H2S） in the range of 15 lOOppb. Monolayer formation of GNPs on glass surface functionalized with 3-aminopropyltrirnethoxysilane （APTMS） is performed for fabricating a chip-based format of the optical transducer. By chemical introduction of short-chain thiol derivatives on cyt c protein shell via its lysine residues, a very fast self-assembled rnonolayer （SAM） of cyt c is formed on the GNPs. Significant shifts in the LSPR peak （△λLSPR） are observed by reacting H2S with cyt c. Results show a linear relationship between △λLSPR and H2S concentration. Furthermore, shifts in the LSPR peak are reversible and the peak positions return to their pre-exposure values once the H2S is removed. The experirnental results strongly indicate that the protein based LSPR chip can be successfully used as a simple, fast, sensitive and quantitative sensor for H2S detection.

Fingerprint Matching Based on Local Relative Orientation Field

A fingerprint matching method based on local relative orientation field is proposed. It extracts local relative orientation field around each minutia for minutiae matching. Local orientation features are also used to sorting minutiae in order to speed up searching a minutia when pairing minutiae. The experimental result reveals that this method achieves improved recognition accuracy. Key words fingerprint matching - ridge-based minutiae matching - local relative orientation field - sorting minutiae CLC number TP 391 Foundation item: Supported by the National Natural Science Foundation of China (60373023)Biography: ZHU En (1976-), male, Ph. D candidate, research direction: pattern recognition, image processing and information security.

Vision-based Localization from Three-Line Structures (TLS)

This paper presents a novel vision based localization algorithm from three-line structure ( TLS) .Two types of TLS are investigated: 1) three parallel lines ( Structure I) ; 2) two parallel lines and one orthogonal line ( Structure II) .From single image of either structure,the camera pose can be uniquely computed for vision localization.Contributions of this paper are as follows: 1 ) both TLS structures can be used as simple and practical landmarks,which are widely available in daily life; 2) the proposed algorithm complements existing localization methods,which usually use complex landmarks,especially in the partial blockage conditions; 3) compared with the general Perspective-3-Lines ( P3L) problem,camera pose can be uniquely computed from either structure.The proposed algorithm has been tested with both simulation and real image data.For a typical simulated indoor condition ( 75 cm-size landmark,less than 7.0 m landmark-to-camera distance,and 0.5-pixel image noises) ,the means of localization errors from Structure I and Structure II are less than 3.0 cm.And the standard deviations are less than 3.0 cm and 1.5 cm,respectively.The algorithm is further validated with two actual image experiments.Within a 7.5 m × 7.5 m indoor situation,the overall relative localization errors from Structure I and Structure II are less than 2.2% and 2.3% ,respectively,with about 6.0 m distance.The results demonstrate that the algorithm works well for practical vision localization.

Range-Based Localization in Wireless Networks Using Density-Based Outlier Detection

Node localization is commonly employed in wireless networks. For example, it is used to improve routing and enhance security. Localization algorithms can be classified as range-free or range-based. Range-based algorithms use location metrics such as ToA, TDoA, RSS, and AoA to estimate the distance between two nodes. Proximity sensing between nodes is typically the basis for range-free algorithms. A tradeoff exists since range-based algorithms are more accurate but also more complex. However, in applications such as target tracking, localization accuracy is very important. In this paper, we propose a new range-based algorithm which is based on the density-based outlier detection algorithm (DBOD) from data mining. It requires selection of the K-nearest neighbours (KNN). DBOD assigns density values to each point used in the location estimation. The mean of these densities is calculated and those points having a density larger than the mean are kept as candidate points. Different performance measures are used to compare our approach with the linear least squares (LLS) and weighted linear least squares based on singular value decomposition (WLS-SVD) algorithms. It is shown that the proposed algorithm performs better than these algorithms even when the anchor geometry about an unlocalized node is poor.

Local Polynomial Regression Estimator of the Finite Population Total under Stratified Random Sampling: A Model-Based Approach

In this paper, auxiliary information is used to determine an estimator of finite population total using nonparametric regression under stratified random sampling. To achieve this, a model-based approach is adopted by making use of the local polynomial regression estimation to predict the nonsampled values of the survey variable y. The performance of the proposed estimator is investigated against some design-based and model-based regression estimators. The simulation experiments show that the resulting estimator exhibits good properties. Generally, good confidence intervals are seen for the nonparametric regression estimators, and use of the proposed estimator leads to relatively smaller values of RE compared to other estimators.

面向月球科研站任务的地月准实时遥操作模拟验证系统设计与关键技术研究

针对月球科研站任务地月遥操作需求和特点,设计地月准实时遥操作模拟验证系统,对其中的关键技术海量多尺度遥感数据环境感知、空地协同高精度定位、基于混合现实的预测仿真等进行了研究。提出了基于海量多尺度遥感数据的联合处理方法,环境感知数据融合可处理米级至厘米级环境感知结果;设计基于空地多运动平台协同定位的原型软件,空地协同地标约束下,定位精度不低于轨道器影像1个像素;开发了带有力觉反馈的预测仿真验证平台,在2~3 s变时延约束下可在仿真环境下协同演示遥操作过程,真实还原从端的遥操作作业场景,提高遥操作的执行效率和安全性。为地面人员的方案设计、操作手训练、故障处置等提供实时支持,为我国未来实施月球科研站任务筑牢基础。

Community Based Flood Risk Management: Local Knowledge and Actor’s Involvement Approach from Lower Karnali River Basin of Nepal

Connecting to the disaster risk reduction (DRR) studies, community-based initiatives are found to be more effective in both developed and developing countries, with a specific focus on the empowerment of local communities to build resilience. Building on social capital theory, the paper investigates on local knowledge (LK) practices experienced by the actors in an emerging economy using the community-based flood risk management (CB-FRM) approach. The qualitative research method was used by collecting data from focused group discussions, and interviews with the key informants including actors from local governments and non-government organizations. Additionally, informal discussions, field visits, and desk studies were undertaken to support the findings. The findings reveal that the local communities carry out various local knowledge experiences to respond during disaster management phases. They own a creative set of approaches based on the LK and that empowers them to live in the flood-prone areas, accepting the paradigm shift from fighting with floods to living with that. The local actor’s involvement is recognized as an essential component for CB-FRM activities. Yet, their program’s implementation is more oriented towards humanitarian assistance in emergency responses. Even, they often overlook the role of LK. Additionally, the results show a high level of presence of local communities during the preparedness and recovery phases, while NGOs and local governments have a medium role in preparedness and low in recovery phase. The lack of local ownership has also emerged as the major challenge. The research provides valuable insights for integrated CB-FRM policies by adopting to LK practices.

Model for a UV Laser Based Local Polymer Surface Halogenation Process Using a Gaseous Precursor

An analytical model describing the physical relations of a UV-based process for halogenation of polymeric surfaces is presented. The process allows, depending on the parameters, a local halogenation with sharp edges at the interfaces to areas where no halogenation is desired. This is achieved via a nonreactive halogen-containing gaseous precursor and a UV source providing photons which dissociate the precursor photolytically. Thus, only where the UV photons affect the precursor, halogens are generated and the polymer is being halogenated.

在地化理论视域下乡村学校课程建设的现实困境与推进路向

乡村学校课程建设是实现乡村教育高质量发展的重要切入点,也是助推乡土文化传承与乡村社会振兴的重要内生力。“地方”是在地化教育的逻辑起点,在地化理论的提出为引领乡村学校课程建设提供了新视域,具有推动乡村学校课程质量提升、实现乡村学生可持续发展及促进乡土文化传承与创新等实践价值。基于在地化理论审视当下乡村学校课程建设的现状发现,乡村学校课程建设正面临着课程取向的价值性冲突、课程建设主体的认同性焦虑、课程文化的本土性失语及课程评价标准的失焦等“离地化”困境。为此,要超越“农本”或“城本”取向的静态线性思维,运用共生思维,将地方知识、地方文化及地方环境融入课程内容,把学生、生态、社会的统一整全作为推进课程建设的突破口,构建“校-家-社”协同育人共同体,促进学生与在地环境的协调发展。为契合当下乡村学校课程建设的现实需要,建议从课程目标建构、课程建设主体专业赋能、课程资源供给及课程评价实施等方面提出针对性的实践路径,以打造乡村学校课程建设的新生态,走出一条中国乡村学校特色的课程建设之路。

西班牙汉语教育的发展现状、挑战与应对

文章从西班牙现行教育体系着手,阐述了汉语教育在西班牙中小学和高等教育体系背景下的成长与发展历程,以及在西班牙公立大学三大本科专业内开设的中文课程特点和使用的教材。借助田野调查所获取的翔实数据分析西班牙汉语教育的现状、教师队伍和AI时代教材本土化问题及所面临的大挑战。提出了汉语教育在西班牙未来发展的方向和当下需要制定的应对措施来迎接一系列挑战。

基于相关性局域化迭代集合平滑反演渗透系数场

在地下水流和溶质运移问题中,有较多研究基于物理距离局域化集合同化方法反演水文地质参数。当反演参数与观测信息之间不存在物理距离时,这种方法不适用。为了克服这个局限,通过渗透系数与水头信息之间的相关性计算局域化方法的阻滞因子,构建基于相关性的局域化迭代集合平滑方法。为了方便比较,将该方法和一种基于物理距离的局域化迭代集合平滑一同用于同化水头信息反演二维孔隙承压含水层的渗透系数场。算例中考虑了不同集合大小、观测误差及观测数量等因子的组合,便于分析其对渗透系数反演精度的影响。研究结果显示:(1)在所有算例中新方法得到的渗透系数均方根误差范围为[0.8307,0.9590],都小于基于物理距离方法的均方根误差,范围为[0.8394,1.0000];(2)基于物理距离方法得到的渗透系数场空间上存在不连续性,而新方法的结果不存在此现象。文章提出了一种新的基于相关性局域化迭代平滑方法,该方法不需要依赖参数与观测信息之间的物理距离且参数反演精度高于基于物理距离的方法,可作为参数反演的科学工具。

Outlier detection based on multi-dimensional clustering and local density

Outlier detection is an important task in data mining. In fact, it is difficult to find the clustering centers in some sophisticated multidimensional datasets and to measure the deviation degree of each potential outlier. In this work, an effective outlier detection method based on multi-dimensional clustering and local density(ODBMCLD) is proposed. ODBMCLD firstly identifies the center objects by the local density peak of data objects, and clusters the whole dataset based on the center objects. Then, outlier objects belonging to different clusters will be marked as candidates of abnormal data. Finally, the top N points among these abnormal candidates are chosen as final anomaly objects with high outlier factors. The feasibility and effectiveness of the method are verified by experiments.

基于最优四基阵的被动式声源定位估计

针对现有被动式声源定位算法测量精度低、适用范围小等问题,提出了一种基于最优四基阵的被动式声源定位估计方法。该方法通过构建最优四基阵阵列结构以实现多阵元点共用,旨在使用较少的阵元总量实现对目标声源的融合定位估计,从而提高定位精度。并就该阵列模型确定空间目标定位方程组,将求解位置坐标问题转换为求解阵元点之间时延差值问题。进而采用二次分数低阶协方差算法求解脉冲噪声环境下的相应阵元间时延差值,即求得阵元信号的自分数低阶协方差和两阵元间信号的互分数低阶协方差之后,再次计算二者的互分数低阶协方差,以期更大程度上抑制脉冲噪声的影响,提高时延差值估计精度;最终将求得的时延估计信息带回定位方程组已实现对空间声源的定位估计。通过数值仿真和实测实验验证了所提方法的可行性及阵列结构的优越性。在实测实验中对声源定位估计误差仅为0.085 1 m,表明所提方法能较高精度的实现脉冲噪声环境下的声源定位,拓展了被动式声源定位算法的应用场景,具有一定的实际应用价值。