Source localization based on field signatures:Laboratory ultrasonic validation

Location awareness in wireless networks is essential for emergency services,navigation,gaming,and many other applications.This article presents a method for source localization based on measuring the amplitude-phase distribution of the field at the base station.The existing scatterers in the target area create unique scattered field interference at each source location.The unique field interference at each source location results in a unique field signature at the base station which is used for source localization.In the proposed method,the target area is divided into a grid with a step of less than half the wavelength.Each grid node is characterized by its field signature at the base station.Field signatures corresponding to all nodes are normalized and stored in the base station as fingerprints for source localization.The normalization of the field signatures avoids the need for time synchronization between the base station and the source.When a source transmits signals,the generated field signature at the base station is normalized and then correlated with the stored fingerprints.The maximum correlation value is given by the node to which the source is the closest.Numerical simulations and results of experiments on ultrasonic waves in the air show that the ultrasonic source is correctly localized using broadband field signatures with one base station and without time synchronization.The proposed method is potentially applicable for indoor localization and navigation of mobile robots.

Virtual local target method for avoiding local minimum in potential field based robot navigation

A novel robot navigation algorithm with global path generation capability is presented. Local minimum is a most intractable but is an encountered frequently problem in potential field based robot navigation.Through appointing appropriately some virtual local targets on the journey, it can be solved effectively. The key concept employed in this algorithm are the rules that govern when and how to appoint these virtual local targets. When the robot finds itself in danger of local minimum, a virtual local target is appointed to replace the global goal temporarily according to the rules. After the virtual target is reached, the robot continues on its journey by heading towards the global goal. The algorithm prevents the robot from running into local minima anymore. Simulation results showed that it is very effective in complex obstacle environments.

Postoperative chemotherapy with S-1 plus oxaliplatin versus S-1alone in locally advanced gastric cancer(RESCUE-GC study): a protocol for a phase Ⅲ randomized controlled trial

Background： The ACTS-GC study had shown postoperative adiuvant therapy with S-1 improved survival of patients with locally advanced gastric cancer. Addition of oxaliplatin to S-1 is considered to be acceptable as one of the treatment options for gastric cancer patients after radical gastrectomy with D2 lymph node excision. Methods： We have commenced a randomized phase III trial in December 2016 to evaluate S-I plus oxaliplatin compared with S-1 alone in the adjuvant setting for locally advanced gastric cancer. A total of 564 patients will be accrued from 13 Chinese institutions in two years. The primary endpoint is 3-year relapse-free survival. The secondary endpoints are 5-year overall survival, proportion of patients who complete the postoperative chemotherapy and incidence of adverse events. Ethic and dissemination： The trial has been approved by the institutional review board of each participating institution and it was activated on December, 2016. The enrollment will be finished in December, 2018. Patient＇s follow-up will be ended until December, 2023. Trial registration： ClinicalTrials.gov, identifier： NCT02867839. Registered on August 4, 2016.

Locally Linear Back-propagation Based Contribution for Nonlinear Process Fault Diagnosis

This paper proposes a novel locally linear backpropagation based contribution(LLBBC) for nonlinear process fault diagnosis. As a method based on the deep learning model of auto-encoder(AE), LLBBC can deal with the fault diagnosis problem through extracting nonlinear features. When the on-line fault diagnosis task is in progress, a locally linear model is firstly built at the current fault sample. According to the basic idea of reconstruction based contribution(RBC), the propagation of fault information is described by using back-propagation(BP) algorithm. Then, a contribution index is established to measure the correlation between the variable and the fault, and the final diagnosis result is obtained by searching variables with large contributions. The smearing effect, which is an important factor affecting the performance of fault diagnosis, can be suppressed as well,and the theoretical analysis reveals that the correct diagnosis can be guaranteed by LLBBC. Finally, the feasibility and effectiveness of the proposed method are verified through a nonlinear numerical example and the Tennessee Eastman benchmark process.

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.

Developing high-efficiency base editors by combining optimized synergistic core components with new types of nuclear localization signal peptide

The clustered regularly interspaced short palindromic repeats(CRISPR)–CRISPR-associated protein(Cas) system has been widely used for genome editing. In this system, the cytosine base editor(CBE) and adenine base editor(ABE) allow generating precise and irreversible base mutations in a programmable manner and have been used in many different types of cells and organisms. However, their applications are limited by low editing efficiency at certain genomic target sites or at specific target cytosine(C) or adenine(A) residues. Using a strategy of combining optimized synergistic core components, we developed a new multiplex super-assembled ABE(sABE) in rice that showed higher base-editing efficiency than previously developed ABEs. We also designed a new type of nuclear localization signal(NLS) comprising a FLAG epitope tag with four copies of a codon-optimized NLS(F4NLS^(r2)) to generate another ABE named F4NLS-sABE. This new NLS increased editing efficiency or edited additional A at several target sites. A new multiplex super-assembled CBE(sCBE) and F4NLS^(r2) involved F4NLS-sCBE were also created using the same strategy. F4NLS-sCBE was proven to be much more efficient than sCBE in rice. These optimized base editors will serve as powerful genome-editing tools for basic research or molecular breeding in rice and will provide a reference for the development of superior editing tools for other plants or animals.

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.

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.

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。

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.

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.

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.

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.

An Intelligent Early Warning Method of Press-Assembly Quality Based on Outlier Data Detection and Linear Regression

Focusing on controlling the press-assembly quality of high-precision servo mechanism,an intelligent early warning method based on outlier data detection and linear regression is proposed.Linear regression is used to deal with the relationship between assembly quality and press-assembly process,then the mathematical model of displacement-force in press-assembly process is established and a qualified press-assembly force range is defined for assembly quality control.To preprocess the raw dataset of displacement-force in the press-assembly process,an improved local outlier factor based on area density and P weight(LAOPW)is designed to eliminate the outliers which will result in inaccuracy of the mathematical model.A weighted distance based on information entropy is used to measure distance,and the reachable distance is replaced with P weight.Experiments show that the detection efficiency of the algorithm is improved by 5.6 ms compared with the traditional local outlier factor(LOF)algorithm,and the detection accuracy is improved by about 2%compared with the local outlier factor based on area density(LAOF)algorithm.The application of LAOPW algorithm and the linear regression model shows that it can effectively carry out intelligent early warning of press-assembly quality of high precision servo mechanism.

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.

Elevating Localization Accuracy in Wireless Sensor Networks:A Refined DV-Hop Approach

Localization is crucial in wireless sensor networks for various applications,such as tracking objects in outdoor environments where GPS(Global Positioning System)or prior installed infrastructure is unavailable.However,traditional techniques involve many anchor nodes,increasing costs and reducing accuracy.Existing solutions do not address the selection of appropriate anchor nodes and selecting localized nodes as assistant anchor nodes for the localization process,which is a critical element in the localization process.Furthermore,an inaccurate average hop distance significantly affects localization accuracy.We propose an improved DV-Hop algorithm based on anchor sets(AS-IDV-Hop)to improve the localization accuracy.Through simulation analysis,we validated that the ASIDV-Hop proposed algorithm is more efficient in minimizing localization errors than existing studies.The ASIDV-Hop algorithm provides an efficient and cost-effective solution for localization in Wireless Sensor Networks.By strategically selecting anchor and assistant anchor nodes and rectifying the average hop distance,AS-IDV-Hop demonstrated superior performance,achieving a mean accuracy of approximately 1.59,which represents about 25.44%,38.28%,and 73.00%improvement over other algorithms,respectively.The estimated localization error is approximately 0.345,highlighting AS-IDV-Hop’s effectiveness.This substantial reduction in localization error underscores the advantages of implementing AS-IDV-Hop,particularly in complex scenarios requiring precise node localization.

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.

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.

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.