Miracle-dz/UniFAR-FTUs huggingface dataset:
Mapping Bottom Topography With X-band Slar

On January 19th, 1988 an experiment with X-band SLAR has been performed to study the possibilities and limi tations for cartographic mapping of sea bottom topography with the aid of imaging radar. The bottom topography of the test area is dominated by sand waves with a height between 2 and 6 m and a crest-to-crest distance of typically 500 m. Simultaneously with the radar measurements wind and wave data were collected on the Noordwijk Tower (MPN) at 20 km from the test area, and the current velocity profile was measured from two ships in the test area. The results of the current measurements agree with the continuity equati­ ons. It is shown that the extremes in the radar backscatter are located right above regions with maximum bottom slope, with an error in position of two pixel sizes (30 m), in agreement with simple relaxati­ on models like the model of Alpers and Hennings (1984).
NON-PARAMETRIC BAYESIAN ANALYSIS OF HETEROGENEOUS DATA

Abstract : Under this grant, my research focused on fusing heterogenous sources of data with Bayesian nonparametric models. We published many papers in the service of this goal. I would like to highlight the following papers about furthering Bayesian nonparametrics and examining the fusion of heterogenous data types in a diversity of settings. This is an extension of last year s report. It is my final report.
Robust fixed-outline floorplanning through evolutionary search

In this paper, we address the pratical problem of fixed-outline VLSI floorplanning with minimizing the objective of area. This problem was shown significantly much more difficult than the well-researched floorplan problems without fixed-outline regime [1]. We successfully develop an algorithm with evolutionary search to efficiently handle the fixed-die floorplanning problem and achieve near 100% successful probability, on the average.
Multichannel video segmentation

A video is a multimedia document which is structured in scenes and shots. Scenes are lists of consecutive shots characterized by common visual and audio features. Shots are sets of consecutive frames separated by cuts, which can be easily recognized by existing techniques. Video segmentation into scenes is a new and open problem. It is needed for scenes retrieval, specially in authoring and interactive video applications. We propose a new approach of video segmentation into scenes, which is based on several media and takes into account the film syntax. We characterize a scene by some similarity between color histograms of the current shot, and of one of the most recent previous shots. Similarity between a shot frame and a frame of a previous shot may indicate the presence of alternate shots, which belong to the same scene. Other techniques based on projective geometry are presented in a companion paper. These techniques enable to detect the movement of the camera. We recognize the speakers of a scene by AR vector model techniques, such as the one proposed by some of the authors in the Orphee system, implemented at Laforia. However the speaker recognition problem is much more difficult when applied to the video CD-I, due to several transition types and various types of noise. We present experimental results, based on this approach. Detection of alternate shots is efficient, but speaker recognition needs improvements.
Pascal pyramid in the space $\mathbf{H}^2\!\times\!\mathbf{R}$

In this article we introduce a new type of Pascal pyramids. A regular squared mosaic in the hyperbolic plane yields a $(h^2r)$-cube mosaic in space $\mathbf{H}^2\! \times\! \mathbf{R}$ and the definition of the pyramid is based on this regular mosaic. The levels of the pyramid inherit some properties from the Euclidean and hyperbolic Pascal triangles. We give the growing method from level to level and show some illustrating figures.
The case of InterCorp, a multilingual parallel corpus

This paper introduces InterCorp, a parallel corpus including texts in Czech and 27 other languages, available for online searches via a web interface. After discussing some issues and merits of a multilingual resource we argue that it has an important role especially for languages with fewer native speakers, supporting both comparative research and studies of the language from the perspective of other languages. We proceed with an overview of the corpus — the strategy and criteria for including new texts, the representation of available languages and text types, linguistic annotation, and a sketch of pre-processing issues. Finally, we present the search interface and suggest some research opportunities.
Combining Cyclic Q Delay and Cell Interleaver for Enhanced Performance DVB-T2 System

DVB-T2 (The second generation Digital Video Broadcasting) is mainly aimed to replace the current standard DVB-T. The main motivation of DVB-T2 is to provide broadcasters with more advanced and efficient alternative to DVB-T standards. The cyclic Q delay and the cell interleaver are both optional modules existing in the DVB-T2 system. The main purpose of the two modules is to increase the overall DVB-T2 system performance. In this paper we propose a new algorithm to combine the two modules. The new algorithm serves as a primary verification step before hardware implementation. The aim of the combined module is to decrease the system complexity, hardware usage and the overall system delay.
Gambierol: a new toxic polyether compound isolated from the marine dinoflagellate Gambierdiscus toxicus

The formation of these two different complexes requires that the macrocycle adopt two opposite conformations in the binding of 2 and 3 (Figure 1). In forming a complex, two opposed nine-base domains move inward and align themselves with the complementary purine strand on opposite sides, forming two or three hydrogen bonds on each side of a base. The unbound nine-base domains then act as loops for bridging the bound domains. In order to bind the other sequence, the pairs of domains reverse roles, with the former binding domains acting as bridging loops and vice versa. Interestingly, a further mixing experiment reveals that this complexation is mutually exclusive: the act of binding to one sequence precludes the binding of the other, even though the necessary bases are unpaired in the loops. When the circle is mixed at varied molar ratios with a one-to-one mixture of the two sequences (Figure 2), the mixing plot shows a stoichiometry of 1:l. This indicates that a given complex cannot bind the second sequence with its loops at 25 OC. While this macrocycle at all times carries the bases necessary for forming complexes with the two sequences, the act of binding causes a conformational switch, holding the loops in an inaccessible or unproductive conformation.I0 To confirm that the macrocycle can recognize these two sequences within a longer strand of DNA, we synthesized a 33nucleotide oligomer having the sequence 5'dCACAAGAGAGAGAATCCCTAAAAAAAAAAACAC (4). This oligomer contains the two recognition sequences separated by seven bases. When mixing experiments are performed (pH 7.0, 25 "C) with this oligomer and the macrocycle 1, a plot of (7) Thermal denaturations were performed on a Cary 1 spectrophotometer in 1 cm path length stoppered quartz microcells under an N2 atmosphere. The cells were monitored at 260 nm, with a temperature rise of 0.5 OC/min. Mixtures were 3 pM in each DNA strand and contained 10 mM Na-PIPES buffer (pH 7.0), 100 mM NaCI, and 10 mM MgCI,. T,'s were taken as the inflection point in the denaturation curve and are estimated to be accurate to within 10.5 OC.
Echo signal modeling of imaging LADAR target simulator

LADAR guidance technology is one of the most promising precision guidance technologies. In the aim of simulating the return waveform of the target, a 3D geometrical model of a target is built and mathematical model of target echo signal for imaging LADAR target simulator is established by using the coordinate transformation, radar equation and ranging equation. First, the 3D geometrical data of the object model is obtained by 3D geometrical modeling. Then, target coordinate system and viewpoint coordinate system are created respectively. 3D geometrical model is built in the target coordinate system. The 3D geometrical model is transformed to the viewpoint coordinate system based on the derived relationship between the two coordinate systems. Furthermore, the range information of the target could be obtained under viewpoint coordinate system. Thus, the data of the target echo signal can be obtained by using radar equation and ranging equation. Finally, the echo signal can be exported through corresponding data interface. In order to validate the method proposed in this paper, the echo signal generated by a typical target is computed and compared with the theory solutions. The signals can be applied to drive target simulator to generate a physical target LADAR image.
Mathematical Programming Based Short Term Load Forecasting Algorithm, Case Study: Turkey 2010

Short-term load forecasting (STLF) is an important problem in the operation of electrical power generation and transmission. In this paper, STLF algorithm was developed for electrical power systems using mathematical programming with Matlab. A fast and efficient computational algorithm has been obtained for STLF. The mean absolute percentage errors (MAPE) of daily loads forecast and weekly loads forecast for Turkey are found as 1,76%, 1,92%, respectively.
The Researches and Applications Based on Video Statistical Analysis of Average Population Density Estimation Methods

This article describes the use of the average population density estimation methods based on video statistical analysis, and mainly discussed the research and application of the air conditioning energy-efficient system in the subway. The distributed intelligent control system in the subway station platform captured video images by more than one camera sensors, according to the computer image processing methods. And it have unique advantages for the fuzzy neural network to model the human nervous system in fuzzy information processing. When tackling the video files, the image boundary is fuzzily set, it can legitimately divide the crowd by achieving the image intelligent analysis data, and whats more, it can help to get the estimation of population density.
Split up: an intelligent decision support system which provides advice upon property division following divorce

In this paper we discuss Split Up, a hybrid rule-based/neural network system which provides advice upon the distribution of property following divorce in Australia. The task of determining what property a Family Court judge may distribute was determined to be rule-based and implemented using directed graphs. A hierarchy of ninety four factors relevant for a percentage split prediction were identified with the help of domain experts. The way some factors combine was later learnt by machine learning algorithms known as neural networks. The way other factors combined was modelled with rules that were derived from expert heuristics. Neural networks provide no explanation for their answers. The Split Up system utilizes a knowledge representation that is based on the argumentation theory of Toulmin. This enables the system to generate an explanation for conclusions reached. The system has been trialed by judges, judicial registrars and registrars of
An Efficient Authentication Scheme between MANET and WLAN on IPv6 Based Internet

One of the major challenges for a wireless network design is the efficient authentication scheme. A mobile node (MN) attached to a WLAN and then moved into an area where the radio signal coverage from the access point (AP) does not exist. The mobile node may reconfigure itself into ad hoc mode and connect to this network. Before the mobile node using the resource, it must be verified whether legally or not. But in Mobile IP protocol, every mobile node must perform the home registration to register with HA every second. If the foreign network is far from the home network, the authentication time delay will be long. To reduce the time delay of authentication and home registration, we provide an efficient registration scheme. We use local authentication to achieve efficiency, and use Mobile IPv6 to support the mobility.
Understanding the Performance of Collaborative Filtering Recommendation through Social Network Analysis

Collaborative filtering (CF), one of the most successful recommendation techniques, has been used in a number of different applications such as recommending web pages, movies, music, articles and products. One of the critical issues in CF is why recommendation performances are different depending on application domains. However, prior literatures have focused on only data characteristics to explain the origin of the difference. Scant attentions have been paid to provide systematic explanation on the issue. To fill this research gap, this study attempts to systematically explain why recommendation performances are different using structural indexes of social network. For this purpose, we developed hypotheses regarding the relationships between structural indexes of social network and recommendation performance of collaboration filtering, and empirically tested them. Results of this study showed that density and inconclusiveness positively affected recommendation performance while clustering coefficient negatively affected it. This study can be used as stepping stone for understanding collaborative filtering recommendation performance. Furthermore, it might be helpful for managers to decide whether they adopt recommendation systems.
Peer Instruction: A User's Manual

I. OVERVIEW. 1. Introduction. 2. Peer Instruction. 3. Motivating the Students. 4. A Step-by-Step Guide to Preparing for a Peer Instruction Lecture. 5. Sample Lecture. 6. Epilogue. II. RESOURCES. 7. Mechanics Baseline Test. 8. Force Concept Inventory. 9. Questionnaire Results. 10. Reading Quizzes. 11. Concept Tests. 12. Conceptual Exam Questions. Appendix: Disk Instructions. Index.
Using Large Scale Distributed Computing to Unveil Advanced Persistent Threats

Besides a large set of malware categories such as worms and Trojan horses, Advanced Persistent Threat (APT) is another more sophisticated and highly targeted attack emerging in the cyber threats environment. In this paper we propose a model of the APT detection problem as well as a methodology to implement it on a generic organization network. The method suggests to closely monitor the possible targets and to use a large scale distributed computing framework, such as MapReduce to consider all possible events and to process all the possible contexts where the attack could take place. Our results show that this approach is feasible to process very large data sets and is flexible enough to accommodate any context processing algorithm, even to detect sophisticated attacks such as APT.
The wall histogram method

Occupancy maps are a standard method for probabilistic spatial representations of the environment of a mobile robot. They are easy to build and maintain and suffice for many navigation tasks. However, for solving more complex tasks the need to integrate metric occupancy maps with topological or relational spatial information arises. An important step in the automatic extraction of symbolic representations from sub-symbolic data such as occupancy maps is the segmentation of occupancy maps into areas with similar occupancy. Formerly suggested methods usually lead to unintuitive segmentation, which are difficult to match with symbolic concepts like rooms or hallways. We present the wall histogram method which makes use of the concepts and algorithms developed in computer vision, to yield more intuitive occupancy map segmentation.
Evolving neural network controllers for unstable systems

The author describes how genetic algorithms (GAs) were used to create recurrent neural networks to control a series of unstable systems. The systems considered are variations of the pole balancing problem: network controllers with two, one, and zero inputs, variable length pole, multiple poles on one cart, and a jointed pole. GAs were able to quickly evolve networks for the one- and two-input pole balancing problems. Networks with zero inputs were only able to valance poles for a few seconds of simulated time due to the network's inability to maintain accurate estimates of their position and pole angle. Also, work in progress on a two-legged walker is briefly described. <<ETX>>
Efficient page layout analysis on small devices

Previously we have designed and implemented new image browsing facilities to support effective offline image contents on mobile devices with limited capabilities: low bandwidth, small display, and slow processing. In this letter, we fulfill the automatic production of cartoon contents fitting small-screen display, and introduce a clustering method useful for various types of cartoon images as a prerequisite stage for preserving semantic meaning. The usage of neural networks is to properly cut the various forms of pages. Texture information that is useful for grayscale image segmentation gives us a good clue for page layout analysis using the multilayer perceptron (MLP) based x-y recursive algorithm. We also automatically frame the segment MLP using agglomerative segmentation. Our experimental results show that the combined approaches yield good results of segmentation for several cartoons.
Incremental delivery using abstract data types and requirements clustering

It has been recognized that abstract data types (ADTs) are important in software development. Developers use ADTs to implement systems. Customers are usually not able to understand ADTs due to lack of technical knowledge. Thus, they may not easily envision (or rather, may not be concerned with) how the use of ADTs meet their requirements. This paper uses the concepts of ADTs to accomplish incremental delivery and develops a methodology to deliver data-dominant systems incrementally. An important outcome of this methodology is an algorithm to cluster requirements based on ADTs obtained from the Onion methodology. A case study demonstrating the methodology is presented. <<ETX>>
Combination of SVM and Score Normalization for Person Identification Based on Audio-Visual Feature Fusion

Aimed at the problem of low accuracy rate for face recognition and speaker recognition in noisy environment, a multi-biometric model fusing face features and speech features is presented by combining Normalization and SVM theory based on the research of feature level fusion. Face features and speech features are first extracted by pulse coupled neural network and VQ-SVM respectively. Then the distance between tested people and template people is calculated after getting the fused feature on the feature level fusion. In order to reduce the computational cost and improve the recognition performance, matching distance is normalized and finally recognized by SVM. Experiment on the ORL database show that even when the signal to noise ratio is declined, recognition rate of the fused system is clearly higher than the single system under noisy environment and the purpose of identity recognition is achieved.
Longitudinal and lateral servoing of vehicles in a platoon

This paper presents the PRAXITELE project which includes a vision technique for platoon driving of a fleet of homogeneous electric cars. These cars, under supervision from a central computer, will form a novel public transportation system now under development in France. In the first version of the system, the public cars are driven by their users but their operation will be automated in some instances when no-one is on board. For later versions, fully automated driving (with passengers) is considered on dedicated tracks and on low-traffic road networks at slower speed. The paper focuses on the vision and control techniques now being developed for the first version. We wish to be able to drive a "convoy" of empty public cars with only one driver (in the front car). This function is needed to pick up cars which have been abandoned in various locations and to redistribute them to appropriate locations. When one car has reached its destination, it can leave the platoon and parallelly park all by itself. This paper presents the platooning technique based on vision with a linear camera.
The development of an integrated GPS/INS/sonar navigation system

The need to successfully navigate in an underwater environment is rapidly becoming an important concern in the 1990s. This paper presents the development of an integrated navigation system for use by autonomous vehicles (AUV) using GPS, INS and sonar. This paper discusses the existing problems with sub-sea navigation, the motivation for an integrated system, the mathematical derivation for an integrated GPS/INS/sonar system, and the results obtained from extensive testing.
Fault diagnosis of vision module on intelligent agent

An algorithm of fault diagnosis is proposed for vision module on the intelligent agent. The basis to generate the expectation is the fundamental matrix, which is an important concept of epipolar geometry. The proposed method of fault diagnosis is mainly composed of two processes. The fundamental matrix is firstly computed and its uncertainty is characterized. This is can be finished off-line. Then a procedure is performed in real time to judge whether the relative orientation of the cameras has been changed. Compared with the conventional methods, our method does not need additional sensors and can be almost automatically executed. Experiments have been carried out with several practical applications and the results show that the method of fault diagnosis is reasonable.
Spectral clustering and community detection in document networks

Document clustering is one of the most active research topics in text mining. In this work two approaches issued from very different fields are explored for document clustering: spectral clustering and community detection in complex networks. Both approaches are based on a representation of the document collection as a graph, of which the nodes represent the documents and the edges represent the similarities between each pair of documents, such that the two approaches have many issues in common. The results of the application of these two types of techniques to benchmark text mining problems show that they are complementary and are useful for finding structure in large collections of documents
Minimising makespan heuristics for scheduling a single batch machine processing machine with non-identical job sizes

In this paper, the problem of minimising maximum completion time on a single batch processing machine is studied. A batch processing is performed on a machine which can simultaneously process several jobs as a batch. The processing time of a batch is determined by the longest processing time of jobs in the batch. The batch processing machine problem is encountered in many manufacturing systems such as burn-in operations in the semiconductor industry and heat treatment operations in the metalworking industries. Heuristics are developed by iterative decomposition of a mixed integer programming model, modified from the successive knapsack problem by Ghazvini and Dupont (1998, Minimising mean flow times criteria on a single batch processing machine with non-identical jobs sizes. International Journal of Production Economics 55: 273–280) and the waste of batch clustering algorithm by Chen, Du, and Huang (2011, Scheduling a batch processing machine with non-identical job sizes: a clustering perspective. International Journal of Production Research 49 (19): 5755–5778). Experimental results show that the suggested heuristics produce high-quality solutions comparable to those of previous heuristics in a reasonable computation time.