Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on the Buffer Management Algorithm in DTN

In Delay and Disruption Tolerant Networks (DTN), because of its intermittent connectivity features, resulting in packet transmission process cannot ensure end to end path, usually employ a "store - carry - forwards" approach to forward the message. Currently the key method to solve information exchange in DTN is select forwarding nodes effectively. Made the information deliver to the destination successfully within a short time and reduce resource overhead. At the same time, the load of selected nodes will be increased correspondingly and the requirement to the buffer also increased, then the management of the buffer become particularly important. The buffer management not only includes the message dropping, but also includes message forwarding. In this paper, we mainly analysis of the existing buffer management algorithm, and also analysis some social attributes in favor of selecting forwarding nodes in DTN. We put forward the buffer management algorithm based on social attributes in DTN in future work.
Automatic Text Recognition from Raster Maps

Text labels in raster maps provide valuable geospatial information by associating geospatial locations with geographical names. Although present commercial optical character recognition (OCR) products can achieve a high recognition rate on documents, text recognition on raster maps is still challenging due to the varying text orientations and the overlapping between text labels. This paper presents an automatic text recognition approach which focuses on locating individual text labels in the map and detecting their orientations and leverages horizontal text recognition of commercial OCR software. We show that our approach detects all strings in the test maps and achieves 96.8% precision and 95.7% recall on character recognition.
Rapid Interactive 3 D Reconstruction from a Single Still Image

We present a method to create photorealistic 3D models from a single still image. The user provides input in the form of “scrib bles”— within a few seconds of user input, our algorithm generates a photorealistic 3D model and a 3D flythrough from the image. Our ap proach uses a machine learning algorithm that learns the rel ation between (single) image features and depth. In our prelimina ry experiments, 1238 users created good 3D models for 84% of the 37 75 images they uploaded.
Research on location prediction of vehicular networks

Location information is very important for many applications of vehicular networks such as routing, network management and safety. However, the mobility of vehicle usually results in many location errors. To solve the problem, various prediction schemes have been proposed. In the paper, by Rough sets theory, a novel location prediction technique LPVN that incorporates the vehicle position information, digital map data and traffic knowledge was presented. Through simulation of traffic simulator, we observe that the LPVN performs better than other schemes LVP, WVP, RTBP.
Energy-Efficient Variable Stiffness Actuators

Variable stiffness actuators are a particular class of actuators that is characterized by the property that the apparent output stiffness can be changed independent of the output position. To achieve this, variable stiffness actuators consist of a number of elastic elements and a number of actuated degrees of freedom, which determine how the elastic elements are perceived at the actuator output. Changing the apparent output stiffness is useful for a broad range of applications, which explains the increasing research interest in this class of actuators. In this paper, a generic, port-based model for variable stiffness actuators is presented, with which a wide variety of designs can be modeled and analyzed. From the analysis of the model, it is possible to derive kinematic properties that variable stiffness actuator designs should satisfy in order to be energy efficient. More specifically, the kinematics should be such that the apparent output stiffness can be varied without changing the potential energy that is stored in the internal elastic elements. A concept design of an energy-efficient variable stiffness actuator is presented and implemented. Simulations of the model and experiments on the realized prototype validate the design principle.
Likelihood-Field-Model-Based Dynamic Vehicle Detection with Velodyne

Dynamic vehicle detection is an important module for Autonomous Land Vehicle (ALV) navigation in outdoor environments. In this paper, we present a novel dynamic vehicle detection algorithm based on the likelihood field model for an ALV equipped with a Velodyne LIDAR. An improved 2D virtual scan is utilized to detect the dynamic objects with the scan differencing operation. For every dynamic object, a vehicle is fitted with the likelihood field model, and the motion evidence and motion consistence of the fitted vehicle are exploited to classify the dynamic object into the vehicle or not. The performance of the algorithm is validated on the data collected by our ALV in various environments.
Subcarrier Allocation for Multiuser Two-Way OFDMA Relay Networks with Fairness Constraints

In this paper, we propose a new adaptive subcarrier allocation for a multiuser two-way OFDMA relay network. In the proposed algorithm, subcarriers are allocated to the user-pairs and relays to maximize the achievable sum-rate over all user-pairs while satisfying the minimum rate requirement for each user-pair. Simulation results show that the proposed algorithm improves spectral efficiency and fairness.
Game theoretic approach to medium access control in wireless networks

This paper proposes a game-theoretic random access model, compliant with the IEEE 802.11 standard, that can be integrated into the distributed coordination function (DCF). The objective is to design a game theoretic model that can potentially optimize throughput and fairness in each node independently and, therefore, minimise channel access delay. We propose a utility function, such that it can decouple the protocol's dynamic adaptation to channel load from collision detection. We demonstrate that our model can reach a Nash equilibrium that results in a relatively stable contention window, provided that a node adapts its behavior to the idle rate of the broadcast channel, coupled with observation of its own transmission activity. The simulation results show that this model is capable of achieving much higher throughput than the standard IEEE 802.11 DCF with absolutely better short-time fairness.
Efficient packet erasure decoding by transforming the systematic generator matrix of an RS code

We consider the erasures-only decoding of Reed-Solomon codes. One class of erasure decoders of algebraic codes can be viewed as a transformation of the systematic generator matrix, as first noticed by Berlekamp. We extend Berlekamppsilas method to Reed-Solomon codes and compare the performance of the decoder with various other Reed-Solomon erasure decoders. The developed decoder reduces the decoding time of packet erasures significantly when used in conjunction with a modified interleaver found in the literature.
On the development of the emotion expression humanoid robot WE-4RII with RCH-1

Among social infrastructure technologies, robot technology (RT) is expected to play an important role in solving the problems of both decrease of birth rate and increase of elderly people in the 21st century, specially (but not only) in Japan where the average age of the population is rising faster than any other nation in the world. In order to achieve this objective, the new generation of personal robots should be capable of a natural communication with humans by expressing human-like emotion. In this sense, human hands play a fundamental role in exploration, communication and interaction with objects and other persons. This paper presents the recent results of the collaboration between the Takanishi Lab of Waseda University, Tokyo, Japan, the Arts Lab of Scuola Superiore Sant'Anna, Pisa, Italy, and ROBOCASA, Tokyo, Japan. At first, the integration of the artificial hand RTR-2 of ARTS lab with the humanoid robotic platform WE-4R during ROBODEX2003 is presented. Then, the paper show the preliminary results of the development of a novel anthropomorphic hand for humanoid robotics RCH-I (ROBOCASA Hand No.1) and its integration into a new humanoid robotic platform, named WE-4RII (Waseda Eye No.4 Refined II).
Modeling of AC-AC matrix converter for wind energy conversion system

This paper introduces the use of a three-phase matrix converter as an alternative to the DC-link back-to-back converter, for doubly-fed induction generator used in wind turbine system. A new dynamic model of matrix converter is proposed together with d-q dynamic representation of doubly fed induction generator. Consequently, the complicated three-phase coupling system is transformed into two independent direct and quadrature axes. The developed model can be used for design of system controller as well as dynamic power system studies. The accuracy of the developed model is validated by comparing the simulation results produced by the developed model with real-switch based model.
Testing the construct validity of the Discounting Inventory – Psychometric properties of a Polish and German samples

: The Discounting Inventory (DI), originally developed in polish language, allows the measurement of individual differences in the delay, probabilistic, effort, and social discounting rates. The present study attempted to validate the DI’s psychometric properties using German university students and to compare the results to those from a sample of Polish university students. Over four hundred participants completed the DI and traditional discounting measures. A confirmatory factor analyses indicated that the original four-factor model of the DI provided an excellent fit for the German data and internal consistency was high. These outcomes were similar, if not superior, to those from the Polish sample. DI scores strongly correlated with traditional discounting measures scores in both samples, replicating previous results. These findings indicate that the DI is a valid measure for use in a sample from another cultural setting, which is potentially useful to both researchers and practitioners.
OPTIMIZATION FOR HIERARCHICAL PRODUCTION PLANNING OF INDUSTRIAL PROCESSES

In this paper, a generalized mathematical model formulation for cellular manufacturing system (CMS) using hierarchical production planning (HPP) approach is proposed. The model is applied to two different real case studies and is solved by using operation research optimization software (Lingo (12.0) program). The model is divided into three main steps as follows: data collection, mathematical model formula, and results. The proposed mathematical model of the optimization can solve the problems of the system under utilizing the limited resources in a production plan. The results show that the proposed mathematical model can be used to minimize manufacturing total costs of products for similar cases. To prove the work, two case studies are introduced; for the first case (electric water heater with capacity 50 liter (EWH1)), the results show that the total cost decreases by 8.46 % for the optimum conditions. For the second case, (electric water heater with capacity 80 liter (EWH2)), as global results, the total cost decreases by 3.7% for the optimum conditions. Index Term-Hierarchical Production Planning (HPP), Cellular Manufacturing System (CMS), Group Technology (GT) and General Manufacturing Company (GMC).
Social Identity, Self-awareness and Intergroup Behaviour

Self-awareness theory and social identity theory both concern the impact of the self-concept on behaviour. Self-awareness theory addresses the process of individual self-regulation in terms of private and public standards. Self-focused attention increases the influence of these standards. Social identity theory presumes that the self can be designed in term s of social category membership. Intergroup behaviour ensues when these social identifications are salient. It is theorised that the public/private self-focus and social/personal identity distinctions are orthogonal. Focus on the private aspects of self may include a social identification and lead to intergroup behaviour. Fenigstein, Scheier and Buss' (1975) Self-Consciousness Scale is assessed and discriminates reliably between private and public self-consciousness. The first experiments reveal no impact of self-awareness manipulations on intergroup ratings. In the next experiment, increased attention to social categorisation raises the amount and consistency of discrimination. The finding that private, but not public, self-focus reinforces social identification under relevant conditions also emerges in the next study. Different standards for 'public' behaviour may prevail when different audiences observe, and 'private' standards may depend on identification with the group. In an experiment testing these propositions identification is accurately reflected only to an ingroup audience - a result which is replicated. In the final experiment, video feedback with an ingroup audience minimises, wheras that without maximises, ingroup bias, due to variations in the impact of different aspects of identity. Ingroup bias reflects identification only when private self-consciousness is high or public self-consciousness is low. Across experiments self-esteem reliably affects intergroup behaviour, and referent informational, rather than normative, influence predominates. The social identification and self-awareness approaches are each enriched by the other. A model of identitifaction in group contexts and the view that behaviour may serve self-preserving motives are proposed. The conceptual and empirical ambiguity between 'salience' and 'attention' remain to be resolved. These cognitive factors in intergroup behaviour may not have simple effects because other, social, factors, exert influences to alter their impact.
Associative patterns among anaerobic fungi, methanogenic archaea, and bacterial communities in response to changes in diet and age in the rumen of dairy cows

The rumen microbiome represents a complex microbial genetic web where bacteria, anaerobic rumen fungi (ARF), protozoa and archaea work in harmony contributing to the health and productivity of ruminants. We hypothesized that the rumen microbiome shifts as the dairy cow advances in lactations and these microbial changes may contribute to differences in productivity between primiparous (first lactation) and multiparous (≥second lactation) cows. To this end, we investigated shifts in the ruminal ARF and methanogenic communities in both primiparous (n = 5) and multiparous (n = 5) cows as they transitioned from a high forage to a high grain diet upon initiation of lactation. A total of 20 rumen samples were extracted for genomic DNA, amplified using archaeal and fungal specific primers, sequenced on a 454 platform and analyzed using QIIME. Community comparisons (Bray–Curtis index) revealed the effect of diet (P < 0.01) on ARF composition, while archaeal communities differed between primiparous and multiparous cows (P < 0.05). Among ARF, several lineages were unclassified, however, phylum Neocallimastigomycota showed the presence of three known genera. Abundance of Cyllamyces and Caecomyces shifted with diet, whereas Orpinomyces was influenced by both diet and age. Methanobrevibacter constituted the most dominant archaeal genus across all samples. Co-occurrence analysis incorporating taxa from bacteria, ARF and archaea revealed syntrophic interactions both within and between microbial domains in response to change in diet as well as age of dairy cows. Notably, these interactions were numerous and complex in multiparous cows, supporting our hypothesis that the rumen microbiome also matures with age to sustain the growing metabolic needs of the host. This study provides a broader picture of the ARF and methanogenic populations in the rumen of dairy cows and their co-occurrence implicates specific relationships between different microbial domains in response to diet and age.
The plasticity of lexical selection mechanism in word production: ERP evidence from short-term language switching training in unbalanced Chinese–English bilinguals

The present study examined the plasticity of the lexical selection mechanism in bilingual word production by training a group of unbalanced Chinese–English bilinguals with a language switching task. The experimental group received an 8-day language switching training, while the control group received no training. Before and after training, the behavioral and event-related potential (ERP) data of both groups in a cued picture naming task were collected. ERP results revealed a training effect such that after training, the N2 peak latency in cue-locked ERPs was shortened only in the experimental group. These results suggest that short-term language switching experience could improve the efficiency to establish the target language task schema, and that the language control mechanism of word production in unbalanced bilinguals could be modulated by language switching experience.
SOLAR PARTICLE EVENT DOSES AND DOSE RATES FOR INTERPLANETARY CREWS: PREDICTIONS USING ARTIFICIAL INTELLIGENCE AND BAYESIAN INFERENCE

ABSTRACT For deep space missions, a major concern is the occurrence of large solar particle events (SPE) which can deliverdoses to critical body organs in excess of 10Gy at dose rates exceeding 1 Gy h -1 over a period of several hours.Accurately predicting the likelihood of occurrence of these events before they begin has been an ongoing problemthroughout the history of mannedspaceflight. Recently, efforts to predict doses and dose rates from large SPEs using only dosimeter readings obtained early in the evolution of an event have been undertaken. Studies usingartificial intelligence and Bayesian inference have been initiated. An innovative, artifiical neural network has beendeveloped to predict total doses from SPEs early in an event, using only doses obtained at times early in that event.The network makes accurate predictions for large events, such as those of August and September 1989. Thenetwork is capable of updating its predictions as additional dose inputs are received. A second thrust has been toimplement Bayesian inference techniques with Markov Chain Monte Carlo (MCMC) methods to calculate posteriorparameter and dose rate distributions as well as predictive distributions for future observations and dose rates. Inthis work preliminary results obtained using these two methodologies are presented.
A Novel Cross Layer Routing Protocol for CR Ad Hoc Network

Ad hoc network is one of the most common application areas of CR technology. This paper presented the design, implementation, and analysis of a novel pre-active Cognitive multi-channel routing protocol that utilizes multiple channels in mobile ad-hoc networks. A new node burden based metric is proposed to evaluate the effectiveness of candidate routes and a new channel switch metric is extended to solve the channel frequent switch problem in this mechanism. For evaluation, simulation is carried out to show the performance of our route protocol. The simulation results illustrate that the proposed route protocol perform well in both single-hop and multi-hop ad hoc networks.
Linear N-Point Camera Pose Determination

The determination of camera position and orientation from known correspondences of 3D reference points and their images is known as pose estimation in computer vision and space resection in photogrammetry. It is well-known that from three corresponding points there are at most four algebraic solutions. Less appears to be known about the cases of four and five corresponding points. We propose a family of linear methods that yield a unique solution to 4- and 5-point pose determination for generic reference points. We first review the 3-point algebraic method. Then we present our two-step, 4-point and one-step, 5-point linear algorithms. The 5-point method can also be extended to handle more than five points. Finally, we demonstrate our methods on both simulated and real images. We show that they do not degenerate for coplanar configurations and even outperform the special linear algorithm for coplanar configurations in practice.
Implementation and Optimization of a Multi Conjugate Adaptive Optics Software System for Vision Research

This report presents the design and implementation of a real-time Adaptive Optics software control system, used for vision research at Göteborg University. The possible ways to implement such an application depends on several factors such as computer platform including operating system and computer hardware, hardware used in the optical setup and the availability of efficient algorithms. These issues has been thoroughly analyzed and tested to give an efficient and well designed application. The software has been designed to provide for a flexible and extendable program that fulfils the performance requirements of the system's correction loop. The software has been implemented on a 64-bit Intel Xeon machine of 1.7 GHz with a maximum correction speed of 13 Hz restricted by the speed of the camera hardware. Analysis shows that this implementation would be able to run at speeds around 30 Hz, assuming the use of a camera with frame rate of 95 Hz. Moreover the Windows 2000 platform has been analyzed and tested with regards to real-time capabilities and has been found to provide for correction speeds of at least 100 Hz on the above machine with good reliability, assuming one or two missed corrections per second can be tolerated. All the work has been performed in the spring of 2005 and finished in the early summer the same year. My thanks go to Zoran and Jörgen who made it possible for me to do a master thesis that combines my interests in both computer science and optics. I also would like to thank Håkan for giving me valuable advice in writing the report in combination with good insights concerning the computer science issues. Since this is a thesis in computer science the optical aspects and considerations are intentionally made superficial. The sections regarding optics are intended mainly for understanding of the computer science considerations, and not for professionals in optics research. Basic knowledge of wave theory is assumed. On the other hand, more elaborate knowledge of methods and paradigms used in computer science are expected of the reader.
Exploiting self-similarity for change detection

Time-series data are often characterized by a large degree of self-similarity, which arises in application domains featuring periodicity or seasonality. While self-similarity has shown to be an effective prior for modeling real data in the signal and image-processing literature, it has received much less attention in time-series literature, where only few works leveraging the self-similarity for anomaly detection have been presented. Here we introduce a novel change-detection test to detect structural changes in time series by analyzing their self-similarity. The core of the proposed solution is the definition of a change indicator to quantitatively assesses the self-similarity of the time-series data over time. In particular, the change indicator is obtained by comparing each patch to be analyzed with its most similar counterpart in a change-free training set. Experimental results on the flow measurements in the water distribution network of the Barcelona city show the effectiveness of the proposed solution.
Building a Simulation Environment for Optimising Control Parameters of an Autonomous Robotic Fish

This paper firstly presents a shor t review on the current research of robotic fish. The construction of a simulation environment for robotic fish is then presented. The exper iment results show that the simulator is a convenient way to develop and test the motion control algor ithm for a robotic fish. Fur thermore, a parameter optimising method for fish’s travelling wave approximation is proposed. A special error function is selected depending on the hydrodynamics theory of fish.