Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust and Energy-Efficient Trajectory Tracking for Mobile Devices

Many mobile location-aware applications require the sampling of trajectory data accurately over an extended period of time. However, continuous trajectory tracking poses new challenges to the overall battery life of the device, and thus novel energy-efficient sensor management strategies are necessary for improving the lifetime of such applications. Additionally, such sensor management strategies are required to provide a high and application-adjustable level of robustness regardless of the user's transportation mode. In this article, we extend and further analyze the sensor management strategies of the EnTrackedT system that intelligently determines when to sample different on-device sensors (e.g., accelerometer, compass and GPS) for trajectory tracking. Specifically, we propose the concept of situational bounding to improve and parameterize the robustness of sensor management strategies for trajectory tracking. We demonstrate the effectiveness of our proposed approach by performing a series of emulation experiments on real world data sets collected from different modes of transportation (including walking, running, biking and commuting by car) on mobile devices from two different platforms. Thorough experimental analyses indicate that our system can save significant amounts of battery power compared to the state-of-the-art position tracking systems, while simultaneously maintaining robustness and accuracy bounds as required by diverse location-aware applications.
Validation of ATMS Calibration Accuracy Using Suomi NPP Pitch Maneuver Observations

The Suomi National Polar-orbiting Partnership (SNPP) satellite was launched on 28 October, 2011, and carries the Advanced Technology Microwave Sounder (ATMS) onboard. Currently, ATMS performance in orbit is very stable and the calibration parameters (e.g., noise and accuracy) meet specifications. This study documents an ATMS calibration error budget model and its results for community reference. The calibration accuracy is also verified with the ATMS pitch maneuver observations of cold space. It is shown that the ATMS pitch maneuver cold space observations at center positions are inconsistent with the values predicted by the instrument calibration error budget model. The biases also depend on scan angle. This scan-angle dependence may be caused by the ATMS plane reflector emission. Thus, a physical model is developed to simulate the radiation emitted from the reflector and is recommended as part of ATMS radiance calibration to further improve the sensor data record (SDR) data quality.
On emptying a wireless network in minimum time

We consider N transmitter-receiver pairs that share a wireless channel and we address the problem of obtaining a schedule for activating subsets of these links so as to empty the transmitter queues in minimum time. Our aim is to provide theoretical insights for the optimality characterization of the problem, using both a cross-layer model formulation, which takes into account the effect of interference on achievable transmission rates, as well as a collision-based model, which does not incorporate the physical layer realities into the problem. We present the basic linear programming formulation of the problem and establish that the optimal schedule need not consist of more than N subset activation frames. We then prove that the problem is NP-hard for all reasonable continuous rate functions. Finally, we obtain sufficient and/or necessary conditions for optimality in a number of special cases.
The Critical Grid Size and Transmission Radius for Local-Minimum-Free Grid Routing in Wireless Ad Hoc and Sensor Networks

In grid routing, the plane is tessellated into equal-sized square cells. Two cells are called neighbor cells if they share a common edge, and two nodes are called routing neighbors if they are in neighbor cells and within each other's transmission range. If communication parties are in the same cell, packets can be transmitted directly; otherwise, packets are forwarded to routing neighbors that are in cells closer to destination cells. As a greedy strategy, grid routing suffers the existence of local minima at which no neighbor nodes exist for relaying packets. To guarantee deliverability, in this paper, we investigate two vital parameters of grid routing, called the grid size and the transmission radius. Assume that nodes are represented by a Poisson point process with rate n over a unit-area square, and let l denote the grid size and r the transmission radius. First, we show that if for some constant β and r = √5l, then β = 1 is the threshold for deliverability. In other words, there almost surely do not exist local minima if β > 1 and there almost surely exist local minima if β 1, we give sufficient and necessary conditions to determine the critical transmission radius (CTR) for deliverability. Then, we show that as β ≅ 1.092, the CTR is the minimum over all β > 1. Simulation results are given to validate this theoretical work.
New Result on Low-Order Controller Design for First-Order Delay Processes Via Eigenvalue Assignment

Abstract This article developed a new result on the eigenvalue distribution for a certain class of time delay systems based on the extension of the Hermite-Biehler Theorem. Such result is applied to proportional-integral (PI) controller parameter design for a first-order plant with time delay via eigenvalue assignment. Using the method provided in this paper, one can assign the rightmost eigenvalues of the closed-loop system to desired positions in the complex plane. Further, on the basis of the previous result, this paper also extended the PI control to the proportional-integral-derivative (PID) case.
Long-Point FFT Processing Based on Twiddle Factor Table Reduction

In this paper, we present a new fast Fourier transform (FFT) algorithm to reduce the table size of twiddle factors required in pipelined FFT processing. The table size is large enough to occupy significant area and power consumption in long-point FFT processing. The proposed algorithm can reduce the table size to half, compared to the radix-22 algorithm, while retaining the simple structure. To verify the proposed algorithm, a 2048-point pipelined FFT processor is designed using a 0.18 μm CMOS process. By combining the proposed algorithm and the radix-22 algorithm, the table size is reduced to 34% and 51% compared to the radix-2 and radix-22 algorithms, respectively. The FFT processor occupies 1.28 mm2 and achieves a signal-to-quantization-noise ratio (SQNR) of more than 50 dB.
User Centred Design in Agile Application Development

Agile methods are becoming increasingly common in application design, with their collaborative customer focus and iterative, test driven approach. They share many common principles, yet it is rare for Agile methods to incorporate user centred design. This paper argues that by incorporating user-centred design (and in particular using low fidelity prototyping as an iterative model for the application rather than time consuming code) better applications can be developed, delivering business benefit with a focus upon the end user and their experience.
Verification of a wireless ATM medium-access protocol

We report on a model checking case study of an industrial medium access protocol for wireless ATM. Since the protocol is too large to be verified by any of the existing checkers as a whole, the verification exploits the layered and modular structure of the protocol's SDL specification and proceeds in a bottom-up, compositional way. The compositional arguments are used in combination with abstraction techniques to further reduce the state space of the system. The verification is primarily aimed at debugging the system. After correcting the specification step by step and validating various untimed and time-dependent properties, a model of the whole control component of the medium-access protocol is built and verified. The significance of the case study is in demonstrating that verification tools can handle complex properties of a model as large as shown.
Mining Web-based Educational Systems to Predict Student Learning Achievements

Educational Data Mining (EDM) is getting great importance as a new interdisciplinary research field related to some other areas. It is directly connected with Web-based Educational Systems (WBES) and Data Mining (DM, a fundamental part of Knowledge Discovery in Databases). The former defines the context: WBES store and manage huge amounts of data. Such data are increasingly growing and they contain hidden knowledge that could be very useful to the users (both teachers and students). It is desirable to identify such knowledge in the form of models, patterns or any other representation schema that allows a better exploitation of the system. The latter reveals itself as the tool to achieve such discovering. Data mining must afford very complex and different situations to reach quality solutions. Therefore, data mining is a research field where many advances are being done to accommodate and solve emerging problems. For this purpose, many techniques are usually considered. In this paper we study how data mining can be used to induce student models from the data acquired by a specific Web-based tool for adaptive testing, called SIETTE. Concretely we have used top down induction decision trees algorithms to extract the patterns because these models, decision trees, are easily understandable. In addition, the conducted validation processes have assured high quality models.
Structural and Theoretical Investigation of Anhydrous 3,4,5-Triacetoxybenzoic Acid

A comprehensive investigation of anhydrous form of 3,4,5-Triacetoxybenzoic acid (TABA) is reported. Single crystal X-ray diffraction, Thermal analysis, Fourier Transform Infrared spectroscopy (FTIR) and DFT calculations were applied for TABA characterization. This anhydrous phase crystallizes in the triclinic P1¯ space group (Z' = 1) and its packing shows a supramolecular motif in a classical R22(4) ring formed by acid-acid groups association. The phase stability is accounted in terms of supramolecular architecture and its thermal behaviour. Conformation search at B3LYP/6-311++G(2d,p) level of theory shows the existence of three stable conformers and the most stable conformation was found experimentally. The reactivity of TABA was investigated using the molecular orbital theory and molecular electrostatic potential. The calculation results were used to simulate the infrared spectrum. There is a good agreement between calculated and experimental IR spectrum, which allowed the assignment of the normal vibrational modes
Using GQM Hypothesis Restriction to Infer Bayesian Network Testing

By definition, the scope of a Bayesian Network uses a complementary technique to restrict the modeling reach. In this paper, the used restriction technique was the Goals, Questions, and Metrics (GQM). The hypothesis to be tested relates cause and effect conditional probabilities in a software test phase of a manufacturing production line. The Bayesian Network concept is related to the specific concept of a Directed Non Cyclic Graph (DNCG), where each one of its nodes represents a random discrete variable and is illustrated by directed arcs of cause and effect relationships between variables. A Bayesian Network is a graphical artifact which restricts problems, incorporating data structures. The major contributions of this paper are conceptualization and implementation of a methodology for using a GQM hypothesis restriction to infer Bayesian network testing with the Netica Bayesian Networks ® computer software.
Considering Side Effects in Service Interactions in Home Automation - an Online Approach

The feature or service interaction problem within home networks is an established topic for the FI community. Interactions between home appliances, services and their environment have been reported previously. Indeed earlier work by the authors introduced a device centric approach which detects undesirable behaviour between appliances and their effects on the environment. However this previous work did not address side-effects between components of the modelled environment. That is some appliances do not only affect the environment through their main function, but may do so also in other ways. For instance, an air conditioner cools the air as its main function, but also decreases the humidity as a side-effect. Here we extend our earlier approach to handle such side effects effectively and discuss previously unreported results.
Reinforcement Learning of Contextual MDPs using Spectral Methods

We propose a new reinforcement learning (RL) algorithm for contextual Markov decision processes (CMDP) using spectral methods. CMDPs are structured MDPs where the dynamics and rewards depend on a smaller number of hidden states or contexts. If the mapping between the hidden and observed states is known a priori, then standard RL algorithms such as UCRL are guaranteed to attain low regret. Is it possible to achieve regret of the same order even when the mapping is unknown? We provide an affirmative answer in this paper. We exploit spectral methods to learn the mapping from hidden to observed states with guaranteed confidence bounds, and incorporate it into the UCRL-based framework to obtain order-optimal regret.
Stock Market Integration in Emerging Countries : Further Evidence from the Philippines and Mexico

This article investigates the stock market integrat ion hypothesis of two emerging countries (the Philippines and Mexico) into the world capital mark et over the last three decades. To check this hypothesis in the short and long run, we use the no nlinear cointegration techniques. Our results show that both stock markets are nonlinearly integrated into the world market, although the degree of integration is higher for Mexico. Furthermore, we s how that the stock market integration process is nonlinear, asymmetric and time-varying.
Analysis of cognitive heuristics for content dissemination in opportunistic networks

When faced with large amounts of data, human brains are able to swiftly react to stimuli and assert relevance of discovered information, even under uncertainty and partial knowledge. These efficient decision-making abilities rely on socalled cognitive heuristics, which are rapid, adaptive, lightweight yet very effective schemes used by the brain to solve complex problems. In a content-centric future Internet where users generate and disseminate large amounts of content through opportunistic networking techniques, individual nodes should exhibit those properties to support a scalable content dissemination system. We therefore study whether such cognitive heuristics can also be used in such a networking environment. To this end, in this paper we develop an analytical model that describes a content dissemination mechanism for opportunistic networks based on one such heuristics, known as the recognition heuristic. Our model takes into account the different popularities of content types, and highlights the impact of the shared memory contributed by individual nodes to make the dissemination process more efficient. Furthermore, our model allows us to investigate the performance of the dissemination process for very large number of nodes, which might be very difficult to carry out through a simulation-based study.
On the role of trust types and levels on inventory replenishment decision

Using a participatory simulation platform, this paper investigates the inventory replenishment decisions made under different trust categories. Depending on the type of trust (trust in supplier versus trust in customer) and level of trust (high versus low), each decision is categorized and analyzed. We investigate how the inventory manager`s ordering behavior varies regarding their type and level of trust. This study provides evidence for the role of trust in inventory replenishment decision. The findings on the influence of trust on inventory decision indicators provide a new perspective in respect to the supply chain management literature, which generally postulates a positive influence of trust on supply chain stability and uncertainty reduction.
Platooning with DSRC-based IVC-enabled autonomous vehicles: Adding infrared communications for IVC reliability improvement

Platooning with IVC-enabled autonomous vehicles may enable a significant increase in lane capacity, if performed with constant spacing policies. However, to be effective, such system is very demanding with respect to communication performance and reliability. Dedicated short range communications (DSRC) is the prominent intervehicle communication (IVC) technology. However, its reliability rises concerns when operating under platooning scenarios. In this paper we identify some specific problems that platooning pose to DSRC, through the simulation of several scenarios implemented in the NS-3 network simulator. Moreover, we propose a new concept of IVC when applied to platoons, using simultaneously two different communication technologies: DSRC and infrared (IR). New guidelines toward more efficient use of IVC transmission media are suggested, e.g., by broadcasting the event-driven type of messages through DSRC, whereas periodic vehicle control-based messages use the IR channel, in unicast. Furthermore, the base architecture of the IVC proposed system is presented.
Streaming of photo-realistic texture mapped on 3D surface

We present a novel technique for efficient coding of texture to be mapped on 3D landscapes. The technique enables to stream the data across the network using a back-channel. The use of wavelet and discrete cosine transforms is investigated and compared. This technology has been proposed has a tool for the emerging MPEG-4 standard.
Multiple feature similarity based for image retrieval

In Bag-of-Words-based image retrieval, the local feature could not describe the global information of an image. It produces many false matches and reduces the retrieval precision. To address this problem, this paper proposes a new method which is based on the global and local feature similarity. The global feature extracted by convolutional neural network is added to the local keypoints extracted in a given image. The local and global features are used together to improve the accuracy and a 2-D inverted index is built to accelerate the speed of retrieval. Experimental results demonstrate that the method proposed in this paper can improve the accuracy significantly. It outperforms the state-of-the-arts on the Ukbench and Holidays datasets.
Imitation Learning of Positional and Force Skills Demonstrated via Kinesthetic Teaching and Haptic Input

A method to learn and reproduce robot force interactions in a human–robot interaction setting is proposed. The method allows a robotic manipulator to learn to perform tasks that require exerting forces on external objects by interacting with a human operator in an unstructured environment. This is achieved by learning two aspects of a task: positional and force profiles. The positional profile is obtained from task demonstrations via kinesthetic teaching. The force profile is obtained from additional demonstrations via a haptic device. A human teacher uses the haptic device to input the desired forces that the robot should exert on external objects during the task execution. The two profiles are encoded as a mixture of dynamical systems, which is used to reproduce the task satisfying both the positional and force profiles. An active control strategy based on task-space control with variable stiffness is then proposed to reproduce the skill. The method is demonstrated with two experiments in which the robot learns an ironing task and a door-opening task.
Accelerated A* Path Planning (Extended Abstract)

The paper addresses the area of path planning for nonholonomic vehicles operating in a large-scale dynamic continuous three-dimensional space where the vehicle has to avoid given obstacles and restricted areas. For vehicles, motion dynamics is defined by means of constraints on the driving manoeuvres and restrictions on smoothness of the trajectory. The paper addresses only the basic spatial path planning problem – a process searching for a spatial arrangement of the trajectory from the given start position and orientation to the given target position and orientation. Extended problems like incremental planning are not considered by this paper. The dynamic environment means that obstacles’ and restricted areas’ definitions are altered almost after each particular search. The field of the path planning problem has been studied by the research community for many decades. The problem is still topical as all intelligent autonomous vehicles have to include path planning into their deliberation mechanisms. There exist very efficient (fast) algorithms based on randomness, e.g. the random-walk planner [3], the rapidly exploring random tree (RRT) [8] and the randomized potential field [2] algorithms. Although there are many extensions of these path-finding concepts, their search processes
Stereo SLAM Using Two Estimators

This paper proposes a new algorithm for Simultaneous Localization and Mapping (SLAM) with omnidirectional stereo vision. In our approach, stereo matching is solved efficiently by using the estimated spatial information of the environment and robot motion. The use of two EKF (Extended Kalman Filter) estimators brings about a more reliable robot trajectory and a better map of the environment with more landmarks. The first estimator (a binocular estimator) mostly focuses on the robot trajectory; the second estimator (a monocular estimator) is devoted to the map building. Reliably matched landmarks are entered into the first estimator to establish the reliable robot position and some of the landmark positions. Other landmarks, which have more uncertainty of stereo combination or are observed by only one camera, are passed to the second estimator to establish their positions. This structure results in more precise estimation of robot trajectory and a map of the environment with more landmarks.
L-turn routing: an adaptive routing in irregular networks

Network-based parallel processing using commodity personal computers has been widely developed. Since such systems require high degree of flexibility and scalability of wiring, a high-speed network with an irregular topology is often needed. In traditional routing algorithms for irregular networks, available paths are considerably restricted in order to avoid deadlocks. In this paper we propose a novel routing algorithm called left-up-first turn routing (L-turn routing), which makes a better traffic balancing in irregular networks by building a specific spanning tree. Result of simulations shows that L-turn routing achieves better performance than traditional ones with each topology.