Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Lifetime in Heterogeneous Wireless Sensor Networks

— Wireless Sensor Networks (WSNs) consist of a large number of energy-limited sensor nodes that are densely deployed in a large geographical region. For WSNs, energy efficiency is always a key design issue to improve the life span of the network. The clustering Algorithm is a kind of key technique used to reduce energy consumption and to increase scalability and lifetime of the network. In this paper, we propose and evaluate a distributed energy-efficient clustering algorithm for heterogeneous WSNs, which is called Improved LEACH-E (ILE). This protocol is a LEACH-E improvement. In ILE, the cluster-heads are elected by a probability based on the ratio between residual energy of each node and the remaining energy of the network. Also, it uses a second hierarchical level by selecting a cluster head for data transmission. Furthermore, we consider a multi-level heterogeneous network in order to study the impact of heterogeneity on wireless sensor Networks. In this case we propose an energy-efficient multi-level clustering algorithm called ILE-M. Finally, Simulation results show that the network lifetime and energy efficiency are much better in our proposed protocols ILE and ILE-M than the existing protocols.
Multi-Agent System for Hadith Processing

Hadith is everything attributed to Prophet Muhammad Peace Be Upon Him (PBUH) such as says, deeds, explanations, decisions or characteristics of his creation. Each Hadith is composed of two main parts: Isnad and Matn. Isnad processing is one of the unique features of Islam nation, which concentrates on the narrations of the prophet. Before 1400 years, Hadith scholars set the rules and wrote the books to distinguish between the right narrations that really related to the prophet and the lied one. All Hadiths are texts written in Arabic classical language. Processing the Arabic language is a difficult task due to the specificities of this language and the scarcity of the digital resources (grammars, lexicon and corpus). In this paper, we suggest a method for Hadith Isnad processing based on the Multi-Agent approach. We designed and implemented a system composed of five agents, which are: preprocessing agent, lexical agent, morphological agent, replacing agent and syntactical agent. These agents cooperate to analyze Hadith Isnad and to provide the parse tree, Isnad tree and the received method. This system is based on a large database that we created using XML technology to ensure the re-usability, the scalability and the interoperability.
Study on a Multi-Hierarchy Topological Sort Algorithm for Automatic Calculation

Topological sort algorithm (Toposort) is widely used for practical problems. However, standard toposort don’t solve the automatic calculation of formula problem (ACFP) directly. To gain the feasible topological order of ACFP, this paper puts forward a modified algorithm based toposort ,which models for ACFP using directed acyclic graph (DAG) firstly, and then puts the 0 in-degree formula nodes into a level list in execution process, lastly decreases the in-degree of other formula nodes ,whose parameters is correlate to the 0 in-degree nodes and in-degree is not equal to 0.Application of the modified algorithm in ACFP with 183 formulas, our system achieves a feasible topological order and uses the order and reflection mechanism to gain the final correct result. Compared to the customer's manual calculation, our algorithm spends a little time but achieves the same results.
A Unified Hollow-Disk Scattering Model: Angle-of-Departure and Time-of-Arrival Statistics

In order to ensure the required wireless communication system performances, it is necessary to have an efficient communication channel model, which is capable of considering all channel degradation factors fairly. In this paper, a unified hollow-disk geometrically based stochastic channel model is derived. This model assumes that the scatterers are distributed in a hollow-disk area around a mobile station. Namely, the scatterer distribution is derived as a projection of $\boldsymbol{d}$-dimensional uniformly distributed scatterers into two-dimensional space. According to this generalization, derived model is capable of obtaining uniform circular, uniform ring, uniform hollow-disk, and unified disk scattering model as special cases, as well as modeling a whole new group of hollow-disk models. Under an assumption of single bounce scattering and omnidirectional antennas at transmitter and receiver, joint probability density function (PDF) of angle-of-departure and angle-of-arrival, as well as the joint PDF of time-of-arrival and angle-of-arrival are derived. Subsequently, the corresponding marginal PDFs are provided in a closed form. Thus, derived PDFs are suitable for any position of the base station, (base station placed inside or outside of scattering region). The proposed model is validated by several measured channels. The curve fitting results shown the high flexibility of the proposed model and outperforming results compared with several other geometric scattering models.
Path Predictions using Object Attributes and Semantic Environment

Path prediction methods with deep learning architectures take into account the interaction of pedestrians and the features of the physical environment in the surrounding area. These methods, however, process all prediction targets as a unified category and it becomes difficult to predict a path suitable for each category. In real scenes, it is necessary to consider not only pedestrians but also automobiles and bicycles. It is considered possible to predict the path corresponding to the type of target by considering the types of multiple targets. Therefore, aiming to achieve path prediction in accordance with individual categories, we propose a path prediction method that represents the target type as an attribute and simultaneously considers the physical environment information. The proposed method inputs feature vectors in a long short-term memory that represents i ) past object trajectory, ii) the attribute, and iii) the semantics of the surrounding area. This makes it possible to predict a path that is proper for each target. Experimental results show that our approach can predict a path with higher precision. Also, changes in accuracy were analyzed by introducing the attribute of the prediction target and the physical environment information.
Structure from Motion with Higher-level Environment Representations

Computer vision is an important area focusing on understanding, extracting and using the information from vision-based sensor. It has many applications such as vision-based 3D reconstruction, simultaneous localization and mapping(SLAM) and data-driven understanding of the real world. Vision is a fundamental sensing modality in many different fields of application. While the traditional structure from motion mostly uses sparse point-based feature, this thesis aims to explore the possibility of using higher order feature representation. It starts with a joint work which uses straight line for feature representation and performs bundle adjustment with straight line parameterization. Then, we further try an even higher order representation where we use Bezier spline for parameterization. We start with a simple case where all contours are lying on the plane and uses Bezier splines to parametrize the curves in the background and optimize on both camera position and Bezier splines. For application, we present a complete end-to-end pipeline which produces meaningful dense 3D models from natural data of a 3D object: the target object is placed on a structured but unknown planar background that is modeled with splines. The data is captured using only a hand-held monocular camera. However, this application is limited to a planar scenario and we manage to push the parameterizations into real 3D. Following the potential of this idea, we introduce a more flexible higher-order extension of points that provide a general model for structural edges in the environment, no matter if straight or curved. Our model relies on linked Bézier curves, the geometric intuition of which proves great benefits during parameter initialization and regularization. We present the first fully automatic pipeline that is able to generate spline-based representations without any human supervision. Besides a full graphical formulation of the problem, we introduce both geometric and photometric cues as well as higher-level concepts such overall curve visibility and viewing angle restrictions to automatically manage the correspondences in the graph. Results prove that curve-based structure from motion with splines is able to outperform state-of-the-art sparse feature-based methods, as well as to model curved edges in the environment.
Resource allocation in RF energy harvesting‐assisted underlay D2D communication

Device to device (D2D) communication is capable to address the increasing demand for data rates in fifth generation (5G) and beyond networks. However, D2D communication is usually convoluted with interference scenarios since both D2D users and cellular users share the same spectrum resources. Furthermore, D2D systems can trace back to limited battery life. The battery life problem is becoming more challenging with the exponential increase of devices in the future networks. Therefore, efficient resource allocation schemes need investigation to offer better quality of service for both cellular and D2D users under the constraints of interference and energy. In this paper, we address these two problems (interference and energy) simultaneously by efficiently allocating resources in energy harvesting‐assisted underlay D2D communication. We propose a deterministic model in which D2D users harvest energy only when required. We propose a resource allocation scheme, which jointly allocate resources and transmit power. We formulate an optimization problem with an objective to maximize sum throughput of D2D system while satisfying constraints on quality of service, power, and interference. To solve the problem, we adopt the nonlinear optimization by mesh adaptive direct search algorithm to obtain the suboptimal solution. We show the effectiveness of the proposed scheme in comparison with existing algorithms through simulation results.
Olfactive robot for gas discrimination over several months using a new optoelectronic nose

A new optoelectronic nose has recently been developed using, for the first time, Surface Plasmon Resonance Imaging. Initial studies indicate that the instrument is very promising, and can detect and recognise a large variety of volatile organic compounds (VOCs) even with very limited sampling time. In this paper, we investigate its use as a continuous monitoring system under real-life conditions, recognising VOCs over several months. To this end, we present a robot-based platform which allows fast, realistic and repeatable measurements of several VOCs. We have generated a substantial data set over several months and under different environmental conditions. Results show that the device can be used to recognise VOCs with high reliability, even when training and testing sessions are months apart. The results are quite encouraging for further studies.
Deep Learning for Relevance Filtering in Syndromic Surveillance: A Case Study in Asthma/Difficulty Breathing

In this paper, we investigate deep learning methods that may extract some word context for Twitter mining for syndromic surveillance. Most of the work on syndromic surveillance has been done on the flu or Influenza- Like Illnesses (ILIs). For this reason, we decided to look at a different but equally important syndrome, asthma/difficulty breathing, as this is quite topical given global concerns about the impact of air pollution. We also compare deep learning algorithms for the purpose of filtering Tweets relevant to our syndrome of interest, asthma/difficulty breathing. We make our comparisons using different variants of the F-measure as our evaluation metric because they allow us to emphasise recall over precision, which is important in the context of syndromic surveillance so that we do not lose relevant Tweets in the classification. We then apply our relevance filtering systems based on deep learning algorithms, to the task of syndromic surveillance and compare the results with real-world syndromic surveillance data provided by Public Health England (PHE). We find that the RNN performs best at relevance filtering but can also be slower than other architectures which is important for consideration in real-time application. We also found that the correlation between Twitter and the real-world asthma syndromic surveillance data was positive and improved with the use of the deep- learning-powered relevance filtering. Finally, the deep learning methods enabled us to gather context and word similarity information which we can use to fine tune the vocabulary we employ to extract relevant Tweets in the first place.
Assessment on Microgrid Reliability Applying Demand Side Management Scenarios

One of the problems with renewable resources inclusion is its intermittency. Its operation always oscillates due to weather conditions, day hour, and season of the year. This paper, using probabilistic distributions of solar radiation and wind speed, evaluates the impact of so Demand Side Management Strategies on the reliability indicators of a theoretical microgrid located in Barranquilla (Colombia). Initial results show a chance of occurring unbalance interruptions in months with low rate of wind speed, and rush hours. When Management Strategies are tested, it is shown decreases of outages and Not Supplied Energy in comparison with the base case. In addition, a switching system is implemented to disconnect several groups from the main bus in case of generation-demand unbalance. Criteria between reliability and billed energy are discussed to define a disconnection sequence.
Exploring the variability of media multitasking choice behaviour using a network approach

ABSTRACT Many researchers have used the Media Multitasking Index (MMI) for investigating media multitasking behaviour. While useful as a means to compare inter-individual multitasking levels, the MMI disregards the variability in media multitasking choice behaviour: certain media combinations are more likely to be selected than others, and these patterns might differ from one population to another. The aim of the present study was to examine media multitasking choices in different populations. For this means, we employed a social network approach to render MMI responses collected in eight different populations into networks. The networks showed that the level of media multitasking as measured by the network densities differed across populations, yet, the pattern of media multitasking behaviour was similar. Specifically, media combinations which involved texting/IMing, listening to music, browsing, and social media were prominent in most datasets. Overall the findings indicate that media multitasking behaviours might be confined within a smaller set of media activities. Accordingly, instead of assessing a large number of media combinations, future studies might consider focusing on a more limited set of media types.
Enhancing Spatial Keyframe Animations with Motion Capture

While motion capture (mocap) achieves realistic character animation at great cost, keyframing is capable of producing less realistic but more controllable animations. In this paper we show how to combine the Spatial Keyframing Framework (SKF) of Igarashi et al.(Igarashi et al., 2005) and multidimensional projection techniques to reuse mocap data in several ways. For instance, by extracting meaningful poses and projecting them on a plane, it is possible to sketch new animations using the SKF. Additionally, we show that multidimensional projection also can be used for visualization and motion analysis. We also propose a method for mocap compaction with the help of SK’s pose reconstruction (backprojection) algorithm. This compaction scheme was implemented for several known projection schemes and empirically tested alongside traditional temporal decimation schemes. Finally, we present a novel multidimensional projection optimization technique that significantly enhances SK-based compaction and can also be applied to other contexts where a back-projection algorithm is available.
Multimode Wireless Broadcasting System using nRF

-In textile industries, the problem is that there is less gain of product due to lack of interest of the workers because of the continuity of the same work. In order to achieve the maximum gain in a shortest period as well as creating interest among the workers, Design of multimode wireless broadcasting system using nRF is proposed. In textile industries, the progress of work completion should be intimated to the workers to complete the assignment in time. Hourly targets are fixed to different teams to complete the work orders. In practical, some microphone based announcement systems are followed to intimate the progress to the workers and some companies follows the chalk and board procedures. Here we proposes a multi-node wireless broadcast system using radio frequency of range 2.4 GHz. In this system, an admin will enter the hourly target in a personnel computer. The proposed wireless system will works for the distance of 1200 meters in line of sight and 500-600 meters in off sight. The wireless device will act as a transceiver and in the receiver node, there will be a LCD/LED monitor to display the targets. For the purpose of compaction, maintenance free and for low power consumption, the LCD/LED is proposed here. Each and every nodes are assigned with different addresses, by the way the admin may easily sent the different data to different nodes.
Voltage Sag in Distribution Systems- A review and comparative study (Reasons, Characteristics, Compensations and Detection)

By quick technological advancements in industrial control processes, major industries demand higher power quality. According to IEEE 1159-1995 standard, any problems in voltage, current or freq‌ue‌ncy that lead to the error or malfunctions in electrical equipment are considered as a power quality problem. Among the power quality phenomena, voltage sag has been an important problem for power systems in meeting advanced industries. This phenomenon happens continuously both in transmission and distribution levels. In this paper, following a general review on the voltage sag phenomenon, short circuit faults, starting big inductive motors and energizing of the transformer are considered as the main factors of providing this event. Then voltage sag characteristics are presented. And then the suggested strategies to reduce this event are evaluated. Applying conventional methods for voltage sag compensation is not able to meet this event completely. In recent years, with rapidly developing in semi-conductor industries and control systems, using compensators which are based on controllable electronic devices with higher speed, has attracted many power experts' attentions. At the end, voltage sag compensation style has been studied through applying different compensators.
Clock Gated Multi Coding Technique for the Estimation of Transition Activities in Digital Circuits

In this paper, estimation of transition activity in multi coding technique with the help of hamming code estimator is done and power consumption is reduced using clock gating technique. The VLSI technology allows thousands of transistors in a single chip. In order to integrate more functions in a single chip and to improve their performance, the feature size of the transistors are continue to shrink. This results in maximum power dissipation of the circuit followed by heat removal and cooling of the circuit. The charging and discharging of the internal node capacitance due to transition activity highly contributes to dynamic power dissipation. Mainly the power dissipation occurs due to the transition activity and this can be estimated using hamming code estimator. Coded data is given to the hamming code estimator from each coding type. Estimated code is compared with the help of comparator, this result in analysis of the minimum transition activity for the code in appropriate coding technique. Also clock gating is used to minimize the unnecessary power consumed in the idle state.
Towards unifying spreadsheets with databases for ad-hoc interactive data management at scale

We are witnessing the increasing availability of data across a spectrum of domains, necessitating the interactive ad-hoc management and analysis of this data, in order to put it to use. Unfortunately, interactive ad-hoc management of very large datasets presents a host of challenges, ranging from performance to interface usability. This thesis introduces a new research direction of manipulation of large datasets using an interactive interface and makes several steps towards this direction. In particular, we develop DataSpread, a tool that enables users to work with arbitrary large datasets via a direct manipulation interface. DataSpread holistically unifies spreadsheets and relational databases to leverage the benefits of both. However, this holistic integration is not trivial due to the differences in the architecture and ideologies of the two paradigms: spreadsheets and databases. We have built a prototype of DataSpread, which, in addition to motivating the underlying challenges, demonstrates the feasibility and usefulness of this holistic integration. We focus on the following challenges encountered while developing DataSpread. (i) Representation—here, we address the challenges of flexibly representing ad-hoc spreadsheet data within a relational database; (ii) Indexing—here, we develop indexing data structures for supporting and maintaining access by position; (iii) Formula Computation—here, we introduce an asynchronous formula computation framework that addresses the challenge of ensuring consistency and interactivity at the same time; and (iv) Organization—here, we develop a framework to best organize data based on a workload, e.g., queries specified on the spreadsheet interface.
Motion Planning Method for In-Pipe Walking Robots Using Height Maps and CNN-Based Pipe Branches Detector

In this chapter, the problem of motion planning for an in-pipe walking robot is studied. One of the key parts of motion planning for a walking robot is a step sequence generation. In the case of in-pipe walking robots it requires choosing a series of feasible contact locations for each of the robot's legs, avoiding regions on the inner surface of the pipe where the robot cannot step to, such as pipe branches. The chapter provides an approach to localization of pipe branches, based on deep convolutional neural networks. This allows including the information about the branches into the so-called height map of the pipeline and plan the step sequences accordingly. The chapter shows that it is possible to achieve prediction accuracy better than 0.5 mm for a network trained on a simulation-based dataset.