Miracle-dz/UniFAR-FTUs huggingface dataset:
Geographic Segmented Opportunistic Routing in Cognitive Radio Ad Hoc Networks Using Network Coding

In cognitive radio ad hoc networks (CRAHNs), the secondary users’ links are interrupted by the arrival of primary users, which leads to a significant increase of the number of transmissions per packet. Since network coding and opportunistic routing can reduce the number of transmissions over unreliable wireless links, they are more suitable for CRAHNs. Network coding-based opportunistic routing has been well studied in previous works for traditional ad hoc networks. However, existing approaches have limitations to handle CRAHNs. First, the routing decisions of these methods aim to find one optimal forwarding set for the whole path from the source to the destination. If the channel conditions change dynamically, the pre-selected forwarding set may become unavailable or non-optimal. Second, their coding schemes are not efficient enough to cope with the dynamic spectrum variation in CRAHNs. In this paper, we attempt to overcome these limitations and propose a network-coding-based geographic segmented opportunistic routing scheme for CRAHNs, by fully embracing its characteristics. In our scheme, the whole path from the source to the destination is cut down into several smaller opportunistic route segments, where the packets are transmitted through multiple segments based on a step-by-step forwarding procedure until all the packets are delivered to the destination. Since our scheme utilizes only local spectrum opportunities, topology information, and geometric conditions to compute the forwarding set for each short-term opportunistic route segment, it can better adapt to dynamic spectrum environments and changing network topologies in CRAHNs. Furthermore, we construct a coding graph to show that our coding problem is a reduction from the maximum clique problem, and we propose an efficient network coding strategy to solve it. Simulation results show that our scheme can achieve a considerable performance, compared with the latest opportunistic routing protocols designed for CRAHNs.
An X ray-cosmic ray combined dual-model reconstruction approach for vehicle/container inspection

Cosmic-ray tomography is an effective non- destructive technology which was originally developed to detect high-Z nuclear materials. Further research showed that after taking part in soft cosmic ray (such as electrons), this technology can discriminate medium- and low-Z materials, but presenting poor material resolution. In order to fight against smuggling and seizing hazardous goods, it’s significant to improve the material resolutionof medium- and low-Z materials. In this paper, we propose an X-ray & cosmic-raycombined dual-model reconstruction approach. X-ray system obtainstransparency, structure and height information of the inspected vehicle/container, then cosmic ray tomography system reconstructs image using both cosmic-ray and X-ray information, where thecargo transparency value helps to divide different Z regions, structure determines the location of stopping effect, andmaterial thickness improves the calculations of stopping power and scattering density. Geant4 simulation program and ROC (receiver operating characteristic) analysis are used to validate the proposed approach. Results show that in 1 minute’s inspection time, the dual-model reconstruction approach can achieve detection rate of above 65%, with AUC (the area under the ROC curve) value of higher than 0.75, much better than those of standalone cosmic-ray approach by 30% and 0.46. The dual- model reconstruction approach can effectively enhance the classification ability of medium- and low-Zmaterials.
Design and Physical Implementation of a Low Frequency On-Chip Oscillator

In this paper, a square wave with 10 kHz frequency is designed to be used as an on-chip oscillator for a Wireless Sensor Node System On Chip (WSN-SOC). The proposed circuit generates high-frequency oscillations without the need for any external crystal oscillator using a ring oscillator. An asynchronous counter is used as a frequency divider circuit to decrease the frequency of the oscillations to the desired frequency. A buffer at the output stage increases the net current so that the oscillator can drive multiple loads. The circuit is designed as per the full custom ASIC flow in the 180 nm technology library provided by the foundry - Semi-Conductor Laboratory, India. The schematic and the layout are done on the Cadence Virtuoso tool. Physical verification is performed using Mentor Graphics Calibre tool. Post-layout simulation is done using Synopsys HSpice tool. The post-layout simulations of the oscillator occupying an area of about 223um x 118 um, show a square wave with a frequency of 10.59 kHz, very close to the desired frequency, consuming 9.408 uW average power at 1.8 V supply.
Dietary assessment toolkits: an overview

Abstract Objective A wide variety of methods are available to assess dietary intake, each one with different strengths and weaknesses. Researchers face multiple challenges when diet and nutrition need to be accurately assessed, particularly in the selection of the most appropriate dietary assessment method for their study. The goal of the current collaborative work is to present a collection of available resources for dietary assessment implementation. Design/Setting/Participants As a follow-up to the 9th International Conference on Diet and Physical Activity Methods held in 2015, developers of dietary assessment toolkits agreed to collaborate in the preparation of the present paper, which provides an overview of each toolkit. The toolkits presented include: the Diet, Anthropometry and Physical Activity Measurement Toolkit (DAPA; UK); the National Cancer Institute’s (NCI) Dietary Assessment Primer (USA); the Nutritools website (UK); the Australasian Child and Adolescent Obesity Research Network (ACAORN) method selector (Australia); and the Danone Dietary Assessment Toolkit (DanoneDAT; France). An at-a-glance summary of features and comparison of the toolkits is provided. Results The present review contains general background on dietary assessment, along with a summary of each of the included toolkits, a feature comparison table and direct links to each toolkit, all of which are freely available online. Conclusions This overview of dietary assessment toolkits provides comprehensive information to aid users in the selection and implementation of the most appropriate dietary assessment method, or combination of methods, with the goal of collecting the highest-quality dietary data possible.
A Low-Rank Tensor Model for Hyperspectral Image Sparse Noise Removal

Hyperspectral image (HSI) has been widely used in target detection and classification. However, various kinds of noise in HSIs affect the applications of HSIs. In this paper, we propose a low-rank (LR) tensor recovery model to remove noise. Considering that the HSI is a 3-D HSI data, and the underlying LR tensor property is used in the model. Then, according to the similarity of adjacent bands images, the regularization on the difference of adjacent bands images is considered. The experiments of removing noise from different noisy HSIs show that our method can achieve better performance on removing sparse noise, especially for strips removal.
Curb Detection for Road and Sidewalk Detection

Curb detection, a significant area of autonomous driving, plays an important role in road detection and obstacle avoidance, etc. However, curb detection is challenging due to the problems like occlusions, shadows and the small size of the target. In this paper, we propose a curb detection paradigm for road and sidewalk detection for mobile robots using stereo vision in the urban residential region. First, the flat area is estimated based on the disparity and v-disparity maps generated from stereo matching. In the estimated flat area, to distinguish curbs from the road and obstacles, we propose an efficient 16-dimensional descriptor based on the appearance, geometry, and disparity characteristics of curbs. The curb points can be extracted by an SVM classifier with the obtained descriptors. Second, the curb points are exploited by a vanishing point constrained Dijkstra road model to find the road region, where two shortest paths, generated from the vanishing point to the last row of the cost map, are searched and regarded as the road borders. Following the curb and road region detection results, the sidewalk area is detected by a region-growing-like method based on the geometry characteristic of the sidewalk area and the layout of road scenes. The proposed method has been tested for road and sidewalk detection tasks on the KITTI dataset. The experimental results of the curb, road, and sidewalk detection demonstrate the accuracy and robustness of this method.
Multivariate Time-Series Similarity Assessment via Unsupervised Representation Learning and Stratified Locality Sensitive Hashing: Application to Early Acute Hypotensive Episode Detection

An accurate and timely prediction of clinically critical events in intensive care unit (ICU) is important for improving care and survival rate. Most of the existing approaches are based on the application of various classification methods on explicitly extracted statistical features from the vital signals of ICU patients, such as the mean, the standard deviation, and the skew. In this article, we propose to eliminate the high cost of engineering hand-crafted features from multivariate time-series of physiologic signals by learning its representation with a sequence-to-sequence autoencoder. We then propose to hash the learned multivariate time-series representations of labeled dataset to enable signal similarity assessment for the prediction of critical events. We evaluate this methodological framework to predict acute hypotensive episodes (AHE) on a large and diverse dataset of vital signal recordings extracted from eICU collaborative research database. Experiments demonstrate the ability of the presented framework in accurately predicting an upcoming AHE.
Optimizing Weighted ELM Based on Gray Wolf Optimizer for Imbalanced Data Classification

Extreme Learning Machine (ELM), which is a Well-known supervised learning algorithm, provides a high generalization performance and a fast learning technique. However, ELM cannot handle the class imbalance. Recently, the weighted ELM was developed to solve this issue. The weighted ELM performance still faced with finding the proper boundary between positive class and negative class. In this paper, we proposed GWO-weighted ELM that is a combination of the weighted ELM integrated with the Gray wolf optimizer (GWO). The main contribution of the paper is the regularization parameter is optimized via the GWO algorithm. The experiments were conducted on 16 real imbalanced datasets. We compared the proposed GWO-weighted ELM with the original weighted ELM. The experimental results showed that the GWO-weighted ELM with Wl is superior to the weighted ELM and the GWO-weighted ELM with W2.
Boosting Search Performance Using Query Variations

Rank fusion is a powerful technique that allows multiple sources of information to be combined into a single result set. Query variations covering the same information need represent one way in which different sources of information might arise. However, when implemented in the obvious manner, fusion over query variations is not cost-effective, at odds with the usual web-search requirement for strict per-query efficiency guarantees. In this work, we propose a novel solution to query fusion by splitting the computation into two parts: one phase that is carried out offline, to generate pre-computed centroid answers for queries addressing broadly similar information needs, and then a second online phase that uses the corresponding topic centroid to compute a result page for each query. To achieve this, we make use of score-based fusion algorithms whose costs can be amortized via the pre-processing step and that can then be efficiently combined during subsequent per-query re-ranking operations. Experimental results using the ClueWeb12B collection and the UQV100 query variations demonstrate that centroid-based approaches allow improved retrieval effectiveness at little or no loss in query throughput or latency and within reasonable pre-processing requirements. We additionally show that queries that do not match any of the pre-computed clusters can be accurately identified and efficiently processed in our proposed ranking pipeline.
LabelTransfer - Integrating Static and Dynamic Label Representation for Focus+Context Text Exploration

In recent years, interactive visualization to analyze text documents has gained an impressive momentum. This is not surprising considering the fast increase of electronically available textual documents of various kinds. These include, for example, patents, scholarly documents, social media messages, and many other sources that contain valuable knowledge and insights for many stakeholders. Interactive text visualization turned out to be an important means for exploring and gaining insights into complex and often large document collections. An established visualization strategy to represent such collections is using projection-based techniques that visualize documents as glyphs in a 2D view aiming to reflect the semantic similarity of documents by the proximity of their placement. Static labels have been suggested to characterize the overall topics contained in the projected data to improve the effectiveness of such visualization techniques. Other approaches employ magic lenses that enable users to explore the 2D spatialization freely on various granularity levels. In this work, we propose a visual exploration approach that combines cluster-based labeling of projected documents with an interaction concept for magic lens techniques. We offer a set of novel interactive features to support a smooth transition between static labels and the magic lens approach while exploiting the different levels of visual abstraction of both techniques without introducing additional clutter through overdraw. Finally, we provide insights gained from a preliminary user study and present the benefits of our approach.
Discovering User-context in Indoor Space

Recently, there has been a significant progress of context awareness due to sensor and mobility technologies and machine learning for their interpretation. A large part of the user's context is determined by her/his location. Since the notion of indoor location differs from the outdoor space, the context in indoor space should be differently interpreted from the outdoor space. The purpose of this study is to discover user context in indoor space by combining her/his activity and location context in indoor space. In order to predict the profile of users, we retrieve the cell from (x, y, z) coordinates and its types such as office room, meeting room, or classroom, where she/he is located, and recognize human activity from smartphone sensors. An experiment was conducted to validate our method and the result shows 93.44% accuracy for user profile prediction.
Reconstruction of Time-of-Flight Projection Data with the STIR reconstruction framework

This manuscript gives an update on the integration of Time-Of-Flight reconstruction into the STIR image reconstruction toolkit. In this iteration we provide significant support to reconstruct TOF-compatible projection data. Most infrastructure classes, such as ProjDataInfo and ProjData, have been extended, and utilities, as lm_to_projdata, used to create sinograms from listmode data, can now handle TOF information. This extension required many modifications in the low level code base of STIR, making it non-trivial and error-prone. Therefore, a thorough validation is required. In this work we provide initial results of the correctness of the extension. Using Monte Carlo simulations, as well as cylindrical and XCAT phantoms analytically projected, we calculate the contrast recovery ratio over a wide range of iterations. The results demonstrate the benefits of TOF under different configurations, which are in good agreement with literature. The ROI mean value converges to the maximum faster with better timing resolution, while larger number of TOF bins improve contrast values in low iterations.
Threshold-Bounded Dominating Set with Incentives

Motivated by some problems in the area of influence spread in social networks, we introduce a new variation on the domination problem which we call Threshold-Bounded Domination with Incentives. Let G = (V,E) be a graph with an influence threshold t(v) for each node. An assignment of external incentives to the nodes of G is a cost function c : V → N0, where c(v) is the incentive given to v ∈ V . The effect of applying incentive c(v) to node v is to decrease its threshold, i.e., to make v more susceptible to be dominated. A node is in the Threshold-Bounded dominating set D if it receives an incentive equal to its threshold, that is, c(v) = t(v). A node, which is not in D, is dominated if the number of its neighbors in D plus the incentives it has received is at least equal to the node threshold t(v). The goal is to minimize the total of the incentives required to ensure that all the nodes are dominated. The problem is log-APXcomplete in general networks with unbounded degree. We prove that a greedy strategy has approximation factor ln∆ + 2, where ∆ is the maximum degree of a node. We also give exact linear time algorithms for some classes of graphs.
Escape mapping to achieve bidirectional block: A case series

Escape mapping is a novel technique that can be used to locate sites of persistent conduction and achieve exit block during an atrial fibrillation ablation. This method allows for mapping solely with the ablation catheter in the left atrium by annotating to a catheter in the coronary sinus. We illustrate the utility escape mapping during an atrial fibrillation ablation where entrance block is achieved without exit block. We further expand upon this technique by describing the first reported case of escape mapping being used to achieve bidirectional block during an atrial flutter ablation.
Experimental Investigation of Parametric Mode and Wavelength Conversion in a 4.7 km Few-Mode Fiber

We demonstrate the potential of few-mode-fibers for all-optical signal processing by converting and receiving three wavelength-multiplexed 30-GBaud QPSK channels from 1533 nm wavelength launched into the LP11a mode to approximately 1549 nm received in the LP01 mode of a 4.7 km FMF.
A Methodology of NB-IoT Mobility Optimization

Narrowband Internet of Things (NB-IoT) specified by 3rd Generation Partnership Project (3GPP) is a new technology of Low Power Wide Area (LPWA). While conventional mobile services such as LTE support excellent mobility with handover and redirection, NB-IoT supports mobility only with cell reselection in idle state. In this regards, it is important for mobile operators to optimize the performance of cell reselection for reliable NB-IoT service. In this paper, we provide operational characteristics of NB-IoT cell reselection and a methodology for optimization of cell reselection parameters with test results in test-bed as well as real operational field environment.
Cracking the TSCH Channel Hopping in IEEE 802.15.4e

Industrial networks typically connect hundreds or thousands of sensors and actuators in industrial facilities, such as steel mills and oil refineries. Although the typical industrial applications operate at low data rates, they pose unique challenges because of their critical demands for reliable and real-time communication in harsh industrial environments. IEEE 802.15. 4 based Wireless Sensor-Actuator Networks (WSANs) technology is appealing for use to construct industrial networks because it can be deployed and maintained inexpensively. Battery-powered wireless modules easily and inexpensively retrofit existing sensors and actuators in industrial facilities without running cabling for communication and power. To address the stringent real-time and reliability requirements, WSANs adopt a set of novel design choices such as employing the Time-Synchronized Channel Hopping (TSCH) technology that distinguish themselves from traditional Wireless Sensor Networks (WSNs) that require only best effort services. The equation-based channel hopping used in TSCH simplifies the network operations at the cost of security. Our case study shows that an attacker can reverse engineer the channel hopping sequence by silently observing the channel activities and then perform smart collision attacks. In this poster proposal, we describe our target problem and present our case study based on a publicly accessible implementation of TSCH.
A Sine Pulse Width Modulation Based Four-Leg Voltage Source Inverter Drive For Asymmetrical Two-Phase Induction Motor

The development is need of today's world. This gives a new direction to work better. And Two-Phase Induction Motor is a path to replace the crisis occurred in Single-Phase Induction Motor (SPIM). The SPIM is broadly used with frequently available single phase supply. The main drawbacks are SPIM is not self-starting and noisy etc. these all crisis are rise above in Two-Phase Induction motor. The Two-Phase Induction Motor is supplied through a four-leg Voltage Source Inverter with a carried-based switching. With this type of configuration open loop v/f control is applied. This v/f control is making this system different as well as novel over SPIM. And the system gives input to save energy.
Microwave Absorber by FSS Using Paper Instead of Dielectric Substrate

The wave absorber is used to suppress unwanted wave reflection. In this paper, a frequency selective surface (FSS) wave absorber is proposed. To get flexibility, silver ink is used for the patch layer, and a dedicated paper for dielectric substrate. 90 % absorption is achieved with a bandwidth of 7.7 % between 9.65 to 10.42 GHz in the case of a normal plane wave incidence. Its absorb characteristics are stable for a oblique incident wave, which is verified by bi-static RCS simulation, the RCS simulation result of the proposed absorber are 10.2 dB lower than the PEC reflector.
Toward Efficient Service Composition Techniques in the Internet of Things

Internet of Things (IoT) is anticipated to bridge various technologies to allow new applications through linking physical things together in the future. Increasing facilities through the composition of services are considered as an essential module in this technology. Several types of services can be supplied in the IoT using the user’s requirements to consider the user needs. However, the service composition in the IoT does not have any systematic and complete study about examining its significant techniques. So, this paper aims to investigate the available methods in this field using a systematic manner. To achieve a comprehensive view of the topic, all of the selected approaches are divided into four distinct categories, including framework, service oriented architecture and RESTful, heuristic, and model-based. The detailed classifications have been presented using various parameters based on the examination of the existing techniques. Also, the benefits and drawbacks of the state-of-the-art service composition techniques and the important challenges of them are discussed. Finally, we have explained future work in the field of service composition in the IoT with detail in order to make an effective and efficient way for researchers in this area. We also have identified four important parameters to examine the selected service composition mechanisms in the IoT. Scalability is improved in 45.4%, execution time in 36.3%, cost in 27.2%, reliability in 22.7%, availability in 18.1%, and response time in 13.6% of the reviewed articles.