Miracle-dz/UniFAR-FTUs huggingface dataset:
Formaldehyde Adsorption using a "Dynamic Water Bubble" in Reactors manufacturing Phenolic Resin

Develop an adsorption system to reduce carcinogen agent, call formaldehyde, is the primary objective of this study. In such way, a proposal to adsorpt the formaldehyde expelled through the gasses venting system in reactors aimed at manufacturing phenolic resin, which is involved chemical reactions of the phenol and formaldehyde, is reported. The task was realized, starting formaldehyde pollution identification was realized using math models, then was designed one adsorption formaldehyde system based on a "Dynamic Water Bubble" built with a set of nozzles. That bubble must insert into the reactor venting line capturing such gas without resistance to the flow. Also, this study follows the ruling issued by the National Cancer Institute (NCI) and the International Agency for Research on Cancer (IARC), whose have reported the formaldehyde as a human carcinogen agent. In this sense, the agencies about the environment have recommended working under of the maximum levels suggested by the World Health Organization (WHO) to avoid cancer risks. However, safe industrial manufacture requires use venting relief system in the reflux line, to avoid runaway reactions ending as a disaster to the reactor. Nevertheless, when there is steam in such line (mix of formaldehyde, phenol, and water), it is expelled to free air introducing pollution. This compound that would fall around the reactor would turn the place into a toxic environment with levels higher than WHO suggested, affecting labor biosecurity. Consequently, the present system turns into a useful tool to reduce this carcinogen agent.
Fog-Assisted SDN Controlled Framework for Enduring Anomaly Detection in an IoT Network

Extensive adoption of intelligent devices with ubiquitous connectivity has increased Internet of Things (IoT) traffic tremendously. The smart devices promise to improve human life through improved safety and security through the implementation of intelligent transportation systems, optimization of power grids, and applications in human health. Devices produce a large amount of data for analytic applications running inside a cloud infrastructure. Unlike core networks, the main objective of an attack on an IoT network is to disrupt the availability of IoT data for the applications by overwhelming devices with information requests. Detection of such an attack cannot be done either in the cloud where the analytical application runs nor on the IoT device itself due to its limited computational resources. Furthermore, the standard networking paradigm does not provide an easy way to instrument and control networking nodes, for an effective mitigation of identified threats. In this paper, we propose a fog-assisted software defined networking (SDN) driven intrusion detection/prevention system (IDPS) for IoT networks. A collocated fog computational arrangement with IoT network equips proposed IDPS for timely identification of various attack models in near real time for effective neutralization of threats using SDN control. We have found our approach more effective from traditional techniques of intrusion detection in the IoT network.
A New Discriminative Collaborative Neighbor Representation Method for Robust Face Recognition

As the representative one of representation-based classification (RBC) methods, collaborative RBC (CRC) has drawn much attention in pattern recognition and machine learning recently. Moreover, the collaborative representation-based face recognition has been extensively studied because of the effective classification performance of CRC. CRC collaboratively represents each query sample as the linear combination of all the training samples and then classifies the query sample according to the categorical representation-based distances. However, most variants of CRC cannot fully consider the locality and discrimination of data and cannot well handle the noise data, which has negative effect on real-world classification problems, such as face recognition. In this paper, a new discriminative collaborative neighbor representation (DCNR) method for face recognition is proposed by integrating class discrimination and data locality. In the proposed method, the locality of data constrains collaborative representation of each query sample to find representative nearest samples of the query sample. Moreover, the class discrimination regularization is taken into account by employing the representation of each class for each query sample. Due to the existing noises, such as corruptions and occlusions in face recognition, we further propose robust DCNR (R-DCNR) for robust classification by using the $\ell _{1}$ -norm representation fidelity. Extensive experiments on face databases demonstrate that the proposed methods achieve competitive classification performance, compared to the state-of-the-art representation-based classification methods.
Continuous Decoding of Self-Paced Movement Intention from EEG Correlates

MRCPs (movement related cortical potentials) are slow negative potentials observed in EEG preceding movement, which represents the processing of the cerebral cortex during planning and preparation. Clinical studies have shown that, rehabilitation therapy with the active participation of the central nervous system can rebuild related nerve function, restore the patient's neural plasticity and characterize the intention to move by means of electroencephalographic activity which can be used in rehabilitation protocols with patients' cortical activity taking an active role during the intervention. We describe our method framework including protocol, data processing and pattern recognition. Then the experimental results are analyzed. The tests were carried out with healthy people and achieved satisfactory performance both in trial and asynchronous detection. Our purpose is to apply to restoration of neural pathway of patients.
Variational Mobility Oriented Channel Tracking for Three-Dimensional Millimeter Wave Massive MIMO System

Channel tracking has been a promising technology to sustain the directional link in the millimeter wave (mmWave) communication. However, the complex dynamic environment alters the movement of the mobile station (MS) and increases the inaccuracy of tracking. To tackle this issue, this paper proposes a novel variational mobility oriented channel tracking (VMCT) algorithm for three-dimensional (3D) mmWave system, where the MS moves with variational direction and velocity. The corresponding angles of arrival (AoA) variation is modeled as a multiple linear regression (MLR) process, which contributes to acquire the temporal correlation of sequential AoA states. To further train the weight coefficients of the MLR model with small-scale dataset, an integration of maximum likelihood function and Bayesian conjugate prior distribution is exploited. Simulation results verify the enhanced mean square error (MSE) performance yielded by our proposed tracking algorithm compared to the existing tracking methods.
Gate Control Optimization of Si/SiC Hybrid Switch for Junction Temperature Balance and Power Loss Reduction

The hybrid switch concept of paralleling a higher-current main Si IGBT and a lower-current auxiliary SiC mosfet offers an improved cost/performance tradeoff in power converters. Currently, the gate control strategy of these two internal devices emphasizes on minimizing the total power loss, and is referred to as the efficiency control mode in this paper. However, there is a serious risk of overheating and reliability degradation of the SiC mosfet if solely relying on this control strategy. In this paper, we propose a new method of gate control optimization, referred to as the thermal balance control mode, to keep the junction temperature of both devices within the specified temperature range, and to minimize the total power loss simultaneously. We first investigate the dependency of the hybrid switch switching losses on the gate control pattern both theoretically and experimentally. We then extensively study control optimization in these two distinct control modes in a dc–dc boost converter. It is found that the thermal balance control mode can achieve almost the same total power loss as the efficiency control mode, but much lower and more balanced junction temperatures of the two internal devices. Experimental results demonstrate that the Si/SiC hybrid switch in an optimal thermal balance control mode can achieve a 163% higher power handling capability in the 20-kHz boost converter or four times higher switching frequency in the 4-kW boost converter than a single IGBT solution with hard switching condition, and yet a considerably lower component cost than a single SiC mosfet solution in the boost converter.
A Study on Idiosyncratic Handwriting with Impact on Writer Identification

In this paper, we study handwriting idiosyncrasy in terms of its structural eccentricity. In this study, our approach is to find idiosyncratic handwritten text components and model the idiosyncrasy analysis task as a machine learning problem supervised by human cognition. We employ the Inception network for this purpose. The experiments are performed on two publicly available databases and an in-house database of Bengali offline handwritten samples. On these samples, subjective opinion scores of handwriting idiosyncrasy are collected from handwriting experts. We have analyzed the handwriting idiosyncrasy on this corpus which comprises the perceptive ground-truth opinion. We also investigate the effect of idiosyncratic text on writer identification by using the SqueezeNet. The performance of our system is promising.
Accelerating an FHE Integer Multiplier Using Negative Wrapped Convolution and Ping-Pong FFT

This brief proposes a novel hardware structure for large integer multiplication in fully homomorphic encryption. We propose a method based on negative wrapped convolution to avoid zero padding in Strassen’s algorithm, which can cut down half of the Fourier transform length. In addition, we also optimize the ping-pong fast Fourier transform algorithm by doubling the transform throughput and generating the round constant on the fly. Based on our proposed method and optimized algorithm, we design and implement a 768 k-bit integer multiplier on Altera Stratix V field-programmable gate array (FPGA). Implementation results on FPGA show that our structure outperforms the current competitors in area efficiency.
Adaptively-Connected Structure for Hybrid Precoding in Multi-User Massive MIMO Systems

Hybrid precoding has been recognized as a promising technology for millimeter wave-based massive MIMO systems. As an improved scheme for conventional hybrid precoding structures, the adaptively-connected structure requires low hardware cost and has attractive system performance. In this paper, we propose an adaptive hybrid precoding (AHP) scheme for multi-user massive MIMO systems. The hybrid precoder design is formulated as minimizing the difference between the hybrid precoder and the full digital precoder, and a rough design and revision based AHP (RDR-AHP) algorithm is developed to seek for a near-optimal solution. The proposed RDR-AHP algorithm is achieved by a three-step process including rough design of baseband precoder based on singular value unification, stepwise design of radio frequency precoder and baseband precoder revision. Simulation results demonstrate that the proposed scheme achieves higher spectral efficiency than existing schemes with same hardware cost in millimeter wave channel.
Fast Transmission Network Expansion Planning Considering the N-2 Security Criterion

This study presents a fast transmission network expansion planning method incorporating the N-2 security criterion. Robust uncertainty set is used to characterize the N-2 security criterion where the combined failures of generators, existing lines. , and candidate lines are explicitly modeled. , and hence a trilevel robust optimization model is derived. The upper level minimizes the construction cost. , the middle level selects the worst-case N-2 contingency that maximizes load shedding. , and the lower level minimizes the operation cost under the realization of the worst-case contingency. A duality-based column-and-constraint generation algorithm is applied to solve the proposed model. The computational efficiency of the proposed method is illustrated by a numeric study on the IEEE RTS 24-bus and IEEE 118-bus test systems.
A Framework for Planning LoRaWan Networks

We set ourselves from the perspective of a LoRaWAN network operator and we introduce a mathematical programming framework to jointly optimize network layout and network configuration at design time. The proposed framework returns the most cost-effective network layout in terms of gateways position, gateways backhaul configuration and LoRaWAN physical parameters configuration under tight constraints of end node coverage, end-to-end message transmission latency and message extraction rate. Numerical results obtained on realistic network instances demonstrate that the proposed approach leads to network configuration with superior performance with respect to coverage-only classical design policies.
Semantic SLAM Based on Object Detection and Improved Octomap

Due to the development of the computer vision, machine learning, and deep learning technologies, the research community focuses not only on the traditional SLAM problems, such as geometric mapping and localization, but also on semantic SLAM. In this paper, we propose a Semantic SLAM system which builds the semantic maps with object-level entities, and it is integrated into the RGB-D SLAM framework. The system combines object detection module that is realized by the deep-learning method, and localization module with RGB-D SLAM seamlessly. In the proposed system, object detection module is used to perform object detection and recognition, and localization module is utilized to get the exact location of the camera. The two modules are integrated together to obtain the semantic maps of the environment. Furthermore, to improve the computational efficiency of the framework, an improved Octomap based on the Fast Line Rasterization Algorithm is constructed. Meanwhile, for the sake of accuracy and robustness of the semantic map, conditional random field is employed to do the optimization. Finally, we evaluate our Semantic SLAM through three different tasks, i.e., localization, object detection, and mapping. Specifically, the accuracy of localization and the mapping speed is evaluated on TUM data set. Compared with ORB-SLAM2 and original RGB-D SLAM, our system, respectively, got 72.9% and 91.2% improvements in dynamic environments localization evaluated by root-mean-square error. With the improved Octomap, the proposed Semantic SLAM is 66.5% faster than the original RGB-D SLAM. We also demonstrate the efficiency of object detection through quantitative evaluation in an automated inventory management task on a real-world data sets recorded over a realistic office.
Metaphorical Representation of a Natural Phenomenon in Newspaper Discourse on Natural Catastrophes

The metaphoric conceptualization of a natural phenomenon employed in newspaper discourse on natural catastrophes is examined through a data-driven analysis. The focus is put on the representation of the 2004 Indian Ocean tsunami and 2005 Hurricane Katrina in three newspapers published in Western English-speaking countries: The New York Times, The Guardian and The Globe and Mail. The major metaphoric themes discerned include the depiction of the natural phenomenon as an ANIMATE BEING, a MONSTER and a WARRIOR. By demonizing nature, such a representation reinforces Western nature-culture dualism, puts the blame for the catastrophe on the natural phenomenon and hides social and historical factors contributing to the disaster.
ICFHR 2018 Competition on Handwritten Document Image Binarization (H-DIBCO 2018)

H-DIBCO 2018 is the international Handwritten Document Image Binarization Contest organized in the context of ICFHR 2018 conference. The general objective of the contest is to record recent advances in document image binarization using established evaluation performance measures. This paper describes the contest details including the evaluation measures used as well as the performance of the 8 submitted methods along with a brief description of each method.
Map Comparison of Lidar-based 2D SLAM Algorithms Using Precise Ground Truth

This paper presents a comparative analysis of three most common ROS-based 2D Simultaneous Localization and Mapping (SLAM) libraries: Google Cartographer, Gmapping and Hector SLAM, using a metrics of average distance to the nearest neighbor (ADNN). Each library was applied to construct a map using data from 2D lidar that was placed on an autonomous mobile robot. All the approaches have been evaluated and compared in terms of inaccuracy constructed maps against the precise ground truth presented by FARO laser tracker in static indoor environment.
FDD Massive MIMO Downlink Channel Estimation via Selective Sparse Coding over AoA/AoD Cluster Dictionaries

Sparse coding over a redundant dictionary has recently been used as a framework for downlink channel estimation in frequency division duplex massive multiple-input multiple-output antenna systems. This usage allows for efficiently reducing the inherently high training and feedback overheads. We present an algorithm for downlink channel estimation via selective sparse coding over multiple cluster dictionaries. A channel training set is divided into clusters based on the angle of the arrival/departure of the majority physical subpaths corresponding to each channel tap. Then, a compact dictionary is trained in each cluster. Channel estimation is done by first identifying the channel cluster and then using its dictionary for reconstruction. This selective sparse coding allows for adaptive regularization via sparse model selection, thereby offering additional regularization to the ill-posed channel estimation problem. We empirically validate the selectivity of the cluster dictionaries. Simulation results show the advantage of the proposed algorithm in achieving better estimation quality at lower computational cost, as compared the case of using standard sparse coding.
Adaptive Optimal Receding-Horizon Robot Navigation via Short-Term Policy Development

We propose a novel optimal receding-horizon navigation approach for a robot in an unknown search environment, towards a known goal position. The search environment includes several obstacles that are distributed at unknown positions. The proposed approach considers multiple objectives, including reference path tracking, reduction of the energy consumption, restraining the robot's mission time, and asymptotic stability towards the goal position. The navigation policy is determined in the detection zone of the robot's detection sensor at particular update time steps for short time. This policy will be updated at the next update time steps. Moreover, we introduce a novel heuristic algorithm for determining the robot's tracking path trajectory that is simply implementable and fast in computations.
Space Balancing in Online Handwriting Recognition Postprocessing using Deep Bidirectional LSTM

The recognition of named entities is challenging for handwriting recognition systems because such entities are usually new and out-of-vocabulary words. Additionally, high recognition accuracy can be required if the recognized entities are intended to be further used by other applications. In this paper, a new method of correcting space character alignment for named entities based on Deep Bidirectional Long Short-Term Memory Recurrent Neural Network architecture is proposed. This solution has been implemented for postprocessing of a handwriting recognition system for nine languages with possible scalability to languages with different writing systems and other peculiarities. It achieves an accuracy improvement up to 7% compared to pure handwriting recognition without space balancing.
Secure Quantum Turbo Coded Superdense Coding Scheme

In this paper we present a novel secure quantum turbo coded (QTC) superdense (SD) coding scheme. We have integrated SD with QTC to benefit from the inherent near optimum performance of turbo codes. We have further incorporated the quantum secure direct communication (QSDC) protocol within the proposed QTC-SD system for the sake of ensuring absolute security. Explicitly, the QSDC protocol exploits dummy entangled pairs for detecting the presence of eavesdropper. Hence, the proposed scheme ensures absolute security, detects the presence of an eavesdropper and considers practical issues when error is induced in a quantum depolarizing channel. Furthermore, we have experimentally determined the noise threshold levels for the proposed QTC-SD system to detect the presence of an eavesdropper.
Generic Resource Allocation in Distribution Grid

This study presents a generic methodology for allocating active and reactive power resources in the distribution network. It is based on the conventional AC load flow model and can provide accurate results without solving sophisticated optimization programs. Numerical results of standard test systems are presented which indicate the proposed method, compared to the optimization based approaches, can quickly attain accurate and easy-to-compute decisions.
Network Slicing with Splittable Flows is Hard

Allocating resources to network slices can be achieved by means of solving virtual network embedding problems, whereby virtual nodes are used to reserve computing resources on cloud nodes, and virtual links are used to reserve bandwidth resources on network paths. Since the associated optimization problem is also NP-hard to approximate, in this paper we focus on a natural simplified setting of interest: the case where the tunnels can be embedded with splittable flows. For this problem, we provide a simple proof that it is NP-hard by a reduction from the 3-SAT problem. Further, using the idea of the multipartite graph, we propose a poly-time heuristic for the loose capacity constraint case, based on linear relaxation and randomized rounding. This heuristic is shown to have small optimality gaps in extensive simulations.
Learning about Fashion exploiting the Big Multimedia Data

We here propose to collect and analyze large amounts of multimedia data from different public and private sources, in the form of text, image, video, to predict relevant information about specific quantities related to fashion brands, such as their sales volumes and/or trends. To this aim, we deem deep learning techniques as the suitable instrument capable of managing extremely large amounts of multimedia data. While a few works exist in literature on learning applications in the fashion area, where text is used to perform sentiment analysis operations, limited research has also considered images and videos in this context. In this paper, starting with an overview of the state of the art of the applications of artificial intelligence for fashion, we set the stage for a holistic approach for the deep learning based analysis of multimedia data related to fashion.
A Capacitor-Less Ripple-Less Hybrid LDO With Exponential Ratio Array and 4000x Load Current Range

A hybrid low-dropout regulator (LDO) with exponential-ratio array (ERA) and analog based transient voltage compensation is implemented in 65-nm CMOS. The hybrid LDO (HLDO) is capable of reducing a voltage drop upon load change by more than 70%, and the ERA enables fast tracking time, enlarging dynamic range of the digital loop. The HLDO achieves 99.7% current efficiency with the output dynamic range of 0.01-to-40 mA (4000x) and obtains the transient output voltage drop of 55 mV for a load current jump of 0.55 mA from 20 <inline-formula> <tex-math notation="LaTeX">$\mu \text{A}$ </tex-math></inline-formula>. When both the dynamic and the static performances are considered, the proposed HLDO shows the state-of-the-art performance among other recent digital LDOs and HLDOs.