Miracle-dz/UniFAR-FTUs huggingface dataset:
Network-coded Cooperative Communication in Virtualized Mobile Small Cells

With the exponential growth of mobile devices in 5G network, massive content delivery in a cellular network is the upcoming challenge for researches. Due to large data traffic, energy efficiency becomes increasingly important as well as the user throughput requirement increases. In this paper, we propose Network-coded Cooperative (NCC) communication on virtualized Mobile Small Cells (MSCs) to minimize energy consumption on MSCs realizing the benefits of Mobile Edge Computing (MEC). The proposed approach adopts MEC and Software Defined Networking (SDN) to evaluate the gains in terms of user throughput that can be achieved when data contents are moved from cloud to the edge of the Radio Access Network. Moreover, the data contents are transferred to user equipments (UEs) in a cooperative approach to reduce the energy consumption in MSCs. The evaluation results show a decrease in the energy consumption per UE of 62% for the best case, due to a decrease of the LTE channel usage in favor of WiFi, which consumes less energy.
Estimation Theoretic Secure Communication via Encoder Randomization

Estimation theoretic secure transmission of a scalar random parameter is investigated in the presence of an eavesdropper. The aim is to minimize the estimation error at the receiver under a secrecy constraint at the eavesdropper; or, alternatively, to maximize the estimation error at the eavesdropper for a given estimation accuracy limit at the receiver. In the considered setting, the encoder at the transmitter is allowed to use a randomized mapping between two one-to-one and continuous functions and the eavesdropper is fully aware of the encoding strategy at the transmitter. For small numbers of observations, both the eavesdropper and the receiver are modeled to employ linear minimum mean-squared error (LMMSE) estimators, and for large numbers of observations, the expectation of the conditional Cramér-Rao bound (ECRB) metric is employed for both the receiver and the eavesdropper. Optimization problems are formulated and various theoretical results are provided in order to obtain the optimal solutions and to analyze the effects of encoder randomization. In addition, numerical examples are presented to corroborate the theoretical results. It is observed that stochastic encoding can bring significant performance gains for estimation theoretic secrecy problems.
iDahon: An Android Based Terrestrial Plant Disease Detection Mobile Application Through Digital Image Processing Using Deep Learning Neural Network Algorithm

This research focuses on the detection of common diseases on terrestrial plants found in the Philippines through the use of image processing through Deep Learning Neural Network. Android-based smartphones were used to capture images of the terrestrial plant to detect the disease of the plant while Deep Learning Neural Network Algorithm is utilized to distinguish the disease of the terrestrial plants. The results were trained using classification models that could identify the diseases at certain rate and accuracy considering the number of images used. Therefore, the Deep Learning Neural Network classification demonstrates the identification of the common disease terrestrial plants found in the Philippines
Hybrid Method for Measuring Rotor Bar-Lamination Contact Resistances

The paper presents a new non-invasive method to estimate the contact resistance between the aluminum bars and the rotor laminations of induction motors. The methodology is based on a mix of experimental data and FEM-based simulation results. It has been applied with success to a 4-poles 15 kW squirrel cage induction motor obtaining interesting outcomes. The quantification of the contact resistance allows accurate computation of the interbar currents as well as the related additional Joule losses, opening a new understanding of this complex phenomenon and its impact on the stray losses.
Towards a Brain-Robot Interface for children

Brain-Computer Interface systems have been widely studied and explored with adults demonstrating the possibility to achieve augmentative communication and control directly from the users’ brain. Nevertheless, the study and the exploitation of the BCI in children seems to be limited. In this paper we propose and present for the first time a Brain-Robot Interface enabling children to mentally drive a robot. With this regards, we exploit the combination of a P300-based Brain-Computer Interface and a shared-autonomy approach to achieve a reliable and safe robot navigation. We tested our system in a pilot study involving five children. Our preliminary results highlight the advantages of using an accumulation framework, thanks to which the performance of the children reached the 81.67 % ± 12.7 on average in terms of accuracy. During the experiments, the shared-autonomy approach involved a low-level intelligent control on board of the robot to avoid obstacles, enabling an effective navigation also with a small number of commands.
Monocular Vision-Based Obstacle Detection and Avoidance for a Multicopter

This article presents a monocular vision-based algorithm for detecting obstacles and identifying obstacle-aware regions, developed to be used for collision avoidance by a multicopter. The first step of our algorithm is to predict a disparity image from a single-view image via implementing a deep encoder-decoder network. All pixels in this disparity prediction are then categorized as one of three classes, obstacle, road, or obstacle-free, by combining V-disparity analysis and a fuzzy inference system. For pixels belonging to obstacle objects, obstacle-aware regions are generated within the field of visual perception. To accommodate the safety margins of a multicopter, intermediate waypoints are then added to obtain a new flyable path that passes through an unknown environment safely. Experimental results verified the effectiveness of the detection of obstacles and the identification of obstacle-aware regions. The accuracy of disparity prediction and monocular depth estimation were quantitatively compared to support the feasibility of monocular vision in obstacle avoidance. Furthermore, the entire algorithm was successfully tested on a robotic platform, autonomously flying a hexacopter in an outdoor space with obstacles. In conclusion, the proposed monocular algorithm performs well for obstacle detection and depth estimation and is potentially an alternative to a binocular solution.
Embedded compensation for DDQ/Bipolar-Q IPT Charging Pads

The performance of inductive power transfer pads deteriorates with the pad misalignment. In recent years, several strategies to keep the power transfer constant at different misaligned conditions have been studied. Hybrid and embedded compensation topologies have been presented for DD and bipolar pads. This paper presents an embedded compensation for bipolar-Q or DDQ coils. The Q-coil is used as the compensation inductor of an LCCL resonant tank. The proposed structure was tested on a 6.6 kW testbench. Almost constant power is maintained for pad misalignments ≤ 15 cm in both x and y directions. The proposed topology requires only 2 capacitors per pad and offers additional flexibility of design. Particularly, the Q-coil dimensions and number of turns can be designed to achieve almost constant power transfer at different pad misalignments.
Vehicle speed prediction with RNN and attention model under multiple scenarios

Motion prediction is an essential feature for autonomous vehicle to understand the intention of nearby vehicles so as to plan its route. Several works have been proposed to predict the motion of nearby vehicles using Kalman filter, RNN, and other machine learning methods. However, many of them rely on either the communication among vehicles or the global information of all the vehicles on the road and have limited applicability. This paper presents the design and implementation of a new machine learning model to predicate the motion of nearby vehicles by observing their motions in last few seconds. The proposed network consists of encoderdecoder, LSTM, and attention model to tackle the challenges so as to predict the vehicle speed using limited observations. The network was trained based on data set collected on public roads. Comparing with Kalman filter, the developed method reduces the prediction error up to 50% and the prediction error is up to 6.5KPH(1.8 meter per second) under all evaluated scenarios, which is less than the tolerance of speedometers on vehicles.
Burn wound healing activity of hydrolyzed virgin coconut oil.

Virgin coconut oil (VCO) contains mainly medium chain fatty acids especially lauric acid, easily absorbed, has the potential to accelerate cell metabolism, moisten wound and has anti-inflammatory activity. Partial hydrolysis of VCO would result in free fatty acids, monoglycerides, and diglycerides. Combination of free fatty acids and monoglycerides especially lauric acid and monolaurin are active as antibacterial. The aim of this study was to investigate the effect of healing properties of partially hydrolyzed VCO on burn wound. VCO used in this study was the product of UD Sinar Nias. VCO was partially hydrolyzed with NaOH solution at the amount of 35% and 70% of the total saponification value. Hydrolysate was acidified with dilute HCl and then extracted with hexane. Hexane extract was evaporated and the evaporated residue was used topically for the treatment of burn induced in rabbits. This study used 10 rabbits (1.5-2 kg) divided in to five treatment groups (negative control, positive control with Bioplacenton®, VCO 0%, 35% hydrolyzed VCO and 70% hydrolyzed VCO). Burn induced by placing a hot metal plate with diameter of 2 cm. Hydrolyzed VCO as tested material was applied topically to the wound every day as much as 0.1 ml. Lesion diameter was measured every day, and the time recorded until the diameter of the healed wound was zero. The results showed that the healing process was the fastest when treated with hydrolyzed VCO at 70% (12 days), followed by VCO hydrolysis at 35% (15.5 days), VCO 0% (17.3 days), Bioplacenton ® (18.1 days) and the longest time of healing was with untreated/negative control (23.5 days). There was significant difference on the rate of healing of burn wound between each treatment. However, there is no significant difference between the positive control group (Bioplacenton®) and VCO 0% (without hydrolysis). The higher the level of hydrolysis of VCO the more effective the healing process of burn wound.
In vivo subsite classification and diagnosis of oral cancers using Raman spectroscopy

Oral cancers suffer from poor disease-free survival rates due to delayed diagnosis. Noninvasive, rapid, objective approaches as adjuncts to visual inspection can help in better management of oral cancers. Raman spectroscopy (RS) has shown potential in identification of oral premalignant and malignant conditions and also in the detection of early cancer changes like cancer-field-effects (CFE) at buccal mucosa subsite. Anatomic differences between different oral subsites have also been reported using RS. In this study, anatomical differences between subsites and their possible influence on healthy vs pathological classification were evaluated on 85 oral cancer and 72 healthy subjects. Spectra were acquired from buccal mucosa, lip and tongue in healthy, contralateral (internal healthy control), premalignant and cancer conditions using fiber-optic Raman spectrometer. Mean spectra indicate predominance of lipids in healthy buccal mucosa, contribution of both lipids and proteins in lip while major dominance of protein in tongue spectra. From healthy to tumor, changes in protein secondary-structure, DNA and heme-related features were observed. Principal component linear discriminant analysis (PC-LDA) followed by leave-one-out-cross-validation (LOOCV) was used for data analysis. Findings indicate buccal mucosa and tongue are distinct entities, while lip misclassifies with both these subsites. Additionally, the diagnostic algorithm for individual subsites gave improved classification efficiencies with respect to the pooled subsites model. However, as the pooled subsites model yielded 98% specificity and 100% sensitivity, this model may be more useful for preliminary screening applications. Large-scale validation studies are a pre-requisite before envisaging future clinical applications.
The lived experience of men entering residential aged care

Objectives: This study examined the experience of 14 men entering 10 residential aged care facilities in the Sydney metropolitan area. The impact of variables including facility location, socioeconomic region, standard of the facility and the facility's accreditation history was also explored. Methods: Fourteen male participants were recruited from the participating residential aged care facilities. An in-depth interview was conducted with each participant and follow up interviews were undertaken where clarification of experiences was required. Results The narrative suggests that the experience of these men entering residential aged care is largely negative. The experiences of the participants did not establish a correlation between negative experiences and poor accreditation outcomes although residents in facilities with poor physical environments did report more negative experiences in relation to privacy and dignity. Conclusion: The participants identified the residential aged care experience as largely negative. Significant perceptions of disempowerment, institutionalisation and social isolation were identified. (author abstract)
Fast Co-Simulation Methodology to Assess Electric Vehicle Penetration in Distribution Networks

The study of the impact generated by a massive connection of electric vehicles in distribution networks requires modeling their connection behavior in different scenarios and considering their stochastic characteristics in annual periods and with short simulation steps. This implies a combinatorial explosion of case studies with a high computational cost. This article presents a methodology aimed at reducing the simulation time required to determine the hosting capacity (HC) of electric vehicles through the co-simulation of hardware and software. Monte Carlo methods and quasi-static time series (QSTS) simulation concepts are used to model the connection features of electric vehicles. The simulation and implementation are done using Matlab and OpenDSS software using parallel computing. The methodology is tested in the IEEE 123 node system observing the dependency of the HC indicator on the location and type of EV connection charger and the reduction of execution times.
A Simulation Study on Handover in LTE Ultra-Small Cell Deployment: A 5G Challenge

Future cellular networks need to support data hungry applications with enhanced data rates possibly via cell densification (ultra-small cells). The key objective of this work is to study the performance of a cellular network by taking into account the user mobility when LTE ultra-small cell network is deployed. Handover (HO) issues are analyzed considering various cell sizes, user speeds, and HO optimization related parameters. This study is very helpful to realize the problem of HO in 5G standalone network deployment as it shows the impact of LTE ultra-small cell deployment on the HO performance. A system level simulator is used to perform simulation and the results show that, as expected, the ultra-densification results in an increased number of HOs. Also, increasing the offset values have a significant impact on the HO reliability while changing the Time to Trigger (TTT) values has a less-significant impact.
The price of privilege

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A Bidirectional Object Tracking and Navigation System using a True-Range Multilateration Method

In the past, several contributions and proposals for the implementation of Ultra-wideband (UWB)-based localization and positioning solutions on the system level were made. However, most of them are limited to a unidirectional approach, i.e. data communication is in one direction (from a transmitter to a receiver). This restricts the systems’ use-case to either navigation or tracking. In this paper, we demonstrate an UWB-based bidirectional localization system which is capable of acting as both a navigation and tracking system in a single wireless platform. Regarding this, we proposed a complete set of such a system and outline the implementation process in the paper. A true-range multilateration method is used as a positioning algorithm in the implemented system, for which we proposed a novel Non-Line-of-Sight (NLOS) mitigation technique. The experimental evaluation of the proposed system was done in comparison with a commercially available UWB system. In the experiments, we used a Vicon camera system as a reference.
Decision Making through Occluded Intersections for Autonomous Driving

Driving in unsignalized intersections poses significant challenges for autonomous vehicles, as various uncertainties are involved. On one hand, behaviors of vehicles in the observable area need to be carefully predicted, on the other hand, the occluded area has to be addressed judiciously, as potential obstacles might cause serious danger. In this paper, decision making through occluded intersection is discussed and both static and dynamic obstacles are considered. A general approach is proposed to deal with different occlusion scenarios. The approach is based on partially observable Markov decision process (POMDP) and customized for autonomous driving. A variety of experiments are conducted to evaluate the proposed approach, and results show that the proposed approach enables autonomous vehicle to exhibit a human-like driving behavior when facing occlusions.
Efficient Agent Based Trust Threshold Model for Healthcare Cloud Applications

The field of agent technology and cloud computing have procured a new discipline called the agent-based cloud computing which provides the application of agent based techniques to cloud computing for further development of resource management, service management, service discovery and security. The emergence and exponential rise in the cloud computing opens up an array of trust and security concerns with regard to this pervasive technology. MAS technology is a different way of approaching the distributed system paradigm where multiple agents contribute to resolving tasks of interest cooperating with each other in a decentralized approach. This paper proposes an agent based trust threshold model that is efficient and secure in integrating and distributing Electronic Health Records (EHR) to the respective user. In addition, we introduce a novel trust model which is dynamically adaptable over time such that the agents can accurately classify users into maximum trust, average trust, minimum trust and malicious/quarantined. The results give a pronounced indication of the importance of considering both the parameters such as the action, Oa and frequency of the transactions of the user, ϒ, in order to accurately classify the respective users to establish a secure foundation for the system to run smoothly.
Characterization of Magnetic Anisotropy for Binder Jet Printed Fe93.25Si6.75

Isotropic magnetic properties are important for electrical machine topologies that require 3D flux paths. Soft magnetic composites, which have been used to manufacture magnetic cores for these designs, have limitations. Additive manufacturing technologies provide flexibility to fabricate ferromagnetic structures that can overcome challenges inherent in manufacturing soft magnetic composites. In this paper, the magnetic anisotropy of binder jet printed iron silicon samples is quantified and compared with commercial soft magnetic composite material.
Improvements on Harmonic Current Distortion for MHz-Operated Discontinuous Current Mode Single Phase Grid-Tied Inverter with GaN-HEMT Device

This paper focuses on the improvement of the harmonic distortion for MHz-operated discontinuous current mode single-phase grid-tied inverter under feed-forward current control. Two harmonic current reduction strategies are proposed. A feedback path, which is used to compensate for the low-order harmonics, is added to the conventional feed-forward structure; and an improved modulation, is proposed to minimize the influences caused by the resonance current during zero current period. The proposed method was verified by the experiment with a fabricated prototype. The total harmonic distortion (THD) of the output current with the maximum current operation was reduced from 7.3% to 4.5%, after the proposed method was applied. It is also shown that chip inductors can be a promising choice for MHz-class discontinuous current mode low-power inverters.
3D Map Optimization with Fully Convolutional Neural Network and Dynamic Local NDT

Due to multi-path effects, GNSS-based localization methods are not always reliable in urban transportation scenes. To solve this problem, matching-based methods, which compare the real-time sensor data with a prior map, are widely used for urban autonomous driving. In these methods, high-precision noise-free 3D map plays an essential role in vehicle localization. This paper proposes a 3D map optimization framework to generate such map with high efficiency and low memory consumption. First, a deep learning based method is designed to automatically filter out non-map objects in mobile laser scans during mapping. Then, a method named dynamic local NDT is introduced for mapping and localization to improve efficiency and reduce memory usage. Furthermore, a road segmentation method is exploited for further optimization. The proposed framework only relies on LIDAR and GNSS-INS, which makes it simple and easily conducted. The mapping and positioning experimental results show that the proposed framework outperforms the conventional NDT method.
Excitation-Inhibition Balanced Spiking Neural Networks for Fast Information Processing

The balance of excitation and inhibition is a fundamental property of neural systems. The present study investigates an excitation and inhibition (E-I) balanced spiking neural network model for neuromorphic computing, in particular, to track rapid changes of external inputs. We analyze the working mechanism of an E-I balanced network and find that the network generates internal noises of a nearly optimal structure which enables neural population firing rates to track input changes almost instantly. Moreover, we extend the network model from homogenous connectivity to local connectivity, so that the network can remain balanced under spatially heterogeneous inputs and retain spatial information. Simulation results confirm that the model works well. This model may serve as a fast responding module for general neuromorphic computing systems.