Miracle-dz/UniFAR-FTUs huggingface dataset:
Dual-Band Harvester with Wide Range Input Power for WPT Applications

This paper discussed the design of a dual-band harvester to efficiently handle a wide input power range while addressing the failure voltage issue in conventional rectifiers by operating a FET as a switch amongst low and high input powers. Designed for the two ISM bands of 915 MHz and 2.4 GHz, it achieved an RF-DC efficiency of more than 40% over a wide-ranging of input power from −11 dBm to 25 dBm and −4 dBm to 21 dBm as well as a maximum power efficiency of 72% at 11 dBm and 67% at 15 dBm for the two respective bands, making it appropriate for Wireless Power Transfer applications.
The role of speech rate for Italian-speaking cochlear implant users: insights for everyday speech perception

Abstract This study investigated the effect of speech rate for Italian-speaking cochlear implant (CI) users with the aim of gaining insight into everyday listening situations where speaker’s rate may vary considerably together with speech level in fluctuating background noise. The test material was prepared by processing the Italian STARR test with temporal compression of sentence material. Twelve adult CI users were divided into two subgroups based on their STARR performance: good and poor performers. The effect of varying speech rate was remarkable for both subgroups. Good performers showed significant performance deterioration for the faster speech rate, whereas outcomes for poor performers improved significantly for the slower speech rate. Background noise is inevitable during daily life and CI users face a variety of speakers, so must cope with changes in both speech level and rate. Traditional tests with a single talker speaking very clearly while using a moderate to low speech rate may overestimate everyday speech perception for CI users. The use of tests that mimic everyday perception is of great importance and should become a routine part of audiometric measures for hearing impaired people.
Performance Analysis of OLSR to Consider Link Quality of OLSR-ETX/MD/ML in Wireless Mesh Networks

In today's era the wireless mesh network have been growing rapidly and is one of the emerging technologies in the wireless era. Many research has been done and is being taken out in the field of the wireless mesh networks. There is been substantial thriving in the wireless mesh networks in all the aspects. The OLSR in the thriving and headway of the networks is used. The OLSR sends the packets among network through the Multiple-point (MPR) which ensures an organized packet delivery in the wireless networks. An implementation of the OLSR routing protocol which is proactive in nature is implemented. The comparison of the OLSR, the OLSR-MD, OLSR-ML and OLSR-ETX is carried out for Link Quality on basis of the Packet Sent Ratio, Throughput, Normalized Routing Load(NRL), Packet Drop.
A New Method of Online Fault Diagnosis Based on Incremental Continuous Attribute Naive Bayesian

Diagnosing faults accurately and updating diagnosis model timely are the two key points for the online fault diagnosis. Considering the higher diagnostic accuracy and the greater model updating efficiency, this paper proposes an improved incremental Gaussian Naive Bayesian learning algorithm (I-IGNB) to facilitate the online fault diagnosis. A crucial assumption in I-IGNB theory, i.e., the continuous characteristic data is generated by a single Gaussian distribution, where the information loss caused by discretization can be efficiently avoided in online fault diagnosis of continuous attributes. The updating mechanism of the proposed I-IGNB is to update the important parameters of the diagnosis model, the prior probability and the conditional probability with several explicit equations, which is quite computationally efficient. Moreover, when a new fault mode occurs in the online diagnosis, it can be accurately identified by the I-IGNB, by which the whole diagnostic accuracy can be further improved. To verify the effectiveness, a double second-order RC active filter circuit is designed to obtain fault data and test the performance of I-IGNB. Experimental results show that the I-IGNB is able to update diagnosis model quite fast and improve the diagnostic accuracy obviously with the model updating.
Constant Modulus Beamforming Via Low-Rank Approximation

We present novel convex-optimization-based solutions to the problem of blind beamforming of constant modulus signals, and to the related problem of linearly constrained blind beamforming of constant modulus signals. These solutions are based on a low-rank approximation, ensure global optimality and are parameter free, namely, do not contain any tuneable parameters and do not require any a-priori parameter settings. The proposed approach outperforms state-of-the-art both in terms of the number of required samples for convergence, and in terms of the beamformer output SINR.
Asymmetric Gaussian Mixtures with Reversible Jump MCMC

We propose a fully Bayesian learning approach using reversible jump Markov chain Monte Carlo (RJMCMC) for asymmetric Gaussian mixtures (AGM). Compared to classic Gaussian mixture model, AGM doesn't imply that target data is symmetric which brings flexibility and better fitting results. This paper also introduces a RJMCMC learning implementation based on Metropolis-Hastings (MH) within Gibbs sampling method. As an improvement of traditional sampling-based MCMC learning, RJMCMC has no assumption concerning the number of components and, therefore, the AGM model itself could be transferred between iterations. For better evaluating models with different mixture components numbers, the model selection is achieved by calculating integrated likelihood using Laplace approximation to figure out the best-fit components number. We selected both synthetic and challenging spam filtering dataset to show the merits of the proposed model.
Integrated Terrestrial-Satellite Wireless Backhauling: Resource Management and Benefits for 5G

With the fifth generation (5G) of cellular communications around the corner, operators are searching for efficient solutions to accommodate the tremendous traffic increase with limited latency [1]. In the past few years, the satellite telecom industry has moved from traditional direct broadcasting and has been a solution in combination with the terrestrial 5G network [2]. Coverage extension, data offloading, and service continuity are the key complementary aspects that a satellite can offer, which demands more partnership and integration between terrestrial and satellite operators.
OFDMA-based Hybrid Channel Access for IEEE 802.11ax WLAN

Recently, IEEE 802.11 started a task group, 802.11ax, to investigate and deliver next generation wireless local area network (WLAN) technologies for dense environments. In 802.11ax, orthogonal frequency-division multiple access (OFDMA) has been adopted as PHY layer protocol to support up-link multiple-users (MU) transmission. However, choosing multiuser PHY in WLAN creates a new challenge as the legacy random access protocol, carrier sense multiple access/collision avoidance (CSMA/CA), was designed for single-user PHY. Hence, designing a new random access protocol for 802.11ax medium access control (MAC) is desperately needed. In this paper, we firstly introduce 802.11ax MAC and its random access protocol. Theoretical limitation of system efficiency of 802.11ax random access protocol is also provided. Next, we discuss the most popular contention resolution algorithm proposed in the 802.11ax task group at present. Finally, we propose a pragmatic and efficient OFDMA-based hybrid channel access (OHCA) method for IEEE 802.11ax. To the best of our knowledge, this is the first paper that study the 802.11ax random access protocol and its system efficiency analysis. Simulations are conducted to evaluate the performance of the proposed scheme. The results show that the proposed scheme provide much better performance than the existing solution, offering low collision rate, short access delay, better fairness, and yet more efficient bandwidth usage.
Projective Weight-Based Unsupervised Laplacian Graph Learning for Person Re-Identification

For unsupervised person re-identification, traditional Laplacian regularisation based dictionary learning methods encounter the fixed weight problem and thus impair the match rate. To address this limitation, we develop a novel unsupervised dictionary learning approach to learn a discriminative representation. The proposed approach takes the efficiency of $l_{2}$ graph regularization with a closed-form solution into account. Our approaches achieve very promising results on the challenging VIPeR dataset.
“I” and “Me”: The Self in the Context of Consciousness

James (1890) distinguished two understandings of the self, the self as “Me” and the self as “I”. This distinction has recently regained popularity in cognitive science, especially in the context of experimental studies on the underpinnings of the phenomenal self. The goal of this paper is to take a step back from cognitive science and attempt to precisely distinguish between “Me” and “I” in the context of consciousness. This distinction was originally based on the idea that the former (“Me”) corresponds to the self as an object of experience (self as object), while the latter (“I”) reflects the self as a subject of experience (self as subject). I will argue that in most of the cases (arguably all) this distinction maps onto the distinction between the phenomenal self (reflecting self-related content of consciousness) and the metaphysical self (representing the problem of subjectivity of all conscious experience), and as such these two issues should be investigated separately using fundamentally different methodologies. Moreover, by referring to Metzinger’s (2018) theory of phenomenal self-models, I will argue that what is usually investigated as the phenomenal-“I” [following understanding of self-as-subject introduced by Wittgenstein (1958)] can be interpreted as object, rather than subject of experience, and as such can be understood as an element of the hierarchical structure of the phenomenal self-model. This understanding relates to recent predictive coding and free energy theories of the self and bodily self discussed in cognitive neuroscience and philosophy.
Design of Efficient Rectifier Circuit in the GSM Band for Energy Harvesting Applications

a single band compact rectifying circuit operating in the GSM-900 band of frequency is proposed. The proposed RF rectifying circuit employs two-stage Dickson charge pump configuration along with the optimized impedance matching unit comprising of lumped components. The RF rectifying circuit is designed using the Advanced Design System (ADS) software, and is fabricated on a low cost FR-4 substrate. The dimension of the fabricated circuit is 6× 4× 1.6 cm3 thus making it quite compact. The overall performance of the designed circuit is evaluated for different types of load conditions. The maximum RF to DC conversion efficiency of 60% is achieved at 0 dBm input RF power for a 5 k load.
On the Impact of Selective Data Acquisition in Mobile Crowd-Sensing Performance

Mobile crowd-sensing (MCS) has appeared as a viable tool to acquire data without dedicating sensors to provide smart services in the Internet of Things (IoT) Era. Ensuring data trustworthiness is a grand challenge in MCS in the presence of adversaries that aim to spread misinformation at the crowd-sensing platform. All MCS frameworks assume that the sensing tasks are opportunistically assigned to the sensing service providers regardless of the recruitment's being participatory or opportunistic. In this paper, we present two scenarios where sensing service providers can join either a high income or a low income community. The decision of selecting a community of sensing service providers is basically turning of the corresponding sensors in the mobile devices while keeping the rest on. Our feasibility study through simulations under realistic scenarios shows that in order to maintain high platform utility and an average reasonable user utility, user-centric reputation-based recruitment of sensing service providers has to be implemented at the platform as opposed to income-based selective data acquisition in mobile crowd-sensing. When low income-based selective data acquisition is adopted, average user utility can be improved by 14%-28% under the best case. On the other hand, we show that high income based selective data acquisition can result in negative platform utilities whereas platform utility of low income-based selective data acquisition can be as low as 25%-50% of non-selective and reputation-based data acquisition even under the best case scenario.
Accommodating High Penetration of PV in Distribution Networks Considering Smart Curtailment

This paper presents an approach for photo-voltaic based DG allocation in distribution networks. The objective of the proposed approach is to minimize the developer's investment cost associated with energy supply requirements over predefined planning period. Uncertainties associated with supply and demand are considered in this work, as well as, the uncertainty associated with energy prices. In addition, this work introduces smart curtailment of renewable resources, which can maximize the penetration and minimize the overall investment. Simulation results on a typical distribution network are provided, which prove the effectiveness and robustness of the proposed resource allocation approach.
Low Complexity Hybrid Precoding Algorithm for GenSM Aided mmWave MIMO Systems

Hybrid precoding is introduced to millimeter wave (mmWave) multiple-input multiple-output (MIMO) System in order to reduce the number of radio frequency (RF) chains. In traditional hybrid precoding scheme, the maximum number of independent data streams is restricted by the number of RF-chains, which limits the achievable spectral efficiency (SE) rate. To further improve the SE rate, the generalized spatial modulation (GenSM) structure is considered. However, current gradient ascent iteration (GAI) algorithm of GenSM aided hybrid precoding is too complicated when the antenna array is large. In this paper we propose a novel low complexity algorithm for the analog and digital precoder design under GenSM hybrid precoding structure. Our approach gives additional unitary constraint to the digital precoder, formulating its design as a masked orthonormal procrustes problem. With this simplification, we accomplish the whole hybrid precoder design via turbo optimization (TO). Finally, numerical simulations are demonstrated to compare the achievable SE performance between our proposed low complexity scheme and some traditional hybrid precoding algorithms including the GAI algorithm.
Machine Learning Fingerprinting Methods in Cyber Security Domain: Which one to Use?

Identification of a communicating device operating system is a fundamental part of network situational awareness. However, current networks are large and change often which implies the need for a system that will be able to continuously monitor the network and handle changes in identified operating systems. The aim of this paper is to compare machine learning methods performance for OS fingerprinting on real-world data in the terms of processing time, memory requirements, and performance measures of accuracy, precision, and recall.
Sensor-Driven Algorithm for Self-Reconfiguration of Modular Robots

The problem addressed is the deterministic 2D reconfiguration of a connected mass of identical hexagonal robots from an initial well-defined and restricted set of arbitrary shape configurations composed of connected modules $(\mathcal{I})$, to overlapping, connected straight chain goal configurations $(\mathcal{G})$. We present algorithms to accomplish system-wide shape change using only a constant amount of information at each processor, relying on the sensory information gathered via light and contact sensors to complete the reconfiguration. In our algorithms, a module uses only sensory information obtained from sensors on its six adjacent cells. No preprocessing is needed in this algorithm if’ $\mathcal{I}$ adheres to certain admissibility conditions. The addition of a preprocessing algorithm increases the number of admissible configurations possible for $\mathcal{I}$ while adding information to be stored at each robot because each module has to be aware of the position of modules on the system periphery.
Connected Roads of the Future: Use Cases, Requirements, and Design Considerations for Vehicle-to-Everything Communications

The ultimate goal of next-generation vehicle-toeverything (V2X) communication systems is enabling accident-free, cooperative automated driving that uses the available roadway efficiently. To achieve this goal, the communication system will need to enable a diverse set of use cases, each with a specific set of requirements. We discuss the main usecase categories, analyze their requirements, and compare them against the capabilities of currently available communication technologies. Based on the analysis, we identify a gap and indicate possible system designs for the fifth-generation (5G) V2X that could close the gap. Furthermore, we discuss an architecture of the 5G V2X radio access network (RAN) that incorporates diverse communication technologies, including current and cellular systems in centimeter wave (cm-wave) and millimeter wave (mm-wave), IEEE Standard 802.11p [1], and vehicular visible light communications (VVLC). Finally, we discuss the role of future 5G V2X systems in enabling more efficient vehicular transportation: from improved traffic flow and reduced intervehicle spacing on highways to coordinated intersections in cities (the cheapest way to increasing the road capacity) to automated smart parking (no more visits to the parking garage! ), all of which will ultimately enable seamless end-to-end personal mobility.
Combined single‐stage enterolysis with pedicle seromuscular bowel flaps, myocutaneous and fasciocutaneous flaps to repair recurrent enterocutaneous fistulas in complex abdominal Wall defects

Reconstruction of abdominal wall defects with enterocutaneous fistulas (ECF) remains challenging. The purpose of this report is to describe a single‐stage approach using combined microscopic enterolysis, pedicle seromuscular bowel flaps, mesh, fasciocutaneous, and myocutaneous flaps.
Classification of Skin Lesion by Interference of Segmentation and Convolotion Neural Network

Classification of skin lesions plays a crucial role in diagnosing various, local and gene related, medical conditions in the field of dermoscopy. Estimation of these biomarkers are used to provide some insight, while detecting cancerous cells and classifying the lesion as either benign or malignant. This paper presents groundwork for detection of skin lesions with cancerous inclination by segmentation and subsequent application of Convolution Neural Network on dermoscopy images. Images included in ISIC-2016 were used as dataset. Images with skin lesions were segmented based on individual channel intensity thresholding. The resultant images were fed into CNN for feature extraction. The extracted features were then used for classification by an ANN classifier. Previously, several approaches have been used for subject diagnostic with varying degree of success. However, room is still available for exploring other techniques for improving proportion of successfully detected malignant lesions. As compared to a previous best of 97%, methodology presented in this paper yielded an accuracy of 98.32%.
A Method for Interoperable Interface Description of Inductive Power Transfer Systems

In this paper we propose a method for inductive power transfer systems (IPTS) that uses an impedance-based approach for the interface description between vehicle and infrastructure. Thus, interoperability between vehicle and infrastructure can be described and both subsystems can be developed independently. The method preserves flexibility in electronic and magnetic design by not imposing specific electrical circuits or coil topologies. We show how an impedance zone can be derived by using the example of an existing IPTS. We then present a test bench that can be applied to verify the requirements for conformance. In this way, interoperability in the field can be assured, with any Vehicle Assembly (VA) being able to receive power from any Ground Assembly (GA) for all possible operation points.
An Implementation of Solar PV Array Based Multifunctional EV Charger

In this paper, a solar PV (Photo-voltaic) array based EV (Electric Vehicle) charger is proposed, which has a bi-directional flow of active and reactive powers. The proposed charger uses a solar PV array energy to charge the EV battery and to feed the grid with the remaining power. In this charger, the VSC (Voltage Source Converter) does the task of harnessing the maximum power from the solar PV array. At the time of high cost of energy, the charger has the provision to inject the battery energy into the grid to earn revenue. In addition to the active power exchange with the grid, the proposed charger also exchanges the reactive power with the grid, simultaneously. In all operating modes, the THD (Total Harmonic Distortion) of the grid current remains within the IEEE 519 standard. The proposed charger is designed for a single phase 230V, 50Hz supply and is experimentally validated in the laboratory.
Brain Signatures of New (Pseudo-) Words: Visual Repetition in Associative and Non-associative Contexts

The contribution of two different training contexts to online, gradual lexical acquisition was investigated by event-related potentials (ERPs) elicited by new, word-like stimuli. Pseudowords were repeatedly preceded by a picture representing a well-known object (semantic-associative training context) or by a hash mark (non-associative training context). The two training styles revealed differential effects of repetition in both behavioral and ERPs data. Repetition of pseudowords not associated with any stimulus gradually enhanced the late positive component (LPC) as well as speeded lexical categorization of these stimuli, suggesting the formation of episodic memory traces. However, repetition under the semantic-associative context caused higher reduction in N400 component and categorization latencies. This result suggests the facilitation in the lexico-semantic processing of pseudowords as a consequence of their progressive associations to picture-concepts, going beyond the visual memory trace that is generated under the non-associative context.
Fast kq-Space Sampling with Motion-Induced Phase in Diffusion MRI

High spatio-angular resolution diffusion MRI has been shown to provide accurate identification of complex fiber configurations. However, its application in clinics remains limited due to the long required acquisition time. Recently, we have developed a method relying on an accelerated kq-space undersampling scheme to recover the intra-voxel fiber configurations. In the current work, we generalize our method to consider a more realistic setting. We include into the kq-space measurement model the phase contamination induced by magnetic field inhomogeneities and motion during the signal acquisition. Simulations are performed on realistic synthetic data, considering both the cases when the phase is perfectly known, and when it is pre-calibrated. Results show that, in both the cases, the phase contamination does not penalize the recovery of the fiber configuration.