Miracle-dz/UniFAR-FTUs huggingface dataset:
Wireless Power Transfer for IoT Devices - A Review

Developments and applications of battery powered devices faces unprecedented technical challenges due to the limitations of low power density, high cost, heavy weight, etc. Wireless sensors are recently emerging to provide solutions for a number of solutions in ordinary homes, industries, health care and communications, just to name a few. On the other hand, Wireless Power Transfer (WPT) technology is gaining increasing interest in 5 G networks and the Internet of Things (IoT) due to scarce energy resources and high-power consumption. The authors through this paper, present an overview of WPT techniques focusing on working mechanisms and technical challenges. We focus on WPT systems and explore current major research topics and discuss future trends and development. This is used by the authors to focus on the recent studies that are specific to near-field WPT and radio frequency energy harvesting, putting more emphasis on the greener energy harvesting techniques and low power. We also look at existing and possible techniques of powering IoT devices wirelessly.
A Residual-Dyad Encoder Discriminator Network for Remote Sensing Image Matching

We propose a new method for remote sensing image matching. The proposed method uses an encoder subnetwork of an autoencoder pretrained on the GTCrossView data to construct image features. A discriminator network trained on the University of California Merced land-use/land-cover data set (LandUse) and the high-resolution satellite scene data set (SatScene) computes a match score between a pair of computed image features. We also propose a new network unit, called residual-dyad, and empirically demonstrate that networks that use residual-dyad units outperform those that do not. We compare our approach with both traditional and more recent learning-based schemes on the LandUse and SatScene data sets, and the proposed method achieves the state-of-the-art result in terms of mean average precision and average normalized modified retrieval rank (ANMRR) metrics. Specifically, our method achieves an overall improvement in performance of 11.26% and 22.41%, respectively, for LandUse and SatScene benchmark data sets.
Randomized Sketching Based Beamforming for Massive MIMO

Massive MIMO system yields significant improvements in spectral and energy efficiency for future wireless communication systems. The regularized zero-forcing (RZF) beamforming is able to provide good performance with the capability of achieving numerical stability and robustness to the channel uncertainty. However, in massive MIMO systems, the matrix inversion operation in RZF beamforming becomes computationally expensive. To address this computational issue, we shall propose a novel randomized sketching based RZF beamforming approach with low computational latency. This is achieved by solving a linear system via randomized sketching based on the preconditioned Richard iteration, which guarantees high quality approximations to the optimal solution. We theoretically prove that the sequence of approximations obtained iteratively converges to the exact RZF beamforming matrix linearly fast as the number of iterations increases. Also, it turns out that the system sum-rate for such sequence of approximations converges to the exact one at a linear convergence rate. Our simulation results verify our theoretical findings.
DTIC (NSC-45388) studies in the southwest oncology group.

: In a phase I study, the best antitumor/toxicity ratio for DTIC was reported to be at a dose of 250 mg/m2/day X 5 repeated at 28-day intervals. Nausea, vomiting, leukopenia, and thrombocytopenia were the major toxic effects noted. The best responses were seen in disseminated melanoma (19%), various sarcomas (22%), and Hodgkin's disease. A subsequent phase II study in refractory lymphomas showed a response rate in Hodgkin's disease of 56%. In disseminated melanomas, DTIC was then combined with vincristine and BCNU and demonstrated a response rate of 23% which did not improve with the addition of chlorpromazine (23%). A response rate of 31% was seen with the combination of DTIC, BCNU, and hydroxyurea which did not improve with the addition of vincristine (30%). Responders had a more significant survival rate as compared to nonresponders.
Fusion based estimation of the a-priori probability distribution of unknown non-stationary processes

Non-stationary processes can be hard to handle, particular if one would like to know their characterizing time dependent probability functions. In this paper the a-priori probability distributions of unknown non-stationary processes are estimated with different combinations of weakly coupled sensors. For quantification of the unknown a-priori probabilities Bayesian Networks (BN) are adopted for data fusion and Dirichlet functions are applied on non-stationary, time-dependent maximum likelihood (ML) parameter learning. In several experiments the adaption of the non-stationary a-priori probability density functions is shown and the accuracy of data fusion regarding the underlying process variables with different characteristics are determined quantitatively. It is shown that the proposed algorithm can improve data fusion in case conditions for specific process and sensor characteristics are met.
MM-Based Solution for Partially Connected Hybrid Transceivers with Large Scale Antenna Arrays

In a mmWave multiple-input multiple-output (MIMO) communication system employing a large-scale antenna array (LSAA), the hybrid transceivers are used to reduce the power consumption and the hardware cost. In a hybrid analog-digital (A/D) transceiver, the pre/post-processing operation splits into a lower-dimensional baseband (BB) pre/postcoder, followed by a network of analog phase shifters. Primarily two kinds of hybrid architectures are proposed in the literature to implement hybrid transceivers namely, the fully- connected and the partially-connected. Implementation of fully-connected architecture has higher hardware complexity, cost and power consumption in comparison with partially- connected. In this paper, we focus on partially- connected hybrid architecture and develop a low- complexity algorithm for transceiver design for a single user point-to-point mmWave MIMO system. The proposed algorithm utilizes the variable elimination (projection) and the minorization- maximization (MM) frameworks and has convergence guarantees to a stationary point. Simulation results demonstrate that the proposed algorithm is easily scalable for LSAA systems and achieves significantly improved performance in terms of the spectral efficiency (SE) of the system compared to the state-of-the-art solution.
Image classification on projection based multilayer sparse representation

This paper describes a novel multilayer-sparse-representation based image classification. This method designs a dictionary for sparse coefficients of each layer with ADMM (Alternating Direction Method of Multipliers) referring training images. For the classification stage, sparse coefficients should also be calculated with ADMM for test images, which needs computational burden. To reduce the burden, this work proposes to project inputs onto dictionary atoms of each layer instead of solving sparse coefficients. This alternative method is inspired by CNNs (Convolutional Neural Networks), and is also faster than solving sparse coefficients. Experimental results show that our method can predict coefficient vectors faster than the conventional methods with almost equivalent classification accuracy.
The G-Good-Neighbor Local Diagnosability of a Hypercube Network Under the PMC Model

A significant indicator used in evaluating the reliability of a multiprocessor system is fault diagnosability. Researchers concentrate on the diagnosability of the entire system while ignoring important local information about the system. In our paper, an innovative concept of fault diagnosability, called <inline-formula> <tex-math notation="LaTeX">$g$ </tex-math></inline-formula>-good-neighbor local diagnosability, is put forward to study the diagnosability of a system at a node under the <inline-formula> <tex-math notation="LaTeX">$g$ </tex-math></inline-formula>-good-neighbor condition. Moreover, we obtain the relationship between the local diagnosability of a system at each node and the whole system’s diagnosability under the <inline-formula> <tex-math notation="LaTeX">$g$ </tex-math></inline-formula>-good-neighbor condition. Under the PMC model, we prove that the <inline-formula> <tex-math notation="LaTeX">$g$ </tex-math></inline-formula>-good-neighbor local diagnosability of an <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>-dimensional hypercube network <inline-formula> <tex-math notation="LaTeX">$Q_{n}$ </tex-math></inline-formula> at each node is at least <inline-formula> <tex-math notation="LaTeX">$2^{g} (n-g+1)-1$ </tex-math></inline-formula> for <inline-formula> <tex-math notation="LaTeX">$0 \leq g \leq n-3$ </tex-math></inline-formula> and that when <inline-formula> <tex-math notation="LaTeX">$n-2 \leq g \leq n-1$ </tex-math></inline-formula>, the <inline-formula> <tex-math notation="LaTeX">$g$ </tex-math></inline-formula>-good-neighbor local diagnosability of <inline-formula> <tex-math notation="LaTeX">$Q_{n}$ </tex-math></inline-formula> at each node is <inline-formula> <tex-math notation="LaTeX">$2^{n-1}-1$ </tex-math></inline-formula>. Further, we easily derive the diagnosability of hypercube <inline-formula> <tex-math notation="LaTeX">$Q_{n}$ </tex-math></inline-formula> under the <inline-formula> <tex-math notation="LaTeX">$g$ </tex-math></inline-formula>-good-neighbor condition.
A Transient Power Quality Signal Denoising Method Based on CEEMDAN and UWT

Aiming at power quality detection and noise pollution, in combination with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), permutation entropy (PE) and wavelet threshold denoising based on undecimated wavelet transform (UWT), a transient power quality denoising method based on CEEMDAN and UWT is proposed. CEEMDAN decomposes the disturbance signal into a series of intrinsic mode function with single-component characteristics, UWT denoising is used to deal with the noise components screened out by PE and reconstruction to obtain the original signal after denoising. The method is implemented in LabVIEW and simulated independently, the results verify the effectiveness of the method. Finally, on the basis of PXI equipment, a detection device based on LabView is built, which provides a new idea for the development of power quality detection system.
Bi-Directional Training for FDD Systems

We study distributed algorithms for joint adaptation of precoding and combining filters in frequency-division duplex (FDD) multiple-input multiple-output (MIMO) cellular systems. Neither the base stations nor the mobiles have a priori channel state information, and the transmit/receive filters are directly adapted through training. We propose extensions of bi- directional training (BiT), designed for time- division duplex (TDD) systems, to FDD systems where uplink-downlink reciprocity may not apply. A direct application of BiT can give mismatched precoders with substantial performance degradation. Our approach assumes angular reciprocity, that is, the channel multipath, which causes the frequency selectivity, is characterized by angles of arrival/departure that vary predictably with frequency. Hence spatial beams corresponding to angles of arrival can be turned around to point towards the corresponding angles of departure in the paired band. We present three methods based on this general approach with different complexity for angular estimation and selection. Simulation results indicate that when the multipath is sufficiently sparse, most of the achievable gain with channel reciprocity and TDD can be recovered.
Handwriting Recognition through Novel Combinational Segmentation Technique and Iterative Blocks Features

We propose a novel technique for segmentation of the handwritten answers written in continuous writings by the candidates during theory based examinations. We estimate digital information from the handwritten words. The problem with cursive writings is that the neighboring characters may be overlapping or may be some times away from each other. Also all the characters are not of same size. A single length window cannot segment all the characters while an adaptive length window would either require multiple scans of the image or multiple windows might be needed, increasing the computational complexity. Here, we propose a novel combinational segmentation scheme where variable length cuts are of the word image are generated as tentative segments and the length of cuts depends upon the vertical pixel density threshold. These segments may have some segments with deficient characters or excess parts of neighboring characters. So recombination of these segments with nearest segments is done to generate final segments. The advantage of the method is that we can segment any handwriting. Then, we train the system to identify a character through the proposed iterative blocks scheme. The features extracted are density per block and number of junctions and end points per block. Text written by the same author is used for the testing. We have tested for 10 subjects with character recognition accuracy more than 90%.
Performance analysis of WDM-FSO PON using RZ OOK in presence of interchannel crosstalk

A hybrid wavelength division multiplexed (WDM) passive optical network (PON) incorporating high speed free space optical (FSO) communication i.e. WDM-FSO PON using OOK modulation, is analyzed for the impact of interchannel crosstalk affected by the atmospheric turbulence. The WDM channels are affected by crosstalk whereas the FSO performance is degraded by scintillation caused due to atmospheric turbulence. This work considers gamma-gamma probability distribution function (pdf) model for atmospheric turbulence. Different noises like thermal noise, shot noise and amplified spontaneous emission (ASE) noise are considered while analyzing the system. The demultiplexer adjacent channel rejection is also considered while including the crosstalk interferer to the system. In this paper, average bit error rate is used to evaluate the performance of this system using RZ OOK modulation format and in presence of single crosstalk interfere.
Detailed Evaluation of a Wind Noise Reduction Method using DNN for 3D Audio Navigation System Audio Augmented Reality for Bicycles

We previously studied DNN as a method for reducing wind noise in bicycles AAR navigation systems. As a result, DNN was able to remove almost all of the wind noise in most cases. However, there were cases where wind noise was not reduced sufficiently when wind noise and environmental noise were simultaneously generated. Another problem was that the timbre of the environmental sound changed and the original quality could not be maintained. In this paper, we evaluated wind noise reduction and environmental sound that passes through using newly trained DNN. As a result, while the wind noise removal performance remained very high, the timbre alteration of the environmental noise was kept to a minimum.
Precoder Design for Two-User Uplink MIMO-NOMA with Simultaneous Triangularization

In this paper, we consider the uplink precoder design for two-user power-domain multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) systems. We propose a novel precoding scheme which utilizes simultaneous triangularization to decompose the MIMO-NOMA channels of the two users into multiple single-input single-output (SISO)-NOMA channels assuming low-complexity self-interference cancellation at the base station, thereby reducing the decoding complexity. The proposed scheme takes advantage of the available excess degrees of freedom at the base station to enhance the ergodic achievable rate. The ergodic achievable rate region of the proposed scheme is characterized using finite-size random matrix theory (RMT). Our results illustrate that the proposed scheme significantly outperforms traditional MIMO-OMA and zero forcing based MIMO-NOMA schemes, and has a small performance gap to the achievable rate region of MIMO-NOMA.
Multi-Sensor Passive Localization Based on Sensor Selection

In passive localization applications, the positioning accuracy for an emitter is highly dependent on the geometry between sensors and the target, the site error and measurement noise of sensors. We propose a sensor selection mechanism which aims to choose a subset of sensors to implement the multi-sensor passive localization. An optimization model is established by minimizing the GDOP (geometric dilution of precision), with or without the constraint on the cardinality of the selected sensor subset. The CEO (cross entropy optimization) is employed to solve the resulting complex combinatorial optimization model. Simulation experiments are conducted and simulation results demonstrate the efficiency of the proposed sensor selection scheme for multi-sensor passive localization.
Edge Computing for Offload-Aware Energy Conservation Using M2M Recommendation Mechanisms

In this work the problem of energy conservation in wireless Internet of Things (IoT) devices is being addressed for machine-to-machine (M2M) communication. IoT connected devices (i.e. glasses, set-top-boxes, home appliances etc.) can affect the energy levels of the IoT ecosystem and can play an active role in the level of QoS/QoE provided to the end-users, for any service demands, on-the-move. To this end, this work proposes a novel offloading methodology that hosts a “resource-aware” recommendation scheme, which allows the efficient monitoring of energy draining applications that run in an IoT ecosystem. The proposed framework allows users to have a continuous on-demand service provision where devices can actively provide the available resources to be exploited in the IoT ecosystem. Considering the latter, this work utilises an Edge-based Computing offload mechanism in M2M communication for resource-aware recommendation. The work assesses the proposed framework in the context of (i) the offered reliability for IoT services by assistive recommendation scheme and (ii) the energy conservation for a number of devices, forming the IoT ecosystem during the offloading process.
MC-CDMA and SCMA Performance Evaluation in 100% and 200% Load Factor Scenarios

In fifth-generation (5G) wireless networks, the perspective of very large number of simultaneous connections is challenging. Hence, an overloaded system becomes a common scenario for new multiple access techniques. An overloaded scenario means that the number of active simultaneous users is greater than the number of system resources, and its ratio determines the load factor. Some multiple access techniques have been proposed for 5G systems and two of them are compared in this paper. These techniques are multi-carrier code division multiple access (MC-CDMA) and sparse code multiple access (SCMA). Their performance, in terms of the mean bit error rate (BER), and the decoding complexity, in terms of number of mathematical operations performed in the receiver, are evaluated considering quaternary constellations. In MC-CDMA, the optimum maximum-likelihood detector (MU-MLD), implemented via sphere decoding (SD), is considered. In SCMA, a near optimal message-passing algorithm (MPA) is used at the receiver. In the literature, SCMA is typically analyzed considering a 150% load factor scenario. Nevertheless, scenarios with lower (100%) and greater (200%) than 150% load factor are considered in this work. Results show that, for equivalent spectral efficiency and uncoded scenarios, MC-CDMA performs better than SCMA and the greater the load factor, SD algorithm becomes less complex than MPA.
Performance Analysis of ℓ0-Norm Constraint Variable Step Size Normalized Least Mean Square Algorithm

This paper incorporates a ℓ0 –norm sparsity constraint into variable step size normalized least mean square (ℓ0 -VSSNLMS) algorithm for identification of sparse system corrupted by additive noise. The proposed ℓ0–VSSNLMS algorithm makes a good fairness between convergence characteristics, filter stability and steady state error. The proposed (ℓ0 -VSSNLMS) algorithm incorporates a variable step size that accelerates the convergence characteristics and lowers the mean square deviation (MSD) error than in ℓ0 - NLMS with constant value of step size. The inclusive theoretical performance of ℓ0 -VSSSNLMS algorithm is analyzed in this paper with white Gaussian input under some assumptions which are suitable for practical purpose. An expression for variant step size is derived under the stability condition of proposed algorithm. Finally the implementation of the proposed algorithm is carried out in MATLAB software to manifest the improved beavior in identification of sparse system.
Building Lepida ScpA BigData Infrastructure

This paper is about the design and the implementation of Lepida ScpA BigData infrastructure. Our goal is to provide the Regional PA with proper tools to address future challenges such as planning the allocation of resources and creating new business models involving public and private organizations. Our first design of the infrastructure started from a specific scenario and addresses a particular aspect: ingesting the Regional public WiFi data traffic and gathering interesting analytics. We describe the challenges we faced and the choices we made during the process and the final results we achieved.
Demonetization(500&1000):Analysis of Sentiments using NLTK Withtwitter for Text and Image

The main objective of our research is to analyse sentiments on the activity that happened in 2016, named as demonetization. In our research we have collected data by using twitter API. Twitter Application provides four unique tokens i.e. consumer key, consumer Secret, access token, access secret. These keys are unique to every user and comes with certain constraints. After collecting tweets from twitter, preprocessing is performed i.e. removal of stop words like removing punctuations, hash tags etc. After that, by using NLTK we classified the tweets into positive, negative and neutral. In the end tweets are classified by using Kmeans clustering also. In the end, we finally came to a result which is also illustrated in fig 7. 44.1% public has shown positive sentiments, 26.5% has shown negative and 29.5% has shown neutral reaction.
Phase 1 study of combination therapy with L-697,661 and zidovudine

We performed a pilot study that examined the clinical and pharmacokinetic interactions between zidovudine (ZDV) and a pyridinone derivative, L-697-661. The results indicate that the drugs were well tolerated, with no important pharmacokinetic interactions, when administered concomitantly for as long as 8 weeks. Although the number of study participants was small, we noted rapid emergence of resistance to L-697,661 among ZDV-naive study subjects who were administered L-697,661 as monotherapy but did not observe isolates of human immunodeficiency virus type 1 (HIV-1) resistant to L-697,661 among those who were administered concomitant ZDV. These results suggest a potential interaction between development of resistance to L-697,661 and ZDV. Although the clinical development of L-697,661 has been halted, our results support the need for further studies to test whether specific interactions among antiretroviral agents administered in combination and the molecular target can delay the emergence of isolates that exhibit resistance to all drugs in the regimen.