Miracle-dz/UniFAR-FTUs huggingface dataset:
Orbital Angular Momentum for Wireless Communications

As the traditional resources (frequency, time, space, and so on) are efficiently utilized, it becomes more and more challenging to satisfy the ever-lasting capacity-growing and users-boosting demand in wireless networks. Recently, the EM wave was found to possess not only linear momentum, but also angular momentum. The OAM is a kind of wavefront with helical phase. The OAM-based vortex wave has different topological charges, which are orthogonal to each other, bridging a new way for multiple access in wireless communications. In this article, we introduce the fundamental theory of OAM and the OAM based wireless communications. The research challenges regarding OAM signal generation, OAM beam converging, and OAM signal reception are discussed. Further, we propose a new multiuser access with different OAM-modes in wireless networks, where multiple OAM-modes are used as a new orthogonal dimension for interference avoidance. Simulation results reveal the inherent property of OAM waves and show that OAM based radio transmission can significantly increase spectrum efficiency in wireless networks.
State-of-the-Art Clustering Schemes in Mobile Ad Hoc Networks: Objectives, Challenges, and Future Directions

Mobile ad hoc networks (MANETs) are self-organized networks without any fixed infrastructure. The topology changes are very frequent in MANETs due to nodes’ mobility. The topology maintenance creates an extra overhead, as the mobility information of a single node is shared with all nodes in the network. To address the topology maintenance overhead problem in MANETs, the researchers proposed different cluster-based algorithms to reduce the size of a routing table. The clusters are formed to locally adjust the topology changes within the cluster. If a node wants to communicate with a node outside the cluster, it only communicates with its cluster head (CH). The CH communicates with other CHs to transmit data toward the destination. To efficiently utilize the clustering mechanism in MANETs, stable and balanced clusters are required. To form good quality and optimized clusters, some metrics, such as relative mobility (node speed and direction), node degree, residual energy, communication workload, and neighbor’s behavior, are required. In this paper, we present a comprehensive survey of recent CAs in MANETs. We also present the objectives, goals, and contributions of recent research. Similarly, the findings, challenges, and future directions are stated. The validation of each proposed work is analyzed critically in terms of the mobility model, the simulation tool used during simulation, simulation metrics, and the performance metrics used in the validation process.
Design of A Video-Based Vehicle Speed Measurement System - An Uncertainty Approach

Speed measurement is one of the key components of intelligent transportation systems. It provides suitable information for traffic management and law enforcement. This paper presents a versatile and analytical model for a video-based speed measurement in form of the probability density function (PDF). In the proposed model, the main factors contributing to the uncertainties of the measurement are considered. Furthermore, a guideline is introduced in order to design a video-based speed measurement system based on the traffic and other requirements. As a proof of concept, the model has been simulated and tested for various speeds. An evaluation validates the strength of the model for accurate speed measurement under realistic circumstances.
Regularized Interference Alignment Transceiver Design for Asymmetric UL-DL Multi-Cell Multi-User MIMO Systems

In a dynamic time division duplex multi-cell system, the uplink (UL) and downlink (DL) transmissions may co-exist. The asymmetric UL-DL modes introduce more complex and diverse interference among the adjacent cells. In this paper, we propose an interference alignment (IA) scheme for asymmetric UL-DL multi-cell multi-user multi-antenna system. This system involves conventional inter-cell and intra-cell interference in symmetric directions and new asymmetric inter-cell interference. We characterize the properties of the new interference and use these properties to implement partial IA and guarantee feasibility of IA. Through minimizing weighted sum minimum mean square error, we design a regularized IA transceiver to compensate the performance degradation in low and moderate signal-noise-ratio region. The numerical simulations demonstrate that the proposed schemes outperform the previous works for the asymmetric UL-DL multi-cell system.
Faster Image Compression Technique Based on LZW Algorithm Using GPU Parallel Processing

As web, mobile, desktop and all other applications use image for different purposes, image compression technique has become one of the most important applications in image analysis as well as in computer science. Though image compression is an old concept, it is still a very time-consuming process which has opened a new field of research in image compression. In this paper, we showcase our extensive research works performed on LZW (Lempel-Ziv-Welch) algorithm, a lossless image compression algorithm that yields faster computation time when it is processed with parallel computation, based on the outcome of our research. The improved time efficiency obtained from our experimental result can be perceived in the context of millisecond scale rather than in the macro level of measurement along with keeping the decoded image in lossless format.
Stochastic Performance Analysis of Network Function Virtualization in Future Internet

Network function virtualization (NFV) has been considered as a promising technology for future Internet to increase the network flexibility, accelerate the service innovation, and reduce the Capital Expenditures and Operational Expenditures costs through migrating network functions from dedicated network devices to commodity hardware. Recent studies reveal that although this migration of network function brings the network operation unprecedented flexibility and controllability, NFV-based architecture suffers from serious performance degradation compared with traditional service provisioning on dedicated devices. In order to achieve a comprehensive understanding of the service provisioning capability of NFV, this paper proposes a novel analytical model based on Stochastic Network Calculus (SNC) to quantitatively investigate the end-to-end performance bound of the NFV networks. To capture the dynamic and on-demand NFV features, both the non-bursty traffic, e.g., the Poisson process, and the bursty traffic, e.g., the Markov Modulated Poisson Process, are jointly considered in the developed model to characterize the arriving traffic. To address the challenges of resource competition and end-to-end NFV chaining, the property of convolution associativity and leftover service technologies of SNC are exploited to calculate the available resources of the Virtual Network Function nodes in the presence of multiple competing traffic and transfer the complex NFV chain into an equivalent system for performance derivation and analysis. Both the numerical analysis and extensive simulation experiments are conducted to validate the accuracy of the proposed analytical model. Results demonstrate that the analytical performance metrics match well with those obtained from the simulation experiments and numerical analysis. In addition, the developed model is used as a practical and cost-effective tool to investigate the strategies of the service chain design and resource allocations in the NFV networks.
Educational Data Mining Application for Estimating Students Performance in Weka Environment

Educational data mining (EDM) is a multi-disciplinary research area that examines artificial intelligence, statistical modeling and data mining with the data generated from an educational institution. EDM utilizes computational ways to deal with explicate educational information keeping in mind the end goal to examine educational inquiries. To make a country stand unique among the other nations of the world, the education system has to undergo a major transition by redesigning its framework. The concealed patterns and data from various information repositories can be extracted by adopting the techniques of data mining. In order to summarize the performance of students with their credentials, we scrutinize the exploitation of data mining in the field of academics. Apriori algorithmic procedure is extensively applied to the database of students for a wider classification based on various categorizes. K-means procedure is applied to the same set of databases in order to accumulate them into a specific category. Apriori algorithm deals with mining the rules in order to extract patterns that are similar along with their associations in relation to various set of records. The records can be extracted from academic information repositories. The parameters used in this study gives more importance to psychological traits than academic features. The undesirable student conduct can be clearly witnessed if we make use of information mining frameworks. Thus, the algorithms efficiently prove to profile the students in any educational environment. The ultimate objective of the study is to suspect if a student is prone to violence or not.
The Effect of Alumina Nanorods on Breakdown Performance of Transformer oil

Investigations on the transformer oil-based nanofluids (NFs) have increased rapidly over last few years. Even though, disparity has been noted in presented conclusions and insufficient insight of mechanisms of enhancement of dielectric NFs, NFs have surfaced as a possible alternate of mineral oils (MOs/TOs) as insulating and heat expulsion fluids for HV machinery. These NFs may be viewed as the future generation insulation fluids as they submit exceptional prospect to upgrade dielectric breakdown (BD) strength, as well as heat transfer conduct, as compared to MOs. A MO-based NF has been developed by dispersing Al2O3 nanorods (NRs) into MO to enhance insulating traits of the host oil. The lightening impulse (LI) breakdown voltage (BDV) of developed samples are determined in accordance to IEC standard at different gap lengths. The outcomes manifested that BDV of NFs under all test voltages are enhanced as compared to MOs. Probable modification mechanisms of Al2O3 NRs on the insulating features of MO was examined relying on the trap features of the host MO and NFs.
Optimal and Robust AN-aided Precoding Design for Cognitive MIMOME Wiretap Channels

In this paper, we investigate a cognitive radio network (CRN) for a multiple-input multiple-output multiple-eavesdropper (MIMOME) secrecy channel with artificial noise. The cognitive transmitter aims to send confidential messages to its receiver in the presence of multiple eavesdroppers. While the artificial noise (AN) approach or masked precoding is applied to provide the information secrecy, the target of this paper is to maximize the achievable secrecy rate with the constraint of the total transmit power, as well as the interference temperature limits (ITL) for the primary user. The original optimization problems are non-convex and challenging, which is efficiently solved via a two-layer decomposition approach. The inner layer problem can be efficiently handled by solving a sequence of semi-definite problems, and the outer layer problem can be recast as a single-variable optimization problem, which is tackled by one-dimensional search. We also generalize the framework to an imperfect CSI case where a worst-case robust secrecy rate max-imization (SRM) formulation is considered. Finally, simulation results are provided to validate the secrecy performance of the proposed methods.
RMapTAFA: Radio Map Construction Based on Trajectory Adjustment and Fingerprint Amendment

With the increasing demands of location-based services, indoor positioning systems based on the received signal strength (RSS) fingerprinting and pedestrian dead reckoning (PDR) have been attracting lots of research interests in both academia and industry. However, the RSS fingerprinting suffers from the burdensome site survey for radio map construction, while the PDR tracking suffers from the accumulative step localization error. In this paper, we propose the RMapTAFA scheme to construct a radio map from pedestrian trajectories to jointly address the two challenges. We first propose a novel sample-fingerprint structure containing two new coefficients for fingerprint composition: the credibility coefficient measures the confidence level of a sample, while the reliability coefficient defines the importance of each element in a fingerprint. Each step RSS sample of a pedestrian trajectory, if being determined eligible, is included into the proposed structure, together with its credibility computed from our proposed fingerprint amendment algorithm. Furthermore, we propose a trajectory adjustment algorithm via selective particle filtering by enjoying the RSS-fingerprinting result obtained from the constructed radio map. Field measurements and experiments validate the RMapTAFA scheme in terms of the improved localization performance of both pedestrian trajectory tracking and stationary point positioning.
Tetrahydroisoquinoline Sulfamates as Potent Microtubule Disruptors: Synthesis, Antiproliferative and Antitubulin Activity of Dichlorobenzyl-Based Derivatives, and a Tubulin Cocrystal Structure

Tetrahydroisoquinoline (THIQ) 6-O-sulfamate-based anticancer agents, inspired by the endogenous steroid 2-methoxyestradiol and its sulfamate derivatives, are further explored for antiproliferative and microtubule disruptor activity. Based on recently designed C3-methyl C7-methoxy-substituted THIQ derivatives, compounds with mono- and dichloro-substitutions on the pendant N-benzyl ring were synthesized and evaluated. Although improved antiproliferative activity was observed, for example, 4a versus 4b and 4b versus 8c, it was relatively modest. Compound 8c, a 2′,5′-dichlorobenzyl derivative was, however, identified as a promising antiproliferative agent with in vitro activities exceeding that of the parent steroid (e.g., GI50 90 nM in DU-145 cells) and was highly potent against a range of tumor cell lines (e.g., GI50 26 nM for OVCAR-3). 8c inhibited the polymerization of tubulin in vitro with an IC50 only twofold less potent than combretastatin A-4 and inhibited colchicine binding to tubulin. Tubulin polymerization assays showed the parent THIQ 4a to be only a very weak inhibitor, but a striking potency difference was seen between compounds with C2′ methoxy and chloro substituents, whereas this was much smaller when these substituents were positioned at C5′. To confirm the target in atomic detail and because 8c is a racemic mixture, an achiral parent THIQ 6-O-sulfamate derivative 10 was successfully cocrystallized with the αβ-tubulin heterodimer. The derivative 10 binds at the colchicine site on tubulin, the first example of this compound class investigated in such detail, with its sulfamate group interacting with residues beyond the reach of colchicine itself, similar to a recently reported quinazolinone sulfamate derivative, 6a. The structure also suggests that for racemic C3-methyl-substituted THIQ derivatives, such as 8c, the (S)-enantiomer is likely to be preferentially accommodated within the colchicine site for steric reasons. The results further confirm the potential of nonsteroidal THIQ sulfamate derivatives for oncology and suggest that the mechanism of microtubule destabilization for the THIQ compound class is to prevent the curved-to-straight conformational transition of tubulin required for polymerization.
Densenet Blstm for Acoustic Modeling in Robust ASR

In recent years, robust automatic speech recognition (ASR) has greatly taken benefit from the use of neural networks for acoustic modeling, although performance still degrades in severe noise conditions. Based on the previous success of models using convolutional and subsequent bidirectional long short-term memory (BLSTM) layers in the same network, we propose to use a densely connected convolutional network (DenseNet) as the first part of such a model, while the second is a BLSTM network. A particular contribution of our work is that we modify the DenseNet topology to become a kind of feature extractor for the subsequent BLSTM network operating on whole speech utterances. We evaluate our model on the 6-channel task of CHiME-4, and are able to consistently outperform a top-performing baseline based on wide residual networks and BLSTMs providing a 2.4% relative WER reduction on the real test set.
Two-Tier Cellular Networks for Throughput Maximization of Static and Mobile Users

In small cell networks, the high mobility of users results in frequent handoff and thus severely restricts the data rate for mobile users (MUs). To alleviate this problem, we propose use of the heterogeneous two-tier network structure where static users (SUs) are served by both macro and micro base stations (BSs), whereas the mobile (i.e., moving) users are served only by the macro BSs having larger cells; the idea is to prevent frequent data outage for MUs due to handoff. We use the classical two-tier Poisson network model with different transmit powers, and assume the independent Poisson process of SUs and doubly stochastic Poisson process of MUs moving at a constant speed along infinite straight lines generated by a Poisson line process. Using tools from stochastic geometry, we calculate the average downlink data rate of the typical static and mobile (i.e., moving) users, and the latter accounted for handoff outage periods. We consider also the average throughput of these two types of users defined as their average data rates divided by the mean total number of users co-served by the same base station. We find that if the density of a homogeneous network and/or the speed of MUs is high, it is advantageous to let the MUs connect only to some optimal fraction of BSs (i.e., an optimal random subset of BSs) to reduce the frequency of handoffs during which the connection is not assured. If a heterogeneous structure of the network is allowed, one can further jointly optimize the mean throughput of MUs and SUs. This joint optimization is done by appropriately tuning the powers of micro and macro BSs subject to some aggregate power constraint ensuring unchanged mean data rates of SUs via the network equivalence property.
Introducing ACO+ for Modeling Gene Regulatory Networks from Microarray Data

Revealing the fundamental cellular progression through systems biology is a long felt task and application of computational models in Inferring Regulations among the genes is a suggested approach to be optimized. Various models have been used to extract the probable structure and dynamics of such networks from gene expression data. However, capturing the complex nonlinear system dynamics is still a big issue to be worked out. In this paper a method based on a unique algorithm named ACO+ has been proposed for reverse engineering Gene Regulatory Network (GRN) from microarray data using the biologically relevant optimization algorithm Ant Colony Optimization (ACO), with an enhancement by incorporating two genetic operators, crossover and mutation. The Linear Time Variant (LTV) Model, being simple and able to simulate also nonlinear system dynamics, has been used for modeling the GRN in a fitted way. Attempting ACO+ to model GRN is of its first kind and optimization has been carried out using ACO and the variants ACODE (ACO followed by Differential Evolution), and ACO+ with synthetic noise free and noise in data. The applicability of the proposed method has been tested on synthetic datasets as well as real expression data set of SOS DNA repair system in Escherichia coli. Of the algorithms attempted, ACO+ simulated the best optimization.
Autorange Thermal Sigma–Delta Converter for Incident Radiation Measurement

Thermal sigma–delta modulator is a closed-loop measurement approach, where the sensing element performs part of the $\Sigma \Delta$ modulation in the thermal domain. A new architecture is proposed in this paper for measuring the thermal incident radiation using thermoresistive sensors, where the modulator input range is automatically adjusted to fit the complete thermal radiation. The proposed architecture is compared with a similar one, which uses the same transducer interface circuit but without range adjustment, and is validated experimentally with a reference pyranometer. The proposed architecture has the main advantage of presenting a signal-to-noise ratio and sensitivity that is independent of the ambient temperature range definition. Experimental results for an ambient temperature range of 45 °C show a gain of 13 dB over the similar one.
I/Q Multiport Sensing Feasibility on Ambient Wireless Energy

This paper presents a multiport sensing system composed of a multiport sensor node and an I/Q multiport receiver. The sensor node comprises a sensor under test and a reference. It is fed by a 100 KHz data rate BPSK signal. The change in reflections that occurs in the sensor (I path) modulates the envelope of the BPSK signal. The receiver is based on a six-port direct demodulator where the I/Q components are obtained using the differential approach to the detected power at the four outputs of the six-port. The real-time measurement results of the demodulated I/Q components give the magnitude and phase variation (sensing) of the sensor in relation to the reference. These results establish the feasibility of using ambient wireless RF energy such as: TV, cellular, and Wi-Fi transmissions in passive sensing. Thus, we avoid overcrowding the available spectrum and facing the expensive process of generating radio waves.
Fundamental Implications for Location Accuracy in Ultra-Dense 5G Cellular Networks

At the threshold of 5G, much is promised based on the predicate of hyper-accurate location-aware communications. Although the unique spectral real estate associated with 5G implies the possibility of such a persistent localization scheme, we submit that precision range based localization is not a generational inevitability but rather achievable only under the correct architectural philosophy. To show this, we consider a fundamental component that limits or enables any positioning system: the underlying infrastructure geometry. The significance of this research follows from several significant results. First, cell ultra densification, although the primary catalyst of Long Term Evolution (LTE)/LTE-Advanced capacity improvement and a major disruptive technology in 5G networks, will not alone provide for more accurate user positioning. To show this, we derive a closed-form solution to the Cramér–Rao lower bound specific to 5G networks given that infrastructure is distributed via the Poisson point process (PPP). This result is used to justify a positioning services architecture that fundamentally embraces a variable number of supporting access points. In fact, we show that without this architecture, a foundational decline in positioning performance may inadvertently be realized. Finally, by numerically comparing our results with other point processes common in cellular network modeling, we present justification for the PPP as an appropriate model for analyzing positioning performance in ultra dense 5G infrastructures.