Miracle-dz/UniFAR-FTUs huggingface dataset:
Generalized Interference Alignment for Multi-Cell Cooperative Transmission over Doubly Selective Channels

The paper studies the multi-cell cooperation scheme under the time and frequency doubly selective channels, in which the multi-cell co-channel interference are jointly eliminated with the inter-symbol interference to enhance the transmission reception. The basic idea of interference alignment is exploited, with which the link-level interference and network-level interference are aligned to the same dimension before canceled. Instead of assuming the perfect channel information is available at the receivers, an embedded pilot framework is also proposed to track and update the channel state information every short time period, making the proposed scheme more feasible for practical application. Additionally, the pilot design is also discussed and an optimal embedded pilot scheme is derived.
On the Crucial Impact of Antennas and Diversity on BLE RSSI-Based Indoor Localization

Due to their low complexity, RSSI-based solutions for indoor localization have become increasingly popular in recent years despite lacking the accuracy of more sophisticated localization solutions. One of the main reasons for this lack of accuracy is the highly fluctuating nature of RSSI values as a result of indoor channel characteristics, hardware imperfections and varying antenna radiation patterns. In this paper, we thus critically analyze the log- normal path loss model and its validity with the focus on indoor localization. We show how chip antennas of typical consumer devices affect the RSSI measurements and clarify in what manner this effect can be incorporated into the log-normal model. In this process, we are also able to quantify the impact of small-scale fading and diversity on RSSI-based distance estimation. We furthermore propose a novel calibration scheme that estimates path loss exponents based on a simple training walk and outperforms linear regression in all our use cases. At last, we combine our findings in an implementation of an indoor localization system for a 14×3 m office corridor. On average, our measurements yield a significantly decreased position RMSE of 1.36 m, which compares to the Cramer- Rao lower bound on the position RMSE of 0.71 m in this environment.
Investigation of Non-Overlapping Time-Domain Windowing for OOB Radiation Reduction of OFDM System

Orthogonal frequency-division multiplexing (OFDM) systems suffer from significant out-of-band (OOB) radiation. All existing techniques have several drawbacks as wasting spectral or time resources. Time domain windowing is a well-known technique where an extended smoothing window is multiplied by the modulated signal. This window reduces the signal spectrum sidelobes, however, it extends the resource Block (RB) length resulting in reduces the system data rate. In this work, we intend to review some outstanding techniques for OOB reduction, and investigate a new method of time-domain windowing that does not require extension of time-domain signal. Simulation results show that the non-overlapping windowing exhibits better peak to sidelobe ratio compared to overlapping windowing, yet at the expense of degradation of the BER versus SNR.
A Novel Matrix for Location Categorization In Human Mobility Mining

Human mobility mining is feasible due to easy availability of the users’ GPS traces. User’s trajectory derived from GPS based track points is the raw data for mining various trends in user mobility. Identification of staypoints and region of interest (RoI) is one of the most important aspects while processing trajectory data for the various location-based application. Staypoints and RoIs are the locations where the user spends some measurable time and performs some activity. These are the important locations of the user’s daily routine and probably interesting locations from the user’s perspective. Each location possesses substantial importance in user’s routine. One of the basic methods to find such locations is to analyze user’s mobility traces to find staypoints from it and then to perform clustering on these staypoints. Existing algorithms gives equal importance to all such identified locations while designing mobility-based applications. However, realistically, all locations cannot have the same importance from the user’s perspective. Also, there are chances to find few locations like home or work almost in each mobility trace even after keeping less time granularity. So, we have proposed a novel location categorization matrix LoCMat to classify location as per their importance in the user’s routine based on frequency and duration of visits to each location. Identifying, retaining and assigning appropriate weightage to all such locations will enhance results of mobility mining. We have tested our proposed matrix with real dataset "Mobi-India". Our method identifies meaningful locations, assigns weightage to each one of them which can be used further for mobility mining.
Secure Authentication Protocol for 5G Enabled IoT Network

The rapid growth of Internet of Things(IoT) along with wide range of applications made IoT popular. Moreover, the network connectivity and communication support is provided by 5G technology. The mobile technology is also becoming the essential component of Social life, which is also becoming the essential interface for IoT. Various services of IoT may be availed from IoT server, which are enabled with 5G network technology. The IoT service access would require a intermediate access network to connect to 5G, and access network may be publicly accessible. Hence, there exists security threat to user's data. Here, In this paper, We provided an application layer security protocol to mitigate all those attacks originating from public access network. We have also tested the security protocol by an automated security testing tool, Scyther. The security protocol exhibits Secrecy, Aliveness, Non-Injective Agreement, and Non-Inective Synchronization. The protocol is resistant to various attacks, which could originate from Confidentiality, Integrity and Availability. The user-credentials and services-request are secretly communicated, thereby preserving the privacy.
High-Precision UAV Localization System for Landing on a Mobile Collaborative Robot Based on an IR Marker Pattern Recognition

We present a novel high-precision UAV localization system for interconnection between two collaborative robots, i.e., unmanned ground robot (UGR) and unmanned aerial vehicle (UAV) capable of autonomous navigation and precise localization in an indoor environment. Based on our localization system we have achieved robust UAV landing on the moving robot using a fusion of 2D LIDAR sensors, camera, and ultrasonic system for localization. In addition, UAV is capable of accurate high-altitude indoor flights (up to 15 m) relative to the ground robot. Localization of UAV is based on the developed adaptive active IR marker system to achieve reliable flight on different altitudes and light conditions. In this paper, we describe the operating principle of the system and present the results of UAV flight experiments. One of promising applications of the developed system is automated inventory management of warehouses.
Finding simple sequence repeats (SSRs) within human genome using MapReduce based K-mer algorithm

Simple sequence repeats (SSRs) is a collection of the different short and repetitive oligonucleotides sequence that is primarily observed in between any large DNA sequence. In comparison to the standard DNA sequence, these short oligonucleotides may have distinct functions and structural properties. In this article, we have presented a massive parallel processing model for basic operations with k-mers from DNA sequences, based on SSRs function. We introduce a novel approach that finds SSRs using k-mer in the first step. We implement the MapReduce based K-mer algorithm for the advantage of parallel execution, which also enables the analysis of large datasets using of the distributed platform. We have compared the result with KAnalyze, a pipelined K-mer toolkit for finding different sizes of k-mer, and found significant improvement in terms of computational time. Our study shows that MapReduce based k-mer has the potential which reduces memory footprint and accurately identifies every SSR of any specified length. Our proposed MapReduce based k-mer method is useful for finding any Simple sequence repeats (SSRs) in large DNA sequences on any distributed platform.
Polar Coding for Physical-Layer Security without Knowledge of the Eavesdropper's Channel

We propose an adaptive secrecy scheme using polar codes with random frozen bits for a general wiretap channel, in which to protect the data from a potential eavesdropper, part or all of the frozen bits are randomly generated per message. To assess the secrecy level of the proposed scheme, three types of decoding strategies are evaluated: a matching decoder which knows the positions of all inserted bits inside the blocklength and tries to estimate them using the same decoding techniques, a blind decoder which treats all the frozen bits as the same value, and a random decoder which considers those dynamic bits as random at the receiver. Results are presented in terms of the system security gap, assuming an adaptive decoding strategy. It is shown that the system achieves combined secrecy and reliability. The proposed scheme does not assume knowledge of the eavesdropper's channel when defining the indices of information and frozen bits.
Joint Estimation of Channel and IQ Imbalance in Media-Based Modulation

In direct conversion radio frequency (RF) front-end architecture, RF impairments such as IQ imbalance (IQI), DC offset, flicker noise, and oscillator leakage can affect the performance. In this work, we study the performance of media-based modulation (MBM), an attractive channel modulation scheme, in the presence of transmit and receiver side IQI and propose efficient compensation techniques. MBM is shown to be more resilient to IQI compared to conventional modulation. A scheme which jointly estimates and compensates the channel and IQI parameters using widely linear least squares estimation is proposed. The proposed scheme is shown to alleviate the transmit and receive IQI-induced BER degradation in MBM.
Selective Transmission Strategy for NOMA in Downlink CoMP

Selective-transmission (ST) strategy is proposed for non-orthogonal multiple access (NOMA) in downlink coordinated multipoint networks. Based on fixed and adaptive power allocation approaches, various ST-NOMA schemes are investigated and compared with conventional NOMA schemes under joint-transmission (JT) and hybrid joint- transmission and strong-channel-selection (JT+SC) strategies. Adaptive power allocation is also analytically derived to guarantee perfect user fairness. Performance results show that fair ST- NOMA outperforms conventional JT-NOMA and JT+SC- NOMA in user fairness, while providing high system throughput.
Efficient Power Allocation for Multi-Cell Uplink NOMA Network

Digital technologies are rapidly shaping the modern concepts of urbanization. It is a key element of developing practical smart cities of the future. In fact, they are the catalyst for the increasing networking of all areas of life in a smart city. Recent development in the domain of communication technologies has opened new avenues to realize the concept of smart cities. One of such communication technology is non-orthogonal multiple access (NOMA) for future cellular communications. This article, therefore, focuses on the interference management of uplink cellular NOMA systems. Specifically, we propose a power optimization technique for NOMA to improve the sum-rate in a multi-cell environment. We also consider Nakagami-m faded links to analyze the applicability of our proposed scheme under various channel conditions. The simulation results show that the proposed NOMA approach outperforms conventional orthogonal multiple access (OMA) technique in the multi-cell uplink scenario.
Analysis of cardiovascular diseases using artificial neural network

In this paper, a study has been made on the possibility and accuracy of early prediction of several Heart Disease using Artificial Neural Network. (ANN). The study has been made in both noise free and noisy environment. The data collected for this analysis are from five Hospitals. Around 1500 heart patient's data has been collected and studied. The data is analysed and the results have been compared with the Doctor's diagnosis. It is found that, in noise free environment, the accuracy varies from 74% to 92%. and in noisy environment (2dB), the results of accuracy varies from 62% to 82%. In the present study, four basic attributes considered are Blood Pressure (BP), Fasting Blood Sugar (FBS), Thalach (THAL) and Cholesterol (CHOL.). sIt has been found that highest accuracy(93%), has been achieved in case of PPI(Post-Permanent-Pacemaker Implementation), around 79% in case of CAD(Coronary Artery disease), 87% in DCM(Dilated Cardiomyopathy), 89% in case of RHD&MS(Rheumatic heart disease with Mitral Stenosis), 75% in case of RBBB +LAFB (Right Bundle Branch Block + Left Anterior Fascicular Block), 72% for CHB(Complete Heart Block) etc. The lowest accuracy has been obtained in case of ICMP(Ischemic Cardiomyopathy), about 38% and AF(Atrial Fibrillation), about 60 to 62%.
A Novel Method for Testing the Inner Temperature of Helix TWT under Operation using FBG

A novel method for testing the inner temperature of the helix TWT under operation using fiber Bragg grating (FBG) is proposed, in which the high-temperature FBG keeps in close contact with the support rod, and the gold-coated fiber stretching out is welded with the tube to keep vacuum tightness. In order to verify the feasibility of the method, simulation for the power performance of the helix TWT with different number of embedded fibers is performed, showing that using only one fiber could minimize the impacts on the operating performance. The helix TWT prototype with an embedded high-temperature FBG has been successfully manufactured, and the next step is to test the temperature inside the tube.
IoT Based Power Efficient Agro Field Monitoring and Irrigation Control System : An Empirical Implementation in Precision Agriculture

Internet of Things (IoT) has been flourishing the communication and networking system in recent years, while experiencing a growing phase in agricultural field. Precision agriculture requires sensor integration, automatic control, and networking and data processing capabilities. With the implementation of rapidly growing IoT, challenges of scalability and data management can be overcome. In this research, we have developed an IoT based smart monitoring system for agricultural practice including client application- web application and android application; and designed a controlling system for power efficient irrigation. We have used LoRa, wireless RF transceiver to overcome the limitations of area coverage and have enabled to established real time data communication. While our android application makes this system feasible for users. Real time data were collected deploying the system and empirical data has been included in the result section.
Repeatability for Spatiotemporal Throughput Measurements in LTE

Evaluating mobile broadband in an operational cellular network is a challenging measurement task that depends on many parameters. Throughput is an essential performance indicator in any data network. Particularly in cellular mobile networks, the throughput is very sensitive to the number of active users--the cell load. Nearly every publication discussing throughput measurements in live mobile networks cites that throughput strongly varies in time. However, none of the studies have attempted to remove the impact of the cell load to achieve a more predictable throughput behavior. In this paper, we propose a new throughput-correction technique. Since we have access to the information on the amount of resources scheduled to the measuring device, we can approximate the total throughput that the device would experience if all the resources would get allocated to it. We focus on LTE and analyze several repeated measurements conducted with user equipments mounted in a car. We measured in a live LTE network along a selected highway segment, at different times, on two different days. The results have revealed that the corrected throughput exhibits significantly smaller variations than the raw throughput does. Finally, we discuss possible limitations--especially frequency selective scheduling combined with a low share of resources--and outline further directions of research.
A Real-Time Convolutional Neural Network Based Speech Enhancement for Hearing Impaired Listeners Using Smartphone

This paper presents a Speech Enhancement (SE) technique based on multi-objective learning convolutional neural network to improve the overall quality of speech perceived by Hearing Aid (HA) users. The proposed method is implemented on a smartphone as an application that performs real-time SE. This arrangement works as an assistive tool to HA. A multi-objective learning architecture including primary and secondary features uses a mapping-based convolutional neural network (CNN) model to remove noise from a noisy speech spectrum. The algorithm is computationally fast and has a low processing delay which enables it to operate seamlessly on a smartphone. The steps and the detailed analysis of real-time implementation are discussed. The proposed method is compared with existing conventional and neural network-based SE techniques through speech quality and intelligibility metrics in various noisy speech conditions. The key contribution of this paper includes the realization of CNN SE model on a smartphone processor that works seamlessly with HA. The experimental results demonstrate significant improvements over the state-of-the-art techniques and reflect the usability of the developed SE application in noisy environments.
State of Charge Estimation of Sodium Ion Battery under different Operating Conditions using Cascade Forward Backpropagation Algorithm

Sodium ion battery (SIB) technology is a promising battery technology for future energy storage systems. Due to sodium’s abundance in nature and lower cost, it can be a good alternative to current Lithium ion battery (LIB) technology. The Na0.7Ni0. 3Mn0.59Co0.1Cu0.01O2 cathode material was synthesized using sol gel method. The sodium ion battery was fabricated and tested resulting in the maximum discharge capacity of 150 mAhg−1 at 0.1C. The transition metal oxide SIB discharge profile contains many voltage plateaus compared to its lithium counterpart, thus necessitate the determination of the state of charge estimation for SIBs. The fabricated SIB was discharged at two different current rates (0.5C & 1C) and three different temperatures (30°C, 45°C & 55°C). These experimental results of discharge cycles were used to train the Cascade Forward Backpropagation (CFB) algorithm and used for the estimation of State of Charge (SOC) of the SIB battery. The result showed a very good estimation of SOC for SIBs.
Functional words removal techniques: A review

With the growth of internet activities, electronic documents has become the key source of data and optimization of this data is very important for various research tasks such as Information Retrieval, Natural Language Processing, Web mining, Text mining etc. The data which is present in the web is a combination of both structured as well as unstructured data which mostly contains textual data. Hence, text processing is required for extracting useful information from that type of data which can then be used for further processes. Preprocessing plays a vital role in all text processing activities. Stop-word removal is one of the most important pre-processing techniques which eliminate the functional words from the document. Thus, helps in improving the performance of the system. In this paper, all the stop-word removal techniques used for Indian text are discussed along with the analysis of results produced by using those techniques for various Indian languages is also presented.
Speech Emotion Recognition using Mel Frequency Cepstral Coefficient and SVM Classifier

Speech Emotion Recognition is the forefront of Human Machine Interaction. In this paper we present a supervised learning approach for Speech Emotion Recognition (SER) using Mel Frequency Cepstral Coefficient (MFCC) feature extraction. Using dimensionality reduction and normalization we increase the efficiency of prediction accuracy. The developed model was trained and tested against the Berlin Database of Emotional Speech recorded by the Technical University Berlin. The model was classified using a Support Vector Machine (SVM). Using validation, the model resulted with highest accuracy of 92.16% against the test samples.
Cost-Optimal Deployment of a C-RAN With Hybrid Fiber/FSO Fronthaul

Centralized radio access network (C-RAN) has been considered as an architectural solution able to reduce capital and operational expenditure in dense 5G cellular networks while allowing better network performance. The C-RAN approach decouples baseband units from antenna sites and places them in selected locations, connected by the so-called fronthaul links. These links require expensive high-capacity connections, thus calling for cost-efficient deployment. This paper presents a hybrid fronthaul solution for C-RAN based on both optical fibers and free-space optics (FSO) to enhance fronthaul flexibility and minimize deployment costs. Two design strategies based on integer linear programming are proposed for both greenfield and brownfield deployments. The first strategy is referred to as joint planning (JP) and is based on the joint minimization of the number of deployed remote radio heads (RRHs) and the cost of the hybrid fiber/FSO fronthaul. The second strategy is based on two-step disjoint planning (DP) that first identifies a cost-optimal RRH placement and then finds the corresponding minimum cost deployment for the fronthaul links. Results obtained with JP and DP are compared in dense urban area scenarios (i.e., with characteristics similar to festivals or concerts), highlighting the advantage of the JP approach compared to DP, both in terms of costs and an enhanced flexibility during the network design process.
Dimensionality-Varied Convolutional Neural Network for Hyperspectral Image Classification With Small-Sized Labeled Samples

Recently, convolutional neural network (CNN) achieves outstanding performance for hyperspectral image (HSI) classification. However, CNN requires large-sized labeled samples for training and tremendous computations for 3-D spectral-spatial feature extraction. In practice, most HSI data are unlabeled and the availability of samples for training is limited. In order to improve the classification performance with a simplified structure, a dimensionality-varied convolutional neural network (DV-CNN) is proposed. DV- CNN reduces the complexity and calculations by changing the dimensionalities of feature maps and improves the classification accuracy of small-sized labeled HSI by deep network. The experiments are compared with some state-of-the-art methods on Indian pines and Pavia University data sets. The experimental results demonstrate that the proposed DV-CNN outperforms other methods and achieves better classification performance.