Miracle-dz/UniFAR-FTUs huggingface dataset:
Coverage Analysis and Cooperative Hybrid Precoding for 5G Cellular Networks

5G innovations have been made in both the network deployment and the transceiver architectures in order to increase coverage, energy- and spectrum-efficiency. Future base stations (BSs) are expected to be densely deployed in places such as walls and lamp posts and cover a smaller area compared to current macro BS systems. Using large spectrum at millimeter-wave (mmWave) frequency bands and highly directional beamforming with large antenna arrays, 5G will bring gigabit-per-second data rate and low-latency communications and enable many novel services such as high-speed mmWave wireless interconnections between devices, vehicular communications, etc. Moreover, mmWave communication systems will be based on novel hybrid beamforming architectures which have reduced hardware power consumption and cost. Thus, for better understanding of 5G performance and limitations, one of the main goals in this thesis is to analyze new models that give tractable performance metrics for dense small BS networks. Another goal in this thesis is to study mmWave hybrid beamforming schemes which enable joint transmissions in multi-cell multi-user systems. In the thesis, we show the advantages of small cells in increasing the coverage probability and reducing the path loss and shadowing, and we show the value of cooperation in terms of power consumption and outage. In [Paper A] we derive analytical expressions for the successful reception probability of the equal gain combining receiver in a network where interfering transmitters are distributed according to a Poisson point process and interfering signals are spatially correlated. The results show that the spatial correlation reduces the successful reception probability and the effect of the spatial correlation increases with the number of antennas. [Paper B] follows to study the performance of a partial zero forcing receiver. The results are simulated in an environment with blockages and are analyzed under both Rayleigh and Rician channels. The coverage probability is shown to be maximized when using a subset of antennas' degree-of-freedom for useful signal enhancement and using the remaining degrees of freedom for canceling the interference from strongest interferers. Finally, in [Paper C], we propose a hybrid beamforming scheme which minimizes the total power consumption of a multi-cell multi-user network, subject to per-user quality-of-service constraints. The proposed scheme is based on decoupling the analog precoding and digital precoding. The analog precoders are only dependent on the local channel state information at each BS. Then, the digital precoders are obtained by solving a relaxed convex optimization for given analog precoders. Simulation results show that the proposed algorithm leads to almost the same RF transmit power as that of fully digital precoding, while saving considerable hardware power due to the reduced number of RF chains and digital-to-analog converters.
Geoparsing and geocoding places in a dynamic space context

The backbone of the proposal in this chapter is an automatic parser and a formal encoder of information describing places, spatial and verbal relations in textual documents in order to reconstruct and map the textually described itinerary. These tools allow us to show how to combine the information expressed in French texts, referring to places, spatial actions associated with them, and data found in external geographical resources to build a geocoded representation of an itinerary. Our approach focuses on the automatic reconstruction of routes and transcribes them in their geographical setting, identifying locations and routes by interpreting spatial information in a dynamic space context.
Interferer selection for binaural cue preservation in joint binaural linearly constrained minimum variance beamforming

Spatial cues allow a listener to determine the direction sound is coming from. In addition, recognising spatially separated sound sources facilitate the listener to focus on specific sound sources. Because of this, preservation of spatial cues in multi-microphone hearing assistive devices is important to the listening experience and safety of the user. A number of linearly-constrained-minimum-variance-based methods exist for this purpose. Most of these are limited in the number of interfering sources for which they can preserve the spatial cues. In this thesis, a method of selecting the most important interfering point sources using convex optimisation is proposed. The method is presented based on two different convex relaxations, which are compared, using simulation experiments, to existing, exhaustive search and randomised methods in terms of noise suppression and localisation errors. Both methods are shown to improve the performance of the joint binaural linearly constrained minimum variance beamformer, an existing method for simultaneous noise reduction and spatial cue preservation, by giving it more degrees of freedom for noise reduction and allowing it to handle a larger number of (virtual) sources present in the scene.
A Robust Authentication Scheme for Multiple Servers Architecture

Multiple servers collaboration technology furnishes an effective storage management platform to the service providers. The massive data, which belong to disparate providers, possesses distinct access policies for each user. Authentication applied to multiple servers architecture is recognized as a remarkable mechanism for access control and authorization of users. In 2017, Jangirala et al. explored problems existing in the current research of authentication for multiple servers environment and presented a new solution. Nevertheless, we find that it is defenseless in impersonation attack, server spoofing attack, and fails to maintain users’ anonymity. In this paper, we detail the aforementioned faults and propose a remedy with a tripartite certification strategy. Correspondingly, the validation of BAN-logic focused on tripartite authentication protocol is put forward to demonstrate the security reliability. Comparative evaluation of other related solutions for the multiple servers architecture observes that our proposal has advantages over security attributes meanwhile provides a reliable guarantee of efficiency.
EXPLORING MACHINE LEARNING CLASSIFICATION ALGORITHMS FOR CROP CLASSIFICATION USING SENTINEL 2 DATA

Abstract. Crop Classification and recognition is a very important application of Remote Sensing. In the last few years, Machine learning classification techniques have been emerging for crop classification. Google Earth Engine (GEE) is a platform to explore the multiple satellite data with different advanced classification techniques without even downloading the satellite data. The main objective of this study is to explore the ability of different machine learning classification techniques like, Random Forest (RF), Classification And Regression Trees (CART) and Support Vector Machine (SVM) for crop classification. High Resolution optical data, Sentinel-2, MSI (10&thinsp;m) was used for crop classification in the Indian Agricultural Research Institute (IARI) farm for the Rabi season 2016 for major crops. Around 100 crop fields (~400 Hectare) in IARI were analysed. Smart phone-based ground truth data were collected. The best cloud free image of Sentinel 2 MSI data (5 Feb 2016) was used for classification using automatic filtering by percentage cloud cover property using the GEE. Polygons as feature space was used as training data sets based on the ground truth data for crop classification using machine learning techniques. Post classification, accuracy assessment analysis was done through the generation of the confusion matrix (producer and user accuracy), kappa coefficient and F value. In this study it was found that using GEE through cloud platform, satellite data accessing, filtering and pre-processing of satellite data could be done very efficiently. In terms of overall classification accuracy and kappa coefficient, Random Forest (93.3%, 0.9178) and CART (73.4%, 0.6755) classifiers performed better than SVM (74.3%, 0.6867) classifier. For validation, Field Operation Service Unit (FOSU) division of IARI, data was used and encouraging results were obtained.
Robust DOBC for Stabilization Loop of a Two-Axes Gimbal System

In this paper, a disturbance compensation strategy based on disturbance observer control (DOBC) is proposed to solve parameter perturbation, friction, coupling and external turbulence for two-axes gimbal control system. Uncertainties, friction, coupling shortcoming of gimbal system is summed up as a disturbance suppression problem, and achieving disturbance compensation through feedforward channel of DOBC. However, the compensation effects of DOBC are determined by modeling accuracy of the nominal plant and feedforward filter design. A <inline-formula> <tex-math notation="LaTeX">$H\infty $ </tex-math></inline-formula>-based filter design is adopted to guarantee robust stability of DOBC when controlled object changed. Because performance weight function of DOBC is derived from stable constraint of closed-loop system, and robust of closed-loop system can also be satisfied. Eventually, we completed proof of <inline-formula> <tex-math notation="LaTeX">$H\infty $ </tex-math></inline-formula>-based robust DOBC algorithm in pod system, and stability performance of the system has been greatly improved.
Grid Solar Hybrid Speed Controller For Electric Vehicle

In the future a significant shift in focus will occur towards electric vehicles as fossils decrease and fuel prices become extremely high. Also use of renewable energy sources will be required in electric vehicles, so as not to depend on thermal or nuclear power indirectly. Characteristic of a IC engine (Internal Combustion Engine) such as higher torque on demand, rapid acceleration and row power are often a hindrance in acceptance of Electric vehicles by mass public. This Research work emphasis’s on a design of a grid solar hybrid electric vehicle Which addresses the above issues effectively and efficiently. The proposed system uses a higher power 3 q induction motor with FOC (Field Oriented control) or vector control to deliver IC Engine vehicles like performance in terms of row power, acceleration, torque etc. with highest energy efficiency achievable. Also the system comprises of an intelligent electronic PWM switching system which facilitates use of Root solar energy in priority and energy deficit to be derived from battery when the vehicle is running. The switching system also provides for priority to on board Root top solar panels for vehicle battery charging and tabs deficit power from grid.
Selected topics on massive MIMO networks

Massive Multiple-Input Multiple-Output (MIMO) technology promises to deliver substantial gains in spectral efficiency and is set to play an important role in next generation mobile networks. This thesis examines the key challenges of pilot contamination, admission control and channel estimation in massive MIMO networks, and derives contributions which are focused on the new paradigm of the large antenna array at the base station (BS). Firstly, motivated by current literature which holds pilot contamination as the dominant impairment in massive MIMO networks, we investigate the uplink signal-to-interference ratio (SIR) of two common receivers, the maximum ratio combining (MRC) and zero-forcer (ZF), under more general pilot allocation schemes. Our analysis reveals that the limiting performances of the MRC and ZF under general forms of pilot allocation are in fact quite different. By employing a more general pilot allocation where every BS uses a different orthogonal set of pilot sequences, we uncover the further counter-intuitive large antenna behaviour of the ZF detector being limited by forms of intra-cell interference as a result of pilot contamination. Secondly, the next generation of networks will need to service an unprecedented leap in the number of devices requiring cellular access, particularly from the influx in smart and machine-tomachine devices operating under the Internet of Things (IoT) paradigm. Motivated by the need to service this increase in connected devices, we investigate the user-load capacity of cellular massive MIMO networks. We compare the pilot allocation scheme widely adopted in massive MIMO literature where all BSs reuse the same pilot orthogonal set, to a more general pilot set allocation, where all BSs use different orthogonal pilot sets. We show that the later is superior in the context of user-load capacity by allowing more connected users on the network for a given quality of service (QoS). Employing the different pilot set allocation, under system models that account for log-normal shadowing and best cell selection, we define efficient and simple user
Using Apache Spark on Hadoop Clusters as Backend for WebLicht Processing Pipelines

Modern annotation tools and pipelines that support automatic text annotation and processing have become indispensable for many linguistic and NLP-driven applications. To simplify their active use and to relieve users from complex configuration tasks, Service-oriented architecture (SOA) based platforms – like CLARIN’s WebLicht – have emerged. However, in many cases the current state of participating endpoints does not allow processing of “big data”-sized text material or the execution of many user tasks in parallel. A potential solution is the use of distributed computing frameworks as a backend for SOAs. These systems and their corresponding software architecture already support many of the features relevant for processing big data for large user groups. This submission describes such an implementation based on Apache Spark and outlines potential consequences for improved processing pipelines in federated research infrastructures.
Cache memory organization for processing in memory

A promising solution for assuring ultra-low latency in data-intensive application processing systems is processing in memory (PIM). Although most studies that have examined PIM-based computing systems have used cache memory, few have adequately explored a reasonable cache management policy for PIM. Therefore, this paper studies cache management policies for PIM-based computing systems and classi ﬁ es existing PIM policies according to where they are located and how they are managed. To evaluate the policies, we model three types of PIM-based computing systems used in an in-memory system architecture. One model employs an internal-single cache, another an external cache hierarchy, and the other internal multiple cache-based PIM. We also simulate the performance and power consumption of the three models by their workloads, each with diverse characteristics. The experimental results show how cache policies in ﬂ uence the performance and power of PIM-based in-memory computing systems.
Genetic diversity in gladiolus by using gamma rays

This investigation was carried out to determine the effects of gamma radiation on germination characteristics of gladiolus corms. This experiment was conducted through a factorial design in a randomized complete block with three replications. Two factors included cultivars of gladiolus (Red Advance, White Prosperity, Amsterdam, and Rose Supreme) and doses of gamma radiation (0, 15, 25, 35, 45, 55, 65, and 75 Gy). However, the results showed that corm weight, size, and the number of daughter corms decreased with increasing radiation doses in contrast to a low dose of (15 Gy) High doses of gamma irradiation (75 Gy) showed lethal effects on various vegetative traits. The low yield at high doses can be due to the reduced vegetative growth due to gamma treatments. The results of this study suggest a dose of 40-65 Gy as the optimal dose to improve the vegetative traits of gladiolus.
OPTIMIZATION OF IDLE PATH IN DRILLING ON CNC MACHINING CENTRE USING THE GA

Optimization of the idle time is the objective of the present work which is done by minimizing the idle tool path in drilling on CNC machining centres to improve productivity as a result of reducing the idle path time. The case is formulated as a traveling salesman problem and is solved using the Genetic Algorithm (GA). The Branch and Bound (B&B) optimization method was used for the sake of comparison with the Genetic Algorithm. The working mechanism of the GA was inspired by the nature which is based on the evolutionary process of biological organisms. The work is done in three steps, first step is the parameterization of the GA parameters (population size, replacement, crossover and mutation); Second step is the numerical study for the GA validation, for comparison between the GA and B&B and comparison between the GA and the current practice, the third step is the experimental verification of GA results.
Efficient Bayesian Model Choice for Partially Observed Processes: With Application to an Experimental Transmission Study of an Infectious Disease

Infectious diseases such as avian influenza pose a global threat to human health. Mathematical and statistical models can provide key insights into the mechanisms that underlie the spread and persistence of infectious diseases, though their utility is linked to the ability to adequately calibrate these models to observed data. Performing robust inference for these systems is challenging. The fact that the underlying models exhibit complex non-linear dynamics, coupled with practical constraints to observing key epidemiological events such as transmission, requires the use of inference techniques that are able to numerically integrate over multiple hidden states and/or infer missing information. Simulation-based inference techniques such as Approximate Bayesian Computation (ABC) have shown great promise in this area, since they rely on the development of suitable simulation models, which are often easier to code and generalise than routines that require evaluations of an intractable likelihood function. In this manuscript we make some contributions towards improving the efficiency of ABC-based particle Markov chain Monte Carlo methods, and show the utility of these approaches for performing both model inference and model comparison in a Bayesian framework. We illustrate these approaches on both simulated data, as well as real data from an experimental transmission study of highly pathogenic avian influenza in genetically modified chickens.
Thin Wing EMA - System on Chip with Synchronized TRust and Assurance (SaSTRA)

Thin Wing Electromechanical Actuator (EMA) offers significant benefits with respect to power density and performance for flight control systems in the next-generation of aircrafts. The cyber physical system (CPS) security design for such EMAs is of immediate need because of the safety critical nature of the application which has not being addressed so far. This research proposes to develop a novel system on chip (SoC) approach using COTS FPGA SoC with Synchronized TRust and Assurance (SaSTRA) to demonstrate resiliency of the Thin Wing EMA in presence of CPS vulnerabilities. The research takes a holistic approach of embedding and synchronizing Root of Trust (RoT) and security monitors within multiple layers of the CPS to provide a mechanism for FPGA assurance at the application level.
Word sense disambiguation using semantic kernels with class-based term values

: In this study, we propose several semantic kernels for word sense disambiguation (WSD). Our approaches adapt the intuition that class-based term values help in resolving ambiguity of polysemous words in WSD. We evaluate our proposed approaches with experiments, utilizing various sizes of training sets of disambiguated corpora (SensEval 1 ). With these experiments we try to answer the following questions: 1. ) Do our semantic kernel formulations yield higher classification performance than traditional linear kernel? , 2. ) Under which conditions a kernel design performs better than others? , 3. ) Does the addition of class labels into standard term-document matrix improve the classification accuracy? , 4. ) Is their combination superior to either type? , 5. ) Is ensemble of these kernels perform better than the baseline? , 6. ) What is the effect of training set size? Our experiments demonstrate that our kernel-based WSD algorithms can outperform baseline in terms of F-score.
Clustering of protein-protein interactions (PPI) and gene ontology molecular function using Markov clustering and fuzzy K partite algorithm

Functional disorders of proteins in the human body can cause a certain disease. The function and role of the protein are represented by Gene Ontology (GO). In this study, the GO molecular function was used to enrich the analysis of protein-protein interaction (PPI). The relationship between PPI and GO molecular function was represented in a bipartite graph. In the pre-processing step, the PPI network was reduced using the Markov clustering algorithm to obtain the group of proteins with the highest modularity score. The fuzzy k partite algorithm was used to cluster the PPI network and GO molecular functions into several groups. The result of Markov clustering showed the accuracy of 84.6% compared to that of the same algorithm using the GIANT package on Cytoscape applications, one of the popular software for network analysis. Proteins obtained from Markov clustering results were used as inputs to obtain their related GO molecular function. Their relationship was represented as a bipartite graph which is used as an input for the fuzzy k partite algorithm. With the dataset of Diabetes Mellitus type II, the results of Markov clustering showed that there were 117 proteins and 328 related GO molecular function. With fuzzy k partite algorithm, the minimum cost for the bipartite graph is 594.175 at the 20 clusters of proteins and 29 clusters of GO molecular functions.
“Design and implementation of a visual-inertial odometry system with visual-inertial sensors on a SoC computational architecture”

- In this document, we proposed a loosely-coupled system to adapt and fuse a visual odometry algorithm with inertial data obtained from an IMU (Inertial Measurement Unit) in a SoC (System on Chip). These process goes from the selection of the needed resources to the experimental tests of the algorithm working with the made adaptations. All the tests performed in each step of the process and the results of each of them are presented as well.
Improved Deep Hashing With Soft Pairwise Similarity for Multi-Label Image Retrieval

Hash coding has been widely used in the approximate nearest neighbor search for large-scale image retrieval. Recently, many deep hashing methods have been proposed and shown largely improved performance over traditional feature-learning methods. Most of these methods examine the pairwise similarity on the semantic-level labels, where the pairwise similarity is generally defined in a hard-assignment way. That is, the pairwise similarity is “1” if they share no less than one class label and “0” if they do not share any. However, such similarity definition cannot reflect the similarity ranking for pairwise images that hold multiple labels. In this paper, an improved deep hashing method is proposed to enhance the ability of multi-label image retrieval. We introduce a pairwise quantified similarity calculated on the normalized semantic labels. Based on this, we divide the pairwise similarity into two situations—“hard similarity” and “soft similarity,” where cross-entropy loss and mean square error loss are adapted respectively for more robust feature learning and hash coding. Experiments on four popular datasets demonstrate that the proposed method outperforms the competing methods and achieves the state-of-the-art performance in multi-label image retrieval.