Big Data improvements in cluster analysis

LAUREA MAGISTRALENegli ultimi anni, si sta parlando sempre più spesso di "Big Data'', riferendosi non solo a grandi moli di dati. Infatti, l'espressione riguarda alcune nuove necessità e le conseguenti sfide, dette le "Tre V'': Volume, cioè gestione di grandi moli; Velocità, cioè rapidità di analisi; Varietà, cioè elaborazione di dati non strutturati, come testi, immagini e video. Questa tesi tratta l'utilizzo di tecniche di clustering in questo nuovo contesto. Il clustering consiste nella segmentazione di un insieme di oggetti in gruppi che siano il più possibile omogenei. Di fronte a grandi moli di dati, il clustering è uno strumento potente che produce un piccolo insieme di gruppi, facilmente trattabile. Inoltre, le tecniche presentate sono particolarmente efficienti, quindi uno strumento di calcolo adeguato risolve il problema della velocità. Per quanto riguarda la varietà, esistono strumenti di clustering che trattano anche dati non strutturati, ma non sono parte della tesi. Il software che è stato scelto è "Hadoop'', molto utilizzato in ambito "Big Data'' in quanto permette di gestire grandi moli di dati con un costo contenuto e di trattare dati non strutturati. Esso è basato sulla gestione di grandi volumi mediante la distribuzione del lavoro su un cluster di computer. A tal fine, gli algoritmi sono stati sviluppati in uno specifico paradigma, detto "MapReduce'', che consente la loro parallelizzazione mediante Hadoop. Per questo motivo, alcuni algoritmi di clustering già esistenti sono stati adattati alla struttura del MapReduce e altri sono stati sviluppati direttamente seguendo questa logica. Il lavoro di tesi è consisito nello sviluppo di alcuni algoritmi, che sono stati poi testati su dataset simulati. La prima fase di testing ha riguardato l'efficacia degli algoritmi, cioè la loro capacità di segmentare correttamente un insieme di oggetti. A tal fine, sono stati utilizzati dataset di piccole dimensioni e aventi caratteristiche particolari. L'altra fase ha riguardato l'efficienza, cioè la rapidità di esecuzione, e il testing è stato condotto su dataset di dimensioni maggiori tramite un cluster Amazon di 5 nodi. Nonostante il volume dei dati trattati sia ancora relativamente piccolo, è possibile stimare le prestazioni su moli maggiori. Infatti, il MapReduce ha la peculiarità di essere scalabile. Questo significa che la potenza di calcolo cresce linearmente all'aumentare delle risorse, quindi è sufficiente aumentare il numero di nodi in proporzione alla mole di dati da processare per ottenere le stesse prestazioni. In conclusione, la parte innovativa del lavoro di tesi consiste nella progettazione e implementazione di algoritmi di clustering in MapReduce. Essi sono basati sulla combinazione di logiche di algoritmi già esistenti, riadattate nel nuovo paradigma.The expression "Big Data'' has become very popular in the last few years though it does not concern exclusively large volumes of data. In fact, it is more connected to the way the data needs to be treated and the consequential challenges, called the "Three V'', that stand for Volume (treatment of large datasets) , Velocity (quickness of analysis), and Variety (handle of unstructured data, such as texts, images, and videos). This paper discusses the use of clustering in this context, though not considering the challenge of variety. Clustering consists in the segmentation of a set of objects through the identification of features, so to group them accordingly as much as possible. By all means, this approach reduces the size of the problems involved, as a wide dataset can be handled as though as it were a small set of clusters. Furthermore, the paper describes efficient algorithms, which can be used to create the appropriate tools to allow quick data processes, thereby dealing effectively with the velocity challenge. For this purpose, the choice of software framework went for Hadoop, as it allows a cheap processing of large volumes of data and the handling of unstructured data. The logic upon which it is based is the parallelization of processes using a cluster of computers. For this purpose, the clustering algorithms have been developed through a specific programming model, i.e. MapReduce, since it allows the parallelization of tasks. Therefore, some of the current clustering algorithms have been converted to the MapReduce structure, while others have been developed straight away in that manor. Once the tools were designed, the testing was conducted on some simulated datasets. The first stage regarded the effectiveness, i.e. the capability of identifying correctly some unusually shaped clusters. Therefore, the used dataset were small-sized. Consequently, the efficiency testing aimed to cluster the big dataset more rapidly. For this stage, the used tool was an Amazon cluster of 5 computers. Although the tested volume was still pretty small, it is possible to estimate the performance changes as the dataset grow. As a matter of fact, one of the MapReduce peculiarities is its scalability, i.e. the capability to increase linearly the computational power as the resources grow. Hence, if the size of the cluster is proportional to the data volume, the performances are approximately constant. In conclusion, the design and the development of these new clustering algorithms in MapReduce combines the logics of two current classes of clustering algorithms. By all means, this approach has the advantages of both and gives, therefore, a new range of efficient analytical methodologies and consequential results

Advancements in Radar Odometry

Radar odometry estimation has emerged as a critical technique in the field of autonomous navigation, providing robust and reliable motion estimation under various environmental conditions. Despite its potential, the complex nature of radar signals and the inherent challenges associated with processing these signals have limited the widespread adoption of this technology. This paper aims to address these challenges by proposing novel improvements to an existing method for radar odometry estimation, designed to enhance accuracy and reliability in diverse scenarios. Our pipeline consists of filtering, motion compensation, oriented surface points computation, smoothing, one-to-many radar scan registration, and pose refinement. The developed method enforces local understanding of the scene, by adding additional information through smoothing techniques, and alignment of consecutive scans, as a refinement posterior to the one-to-many registration. We present an in-depth investigation of the contribution of each improvement to the localization accuracy, and we benchmark our system on the sequences of the main datasets for radar understanding, i.e., the Oxford Radar RobotCar, MulRan, and Boreas datasets. The proposed pipeline is able to achieve superior results, on all scenarios considered and under harsh environmental constraints

Performance Enhancement of EAD Thrusters With Nonuniform Emitters Array

This work presents an experimental campaign to optimize the performance of electro-aerodynamic (EAD) thrusters through nonuniform emitter arrangements. The study examines the impact of emitter configurations on thrust and thrust-to-power coefficients CT and CT P . Different emitter arrangements, including collinear and staggered arrays, are tested using thrust, electrical, and velocity measurements as diagnostics. The tests are parametrically performed for different sizes (chords) of the collector electrodes. Results reveal that nonuniform emitter configurations outperform standard arrays, in particular, with short chord collectors, widely spaced apart. An optimal configuration for CT is identified among the staggered ones. In addition, droplet collectors demonstrate competitive performance compared to airfoil collectors

Wind tunnel testing and performance modeling of an atmospheric ion thruster

In this work a complete atmospheric electro–hydro-dynamic (EHD) thruster is tested in a subsonic wind tunnel, with the purpose of evaluating changes in performance due to simulated flight conditions and, for the first time, comparing them with a physical model of the drift region. An aerodynamic frame was designed to accommodate the electrodes inside the wind tunnel. Propulsive force and electrical measurements were conducted to assess performance exploiting dimensionless coefficients derived from one-dimensional theory. The results, on top of validating the theory, show how EHD thrusters can operate with a non-zero bulk velocity and highlight the importance of optimized frames and electrodes to enhance the capabilities of flying demonstrators. The test campaign revealed that the operating voltage envelope extends with increasing bulk velocity, leading to an increase in maximum thrust

RadarLCD: Learnable Radar-based Loop Closure Detection Pipeline

Loop Closure Detection (LCD) is an essential task in robotics and computer vision, serving as a fundamental component for various applications across diverse domains. These applications encompass object recognition, image retrieval, and video analysis. LCD consists in identifying whether a robot has returned to a previously visited location, referred to as a loop, and then estimating the related roto-translation with respect to the analyzed location. Despite the numerous advantages of radar sensors, such as their ability to operate under diverse weather conditions and provide a wider range of view compared to other commonly used sensors (e.g., cameras or LiDARs), integrating radar data remains an arduous task due to intrinsic noise and distortion. To address this challenge, this research introduces RadarLCD, a novel supervised deep learning pipeline specifically designed for Loop Closure Detection using the FMCW Radar (Frequency Modulated Continuous Wave) sensor. RadarLCD, a learning-based LCD methodology explicitly designed for radar systems, makes a significant contribution by leveraging the pre-trained HERO (Hybrid Estimation Radar Odometry) model. Being originally developed for radar odometry, HERO's features are used to select key points crucial for LCD tasks. The methodology undergoes evaluation across a variety of FMCW Radar dataset scenes, and it is compared to state-of-the-art systems such as Scan Context for Place Recognition and ICP for Loop Closure. The results demonstrate that RadarLCD surpasses the alternatives in multiple aspects of Loop Closure Detection.Comment: 7 pages, 2 figure

Osteochondral Lesions of the Talus and Autologous Matrix-Induced Chondrogenesis: Is Age a Negative Predictor Outcome?

Purpose To assess and evaluate healing and functional outcomes after arthroscopic talus autologous matrix-induced chondrogenesis (AT-AMIC) in 2 age groups: patients older than 33 years versus patients 33 years or younger. Methods A total of 31 patients, of whom 17 were 33 years or younger (G 1 ) and 14 older than 33 years (G 2 ), were evaluated. All patients were treated with AT-AMIC repair for osteochondral talar lesion. Magnetic resonance imaging (MRI) and computed tomography (CT)-scan evaluations, as well as clinical evaluations measured by the visual analog scale (VAS) score for pain, American Orthopaedic Foot and Ankle Society Ankle and Hindfoot score (AOFAS), and Short Form-12, were performed preoperatively (T 0 ) and at 6 (T 1 ), 12 (T 2 ), and 24 (T 3 ) months postoperatively. Results G 1 consisted of 17 patients (mean age: 25 years, standard deviation: ±5), whereas G 2 consisted of 14 patients (mean age: 47 years, standard deviation: ±9). In both groups, we found a significant difference for clinical and radiological parameters with the analysis of variance for repeated measures through 4 time points ( P 1 , AOFAS improved significantly between T 0 and T 1 ( P = .025) and T 1 and T 2 ( P = .011); CT showed a significant decrease between T 1 and T 2 ( P = .003) and T 2 and T 3 ( P 2 , AOFAS improved between T 0 and T 1 ( P = .011) and T 2 and T 3 ( P = .018); CT decreased between T 1 and T 2 ( P = .025), whereas MRI showed a reduction between T 1 and T 2 ( P = .029) and T 2 and T 3 ( P = .006). AOFAS in G 1 was significantly higher at T 0 ( P = .017), T 2 ( P = .036), and T 3 ( P = .039) compared with G 2 . A negative linear correlation between AOFAS and VAS at T 1 ( R = −0.756), T 2 ( R = −0.637), and T 3 ( R = −0.728) was found in G 1 , whereas in G 2 , AOFAS was negatively correlated with VAS at T 1 ( R = −0.702). Conclusions The study revealed that osteochondral lesions of the talus were characterized by similar sizes and features, both in young and old patients. We conclude that AT-AMIC can be considered a safe and reliable procedure that allows effective healing, regardless of age, with a significant clinical improvement; in particular, clinical results are related to starting conditions of the ankle. Level of Evidence Level IV, therapeutic case series

Maioregen osteochondral substitute for the treatment of knee defects: A systematic review of the literature

Background: This study aims to investigate the clinical and radiological efficacy of three-dimensional acellular scaffolds (MaioRegen) in restoring osteochondral knee defects. Methods: MEDLINE, Scopus, CINAHL, Embase, and Cochrane Databases were searched for articles in which patients were treated with MaioRegen for osteochondral knee defects. Results: A total of 471 patients were included in the study (mean age 34.07 ± 5.28 years). The treatment involved 500 lesions divided as follows: 202 (40.4%) medial femoral condyles, 107 (21.4%) lateral femoral condyles, 28 (5.6%) tibial plateaus, 46 (9.2%) trochleas, 74 (14.8%) patellas, and 43 (8.6%) unspecified femoral condyles. Mean lesion size was 3.6 ± 0.85 cm2. Only four studies reported a follow-up longer than 24 months. Significant clinical improvement has been reported in almost all studies with further improvement up to 5 years after surgery. A total of 59 complications were reported of which 52 (11.1%) experienced minor complications and 7 (1.48%) major complications. A total of 16 (3.39%) failures were reported. Conclusion: This systematic review describes the current available evidence for the treatment of osteochondral knee defects with MaioRegen Osteochondral substitute reporting promising satisfactory and reliable results at mid-term follow-up. A low rate of complications and failure was reported, confirming the safety of this scaffold. Considering the low level of evidence of the study included in the review, this data does not support the superiority of the Maioregen in terms of clinical improvement at follow-up compared to conservative treatment or other cartilage techniques