Riabilitazione protesica ed implanto-protesica di pazienti in età evolutiva affetti da Displasia Ectodermica

Introduction. Ectodermal Dysplasias are a heterogeneous group of inherited disorders characterized by dysplasia of tissues of ectodermal origin (hair, nails, teeth, skins and glands). Clinically, it may be divided into two broad categories: the X-linked hypoidrotic form and the hidrotic form. Hypohidrotic Ectodermal Dysplasia (H.E.D) is characterized by the triad oligo-anodontia, hypotricosis, hypo-anhydrosis (Christ-Siemens-Tourane syndrome). The incidence of HED is about 1/100,000. Mutation in the actodysplasin-A (EDA) and ectodysplasin-A receptor (EDAR) genes are responsible for X-linked and autosomal HED. The clinical features include sparse, fine hair, missing or conical-shaped teeth, decreased sweat and mucous glands, hypoplastic skin, and heat intolerance with exercise or increased ambient temperature. Complete or partial anodontia and malformation of teeth are the most frequent dental findings. Incisors and canines are often conical-shaped while primarily second molars, if present, are mostly affected by taurodontism. Treatment is supportive and includes protection from heat exposure, early prosthetic rehabilitation, skin, hair ear, nose and nail care, and genetic counseling for family planning. The diagnosis of HED in the neonatal and early infancy period may be difficult since sparse hair and absent teeth are normal finding at this age. In childhood the diagnosis is more easily made on the basis of history and clinical examination. Dental abnormalities are the most common complaint. Prosthetic rehabilitation has been recommended as an essential part of the management of HED because is important from functional, esthetic, and psychological standpoint. A team approach that includes input from a pediatric dentist, an orthodontist, a prosthodontist, and an oral and maxillofacial surgeon is necessary for a successful outcome. Conventional prosthodontic rehabilitation in young patient is often difficult because of the anatomical abnormalities of existing teeth and alveolar ridges. The conical shaped teeth and “knife-edge” alveolar ridges result in poor retention and instability of dentures. Moreover, denture must permit jaws expansion and a correct pattern of growth. Materials and Methods. Complete removable dentures were provided to allow for normal physiological development and a corrected masticatory function. Initial maxillary and mandibular impressions were made with smallest stock trays and irreversible hydrocolloid and then final impressions ware made with light-bodied polysulfide rubber base impression material. A base of autopolymerizing resin was constructed and a wax rim was added to the base. The patient’s vertical dimension of occlusion was established by assessing phonetic and esthetic criteria. Preliminary occlusal relations were recorded, and the mandibular cast was mounted on the articulator. Acrylic resin teeth specific for children dentures were selected and mounted. The dentures were tried in and, after proper adjustments, were inserted. The patients were monitored clinically every month to fit prostheses. Cephalometric radiographs were taken every 6 month with the prostheses in place in order to evaluate correct pattern of growth. Cephalometric measurements were realized and used to evaluate the effect of rehabilitation on craniofacial growth. Cephalometric measurements of sound patients were compared with ED patients. After two month expander screws (three-way screw in the upper denture and two-way the lower one)were inserted in each denture in order to permit the expansion of the denture and the jaws growth. Where conical teeth were present, composite crown were realized and luted to improve the esthetic and phonesis. In order to improve retention the placement of endosseous implants was carried out. TC 3D Accuitomo was performed and a resin model of mandibular bone of the patient was realized. At the age of 11 years two implants were inserted into anterior mandible in a child with anodontia. Despite a remarkable multi-dimensional atrophy of the mandibular alveolar process, the insertion of two tapered screw implants (SAMO Smiler, diameter 3.8, length 10 mm). After a submerged healing period of two-three month, the implants were exposed. Implants were connected with an expansion guide that permits mandibular growth and prosthetic retention. The amount of mandibular growth was also evaluate dusing the expansion guide. Results. Early oral rehabilitation improve oral function, phonesis and esthetic, reducing social impairment. Treated patients showed normal cephalometric measurement. Early rehabilitation is able to prevent the prognatissm of the mandibula . The number of teeth was significantly related to several changes in craniofacial morphology. Discussion. In the present study the 5,3% of ED patients showed hypodontia, the l’89,4% di oligodontia, and the 5,3% di anodontia. The cephalometric analysis supports that ED patients showed midface hypoplasia. ED groups showed an increased pogonion to nasion measurement than sound patients, indicative of class III tendency. The present study demonstrated that number of teeth was significantly correlated with deviation of cephalometric measurements from normality. Oligoanodontia is responsible for changing of cephalometric measuraments also on sagittal plane with a class III tendency. Maxillary jaw showed a retrused position related to the presence of hypodontia.

Enabling Human Centric Smart Campuses via Edge Computing and Connected Objects

Early definitions of Smart Building focused almost entirely on the technology aspect and did not suggest user interaction at all. Indeed, today we would attribute it more to the concept of the automated building. In this sense, control of comfort conditions inside buildings is a problem that is being well investigated, since it has a direct effect on users’ productivity and an indirect effect on energy saving. Therefore, from the users’ perspective, a typical environment can be considered comfortable, if it’s capable of providing adequate thermal comfort, visual comfort and indoor air quality conditions and acoustic comfort. In the last years, the scientific community has dealt with many challenges, especially from a technological point of view. For instance, smart sensing devices, the internet, and communication technologies have enabled a new paradigm called Edge computing that brings computation and data storage closer to the location where it is needed, to improve response times and save bandwidth. This has allowed us to improve services, sustainability and decision making. Many solutions have been implemented such as smart classrooms, controlling the thermal condition of the building, monitoring HVAC data for energy-efficient of the campus and so forth. Though these projects provide to the realization of smart campus, a framework for smart campus is yet to be determined. These new technologies have also introduced new research challenges: within this thesis work, some of the principal open challenges will be faced, proposing a new conceptual framework, technologies and tools to move forward the actual implementation of smart campuses. Keeping in mind, several problems known in the literature have been investigated: the occupancy detection, noise monitoring for acoustic comfort, context awareness inside the building, wayfinding indoor, strategic deployment for air quality and books preserving.

Flexible Computing Systems For AI Acceleration At The Extreme Edge Of The IoT

Embedding intelligence in extreme edge devices allows distilling raw data acquired from sensors into actionable information, directly on IoT end-nodes. This computing paradigm, in which end-nodes no longer depend entirely on the Cloud, offers undeniable benefits, driving a large research area (TinyML) to deploy leading Machine Learning (ML) algorithms on micro-controller class of devices. To fit the limited memory storage capability of these tiny platforms, full-precision Deep Neural Networks (DNNs) are compressed by representing their data down to byte and sub-byte formats, in the integer domain. However, the current generation of micro-controller systems can barely cope with the computing requirements of QNNs. This thesis tackles the challenge from many perspectives, presenting solutions both at software and hardware levels, exploiting parallelism, heterogeneity and software programmability to guarantee high flexibility and high energy-performance proportionality. The first contribution, PULP-NN, is an optimized software computing library for QNN inference on parallel ultra-low-power (PULP) clusters of RISC-V processors, showing one order of magnitude improvements in performance and energy efficiency, compared to current State-of-the-Art (SoA) STM32 micro-controller systems (MCUs) based on ARM Cortex-M cores. The second contribution is XpulpNN, a set of RISC-V domain specific instruction set architecture (ISA) extensions to deal with sub-byte integer arithmetic computation. The solution, including the ISA extensions and the micro-architecture to support them, achieves energy efficiency comparable with dedicated DNN accelerators and surpasses the efficiency of SoA ARM Cortex-M based MCUs, such as the low-end STM32M4 and the high-end STM32H7 devices, by up to three orders of magnitude. To overcome the Von Neumann bottleneck while guaranteeing the highest flexibility, the final contribution integrates an Analog In-Memory Computing accelerator into the PULP cluster, creating a fully programmable heterogeneous fabric that demonstrates end-to-end inference capabilities of SoA MobileNetV2 models, showing two orders of magnitude performance improvements over current SoA analog/digital solutions.

Edge and Big Data technologies for Industry 4.0 to create an integrated pre-sale and after-sale environment

The fourth industrial revolution, also known as Industry 4.0, has rapidly gained traction in businesses across Europe and the world, becoming a central theme in small, medium, and large enterprises alike. This new paradigm shifts the focus from locally-based and barely automated firms to a globally interconnected industrial sector, stimulating economic growth and productivity, and supporting the upskilling and reskilling of employees. However, despite the maturity and scalability of information and cloud technologies, the support systems already present in the machine field are often outdated and lack the necessary security, access control, and advanced communication capabilities. This dissertation proposes architectures and technologies designed to bridge the gap between Operational and Information Technology, in a manner that is non-disruptive, efficient, and scalable. The proposal presents cloud-enabled data-gathering architectures that make use of the newest IT and networking technologies to achieve the desired quality of service and non-functional properties. By harnessing industrial and business data, processes can be optimized even before product sale, while the integrated environment enhances data exchange for post-sale support. The architectures have been tested and have shown encouraging performance results, providing a promising solution for companies looking to embrace Industry 4.0, enhance their operational capabilities, and prepare themselves for the upcoming fifth human-centric revolution.

Optimizing AI at the Edge: from network topology design to MCU deployment

The first topic analyzed in the thesis will be Neural Architecture Search (NAS). I will focus on two different tools that I developed, one to optimize the architecture of Temporal Convolutional Networks (TCNs), a convolutional model for time-series processing that has recently emerged, and one to optimize the data precision of tensors inside CNNs. The first NAS proposed explicitly targets the optimization of the most peculiar architectural parameters of TCNs, namely dilation, receptive field, and the number of features in each layer. Note that this is the first NAS that explicitly targets these networks. The second NAS proposed instead focuses on finding the most efficient data format for a target CNN, with the granularity of the layer filter. Note that applying these two NASes in sequence allows an "application designer" to minimize the structure of the neural network employed, minimizing the number of operations or the memory usage of the network. After that, the second topic described is the optimization of neural network deployment on edge devices. Importantly, exploiting edge platforms' scarce resources is critical for NN efficient execution on MCUs. To do so, I will introduce DORY (Deployment Oriented to memoRY) -- an automatic tool to deploy CNNs on low-cost MCUs. DORY, in different steps, can manage different levels of memory inside the MCU automatically, offload the computation workload (i.e., the different layers of a neural network) to dedicated hardware accelerators, and automatically generates ANSI C code that orchestrates off- and on-chip transfers with the computation phases. On top of this, I will introduce two optimized computation libraries that DORY can exploit to deploy TCNs and Transformers on edge efficiently. I conclude the thesis with two different applications on bio-signal analysis, i.e., heart rate tracking and sEMG-based gesture recognition.

Development and characterization of cutting-edge equipment for EMC measurements and advanced power line filter design

This Doctoral Thesis aims at studying, developing, and characterizing cutting edge equipment for EMC measurements and proposing innovative and advanced power line filter design techniques. This document summarizes a three-year work, is strictly industry oriented and relies on EMC standards and regulations. It contains the main results, findings, and effort with the purpose of bringing innovative contributions at the scientific community. Conducted emissions interferences are usually suppressed with power line filters. These filters are composed by common mode chokes, X capacitors and Y capacitors in order to mitigate both the differential mode and common mode noise, which compose the overall conducted emissions. However, even at present days, available power line filter design techniques show several disadvantages. First of all, filters are designed to be implemented in ideal 50 Ω systems, condition which is far away from reality. Then, the attenuation introduced by the filter for common or differential mode noise is analyzed independently, without considering the possible mode conversion that can be produced by impedance mismatches, or asymmetries in either the power line filter itself or the equipment under test. Ultimately, the instrumentation used to perform conducted emissions measurement is, in most cases, not adequate. All these factors lead to an inaccurate design, contributing at increasing the size of the filter, making it more expensive and less performant than it should be.

6G enabled IoV scenarios with distributed intelligence mechanisms

The Internet of Vehicles (IoV) paradigm has emerged in recent times, where with the support of technologies like the Internet of Things and V2X , Vehicular Users (VUs) can access different services through internet connectivity. With the support of 6G technology, the IoV paradigm will evolve further and converge into a fully connected and intelligent vehicular system. However, this brings new challenges over dynamic and resource-constrained vehicular systems, and advanced solutions are demanded. This dissertation analyzes the future 6G enabled IoV systems demands, corresponding challenges, and provides various solutions to address them. The vehicular services and application requests demands proper data processing solutions with the support of distributed computing environments such as Vehicular Edge Computing (VEC). While analyzing the performance of VEC systems it is important to take into account the limited resources, coverage, and vehicular mobility into account. Recently, Non terrestrial Networks (NTN) have gained huge popularity for boosting the coverage and capacity of terrestrial wireless networks. Integrating such NTN facilities into the terrestrial VEC system can address the above mentioned challenges. Additionally, such integrated Terrestrial and Non-terrestrial networks (T-NTN) can also be considered to provide advanced intelligent solutions with the support of the edge intelligence paradigm. In this dissertation, we proposed an edge computing-enabled joint T-NTN-based vehicular system architecture to serve VUs. Next, we analyze the terrestrial VEC systems performance for VUs data processing problems and propose solutions to improve the performance in terms of latency and energy costs. Next, we extend the scenario toward the joint T-NTN system and address the problem of distributed data processing through ML-based solutions. We also proposed advanced distributed learning frameworks with the support of a joint T-NTN framework with edge computing facilities. In the end, proper conclusive remarks and several future directions are provided for the proposed solutions.