Selfie acustiche con il progetto selfear: un'applicazione mobile per l'acquisizione a basso costo di pinna-related transfer function

Le esperienze di realta virtuale e aumentata stanno riscontrando una grande diffusione e le tecnologie per la spazializzazione del suono in cuffia saranno fondamentali per la diffusione di scenari applicativi immersivi in supporti mobile. Questo articolo affronta le problematiche legate alla acquisizione di head-related tranfer function (HRTF) con dispositivi a basso costo, accessibili a chiunque, in qualsiasi luogo e che forniscano delle misurazioni fruibili in tempi brevi. In particolare la soluzione proposta denominata "the SelfEar project" si focalizza sull'acquisizione delle trasformazioni spettrali ad opera dell'orecchio esterno contenute nella pinna-related transfer function (PRTF); l'utente viene guidato nella misurazione di HRTF in ambiente non anecoico attraverso una procedura auto-regolabile. Le informazioni acustiche sono infatti acquisite tramite un headset per la realt`a acustica aumentata che include un set di microfoni posizionati in prossimit\ue0 dei canali uditivi dell'ascoltatore. Proponiamo una sessione di misurazione con l'obiettivo di acquisire le caratteristiche spettrali della PRTF di un manichino KEMAR, confrontandoli con i risultati che si otterrebbero con una procedura in ambiente anecoico. In entrambi i casi i risultati dipendono fortemente dalla posizione dei microfoni, senza considerare in questo scenario il problema legato ai movimenti di un eventuale soggetto umano. Considerando la qualit\ue0 generale e la variabilit\ue0 dei risultati, cos\uec come le risorse totali necessarie, il progetto SelfEar propone una promettente soluzione per una procedura a basso costo di acquisizione di PRTF, e pi\uf9 in generale di HRTF

Seepage Analysis and Optimization of Reservoir Earthen Embankment with Double Textured HDPE Geo-Membrane Barrier

This research paper focuses on conducting a steady state seepage analysis along with the downstream slope factor of safety using the Modified Bishops method in a poorly compacted earthen embankment and optimizing the same reservoir earthen embankment in a case study located near Sadiyavav village in Junagadh district in Gujarat, India. The study site, situated at 21°32'06.5"N and 70°37'26.7"E, is renowned for its Asiatic lions. The analysis and optimization were performed with a double-textured High-Density Polyethylene (HDPE) Geo-membrane barrier. Previously, designs and numerical solutions proposed homogenous embankments and too poorly compacted with no drainage arrangements, which led to anisotropic conditions within the section and water seeping out, cutting the phreatic line. The paper presents the documented improvements in the factor of safety achieved through the seepage analysis and the optimization of the HDPE Geo-membrane barrier. Two improvement techniques were studied using the “Limiting Equilibrium-Finite Element Method” (LS-FEM). The first using (HDPE) Geo-membrane stabilized with gabions, and the second alternative using HDPE Geo-membrane with gabions in addition to rock toe. The study results showed improvements in the downstream slope stability for the two alternatives by 3% and 10%, respectively. Doi: 10.28991/CEJ-2023-09-11-07 Full Text: PD

Design and Simulation of a Model Predictive Controller (MPC) for a Seismic Uniaxial Shake Table

Shake table is one of the apparatus that aids in researches to generate techniques, structural developments, and strategies to prevent, prepare, and minimize an earthquake’s devastating effects. One important factor that should be considered in a shake table is the system dynamics due to control-structural interactions, which could either be linear or non-linear. To accurately model both has always been the challenge but becomes more plausible with the availability of faster hardware and computers and the continuous decrease in latency. Model Predictive Controller (MPC) is a type of controller extensively used in the industry that can be used on linear and non-linear systems. This study presents the design and simulation of an MPC for a uniaxial shake table intending to analyze the system’s behavior and accuracy. MATLAB Simulink was utilized to handle the simulation analysis of the controller. Different MPC parameters such as sample time, prediction horizon, control horizon, and closed-loop performance were manipulated and adjusted to observe their effects on the output of the system. A signal that mimics the actual earthquake data was inputted into the controller, and the system's behavior and outputs were measured and presented through graphical representations. To determine the accuracy of the system’s output, its relationship with the reference signal was compared. From the simulation produced, the system demonstrated high accuracy levels and could be adjusted depending on the set performance aggressiveness of the system

Optimization of CO2 Laser Cutting Parameters Using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Laser cutting is a manufacturing technology that uses laser light to cut almost any materials. This type of cutting technology has been applied in many industrial applications. Problems seen with a laser is the cutting efficiency and the quality wherein these two parameters are both affected by the laser power and its process speed. This study presents the modelling and simulation of an intelligent system for predicting and optimising the process parameters of CO2 laser cutting. The developed model was trained and tested using actual data gathered from actual laser cut runs. For the system parameters, two inputs were used: the type of material used and the material thickness (mm). For the desired response, the output is the process speed or cutting rate (mm/min). Adaptive neuro-fuzzy inference system (ANFIS) was the tool used to model the optimisation cutting process. Moreover, grid partition (GP) and subtractive clustering were both used in designing the fuzzy inference system (FIS). Among the training models used, GP Gaussian bell membership function (Gbellmf) provided the highest performance with an accuracy of 99.66%

Improving diagnosis for rare diseases: the experience of the Italian undiagnosed Rare diseases network

Background For a number of persons with rare diseases (RDs) a definite diagnosis remains undiscovered with relevant physical, psychological and social consequences. Undiagnosed RDs (URDs) require other than specialised clinical centres, outstanding molecular investigations, common protocols and dedicated actions at national and international levels; thus, many "Undiagnosed RDs programs" have been gradually developed on the grounds of a well-structured multidisciplinary approach. Methods The Italian Undiagnosed Rare Diseases Network (IURDN) was established in 2016 to improve the level of diagnosis of persons with URD living in Italy. Six Italian Centres of Expertise represented the network. The National Centre for Rare Diseases at the Istituto Superiore di Sanita coordinates the whole project. The software PhenoTips was used to collect the information of the clinical cases. Results One hundred and ten cases were analysed between March 2016 and June 2019. The age of onset of the diseases ranged from prenatal age to 51 years. Conditions were predominantly sporadic; almost all patients had multiple organs involvements. A total of 13/71 family cases were characterized by WES; in some families more than one individual was affected, so leading to 20/71 individuals investigated. Disease causing variants were identified in two cases and were associated to previously undescribed phenotypes. In 5 cases, new candidate genes were identified, although confirmatory tests are pending. In three families, investigations were not completed due to the scarce compliance of members and molecular investigations were temporary suspended. Finally, three cases (one familial) remain still unsolved. Twelve undiagnosed clinical cases were then selected to be shared at International level through PhenomeCentral in accordance to the UDNI statement. Conclusions Our results showed a molecular diagnostic yield of 53,8%; this value is comparable to the diagnostic rates reported in other international studies. Cases collected were also pooled with those collected by UDNI International Network. This represents a unique example of global initiative aimed at sharing and validating knowledge and experience in this field. IURDN is a multidisciplinary and useful initiative linking National and International efforts aimed at making timely and appropriate diagnoses in RD patients who still do not have a confirmed diagnosis even after a long time