Topical Tocopherol for treatment of reticular oral lichen planus: randomized, double-blind, crossover study

This randomized, double-blind, placebo-controlled crossover study assessed the efficacy of topical tocopherol acetate compared with placebo in easing oral discomfort in patients with reticular oral lichen planus (ROLP)

Femtosecond Laser and Big-Bubble Deep Anterior Lamellar Keratoplasty: A New Chance

Purpose. To report the 12-month follow-up after big-bubble deep anterior lamellar keratoplasty (DALK) assisted by femtosecond laser that we have called IntraBubble. Methods. A 60 kHz IntraLase femtosecond laser (Abbott Medical Optics) firstly created a 30° angled intrastromal channel to insert the air injection cannula, 50 μ above the thinnest corneal site measured by Sirius Scheimpflug camera (CSO, Firenze, Italy), then performed a full lamellar cut 100 μ above the thinnest corneal point, and from the same corneal depth, created a mushroom incision. The lamella was removed, and the smooth cannula of Fogla was inserted into the stromal channel and air was injected to achieve a big bubble. The follow up is 12 months, and sutures were removed by the 10th postoperative month in all patients. Best Corrected Visual Acuity (BCVA), spherical equivalent and, by Sirius Scheimpflug camera (CSO, Firenze, Italy) keratometric astigmatism were evaluated. Results. All procedures were completed as DALK except 2 converted to PK because an inadvertent intraoperative macroperforation occurred. Mean postoperative BCVA was 0.8, mean spherical equivalent was -3.5 ± 1.7 D, and mean keratometric astigmatism was 4.8 ± 3.1 D. Conclusion. The femtosecond laser could standardize the big-bubble technique in DALK, reducing the risk of intraoperative complications and allowing good refractive outcomes

CVA6 RISC-V Virtualization: Architecture, Microarchitecture, and Design Space Exploration

Virtualization is a key technology used in a wide range of applications, from cloud computing to embedded systems. Over the last few years, mainstream computer architectures were extended with hardware virtualization support, giving rise to a set of virtualization technologies (e.g., Intel VT, Arm VE) that are now proliferating in modern processors and SoCs. In this article, we describe our work on hardware virtualization support in the RISC-V CVA6 core. Our contribution is multifold and encompasses architecture, microarchitecture, and design space exploration. In particular, we highlight the design of a set of microarchitectural enhancements (i.e., G-Stage Translation Lookaside Buffer (GTLB), L2 TLB) to alleviate the virtualization performance overhead. We also perform a Design Space Exploration (DSE) and accompanying post-layout simulations (based on 22nm FDX technology) to assess Performance, Power ,and Area (PPA). Further, we map design variants on an FPGA platform (Genesys 2) to assess the functional performance-area trade-off. Based on the DSE, we select an optimal design point for the CVA6 with hardware virtualization support. For this optimal hardware configuration, we collected functional performance results by running the MiBench benchmark on Linux atop Bao hypervisor for a single-core configuration. We observed a performance speedup of up to 16% (approx. 12.5% on average) compared with virtualization-aware non-optimized design at the minimal cost of 0.78% in area and 0.33% in power. Finally, all work described in this article is publicly available and open-sourced for the community to further evaluate additional design configurations and software stacks

SelfAct: Personalized Activity Recognition based on Self-Supervised and Active Learning

Supervised Deep Learning (DL) models are currently the leading approach for sensor-based Human Activity Recognition (HAR) on wearable and mobile devices. However, training them requires large amounts of labeled data whose collection is often time-consuming, expensive, and error-prone. At the same time, due to the intra- and inter-variability of activity execution, activity models should be personalized for each user. In this work, we propose SelfAct: a novel framework for HAR combining self-supervised and active learning to mitigate these problems. SelfAct leverages a large pool of unlabeled data collected from many users to pre-train through self-supervision a DL model, with the goal of learning a meaningful and efficient latent representation of sensor data. The resulting pre-trained model can be locally used by new users, which will fine-tune it thanks to a novel unsupervised active learning strategy. Our experiments on two publicly available HAR datasets demonstrate that SelfAct achieves results that are close to or even better than the ones of fully supervised approaches with a small number of active learning queries

RISC-V Virtualization for a CVA6-based SoC

In this work, we describe the implementation of the latest version of the RISC-V Hypervisor extension (v1.0) specification in a RISC-V CVA6-based (64-bit) SoC. We also report the results of performing an extensive evaluation on the current design and we share our experience about the design space exploration for a few microarchitectural optimizations to the memory subsystem. To complete, we have also enhanced the timer infrastructure by implementing the privileged timer Sstc extension. All these efforts we conducted in an attempt to improve performance without compromising area and power

HULK-V: a Heterogeneous Ultra-low-power Linux capable RISC-V SoC

IoT applications span a wide range in performance and memory footprint, under tight cost and power constraints. High-end applications rely on power-hungry Systems-on-Chip (SoCs) featuring powerful processors, large LPDDR/DDR3/4/5 memories, and supporting full-fledged Operating Systems (OS). On the contrary, low-end applications typically rely on Ultra-Low-Power ucontrollers with a "close to metal" software environment and simple micro-kernel-based runtimes. Emerging applications and trends of IoT require the "best of both worlds": cheap and low-power SoC systems with a well-known and agile software environment based on full-fledged OS (e.g., Linux), coupled with extreme energy efficiency and parallel digital signal processing capabilities. We present HULK-V: an open-source Heterogeneous Linux-capable RISC-V-based SoC coupling a 64-bit RISC-V processor with an 8-core Programmable Multi-Core Accelerator (PMCA), delivering up to 13.8 GOps, up to 157 GOps/W and accelerating the execution of complex DSP and ML tasks by up to 112x over the host processor. HULK-V leverages a lightweight, fully digital memory hierarchy based on HyperRAM IoT DRAM that exposes up to 512 MB of DRAM memory to the host CPU. Featuring HyperRAMs, HULK-V doubles the energy efficiency without significant performance loss compared to featuring power-hungry LPDDR memories, requiring expensive and large mixed-signal PHYs. HULK-V, implemented in Global Foundries 22nm FDX technology, is a fully digital ultra-low-cost SoC running a 64-bit Linux software stack with OpenMP host-to-PMCA offload within a power envelope of just 250 mW.Comment: This paper has been accepted as full paper at DATE23 https://www.date-conference.com/date-2023-accepted-papers#Regular-Paper

Position control study of a bearingless multi-sector permanent magnet machine

Bearingless motors combine in the same structure the characteristics of conventional motors and magnetic bearings. Traditional bearingless machines rely on two independent sets of winding for suspension force and torque production, respectively. The proposed Multi-Sector Permanent Magnet (MSPM) motor exploits the spatial distribution of the multi-three-phase windings within the stator circumference in order to produce a controllable suspension force. Therefore, force and torque generation are embedded in the same winding setting. In this paper the force and torque generation principles are investigated and a mathematical model is presented considering the rotor displacement. A two Degree of freedom (DOF) position controller is designed taking into consideration the rotor overall dynamic system and a controller gains selection strategy is suggested. A simulation study of the bearingless system in different operating conditions is presented and the suspension force and torque produced are validated through Finite Element Analysis (FEA)

Radial force control of multi-sector permanent magnet machines for vibration suppression

Radial force control in electrical machines has been widely investigated for a variety of bearingless machines, as well as for the conventional structures featuring mechanical bearings. This paper takes advantage of the spatial distribution of the winding sets within the stator structure in a multisector permanent-magnet (MSPM) machine toward achieving a controllable radial force. An alternative force control technique for MSPM machines is presented. The mathematical model of the machine and the theoretical investigation of the force production principle are provided. A novel force control methodology based on the minimization of the copper losses is described and adopted to calculate the d–q axis current references. The predicted performances of the considered machine are benchmarked against finite-element analysis. The experimental validation of the proposed control strategy is presented, focusing on the suppression of selected vibration frequencies for different rotational speeds

Radial force control of multi-sector permanent magnet machines

The paper presents an alternative radial force control technique for a Multi-Sector Permanent Magnet machine (MSPM). Radial force control has been widely investigated for a variety of bearingless machines and can be also applied to conventional PMSM aiming the reduction of the mechanical stress on the bearings as well as reduce the overall vibration. Traditional bearingless motors rely on two independent sets of windings dedicated to torque and suspension respectively. The work presented in this paper takes advantage of the spatial distribution of the winding sets within the stator structure towards achieving a controllable net radial force. In this paper the α-β axis model for the MSPM and the theoretical investigation of the force production principle is presented. A novel force control methodology based on the Single Value Decomposition (SVD) technique is described. The predicted performances of the MSPM have been validated using Finite Element simulations and benchmarked against state of the art control techniques

Performance improvement of bearingless multi-sector PMSM with optimal robust position control

Bearingless machines are relatively new devices that consent to suspend and spin the rotor at the same time. They commonly rely on two independent sets of three-phase windings to achieve a decoupled torque and suspension force control. Instead, the winding structure of the proposed multi-sector permanent magnet (MSPM) bearingless machine permits to combine the force and torque generation in the same three-phase winding. In this paper the theoretical principles for the torque and suspension force generation are described and a reference current calculation strategy is provided. Then, a robust optimal position controller is synthesized. A Multiple Resonant Controller (MRC) is then integrated in the control scheme in order to suppress the position oscillations due to different periodic force disturbances and enhance the levitation performance. The Linear-Quadratic Regulator (LQR) combined with the Linear Matrix Inequalities (LMI) theory have been used to obtain the optimal controller gains that guarantee a good system robustness. Simulation and experimental results will be presented to validate the proposed position controller with a prototype bearingless MSPM machine