1,865 research outputs found
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
May lamotrigine be an alternative to topiramate in the prevention of migraine with aura? Results of a retrospective study
Evidence suggests that lamotrigine could be effective in reducing aura frequency and duration. However, studies comparing lamotrigine to other, first-line prophylactic agents solely involving patients suffering from migraine with aura are still lacking. The aim of this study was to compare the efficacy of lamotrigine and topiramate for the preventive treatment of migraine with aura
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