Optimisation- based time slot assignment and synchronisation for TDMA MAC in industrial wireless sensor network

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/166209/1/cmu2bf02232.pd

Kinematic strategies for obstacle-crossing in older adults with mild cognitive impairment

IntroductionMild cognitive impairment (MCI) is considered a transitional stage between soundness of mind and dementia, often involving problems with memory, which may lead to abnormal postural control and altered end-point control when dealing with neuromechanical challenges during obstacle-crossing. The study aimed to identify the end-point control and angular kinematics of the pelvis-leg apparatus while crossing obstacles for both leading and trailing limbs.Methods12 patients with MCI (age: 66.7 ± 4.2 y/o; height: 161.3 ± 7.3 cm; mass: 62.0 ± 13.6 kg) and 12 healthy adults (age: 67.7 ± 2.9 y/o; height: 159.3 ± 6.1 cm; mass: 61.2 ± 12.0 kg) each walked and crossed obstacles of three different heights (10, 20, and 30% of leg length). Angular motions of the pelvis and lower limbs and toe-obstacle clearances during leading- and trailing-limb crossings were calculated. Two-way analyses of variance were used to study between-subject (group) and within-subject (obstacle height) effects on the variables. Whenever a height effect was found, a polynomial test was used to determine the trend. A significance level of α = 0.05 was set for all tests.ResultsPatients with MCI significantly increased pelvic anterior tilt, hip abduction, and knee adduction in the swing limb during leading-limb crossing when compared to controls (p < 0.05). During trailing-limb crossing, the MCI group showed significantly decreased pelvic posterior tilt, as well as ankle dorsiflexion in the trailing swing limb (p < 0.05).ConclusionPatients with MCI adopt altered kinematic strategies for successful obstacle-crossing. The patients were able to maintain normal leading and trailing toe-obstacle clearances for all tested obstacle heights with a specific kinematic strategy, namely increased pelvic anterior tilt, swing hip abduction, and knee adduction during leading-limb crossing, and decreased pelvic posterior tilt and swing ankle dorsiflexion during trailing-limb crossing. The current results suggest that regular monitoring of obstacle-crossing kinematics for reduced toe-obstacle clearance or any signs of changes in crossing strategy may be helpful for early detection of compromised obstacle-crossing ability in patients with single-domain amnestic MCI. Further studies using a motor/cognitive dual-task approach on the kinematic strategies adopted by multiple-domain MCI will be needed for a complete picture of the functional adaptations in such a patient group

Towards General-Purpose Text-Instruction-Guided Voice Conversion

This paper introduces a novel voice conversion (VC) model, guided by text instructions such as "articulate slowly with a deep tone" or "speak in a cheerful boyish voice". Unlike traditional methods that rely on reference utterances to determine the attributes of the converted speech, our model adds versatility and specificity to voice conversion. The proposed VC model is a neural codec language model which processes a sequence of discrete codes, resulting in the code sequence of converted speech. It utilizes text instructions as style prompts to modify the prosody and emotional information of the given speech. In contrast to previous approaches, which often rely on employing separate encoders like prosody and content encoders to handle different aspects of the source speech, our model handles various information of speech in an end-to-end manner. Experiments have demonstrated the impressive capabilities of our model in comprehending instructions and delivering reasonable results.Comment: Accepted to ASRU 202

Transformer-based Learned Image Compression for Joint Decoding and Denoising

This work introduces a Transformer-based image compression system. It has the flexibility to switch between the standard image reconstruction and the denoising reconstruction from a single compressed bitstream. Instead of training separate decoders for these tasks, we incorporate two add-on modules to adapt a pre-trained image decoder from performing the standard image reconstruction to joint decoding and denoising. Our scheme adopts a two-pronged approach. It features a latent refinement module to refine the latent representation of a noisy input image for reconstructing a noise-free image. Additionally, it incorporates an instance-specific prompt generator that adapts the decoding process to improve on the latent refinement. Experimental results show that our method achieves a similar level of denoising quality to training a separate decoder for joint decoding and denoising at the expense of only a modest increase in the decoder's model size and computational complexity.Comment: Accepted to PCS 202

Strong and broadly tunable plasmon resonances in thick films of aligned carbon nanotubes

Low-dimensional plasmonic materials can function as high quality terahertz and infrared antennas at deep subwavelength scales. Despite these antennas' strong coupling to electromagnetic fields, there is a pressing need to further strengthen their absorption. We address this problem by fabricating thick films of aligned, uniformly sized carbon nanotubes and showing that their plasmon resonances are strong, narrow, and broadly tunable. With thicknesses ranging from 25 to 250 nm, our films exhibit peak attenuation reaching 70%, quality factors reaching 9, and electrostatically tunable peak frequencies by a factor of 2.3x. Excellent nanotube alignment leads to the attenuation being 99% linearly polarized along the nanotube axis. Increasing the film thickness blueshifts the plasmon resonators down to peak wavelengths as low as 1.4 micrometers, promoting them to a new near-infrared regime in which they can both overlap the S11 nanotube exciton energy and access the technologically important infrared telecom band.Comment: 19 pages, 5 figures, main text followed by supporting informatio

A Giant Protocluster of Galaxies at Redshift 5.7

Galaxy clusters trace the largest structures of the Universe and provide ideal laboratories for studying galaxy evolution and cosmology. Clusters with extended X-ray emission have been discovered at redshifts up to z ~ 2.5. Meanwhile, there has been growing interest in hunting for protoclusters, the progenitors of clusters, at higher redshifts. It is, however, very challenging to find the largest protoclusters at early times when they start to assemble. Here we report a giant protocluster of galaxies at redshift z = 5.7, when the Universe was only one billion years old. This protocluster occupies a volume of about 35x35x35 cubic co-moving megaparsecs. It is embedded in an even larger overdense region with at least 41 spectroscopically confirmed, luminous Lyman-alpha emitting galaxies (Lyman-alpha Emitters, or LAEs), including several previously reported LAEs. Its LAE density is 6.6 times the average density at z ~ 5.7. It is the only one of its kind in an LAE survey in four square degrees on the sky. Such a large structure is also rarely seen in current cosmological simulations. This protocluster will collapse into a galaxy cluster with a mass of (3.6+/-0.9) x 10^{15} solar masses, comparable to those of the most massive clusters or protoclusters known to date.Comment: Published in Nature Astronomy on Oct 15, 2018 (DOI: 10.1038/s41550-018-0587-9