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IEEE JSTSP Article

Clinical literature provides convincing evidence that language deficits in Alzheimer's disease (AD) allow for distinguishing patients with dementia from healthy subjects. Currently, computational approaches have widely investigated lexicosemantic aspects of discourse production, while pragmatic aspects like cohesion and coherence, are still mostly unexplored.

Obstructive sleep apnea (OSA) is a sleep disorder in which pharyngeal collapse during sleep causes complete (apnea) or partial (hypopnea) airway obstruction. OSA is common and can have severe implications, but often remains undiagnosed. The most widely used objective measure of OSA severity is the number of obstructive events per hour of sleep, known as the apnea-hypopnea index (AHI).

Obstructive Sleep Apnea (OSA) is a sleep breathing disorder affecting at least 3–7% of male adults and 2–5% of female adults between 30 and 70 years. It causes recurrent partial or total obstruction episodes at the level of the pharynx which causes cessation of breath during sleep. 

Approximately one-fifth of the world's population suffer or have suffered from voice and speech production disorders due to diseases or some other dysfunction. Thus, there is a clear need for objective ways to evaluate the quality of voice and speech as well as its link to vocal fold activity, to evaluate the complex interaction between the larynx and voluntary movements of the articulators (i.e., lips, teeth, tongue, velum, jaw, etc), or to evaluate disfluencies at the language level.

Panoramic videos are becoming more and more easily obtained for common users. Although these videos have 360 field of view, they are usually displayed with perspective views, which needs the saliency informations for viewing angle selection. In this paper, we propose a saliency prediction network for 360 videos. Our network takes video frames and optical flows in cube map format as input, thus it does not suffer from image distorations of panoramic frames. 

Panoramic videos are becoming more and more easily obtained for common users. Although these videos have 360 field of view, they are usually displayed with perspective views, which needs the saliency informations for viewing angle selection. In this paper, we propose a saliency prediction network for 360 videos. Our network takes video frames and optical flows in cube map format as input, thus it does not suffer from image distorations of panoramic frames. 

Nowadays, 360° video/image has been increasingly popular and drawn great attention. The spherical viewing range of 360° video/image accounts for huge data, which pose the challenges to 360° video/image processing in solving the bottleneck of storage, transmission, etc. Accordingly, the recent years have witnessed the explosive emergence of works on 360° video/image processing.

Recent years have witnessed the rapid development of virtual reality (VR). Above 90% of VR content is in the form of 360° video, also called omnidirectional video or panoramic video. Generally speaking, 360° video offers immersive and interactive viewing experience, as the viewers are able to freely move their heads in the range of 360° × 180° to access different viewports.

This paper proposes nonorthogonal sharing of available resources between latency-critical and latency-tolerant communication for fulfilling tight requirements of ultrareliable low-latency communication (URLLC) as well as avoiding inefficient spectrum utilization of grant-based (GB) access for sporadic URLLC traffic.

For the Internet of Things (IoT), there might be a large number of devices to be connected to the Internet through wireless technologies. In general, IoT devices would have various constraints due to limited processing capability, memory, energy source, and so on, and it is desirable to employ efficient wireless transmission schemes, especially for uplink transmissions.

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