The Construction of a Clinical Decision Support System Based on Knowledge Base

Part 7: e-Health, the New Frontier of Service Science InnovationInternational audienceBased on a review of domestic and foreign research, application status, classification, composition, and the main problem of a clinical decision support system, this paper proposed a CDSS mode based on a knowledge base. On KB-CDSS mode, this paper discussed the architecture, principle, process, construction of the knowledge base, system design, and application value, then introduced the application WanFang Data Clinical Diagnosis and Treatment Knowledge Base

IDMA-Based MAC Protocol for Satellite Networks with Consideration on Channel Quality

In order to overcome the shortcomings of existing medium access control (MAC) protocols based on TDMA or CDMA in satellite networks, interleave division multiple access (IDMA) technique is introduced into satellite communication networks. Therefore, a novel wide-band IDMA MAC protocol based on channel quality is proposed in this paper, consisting of a dynamic power allocation algorithm, a rate adaptation algorithm, and a call admission control (CAC) scheme. Firstly, the power allocation algorithm combining the technique of IDMA SINR-evolution and channel quality prediction is developed to guarantee high power efficiency even in terrible channel conditions. Secondly, the effective rate adaptation algorithm, based on accurate channel information per timeslot and by the means of rate degradation, can be realized. What is more, based on channel quality prediction, the CAC scheme, combining the new power allocation algorithm, rate scheduling, and buffering strategies together, is proposed for the emerging IDMA systems, which can support a variety of traffic types, and offering quality of service (QoS) requirements corresponding to different priority levels. Simulation results show that the new wide-band IDMA MAC protocol can make accurate estimation of available resource considering the effect of multiuser detection (MUD) and QoS requirements of multimedia traffic, leading to low outage probability as well as high overall system throughput

An Energy-Efficient Routing Algorithm in Three-Dimensional Underwater Sensor Networks Based on Compressed Sensing

Compressed sensing (CS) has become a powerful tool to process data that is correlated in underwater sensor networks (USNs). Based on CS, certain signals can be recovered from a relatively small number of random linear projections. Since the battery-driven sensor nodes work in adverse environments, energy-efficient routing well-matched with CS is needed to realize data gathering in USNs. In this paper, a clustering, uneven-layered, and multi-hop routing based on CS (CS-CULM) is proposed. The inter-cluster transmission and fusion are fulfilled by an improved LEACH protocol, then the uneven-layered, multi-hop routing is adopted to forward the packets fused to sink node for data reconstruction. Simulation results show that CS-CULM can achieve better performances in energy saving and data reconstruction

Liuzijue Qigong vs traditional breathing training for patients with post-stroke dysarthria complicated with abnormal respiratory control: study protocol of a single center randomized controlled trial

Abstract Background Stroke-induced dysarthria is caused by muscle weakness, sacral or muscular dystonia, and incoordination of the articulatory organ formed by organic lesions caused by cerebral vascular obstruction or sudden bursting of blood vessels in the brain, which may cause abnormal breathing patterns, pronunciation, resonance, rhythm, and unclear articulation. The Six Character Formula, or Liuzijue qigong (LQG), is an essential part of Chinese traditional exercises and focuses on breathing–speech synchronization. The purpose of the present study was to compare the effects of LQG with traditional breathing training (combined with basic articulation training in both groups) in patients with post-stroke dysarthria. Methods/design The proposed study will be a single-center randomized controlled trial. A total of 100 patients, with a modified Frenchay Dysarthria Assessment (FDA) dysarthria assessment score < 27 and with a FDA speech breathing level ≥ b will be randomly divided into study (LQG, n = 50) and control (conventional breathing training, n = 50) groups. Basic articulation training will be conducted once a day, five times a week for 3 weeks. Data collection will be conducted at baseline, 1 week, and 2 weeks post-treatment initiation and after completion of the treatment (3 weeks). Comprehensive analyses will be conducted to measure and compare any differences in speech breathing dysfunction levels, comprehensive evaluation of dysarthria, maximum phonation time (MPT), maximal counting ability, signal-noise (S/Z) ratio, and loudness scales between the study and control groups. Discussion This trial will provide evidence about the effectiveness of LQG for improvement of speech breathing function and speech ability in patients with post-stroke dysarthria complicated with abnormal breathing. Trial registration Chinese Clinical Trial Registry, ChiCTR-INR-16010215. Registered 21 December 2016

Multi-User Detection for Sporadic IDMA Transmission Based on Compressed Sensing

The Internet of Things (IoT) is placing new demands on existing communication systems. The limited orthogonal resources do not meet the demands of massive connectivity of future IoT systems that require efficient multiple access. Interleave-division multiple access (IDMA) is a promising method of improving spectral efficiency and supporting massive connectivity for IoT networks. In a given time, not all sensors signal information to an aggregation node, but each node transmits a short frame on occasion, e.g., time-controlled or event-driven. The sporadic nature of the uplink transmission, low data rates, and massive connectivity in IoT scenarios necessitates minimal control overhead communication schemes. Therefore, sensor activity and data detection should be implemented on the receiver side. However, the current chip-by-chip (CBC) iterative multi-user detection (MUD) assumes that sensor activity is precisely known at the receiver. In this paper, we propose three schemes to solve the MUD problem in a sporadic IDMA uplink transmission system. Firstly, inspired by the observation of sensor sparsity, we incorporate compressed sensing (CS) to MUD in order to jointly perform activity and data detection. Secondly, as CS detection could provide reliable activity detection, we combine CS and CBC and propose a CS-CBC detector. In addition, a CBC-based MUD named CBC-AD is proposed to provide a comparable baseline scheme

A new joint channel equalization and estimation algorithm for underwater acoustic channels

Abstract Underwater acoustic channel (UAC) is one of the most challenging communication channels in the world, owing to its complex multi-path and absorption as well as variable ambient noise. Although adaptive equalization could effectively eliminate the inter-symbol interference (ISI) with the help of training sequences, the convergence rate of equalization in sparse UAC decreased remarkably. Besides, channel estimation algorithms could roughly figure out channel impulse response and other channel parameters through several specific mathematical criterions. In this paper, a typical channel estimation method, least square (LS) algorithm, is applied in adaptive equalization to obtain the initial tap weights of least mean square (LMS) algorithm. Simulation results show that the proposed method significantly enhances the convergence rate of the LMS algorithm

ANFIS-EKF-Based Single-Beacon Localization Algorithm for AUV

Singe-beacon localization technology can help Autonomous Underwater Vehicles (AUVs) to obtain precise positions by deploying only one beacon. It is considered as a promising way, benefiting from saving much time and labor compared with traditional Long-Baseline Localization (LBL). A typical single-beacon localization scheme contains two essential questions: the initial observability problem and long-endurance trajectory tracking problem. Aiming at these core problems, a comprehensive solution for single-beacon localization is described in this paper. An multi-hypothesis initial position discriminant method is proposed firstly, which helps to achieve accurate initial location based on observability analysis. Then, an Adaptive Network Fuzzy Inference System (ANFIS)-improved Extended Kalman Filter (EKF) method is proposed, in which single-beacon measuring information is fused with off-the-shelf sensors, including DVL, Compass, etc. ANFIS-EKF can help to improve trajectory tracking precisions by restraining the heavy loss of linearization in conventional EKF. Both simulation and field tests are conducted to verify the performance of the proposed algorithms