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The Linearized Auto-Localization (LAL) algorithm estimates the position of beacon nodes in Local Positioning Systems (LPSs) which are based on the transmission of ultrasonic signals and proposed for indoor positioning of mobile robots. In this paper we propose an improved auto-localization algorithm based on weighted least squares (WLS). The improved algorithm depends on the different error estimations which caused by the different relative positions of the beacons and the measurements nodes. Simulation results show that our WLS-based Linearized Auto-Localization Algorithm can provide improved accuracy in both distance estimation and position estimation.

For many applications in wireless sensor network (WSN) node positioning by unmanned aerial vehicle (UAV) need a path planning algorithm optimizing positioning error. In this article, we present an algorithm combining Voronoi diagram and geometric dilution of precision (GDOP) to make track points, considering both the efficiency of flying and accuracy of positioning. Then, we connect all the track points using genetic algorithm and we get the minimized flight path.

In both industrial field and office building, the accurate statistic of people who enter or leave the elevator has important practical meaning in security and analysis of passenger flow. We present a binocular vision system to count the people pass by. The camera is set in the height of 2.45 meters to monitor the people overhead in order to reduce the overlap of pedestrians. The object segment and tracking method proposed in this paper show good result with the disparity map gained by the dual-camera. Dynamic promotion of threshold is used in the object segmentation. Feature matching is used to track the moving objects. The system can get the number of people accurate and timely. Experiment results show that our system has good performance under relatively complex circumstance.

In this paper, we propose a wireless sensor network self-location improved algorithm based on the Unmanned Aerial Vehicle (UAV). The path and the signal emission mechanism of the UAV are rational planning. The k-3 covering method is used to programming the working point and the path of UAV. We also set up a wireless sensor network localization experiment platform based on the UAV and then we carry on several experimentations and curve fitting on the surface-to-surface and surface-to-air distance curve. Experimental results show that the self-location system built based on the theories this paper proposed has an air-to-surface actual distance error of 4.42m and the algorithms positioning error is 3.67m which has very high work efficiency.

This paper describes an efficient Bayesian framework for localization based on Ultra-wide Bandwidth (UWB) system. Approximate grid-based method based on the Hidden Markov Model (HMM) is an effective method to estimate the position of the Moving Terminal (MT) with the mixed line-of-sight/non-line-of-sight (LOS/NLOS) situation. This article proposes an algorithm by modifying the Position Transition Probability (PTP) according to the practical dynamic model and uses the information fusion effectively. We compare the Maximum Likelihood (ML) estimation with Detection/Tracking Algorithm (D/TA) estimation and its improved algorithm by simulation, in which the localization to an identical trajectory has been tested. The results of the analysis show that the proposed method has better accuracy and stability.

In wireless sensor networks, NLOS propagation often enlarges the errors of position estimates when time-of-arrival (TOA) measurements are used. To mitigate the effects caused by NLOS propagation, herein, an EKF-based robust non-parametric approach is proposed. In this paper, we utilize the variable kernel method to obtain an approximate noise density function, which is inexpensively computational and then used to improve the mobile positioning accuracy. Note that the standard EKF often works well when NLOS error is adequately low. This property could also be used to improve the accuracy of mobile positioning. In the proposed algorithm, a hard decision is employed to choose the rational position estimate which may come from the non-parametric approach or the standard EKF. Numerical simulations show a significant improvement over the standard EKF.

This study is concerned with dynamic output feedback (DOF) H ∞ control for continuous-time networked control systems (NCSs). The packet dropouts and network-induced delays in the sensor-to-controller channel, and network-induced delays in the controller-to-actuator channel are taken into account. A linear estimation-based method is proposed to estimate the measurement output, and the non-uniform distribution characteristic of the measurement output arrival instant is taken into full consideration to establish a new model for the considered NCSs. Based on the newly established model and a new Lyapunov functional, the problem of DOF controller design is studied. A numerical example is given to illustrate the effectiveness of the proposed DOF controller design.

This study is concerned with the problem of dual communication channels sharing-based network-induced delay and packet dropout compensation for continuous-time networked control systems (NCSs). By introducing the dual communication channels sharing-based compensation method, a new closed-loop model for continuous-time NCSs, which can receive more than one control input during a control input updating time interval, is established. Based on the newly established model and a new Lyapunov functional, an effective controller design criterion is derived to optimise the H ∞ performance of the considered systems. Even for NCSs without considering network-induced delay and packet dropout compensation, the newly derived controller design method is also applicable. Two numerical examples are given to illustrate the merits and effectiveness of the proposed compensation method.

This study introduces a multivariable adaptive decoupling control strategy using multiple models and neural networks for a class of discrete-time nonlinear dynamic systems. The adaptive decoupling switching control strategy based on generalised predictive control is composed of a linear adaptive decoupling controller, a neural-network-based nonlinear adaptive decoupling controller and a switching mechanism. First, the linear adaptive decoupling controller is designed to ensure the boundedness of the input and output signals of the closed-loop system. Second, the neural-network-based nonlinear adaptive decoupling controller is developed to improve the transient performance of the closed-loop system. Third, the stability and convergence of the closed-loop system are achieved simultaneously by using multiple-models-based switching mechanism. Simulation studies are provided for a numerical example and a forced-circulation evaporation process of an alumina production system so as to demonstrate the effectiveness of the proposed method.

Due to uncertainties in target motion and randomness of deployed sensor nodes, the problem of imbalance of energy consumption arises from sensor scheduling. This paper presents an energy-efficient adaptive sensor scheduling for a target monitoring algorithm in a local monitoring region of wireless sensor networks. Owing to excessive scheduling of an individual node, one node with a high value generated by a decision function is preferentially selected as a tasking node to balance the local energy consumption of a dynamic clustering, and the node with the highest value is chosen as the cluster head. Others with lower ones are in reserve. In addition, an optimization problem is derived to satisfy the problem of sensor scheduling subject to the joint detection probability for tasking sensors. Particles of the target in particle filter algorithm are resampled for a higher tracking accuracy. Simulation results show this algorithm can improve the required tracking accuracy, and nodes are efficiently scheduled. Hence, there is a 41.67% savings in energy consumption.

Background: Dysnatremia, which is associated with increased mortality in general intensive care units (ICU), has not been thoroughly studied in neurologic ICU (NICU). Methods: Prevalence of dysnatremia was retrospectively assessed. The multivariable binary logistic regression model was used to determine the influence of dysnatremia on mortality. Results: Of 519 patients, 106 (20.4%) were admitted with hyponatremia and 177 (34.10%) with hypernatremia. Hypernatremia was detected in 69 (13.29%) patients on admission to NICU and in 108 patients (20.81%) during the ICU stay. However, the incidence of dysnatremia did not differ across the neurological categories (p = 0.4690). ICU stay in patients with acquired hypernatremia (22.3 w 25.35 days) was longer than those with admission hypernatremia (13.5 w 12.8 days) or with consistent normonatremia (16.16 w 20.06 days). The other indicators such as Acute Physiology and Chronic Health Evaluation II, Glasgow Coma Scale score, urinary catheterization, and incidence of pneumonia were also associated with the serum sodium concentrations. Hypernatremia both on admission and acquired in NICU could significantly differentiate between survivors and nonsurvivors (p = 0.002 and Conclusions: Dysnatremia is more common in NICU, whereas only acquired-hypernatremia was independently associated with outcome. i 2014 S. Karger AG, Basel

Little quantitative evidence was available regarding the development of NICUs in China. The purpose of this survey was to evaluate the current situation of neurointensive care units (NICUs) across China.The directors of NICUs from 100 tertiary care hospitals across China were contacted and asked to complete a closed response questionnaire regarding their NICUs. Basic information, equipment, and technology information available in the units, as well as staffing information were investigated.Seventy-six questionnaires were returned (a 68% response rate). Of 76 NICUs, 43 units constituted the majority. The number of each NICU bed varied from 4 to 45, occupying 2%-30% of the total department beds. Over 70% of NICUs were equipped with many emergency treatment equipments as well as physiological and biochemical monitoring equipments, while 34%-70% of NICUs still lacked some kinds of equipments such as defibrillators. Some specialist equipments were still partially lacking in 62%-95% of NICUs. A vast majority of the NICUs were equipped with neurocritical care directors, full-time attending physicians, and head nurses, but full-time NICU residents and neurocritical care nurses were still lacking in nearly half (53%) and one-third (33%-37%) of NICUs, respectively. In 76 NICUs, full-time neurointensivists and nurses added up to 359 and 852, respectively. In addition, 78%-97% of all the surveyed NICUs were severely short of non-neurological professional staffs.In China, neurocritical care has developed rapidly, but there is still a shortage of well-equipped and well-staffed NICUs across the nation currently.

Severity scoring systems are useful tools for measuring the severity of the disease and its outcome. This pilot study was to verify and compare the prognostic performance of the Simplified Acute Physiology Score II (SAPS II) and Glasgow Coma Scale (GCS) in neuro-intensive care unit (N-ICU) patients.A total of 1684 patients consecutively admitted to the N-ICU at Xuanwu Hospital between January 1, 2005 and December 31, 2011 were enrolled in this study. The data-base included admission data, at 24-, 48-, and 72-hour SAPS II and GCS. Repeated measure data analysis of variance, Logistic regression analysis, the Hosmer-Lemeshow goodness-of-fit statistic, and the area under the receiver operating characteristic were used to evaluate the performance.There was a significant difference between the SAPS II or GCS score at four time points (F = 16.110, P = 0.000 or F = 8.108, P = 0.000). The SAPS II scores or GCS score at four time points interacted with the outcomes with significant difference (F = 116.771, P = 0.000 or F = 65.316, P = 0.000). Calibration of the SAPS II or GCS score at each time point on all patients was good. The percentage of a risk estimate prediction corresponding to observed mortality was also good. The 72-hour score have the greatest consistency. Discriminations of the SAPS II or GCS score at each time were all satisfactory. The 72-hour score had the greatest discriminative power. The cut-off value was 33 (sensitivity of 85.2% and specificity of 74.3%) and 6 (sensitivity of 70.6% and specificity of 65.0%). The SAPS II at each time point on all patients showed better calibration, consistency and discrimination than GCS. The binary Logistic regression analysis identified physiological variables, GCS, age, and disease category as significant independent risk factors of death. After the two variables including underlying disease and type of admission were excluded, we built the simplified SAPS II model. A correlation was suggested between the simplified SAPS II score at each time point and outcome, regardless of the diagnosis.The GCS scoring system tends to be a little weaker in the predictive power than the SAPS II scoring system in this Chinese cohort of N-ICU patients. The advantage of SAPS II scoring system still exists that it dose not need to take into account the diagnosis or diseases categories, even in the special N-ICU. The simplified SAPS II scoring system is considered a new idea for the estimation of effectiveness.

Decompressive hemicraniectomy (DHC) has proven efficacious for the treatment of malignant middle cerebral artery infarction (mMCAI) only in patients less than 60 years. This study aimed to assess the effectiveness of DHC in patients up to 80.This is a prospective, randomized, controlled trail comparing the outcomes with or without DHC in patients aged 18-80 with mMCAI (ChiCTR-TRC-11001757). The primary outcome measure was the modified Rankin Scale (mRS) scores at 6 months. The secondary outcome measures included the 6- and 12-month mortality and the mRS scores after 1 year. The prognosis of patients was evaluated independently by two blinded investigators. In addition, subgroup analyses were done for those above 60 years of age. All analyses were by intention-to-treat.A significant reduction in the poor outcome (mRS 4) following DHC was reached after 36 patients had completed the follow-up period of 6 months. The trial was then terminated when 47 participants (24 in the surgical group vs. 23 in the medical group) had been recruited. The final analysis, based on the outcome data of the 47 patients, showed that DHC significantly reduced mortality at 6 and 12 months (12.5 vs. 60.9 %, P = 0.001 and 16.7 vs. 69.6 %, P 0.001, respectively), and significantly fewer patients had a mRS score 4 after surgery (33.3 vs. 82.6 %, P = 0.001 and 25.0 vs. 87.0 %, P 0.001, respectively). Similar results were present in the subgroup analyses of elderly participantsFor patients up to 80 years who suffered mMCAI, DHC within 48 h of stroke onset not only is a life-saving treatment, but also increases the possibility of surviving without severe disability (mRS = 5).

In this paper, a particle swarm optimization approach for the energy-based acoustic source localization of a wireless sensor network is presented. For this work, it is assumed that there is one acoustic source with unknown localizations which transmit acoustic signals that can be received by the nodes. The only available information to the system is the received signal energy which is not very accurate in general because of the attenuation in the process of propagation. To obtain better estimated localization of the acoustic source, maximum likelihood method is applied to transform it into extremal function, the particle swarm optimization scheme searches the optimal solution. Experimental results show that the proposed approach has the advantages of higher precision and lower computational complexity.

One rare case of meningoencephalitis caused by Angiostrongylus cantonensis is reported. The development of the disease, clinical features, accessory examinations, and the subsequent treatment were described and analyzed briefly.

To confirm an unknown peak (Pu) in proton magnetic resonance spectra ((1)H-MRS) in cats with permanent focal cerebral ischemia, and surmise its potential value of application.After focal cerebral ischemia, diffusion-weighted imaging (DWI) was used to identify regions of ischemia for (1)H-MRS voxel localization with the assistance of stereotaxic atlas of cat brain. (1)H-MRS was used to monitor the progression of focal cerebral ischemia in 6 cats over a period of 7 d following middle cerebral artery occlusion (MCAO). The changes in lactate, N-acetyl-aspartate (NAA), choline, creatine, and Pu were observed.In the involved regions, lactate was elevated almost immediately after the onset of cerebral ischemia, and NAA declined within several hours of acute infarction. The Pu at 1.2 ppm was persistently detected in the affected cerebral areas 2 to 7 d after MCAO.Pu has a close relationship with cerebral ischemia necrosis and may be a intrinsic production of the necrotic tissue, which can be utilized as a specific marker and therefore has important clinical diagnostic value.