Mohammed F. A. Ahmed

Collaborative beamforming for wireless sensor networks with Gaussian distributed sensor nodes

Collaborative beamforming has been recently introduced in the context of wireless sensor networks (WSNs) to increase the transmission range of individual sensor nodes. The challenge in using collaborative beamforming in WSNs is the uncertainty regarding the sensor node locations. However, the actual sensor node spatial distribution can be modeled by a properly selected probability density function (pdf). In this paper, we model the spatial distribution of sensor nodes in a cluster of WSN using Gaussian pdf. Gaussian pdf is more suitable in many WSN applications than, for example, uniform pdf which is commonly used for flat ad hoc networks. The average beampattern and its characteristics, the distribution of the beampattern level in the sidelobe region, and the distribution of the maximum sidelobe peak are derived using the theory of random arrays. We show that both the uniform and Gaussian sensor node deployments behave qualitatively in a similar way with respect to the beamwidths and sidelobe levels, while the Gaussian deployment gives wider mainlobe and has lower chance of large sidelobes.

Dopamine Levels After Repetitive Transcranial Magnetic Stimulation of Motor Cortex in Patients with Parkinson's Disease: Preliminary Results

Background: Repeated sessions of repetitive transcranial magnetic stimulation (rTMS) over motor cortex have been reported to produce significant improvement of motor performance in patients with Parkinsons disease (PD). In addition, it is known that a single session of rTMS over motor cortex transiently increases DA in striatum. Here, we test whether repeated sessions of rTMS increase serum dopamine in PD patients and whether this correlates with changes in clinical rating scales. Material and Methods: Twenty untreated PD patients with moderate to severe symptoms (Hoehn & Yahr stage III–V 1967) were assessed on the Unified Parkinsons Disease Rating Scale (UPDRS), and with an enzyme immunoassay for quantitative determination of plasma dopamine before and after six daily sessions of 25 Hz rTMS with 3,000 stimuli over the right and left hand and leg motor cortex. Results: There was significant improvement in UPDRS compared with the baseline. Serum dopamine level also was significantly elevated ever the same interval. There was a significant correlation between UPDRS and serum dopamine level before and after treatment. Conclusion: Improved motor performance in PD after repeated sessions of rTMS may be related to an elevation of serum dopamine concentration.

Sidelobe Control in Collaborative Beamforming via Node Selection

Collaborative beamforming (CB) is a power efficient method for data communications in wireless sensor networks (WSNs) which aims at increasing the transmission range in the network by radiating the power from a cluster of sensor nodes in the directions of the intended base stations or access points (BSs/APs). The CB average beampattern shows a deterministic behavior and the mainlobe of the CB sample beampattern is independent of the particular node locations. However, the CB for a cluster of a finite number of collaborative nodes results in a sample beampattern with sidelobes that severely depend on the particular node locations. High level sidelobes can cause unacceptable interference when they occur at directions of unintended BSs/APs. Therefore, sidelobe control in CB has a potential to decrease the interference at unintended BSs/APs and increase the network transmission rate by enabling simultaneous multilink CB. Traditional sidelobe control techniques are proposed for centralized antenna arrays and are not suitable for WSNs. In this paper, we show that scalable and low-complexity sidelobe control techniques suitable for CB in WSNs can be developed based on a node selection technique which makes use of the randomness of the node locations. A node selection algorithm with low-rate feedback is developed to search over different node combinations. The performance of the proposed algorithm is analyzed in terms of the average number of search trials required for selecting the collaborative nodes, the resulting interference, and the corresponding transmission rate improvements. Our simulation results show that the interference can be significantly reduced and the transmission rate can be significantly increased when node selection is implemented with CB. The simulation results also show close agreement with our theoretical results.

Performance characteristics of collaborative beamforming for wireless sensor networks with Gaussian distributed sensor nodes

Collaborative beamforming has been recently introduced in the context of wireless sensor networks (WSNs) to increase the transmission range of individual sensor nodes. In this paper, it is proposed to model the spatial distribution of sensor nodes in a cluster using Gaussian probability density function (pdf). Gaussian pdf is more appropriate for many WSN applications than the previously considered uniform pdf which is more suitable when sensor nodes are deployed one at a time. The average beampattern and its characteristics, the distribution function of the beampattern level in the sidelobe region, and the upper bound on the outage probability of sidelobes are derived using the theory of random arrays.

Node selection for sidelobe control in collaborative beamforming for wireless sensor networks

Collaborative beamforming (CB) is a new technique for energy-efficient long-distance communications in wireless sensor networks (WSNs). It is based on the fact that the distributed nodes of a WSN can synchronize their carrier phases to form a beampattern with a stable mainlobe (independent on the node locations). However, sidelobes of such beampattern are found to be severely dependent on the particular node locations. High level sidelobes can cause unacceptable interference to unintended base stations and access points (BSs/APs). Therefore, controlling the sidelobes of CB has the potential to increase the network capacity and wireless channel availability. In this paper, we propose node selection for CB sidelobe control. A selection algorithm with low implementation complexity is developed to search over different node combinations. It aims at minimizing the interference at unintended BSs/APs. The performance of the proposed algorithm is analyzed in terms of the average number of trials and the achieved interference suppression. Simulation results match the analytical approximations and show the effectiveness of node selection for limiting the interference.

Incidence and outcomes of acute kidney injury following orthotopic lung transplantation: a population-based cohort study

BACKGROUND Acute kidney injury (AKI) is a serious complication following lung transplantation (LTx). We aimed to describe the incidence and outcomes associated with AKI following LTx. METHODS A retrospective population-based cohort study of all adult recipients of LTx at the University of Alberta between 1990 and 2011. The primary outcome was AKI, defined and classified according to the Kidney Disease: Improving Global Outcomes (KDIGO) criteria, in the first 7 post-operative days. Secondary outcomes included risk factors, utilization of renal replacement therapy (RRT), occurrence of post-operative complications, mortality and kidney recovery. RESULTS Of 445 LTx recipients included, AKI occurred in 306 (68.8%), with severity classified as Stage I in 38.9% (n = 173), Stage II in 17.5% (n = 78) and Stage III in 12.4% (n = 55). RRT was received by 36 (8.1%). Factors associated with AKI included longer duration of cardiopulmonary bypass [per minute, odds ratio (OR) 1.003; 95% confidence interval (CI), 1.001-1.006; P = 0.02], and mechanical ventilation [per hour (log-transformed), OR 5.30; 95% CI, 3.04-9.24; P < 0.001], and use of cyclosporine (OR 2.03; 95% CI, 1.13-3.64; P = 0.02). In-hospital and 1-year mortality were significantly higher in those with AKI compared with no AKI (7.2 versus 0%; adjusted P = 0.001; 14.4 versus 5.0%; adjusted P = 0.02, respectively). At 3 months, those with AKI had greater sustained loss of kidney function compared with no AKI [estimated glomerular filtration rate, mean (SD): 68.9 (25.7) versus 75.3 (22.1) mL/min/1.73 m(2), P = 0.01]. CONCLUSIONS By the KDIGO definition, AKI occurred in two-thirds of patients following LTx. AKI portended greater risk of death and loss of kidney function.

Beampattern random behavior in wireless sensor networks with Gaussian distributed sensor nodes

Collaborative beamforming (CB) has been introduced in wireless sensor networks (WSNs) to increase the transmission range of sensor nodes. CB improves the power efficiency of the transmission. However, the CB beampattern is random in the sidelobe region. Therefore, it is important to characterize the power level in the sidelobe region to predict the interference to neighboring sensor node clusters. In this paper, we assume that sensor nodes in a cluster of WSN are Gaussian distributed and study the random behavior of the beampattern. To characterize the beampattern in the sidelobe region, we first model the array factor as a complex random variable and find the corresponding mean and variance. The distribution function of beampattern level and the outage probability of sidelobes is derived and compared with the corresponding characteristics resulting from uniform distributed sensor nodes.

Association between transient acute kidney injury and morbidity and mortality after lung transplantation: a retrospective cohort study.

PURPOSE Acute kidney injury (AKI) is a common occurrence after lung transplantation (LTx). Whether transient AKI or early recovery is associated with improved outcome is uncertain. Our aim was to describe the incidence, factors, and outcomes associated with transient AKI after LTx. MATERIALS AND METHODS We performed a retrospective cohort study of all adult recipients of LTx at the University of Alberta between 1990 and 2011. Our primary outcome transient AKI was defined as return of serum creatinine below Kidney Disease-Improving Global Outcome AKI stage I within 7days after LTx. Secondary outcomes included occurrence of postoperative complications, mortality, and long-term kidney function. RESULTS Of 445 LTx patients enrolled, AKI occurred in 306 (68.8%) within the first week after LTx. Of these, transient AKI (or early recovery) occurred in 157 (51.3%). Transient AKI was associated with fewer complications including tracheostomy (17.2% vs 38.3%; P<.001), reintubation (16.4% vs 41.9%; P<.001), decreased duration of mechanical ventilation (median [interquartile range], 69 [41-142] vs 189 [63-403] hours; P<.001), and lower rates of chronic kidney disease at 3 months (28.5% vs 51.1%, P<.001) and 1 year (49.6% vs 66.7%, P=.01) compared with persistent AKI. Factors independently associated with persistent AKI were higher body mass index (per unit; odds ratio [OR], 0.91; 95% confidence interval, 0.85-0.98; P=.01), cyclosporine use (OR, 0.29; 0.12-0.67; P=.01), longer duration of mechanical ventilation (per hour [log transformed]; OR, 0.42; 0.21-0.81; P=.01), and AKI stages II to III (OR, 0.16; 0.08-0.29; P<.001). Persistent AKI was associated with higher adjusted hazard of death (hazard ratio, 1.77 [1.08-2.93]; P=.02) when compared with transient AKI (1.44 [0.93-2.19], P=.09) and no AKI (reference category), respectively. CONCLUSIONS Transient AKI after LTx is associated with fewer complications and improved survival. Among survivors, persistent AKI portends an increased risk for long-term chronic kidney disease.

Distributed Estimation Based on Observations Prediction in Wireless Sensor Networks

We consider wireless sensor networks (WSNs) used for distributed estimation of unknown parameters. Due to the limited bandwidth, sensor nodes quantize their noisy observations before transmission to a fusion center (FC) for the estimation process. In this letter, the correlation between observations is exploited to reduce the mean-square error (MSE) of the distributed estimation. Specifically, sensor nodes generate local predictions of their observations and then transmit the quantized prediction errors (innovations) to the FC rather than the quantized observations. The analytic and numerical results show that transmitting the innovations rather than the observations mitigates the effect of quantization noise and hence reduces the MSE.

The Effect of Correlated Observations on the Performance of Distributed Estimation

Estimating unknown signal in Wireless Sensor Networks (WSNs) requires sensor nodes to transmit their observations of the signal over a multiple access channel to a Fusion Center (FC). The FC uses the received observations, which is corrupted by observation noise and both channel fading and noise, to find the minimum Mean Square Error (MSE) estimate of the signal. In this paper, we investigate the effect of the source-node correlation (the correlation between sensor node observations and the source signal) and the inter-node correlation (the correlation between sensor node observations) on the performance of the Linear Minimum Mean Square Error (LMMSE) estimator for three correlation models in the presence of channel fading. First, we investigate the asymptotic behavior of the achieved distortion (i.e., MSE) resulting from both the observation and channel noise in a non-fading channel. Then, the effect of channel fading is considered and the corresponding distortion outage probability, the probability that the distortion exceeds a certain value, is found. By representing the distortion as a ratio of indefinite quadratic forms, a closed-form expression is derived for the outage probability that shows its dependency on the correlation. Finally, the new representation of the outage probability allows us to propose an iterative solution for the power allocation problem to minimize the outage probability under total and individual power constraints. Numerical simulations are provided to verify our analytic results.