Information-intensive wireless sensor networks: potential and challenges

Conventional wireless sensor networks rely mostly on simple scalar data (such as temperature or humidity) and specialize in single-purpose applications. Taking a fundamental departure, in this article we motivate information-rich wireless video sensor networks that emulate the compound eyes found in certain arthropods. Although constrained by scarce resources, sensor nodes can only serve extremely low-resolution video streams; the availability of vast amount of such streams due to deployment redundance can suffice for the need of information hungry applications. Unfortunately, the unique characteristics of wireless video sensor networks will introduce novel uncertainty-driven challenges in the information-intensive and yet resource-constrained environment. Correspondingly, we describe key research problems in the areas of networking, security, sensor design, and video-data analysis     

Erosion-corrosion behavior of SiC particle-reinforced Al-Si alloy in NaOH slurry

The erosion-corrsosion behavior of SiC particle-reinforced Al-Si alloy has been studied in NaOH slurry simulating the mining atmosphere. The study was performed at two different sand concentrations, namely, 20 and 30 wt pct, and at a speed of 900 rpm. It is depicted that the wear rates decreased with increasing sand content, indicating that corrosion is the dominating mode of material removal. Further composite exhibited lower wear resistance than the laloys irrespective of the sand concentration. Scanning electron microscope (SEM) observations indicated the dissolution of dendrites of Al due to severe corrosion, leaving behind the network of Si. This ultimately results in the falling of Si particles from the matrix, leaving behind voids. This also results in the formation of voids around the SiC particles and leads to pullout of SiC particles from the matrix during the wear process.     

Stress intensity factors for an interfacial crack between an orthotropic half-plane bonded to a dissimilar orthotropic layer with a punch

The plane strain problem of determining Stress Intensity Factors (SIF) for a moving interfacial Griffith crack between an elastic orthotropic half-plane and a dissimilar orthotropic layer with a moving punch situated along the boundary of the layer have been considered. The problem is reduced to the solution of three simultaneous singular integral equations with Cauchy-type singularities. Expressions for SIF for the case of a general loading are obtained. Numerical results for some particular cases are also presented graphically.     

A Novel Neurofilament Light Chain ELISA Validated in Patients with Alzheimer’s Disease,Frontotemporal Dementia,and Subjective Cognitive Decline,and the Evaluation of Candidate Proteins for Immunoassay Calibration

Neurofilament light chain (Nf-L) is a well-known biomarker for axonal damage; however, the corresponding circulating Nf-L analyte in cerebrospinal fluid (CSF) is poorly characterized. We therefore isolated new monoclonal antibodies against synthetic peptides, and these monoclonals were characterized for their specificity on brain-specific intermediate filament proteins. Two highly specific antibodies, ADx206 and ADx209, were analytically validated for CSF applications according to well-established criteria. Interestingly, using three different sources of purified Nf-L proteins, a significant impact on interpolated concentrations was observed. With a lower limit of analytical sensitivity of 100 pg/mL using bovine Nf-L as the calibrator, we were able to quantify the Nf-L analyte in each sample, and these Nf-L concentrations were highly correlated to the Uman diagnostics assay (Spearman rho = 0.97, p < 0.001). In the clinical diagnostic groups, the new Nf-L ELISA could discriminate patients with Alzheimer’s disease (AD, n = 20) from those with frontotemporal lobe dementia (FTD, n = 20) and control samples with subjective cognitive decline (SCD, n = 20). Henceforth, this novel Nf-L ELISA with well-defined specificity and epitopes can be used to enhance our understanding of harmonizing the use of Nf-L as a clinically relevant marker for neurodegeneration in CSF.     

Aluminum industry and climate change—Assessment and responses

It is now possible to assess the impact of the production processes of aluminum on the environment and to describe some of the ongoing responses and opportunities for improvement. This is compared with the benefits of aluminum in transportation, where the growing usage in various forms of transport due to its low density, high strength, and ability to be recycled enables reduced mass, increased fuel efficiency, reduced emissions and increased safety. It is the purpose of this paper to compare and contrast the emissions generated in the production of aluminum with the benefits accruing from its increased use in transportation.     

A selective location update strategy for PCS users

A new location update strategy for personal communication services (PCS) networks and its implementation using a genetic algorithm are proposed. Most of the practical cellular mobile systems partition a geographical region into location areas (LAs) and users are made to update on entering a new LA. The main drawback of this scheme is that it does not consider the individual user mobility and call arrival patterns. Combining these factors with the LAbased approach, we propose an optimal update strategy which determines whether or not a user should update in each LA, and minimizes the average location management cost derived from a userspecific mobility model and call generation pattern. The location management cost optimization problem is also elegantly solved using a genetic algorithm. Detailed simulation experiments are conducted to capture the effects of mobility and callarrival patterns on the location update strategy. The conclusion from this work is that skipping location updates in certain LAs leads to the minimization of the overall location management cost for a user with a specific mobility pattern and even with moderately high call arrival rate.     

Routing Correlated Data with Fusion Cost in Wireless Sensor Networks

In this paper, we propose a routing algorithm called minimum fusion Steiner tree (MFST) for energy efficient data gathering with aggregation (fusion) in wireless sensor networks. Different from existing schemes, MFST not only optimizes over the data transmission cost, but also incorporates the cost for data fusion, which can be significant for emerging sensor networks with vectorial data and/or security requirements. By employing a randomized algorithm that allows fusion points to be chosen according to the nodes' data amounts, MFST achieves an approximation ratio of 5/4log(k + 1), where k denotes the number of source nodes, to the optimal solution for extremely general system setups, provided that fusion cost and data aggregation are nondecreasing against the total input data. Consequently, in contrast to algorithms that only excel in full or nonaggregation scenarios without considering fusion cost, MFST can thrive in a wide range of applications     

Improved Thermal Performance of AlGaN/GaN HEMTs by an Optimized Flip-Chip Design

AlGaN/GaN high electron mobility transistors (HEMT) on sapphire substrates have been studied for their potential application in RF power applications; however, the low thermal conductivity of the sapphire substrate is a major drawback. Aiming at RF system-in-a-package, the authors propose a flip-chip-integration approach, where the generated heat is dissipated to an AlN carrier substrate. Different flip-chip-bump designs are compared, using thermal simulations, electrical measurements, micro-Raman spectroscopy, and infrared thermography. The authors show that a novel bump design, where bumps are placed directly onto both source and drain ohmic contacts, improves the thermal performance of the HEMT     

An automated patient monitoring using discrete‐time wireless sensor networks

In recent years, Internet of Things (IoT) devices are used for remote health monitoring. For remotely monitoring a patient, only the health information at different time points are not sufficient; predicted values of biomarkers (for some future time points) are also important. In this article, we propose a powerful statistical model for an efficient dynamic patient monitoring using wireless sensor nodes through Bayesian Learning (BL). We consider the setting where a set of correlated biomarkers are measured from a patient through wireless sensors, but the sensors only report the ordinal outcomes (say, good, fair, high, or very high) to the sink based on some prefixed thresholds. The challenge is to use the ordinal outcomes for monitoring and predicting the health status of the patient under consideration. We propose a linear mixed model where interbiomarker correlations and intrabiomarker dependence are modeled simultaneously. The estimated and the predicted values of the biomarkers are transferred over the internet so that health care providers and the family members of the patient can remotely monitor the patient. Extensive simulation studies are performed to assess practical usefulness of our proposed joint model, and the performance of the proposed joint model is compared to that of some other traditional models used in the literature.     

Channel Models for Inter-body Communication in Ultra Wideband-Based Body Area Networks

Wireless Personal Communications - Body area networks (BANs) are evolving tremendously over the years and with the progress in the area of internet of things, the BANs are more important than ever....