Gradient Boundary Detection for Time Series Snapshot Construction in Sensor Networks

In many applications of sensor networks, the sink needs to keep track of the history of sensed data of a monitored region for scientific analysis or supporting historical queries. We call these historical data a time series of value distributions or snapshots. Obviously, to build the time series snapshots by requiring all of the sensors to transmit their data to the sink periodically is not energy efficient. In this paper, we introduce the idea of gradient boundary and propose the gradient boundary detection (GBD) algorithm to construct these time series snapshots of a monitored region. In GBD, a monitored region is partitioned into a set of subregions and all sensed data in one subregion are within a predefined value range, namely, the gradient interval. Sensors located on the boundaries of the subregions are required to transmit the data to the sink and, then, the sink recovers all subregions to construct snapshots of the monitored area. In this process, only the boundary sensors transmit their data and, therefore, energy consumption is greatly reduced. The simulation results show that GBD is able to build snapshots with a comparable accuracy and has up to 40 percent energy savings compared with the existing approaches for large gradient intervals.     

On coherence, random-self-reducibility, and self-correction

We study three types of self‐reducibility that are motivated by the theory of program verification. A set A is random‐self‐reducible if one can determine whether an input x is in A by making random queries to an A‐oracle. The distribution of each query may depend only on the length of x. A set B is self‐correctable over a distribution if one can convert a program that is correct on most of the probability mass of to a probabilistic program that is correct everywhere. A set C is coherent if one can determine whether an input x is in C by asking questions to an oracle for C–{x}.?We first show that adaptive coherence is more powerful than nonadaptive coherence, even if the nonadaptive querier is nonuniform. Blum et al.(1993) showed that every random‐self‐reducible function is self‐correctable. It is unknown, however, whether self‐correctability implies random‐self‐reducibility. We show, assuming a reasonable complexity‐theoretic hypothesis, that certain hard, sparse, tally sets exist, and that there is a self‐correctable function which is not random‐self‐reducible. For easily samplable distributions, however, we show that constructing a self‐correctable function that is not random‐self‐reducible is as hard as proving that P is different from PP. Received: 14 June, 1996     

Efficient computation of unique input/output sequences in finitestate machines

This paper makes two contributions toward computing unique input/output (UIO) sequences in finite-state machines. Our first contribution is to compute all UIO sequences of minimal lengths in a finite-state machine. Our second contribution is to present a generally efficient algorithm to compute a UIO sequence for each state, if it exists. We begin by defining a path vector, vector perturbation, and UIO tree. The perturbation process allows us to construct the complete UIO tree for a machine. Each sequence of input/output from the initial vector of a UIO tree to a singleton vector represents a UIO sequence. Next, we define the idea of an inference rule that allows us to infer UIO sequences of a number of states from the UIO sequence of some state. That is, for a large class of machines, it is possible to compute UIO sequences for all possible states from a small set of initial UIOs. We give a modified depth-first algorithm, called the hybrid approach, that computes a partial UIO tree, called an essential subtree, from which UIO sequences of all possible states can be inferred. Using the concept of projection machines, we show that sometimes it is unnecessary to construct even a partial subtree. We prove that if a machine remains strongly connected after deleting all the converging transitions, then all of the states have UIO sequences. To demonstrate the effectiveness of our approach, we develop a tool to perform experiments using both small and large machines     

Unlocking the Latent Antimicrobial Potential of Biomimetically Synthesized Inorganic Materials

Inspired by biomineralization, biomimetic approaches utilize biomolecules and synthetic analogs to produce materials of controlled chemistry, morphology, and function under relatively benign conditions. A common characteristic of biological and biomimetic mineral‐forming processes is the generation of mineral/biomolecule nanocomposites. In this work, it is demonstrated that a facile chemical reaction may be utilized to halogenate the nitrogen‐containing moieties of the organics entrapped within bio‐inorganic composites to yield halamine compounds. This process provides rapid and potent bactericidal activity to biomimetically and biologically produced materials that otherwise lack such functionality. Additionally, bio‐inorganic composites containing the chlorinated peptide protamine are effective in rapidly neutralizing Bacillus spores (≥99.97% reduction in colony forming units within 10 min). The straightforward nature of the described process, and the efficacy of halamine compounds in neutralizing biological and chemical agents, provide new applicability to biogenic and biomimetic materials.     

Edge Effect Elimination in Single-Mixture Blind Source Separation

Blind source separation (BSS) of single-channel mixed recording is a challenging task that has applications in the fields of speech, audio and bio-signal processing. Ensemble empirical mode decomposition (EEMD)-based methods are commonly used for blind separation of single input multiple outputs. However, all of these EEMD-based methods appear in the edge effect problem when cubic spline interpolation is used to fit the upper and lower envelopes of the given signals. It is therefore imperative to have good methods to explore a more suitable design choice, which can avoid the problems mentioned above as much as possible. In this paper we present a novel single-mixture blind source separation method based on edge effect elimination of EEMD, principal component analysis (PCA) and independent component analysis (ICA). EEMD represents any time-domain signal as the sum of a finite set of oscillatory components called intrinsic mode functions (IMFs). In extreme point symmetry extension (EPSE), optimum values of endpoints are obtained by minimizing the deviation evaluation function of signal and signal envelope. Edge effect is turned away from signal by abandoning both ends’ extension parts of IMFs. PCA is applied to reduce dimensions of IMFs. ICA finds the independent components by maximizing the statistical independence of the dimensionality reduction of IMFs. The separated performance of edge EPSE-EEMD-PCA-ICA algorithm is compared with EEMD-ICA and EEMD-PCA-ICA algorithms through simulations, and experimental results show that the former algorithm outperforms the two latter algorithms with higher correlation coefficient and lower relative root mean square error (RRMSE).     

Adaptive Empirical Path Loss Prediction Models for Indoor WLAN

This paper presents robust empirical path loss models to characterize indoor propagation for access point (AP) deployed at different heights. The proposed models are developed with wireless local area network infrastructure at 2.4 GHz. The models are backed by extensive received signal strength (RSS) measurements acquired in line of sight and obstructed line of sight regions. The models are developed for two conditions, viz; quasi realistic and realistic RSS measurements. The quasi realistic measurements are taken after suppressing human intervention and electrical interferences to minimum. While the realistic RSS measurements are made in presence of all the human interventions and electrical interferences. The shadow fading component for both quasi realistic and realistic conditions is statistically modeled with the dependency on AP height. The proposed technique can be applied with higher confidence level to the buildings with similar construction features where RSS measurements are made upon. The results reveal that the performance of the proposed propagation models is significantly higher than the existing International Telecommunication Union-path loss model. The results also demonstrate that the realistic path loss model is more robust than the quasi realistic model.     

Graphene oxide nanoflakes from various agrowastes

Nowadays, the synthesis of graphene/ graphene oxide from graphite precursor using oxidizing agents is the most common procedure, but the direct synthesis of graphene or graphene oxide from a non-graphitic carbonaceous material without using inert atmosphere is really a great challenge. Besides, the chemistry behind the development of graphitic structure from a non-graphitic material during the thermal heating is still not clearly understood. In this research work, three agrowaste materials viz. rice husk, sugarcane bagasse and newspaper were selected and subjected to pyrolysis in presence of trace amount of air. The continued heating at the optimum temperature has resulted in aromatization and condensation along with the oxidation within the cellulosic structure of the agrowaste, which finally resulted in the formation of graphene oxide nanoflakes directly. The mechanism of formation of graphene oxide from these agrowaste materials was studied, which suggested that any carbonaceous waste materials can be converted to graphene oxide by optimizing the thermal heating conditions.