Simulation of a novel piezoelectric energy harvester

This paper focuses on a novel piezoelectric energy harvester for nanofiber PVDF to capture energy from vibration environment. A Resembling CMOS(R-CMOS) circuit consisting of two pMOS transistors and two nMOS transistors is presented, which can greatly increase the energy efficiency and reduce the power dissipation tremendously. Meanwhile, the novel harvester supplies smooth direct current. Simulation result of MULTISIM has shown that by using this novel piezoelectric energy harvester the input voltage (5v) can be rectified to be an output voltage (4.24v). The voltage conversion rate of the novel harvester is as high as 84.8% which is much larger than the rate of traditional rectifier circuit. Its potential application is in micro sensors, wireless transducers, and sensor networks.

A preliminary investigation of adult defence style and physiological reactivity to infant distress signals

Species whose offspring require extended care-giving ought to be predisposed to being biologically responsive to their infant's signalling. This paper examined the interplay between biological and psychological aspects of adult response to an infant's distress. HR (heart rate) and GSR (galvanic skin response) were recorded continuously, while 50 adults listened to white noise and an infant cry audio recording. Participants completed the defence style questionnaire and the state trait anxiety inventory. HR acceleration occurred in response to the control sound, while HR decelerated in response to the infant cry. GSR responsiveness was positively correlated with immature and neurotic defence styles. When controlling for other variables, immature defence was a unique and independent predictor of GSR change in response to infant distress. Defence demonstrated a stronger relationship than self-reported anxiety, than that with physiological responsiveness. Employing defence mechanisms appears to reduce an individual's perceived anxiety, though it has little effect on physiological arousal levels.

Novel Z-Domain precoding method for blind separation of spatially correlated signals

In this paper, we address the problem of blind separation of spatially correlated signals, which is encountered in some emerging applications, e.g., distributed wireless sensor networks and wireless surveillance systems. We preprocess the source signals in transmitters prior to transmission. Specifically, the source signals are first filtered by a set of properly designed precoders and then the coded signals are transmitted. On the receiving side, the Z-domain features of the precoders are exploited to separate the coded signals, from which the source signals are recovered. Based on the proposed precoders, a closed-form algorithm is derived to estimate the coded signals and the source signals. Unlike traditional blind source separation approaches, the proposed method does not require the source signals to be uncorrelated, sparse, or nonnegative. Compared with the existing precoder-based approach, the new method uses precoders with much lower order, which reduces the delay in data transmission and is easier to implement in practice.

Human identification from ECG signals via sparse representation of local segments

This work proposes a novel framework to extract compact and discriminative features from Electrocardiogram (ECG) signals for human identification based on sparse representation of local segments. Specifically, local segments extracted from an ECG signal are projected to a small number of basic elements in a dictionary, which is learned from training data. A final representation is extracted by performing a max pooling procedure over all the sparse coefficient vectors in the ECG signal. Unlike most of existing methods for human identification from ECG signals which require segmentation of individual heartbeats or extraction of fiducial points, the proposed method does not need to segment individual heartbeats or detect any fiducial points. The method achieves an 99.48% accuracy on a 100 subjects dataset constructed from a publicly available database, which demonstrates that both local and global structural information are well captured to characterize the ECG signals.

Extraction of latent patterns and contexts from social honest signals using hierarchical Dirichlet processes

A fundamental task in pervasive computing is reliable acquisition of contexts from sensor data. This is crucial to the operation of smart pervasive systems and services so that they might behave efficiently and appropriately upon a given context. Simple forms of context can often be extracted directly from raw data. Equally important, or more, is the hidden context and pattern buried inside the data, which is more challenging to discover. Most of existing approaches borrow methods and techniques from machine learning, dominantly employ parametric unsupervised learning and clustering techniques. Being parametric, a severe drawback of these methods is the requirement to specify the number of latent patterns in advance. In this paper, we explore the use of Bayesian nonparametric methods, a recent data modelling framework in machine learning, to infer latent patterns from sensor data acquired in a pervasive setting. Under this formalism, nonparametric prior distributions are used for data generative process, and thus, they allow the number of latent patterns to be learned automatically and grow with the data - as more data comes in, the model complexity can grow to explain new and unseen patterns. In particular, we make use of the hierarchical Dirichlet processes (HDP) to infer atomic activities and interaction patterns from honest signals collected from sociometric badges. We show how data from these sensors can be represented and learned with HDP. We illustrate insights into atomic patterns learned by the model and use them to achieve high-performance clustering. We also demonstrate the framework on the popular Reality Mining dataset, illustrating the ability of the model to automatically infer typical social groups in this dataset. Finally, our framework is generic and applicable to a much wider range of problems in pervasive computing where one needs to infer high-level, latent patterns and contexts from sensor data.

Tuning the amino acid sequence of minimalist peptides to present biological signals via charge neutralised self assembly

Nanofibrous materials yielded by the self-assembly of peptides are rich in potential; particularly for the formation of scaffolds that mimic the landscape of the host environment of the cell. Here, we report a novel methodology to direct the formation of supramolecular structures presenting desirable amino acid sequences by the self-assembly of minimalist peptides which cannot otherwise yield the desired scaffold structures under biologically relevant conditions. Through the rational modification of the pK?, we were able to optimise ordered charge neutralised assembly towards in vivo conditions.

Measurement error causes scale-dependent threshold erosion of biological signals in animal movement data

Recent advances in telemetry technology have created a wealth of tracking data available for many animal species moving over spatial scales from tens of meters to tens of thousands of kilometers. Increasingly, such data sets are being used for quantitative movement analyses aimed at extracting fundamental biological signals such as optimal searching behavior and scale-dependent foraging decisions. We show here that the location error inherent in various tracking technologies reduces the ability to detect patterns of behavior within movements. Our analyses endeavored to set out a series of initial ground rules for ecologists to help ensure that sampling noise is not misinterpreted as a real biological signal. We simulated animal movement tracks using specialized random walks known as Lévy flights at three spatial scales of investigation: 100-km, 10-km, and 1-km maximum daily step lengths. The locations generated in the simulations were then blurred using known error distributions associated with commonly applied tracking methods: the Global Positioning System (GPS), Argos polar-orbiting satellites, and light-level geolocation. Deviations from the idealized Lévy flight pattern were assessed for each track after incrementing levels of location error were applied at each spatial scale, with additional assessments of the effect of error on scale-dependent movement patterns measured using fractal mean dimension and first-passage time (FPT) analyses. The accuracy of parameter estimation (Lévy μ, fractal mean D, and variance in FPT) declined precipitously at threshold errors relative to each spatial scale. At 100-km maximum daily step lengths, error standard deviations of ≥10 km seriously eroded the biological patterns evident in the simulated tracks, with analogous thresholds at the 10-km and 1-km scales (error SD ≥ 1.3 km and 0.07 km, respectively). Temporal subsampling of the simulated tracks maintained some elements of the biological signals depending on error level and spatial scale. Failure to account for large errors relative to the scale of movement can produce substantial biases in the interpretation of movement patterns. This study provides researchers with a framework for understanding the limitations of their data and identifies how temporal subsampling can help to reduce the influence of spatial error on their conclusions.

Robust patchwork-based watermarking method for stereo audio signals

This paper presents a patchwork-based watermarking method for stereo audio signals, which exploits the similarity of the two sound channels of stereo signals. Given a segment of stereo signal, we first compute the discrete Fourier transforms (DFTs) of the two sound channels, which yields two sets of DFT coefficients. The DFT coefficients corresponding to certain frequency range are divided into multiple subsegment pairs and a criterion is proposed to select those suitable for watermark embedding. Then a watermark is embedded into the selected subsegment pairs by modifying their DFT coefficients. The exact way of modification is determined by a secret key, the watermark to be embedded, and the DFT coefficients themselves. In the decoding process, the subsegment pairs containing watermarks are identified by another criterion. Then the secret key is used to extract the watermark from the watermarked subsegments. Compared to the existing patchwork methods for audio watermarking, the proposed method does not require knowledge of which segments of the watermarked audio signal contain watermarks and is more robust to conventional attacks.