Biomedical signal filtering for noisy environments

 Luke's work addresses issue of robustly attenuating multi-source noise from surface EEG signals using a novel Adaptive-Multiple-Reference Least-Means-Squares filter (AMR-LMS). In practice, the filter successfully removes electrical interference and muscle noise generated during movement which contaminates EEG, allowing subjects to maintain maximum mobility throughout signal acquisition and during the use of a Brain Computer Interface.

Analysis of phasic and tonic electromyographic signal characteristics: electromyographic synthesis and comparison of novel morphological and linear-envelope approaches

The pattern of tonic and phasic components in an EMG signal reflects the underlying behaviour of the central nervous system (CNS) in controlling the musculature. One avenue for gaining a better understanding of this behaviour is to seek a quantitative characterisation of these phasic and tonic components. We propose that these signal characteristics can range between unvarying, tonic and intermittent, phasic activation through a continuum of EMG amplitude modulation. In this paper, we present two new algorithms for quantifying amplitude modulation: a linear-envelope approach, and a mathematical morphology approach. In addition we present an algorithm for synthesising EMG signals with known amplitude modulation. The efficacy of the synthesis algorithm is demonstrated using real EMG data. We present an evaluation and comparison of the two algorithms for quantifying amplitude modulation based on synthetic data generated by the proposed synthesis algorithm. The results demonstrate that the EMG synthesis parameters represent 91.9% and 96.2% of the variance of linear-envelopes extracted from lumbo-pelvic muscle EMG signals collected from subjects performing a repetitive-movement task. This depended, however, on the muscle and movement-speed considered (F=4.02, p<0.001). Coefficients of determination between input and output amplitude modulation variables were used to quantify the accuracy of the linear-envelope and morphological signal processing algorithms. The linear-envelope algorithm exhibited higher coefficients of determination than the most accurate morphological approach (and hence greater accuracy, T=8.16, p<0.001). Similarly, the standard deviation of the coefficients of determination was 1.691 times smaller (p<0.001). This signal processing algorithm represents a novel tool for the quantification of amplitude modulation in continuous EMG signals and can be used in the study of CNS motor control of the musculature in repetitive-movement tasks.

Unrelated helpers neither signal contributions nor suffer retribution in chestnut-crowed babblers

Alloparental care by distant/nonkin that accrue few kin-selected benefits requires direct fitness benefits to evolve. The pay-to-stay hypothesis, under which helpers contribute to alloparental care to avoid being expelled from the group by dominant individuals, offers one such explanation. Here, we investigated 2 key predictions derived from the pay-to-stay hypothesis using the chestnut-crowed babbler, Pomatostomus ruficeps, a cooperatively breeding bird where helping by distant/nonkin is common (18% of nonbreeding helpers). First, we found no indication that distant or nonkin male helpers advertised their contributions toward the primary male breeder. Helpers unrelated to both breeders were unresponsive to provisioning rates of the dominant male, whereas helpers that were related to either the breeding male or to both members of the pair were responsive. In addition, unrelated male helpers did not advertise their contributions to provisioning by disproportionately synchronizing their provisioning events with those of the primary male breeder or by provisioning nestlings immediately after him. Second, no helper, irrespective of its relatedness to the dominant breeders, received aggression when released back into the group following temporary removal for 1-2 days. We therefore find no compelling support for the hypothesis that pay-to-stay mechanisms account for the cooperative behavior of unrelated males in chestnut-crowned babblers.

Loss and re-adaptation of lumbar intervertebral disc water signal intensity after prolonged bedrest

© 2015, International Society of Musculoskeletal and Neuronal Interactions. All right reserved. The adaptation and re-adaptation process of the intervertebral disc (IVD) to prolonged bedrest is important for understanding IVD physiology and IVD herniations in astronauts. Little information is available on changes in IVD composition. In this study, 24 male subjects underwent 60-day bedrest and In/Out Phase magnetic resonance imaging sequences were performed to evaluate IVD shape and water signal intensity. Scanning was performed before bedrest (baseline), twice during bedrest, and three, six and twenty-four months after bedrest. Area, signal intensity, average height, and anteroposterior diameter of the lumbar L3/4 and L4/5 IVDs were measured. At the end of bedrest, disc height and area were significantly increased with no change in water signal intensity. After bedrest, we observed reduced IVD signal intensity three months (p=0.004 versus baseline), six months (p=0.003 versus baseline), but not twenty-four months (p=0.25 versus baseline) post-bedrest. At these same time points post-bedrest, IVD height and area remained increased. The reduced lumbar IVD water signal intensity in the first months after bedrest implies a reduction of glycosaminoglycans and/or free water in the IVD. Subsequently, at two years after bedrest, IVD hydration status returned towards pre-bedrest levels, suggesting a gradual, but slow, re-adaptation process of the IVD after prolonged bedrest.

Simple and signal-off electrochemical biosensor for mercury(II) based on thymine-mercury-thymine hybridization directly on graphene

Abstract A simple, signal-off and reusable electrochemical biosensor was developed for sensitive and selective detection of mercury(II) based on thymine-mercury(II)-thymine (T-Hg²⁺-T) complex and the remarkable difference in the affinity of graphene with double strand DNA (ds-DNA) and single strand DNA (ss-DNA). Our system was composed of ferrocene-tagged probe DNA and graphene. Due to the noncovalent assembly, the ferrocene-tagged probe ss-DNA was immobilized on the surface of graphene nanosheets directly and employed to amplify the electrochemical signal. In the presence of Hg²⁺, the ferrocene-labeled T-rich DNA probe hybridized with target probe to form ds-DNA via the Hg²⁺-mediated coordination of T-Hg²⁺-T base pairs. As a result, the duplex DNA complex kept away from the graphene surface due to the weak affinity of graphene and ds-DNA, and the redox current decreased substantially. Meanwhile, the graphene decorated GCE surface was released for the reusability. Under the optimal conditions, the proposed sensor showed a linear concentration range from 25 pM to 10 μM with a detection limit of 5 pM for Hg²⁺ detection. The strategy afforded exquisite selectivity for Hg²⁺ against other metal ions in real environmental samples.

An audio signal based model for condition monitoring of sheet metal stamping process

Tool condition monitoring is an important factor in ensuring manufacturing efficiency and product quality. Audio signal based methods are a promising technique for condition monitoring. However, the influence of interfering signals and background noise has hindered the use of this technique in production sites. Blind signal separation (BSS) has the potential to solve this problem by recovering the signal of interest out of the observed mixtures, given that the knowledge about the BSS model is available. In this paper, we discuss the development of the BSS model for sheet metal stamping with a mechanical press system, so that the BSS techniques based on this model can be developed in future. This involves conducting a set of specially designed machine operations and developing a novel signal extraction technique. Also, the link between stamping process conditions and the extracted audio signal associated with stamping was successfully demonstrated by conducting a series of trials with different lubrication conditions and levels of tool wear.

On the security of compressed sensing-based signal cryptosystem

With the development of the cyber-physical systems (CPS), the security analysis of the data therein becomes more and more important. Recently, due to the advantage of joint encryption and compression for data transmission in CPS, the emerging compressed sensing (CS)-based cryptosystem has attracted much attention, where security is of extreme importance. The existing methods only analyze the security of the plaintext under the assumption that the key is absolutely safe. However, for sparse plaintext, the prior sparsity knowledge of the plaintext could be exploited to partly retrieve the key, and then the plaintext, from the ciphertext. So, the existing methods do not provide a satisfactory security analysis. In this paper, it is conducted in the information theory frame, where the plaintext sparsity feature and the mutual information of the ciphertext, key, and plaintext are involved. In addition, the perfect secrecy criteria (Shannon-sense and Wyner-sense) are extended to measure the security. While the security level is given, the illegal access risk is also discussed. It is shown that the CS-based cryptosystem achieves the extended Wyner-sense perfect secrecy, but when the key is used repeatedly, both the plaintext and the key could be conditionally accessed.

Design of an optimal ANFIS traffic signal controller by using cuckoo search for an isolated intersection

An optimal design of Adaptive Neuro-Fuzzy Inference System (ANFIS) traffic signal controller is presented in this paper. The proposed controller aims to adjust a set of green times for traffic lights in a single intersection with the purpose of minimizing travel delay time and traffic congestion. The ANFIS controller is trained, to learned how to set green times for each traffic phase. This intelligent controller uses the Cuckoo Search (CS) algorithm to tune its parameters during the learning pried. Evaluating the performance of the proposed controller in comparison with the performance of a FLS controller (FLC) with predefined rules and membership functions, and also three fixed-Time controllers, illustrates the better performance of the optimal ANFIS controller against the other benchmark controllers.

EEG signal analysis for BCI application using fuzzy system

An approach to EEG signal classification for brain-computer interface (BCI) application using fuzzy standard additive model is introduced in this paper. The Wilcoxon test is employed to rank wavelet coefficients. Top ranking wavelets are used to form a feature set that serves as inputs to the fuzzy classifiers. Experiments are carried out using two benchmark datasets, Ia and Ib, downloaded from the BCI competition II. Prevalent classifiers including feedforward neural network, support vector machine, k-nearest neighbours, ensemble learning Adaboost and adaptive neuro-fuzzy inference system are also implemented for comparisons. Experimental results show the dominance of the proposed method against competing approaches.

Computational intelligence-based traffic signal timing optimization

 Traffic congestion has explicit effects on productivity and efficiency, as well as side effects on environmental sustainability and health. Controlling traffic flows at intersections is recognized as a beneficial technique, to decrease daily travel times. This thesis applies computational intelligence to optimize traffic signals' timing and reduce urban traffic.

Design of distributed adaptive neural traffic signal timing controller by cuckoo search optimization

This paper focuses on designing an adaptive controller for controlling traffic signal timing. Urban traffic is an inevitable part in modern cities and traffic signal controllers are effective tools to control it. In this regard, this paper proposes a distributed neural network (NN) controller for traffic signal timing. This controller applies cuckoo search (CS) optimization methods to find the optimal parameters in design of an adaptive traffic signal timing control system. The evaluation of the performance of the designed controller is done in a multi-intersection traffic network. The developed controller shows a promising improvement in reducing travel delay time compared to traditional fixed-time control systems.

Mixed-signal analog-digital circuits design on the pre-diffused digital array using trapezoidal association of transistors

The mixed-signal and analog design on a pre-diffused array is a challenging task, given that the digital array is a linear matrix arrangement of minimum-length transistors. To surmount this drawback a specific discipline for designing analog circuits over such array is required. An important novel technique proposed is the use of TAT (Trapezoidal Associations of Transistors) composite transistors on the semi-custom Sea-Of-Transistors (SOT) array. The analysis and advantages of TAT arrangement are extensively analyzed and demonstrated, with simulation and measurement comparisons to equivalent single transistors. Basic analog cells were also designed as well in full-custom and TAT versions in 1.0mm and 0.5mm digital CMOS technologies. Most of the circuits were prototyped in full-custom and TAT-based on pre-diffused SOT arrays. An innovative demonstration of the TAT technique is shown with the design and implementation of a mixed-signal analog system, i. e., a fully differential 2nd order Sigma-Delta Analog-to-Digital (A/D) modulator, fabricated in both full-custom and SOT array methodologies in 0.5mm CMOS technology from MOSIS foundry. Three test-chips were designed and fabricated in 0.5mm. Two of them are IC chips containing the full-custom and SOT array versions of a 2nd-Order Sigma-Delta A/D modulator. The third IC contains a transistors-structure (TAT and single) and analog cells placed side-by-side, block components (Comparator and Folded-cascode OTA) of the Sigma-Delta modulator.

Do dividends signal more earnings?

Signaling models have contributed to the corporate finance literature by formalizing "the informational content of dividends" hypothesis. However, these models are under criticism of empirical literature, as weak evidences were found supporting one of the main predicitions: the positive relation between changes in dividends and changes in earnings. We claim thaht the failure to verify this prediction does not invalidate the signaling approach. The models developed up to now assume or derive utility functions with the single-crossing property. We show thaht signaling is possible in the absence of this property and, in this case, changes in dividend and changes in earnings can be positively or negatively related.