Realistically coupled neural mass models can generate EEG rhythms

We study the generation of EEG rhythms by means of realistically coupled neural mass models. Previous neural mass models were used to model cortical voxels and the thalamus. Interactions between voxels of the same and other cortical areas and with the thalamus were taken into account. Voxels within the same cortical area were coupled (short-range connections) with both excitatory and inhibitory connections, while coupling between areas (long-range connections) was considered to be excitatory only. Short-range connection strengths were modeled by using a connectivity function depending on the distance between voxels. Coupling strength parameters between areas were defined from empirical anatomical data employing the information obtained from probabilistic paths, which were tracked by water diffusion imaging techniques and used to quantify white matter tracts in the brain. Each cortical voxel was then described by a set of 16 random differential equations, while the thalamus was described by a set of 12 random differential equations. Thus, for analyzing the neuronal dynamics emerging from the interaction of several areas, a large system of differential equations needs to be solved. The sparseness of the estimated anatomical connectivity matrix reduces the number of connection parameters substantially, making the solution of this system faster. Simulations of human brain rhythms were carried out in order to test the model. Physiologically plausible results were obtained based on this anatomically constrained neural mass model.     

Optimal Weighted Extreme Learning Machine for Cybersecurity Fake News Classification

Fake news and its significance carried the significance of affecting diverse aspects of diverse entities, ranging from a city lifestyle to a country global relativity, various methods are available to collect and determine fake news. The recently developed machine learning (ML) models can be employed for the detection and classification of fake news. This study designs a novel Chaotic Ant Swarm with Weighted Extreme Learning Machine (CAS-WELM) for Cybersecurity Fake News Detection and Classification. The goal of the CAS-WELM technique is to discriminate news into fake and real. The CAS-WELM technique initially pre-processes the input data and Glove technique is used for word embedding process. Then, N-gram based feature extraction technique is derived to generate feature vectors. Lastly, WELM model is applied for the detection and classification of fake news, in which the weight value of the WELM model can be optimally adjusted by the use of CAS algorithm. The performance validation of the CAS-WELM technique is carried out using the benchmark dataset and the results are inspected under several dimensions. The experimental results reported the enhanced outcomes of the CAS-WELM technique over the recent approaches.     

A new shadow detection index for 8-band very high-resolution satellite images

ABSTRACT

The new generation of remote sensing satellite with very high-resolution images has provided a high level of details, which make them a reliable source of information. Presence of shadow can reduce the amount of information that can be extracted from these images. Shadow can be confused with dark objects such as water and dark vegetation. The main aim of this research is to develop a new index to detect shadow in the presence of dark objects using the capabilities of the new remote sensing satellite images. For this study, WorldView-2 (WV-2) remote sensing satellite images with eight spectral bands were used. A spectral reflectance analysis for the main ground features has been studied along the eight spectral bands to determine the most effective bands for shadow detection. These bands are employed with the Hue-Saturation-Intensity colour model for producing the new proposed Saturation Intensity Shadow Detection Index (SISDI). The proposed index is applied to four study areas and compared with two state-of-the-art indices of shadow detection. Results of this comparison demonstrate the more accuracy effectiveness and feasibility of that proposed index. The proposed index achieves the highest overall accuracy (average of 97.8%) and has the ability for detecting small shadow areas.     

Monitoring and risk assessment of tetracycline residues in foods of animal origin

Food Science and Biotechnology - A total of 450 samples consisting of red meat, poultry meat, aquatic product and raw milk were collected during winter 2016 and summer 2017. 22.2% (100/450) of...     

Beam shaping by use of hybrid acousto-optics with feedback

We theoretically demonstrate beam shaping through adaptive feedback in an acousto-optic device with electrical feedback by using experimentally determined parameters. Cases of positive feedback and negative feedback from undiffracted and diffracted orders are investigated. In addition, we demonstrate the dependence of the final value of the induced grating strength in the acousto-optic cell on the feedback parameters.     

Adaptive denoising of event-related functional magnetic resonance imaging data using spectral subtraction

A new adaptive signal-preserving technique for noise suppression in event-related functional magnetic resonance imaging (fMRI) data is proposed based on spectral subtraction. The proposed technique estimates a parametric model for the power spectrum of random noise from the acquired data based on the characteristics of the Rician statistical model. This model is subsequently used to estimate a noise-suppressed power spectrum for any given pixel time course by simple subtraction of power spectra. The new technique is tested using computer simulations and real data from event-related fMRI experiments. The results show the potential of the new technique in suppressing noise while preserving the other deterministic components in the signal. Moreover, we demonstrate that further analysis using principal component analysis and independent component analysis shows a significant improvement in both convergence and clarity of results when the new technique is used. Given its simple form, the new method does not change the statistical characteristics of the signal or cause correlated noise to be present in the processed signal. This suggests the value of the new technique as a useful preprocessing step for fMRI data analysis.     

Application of Lean Concepts and Simulation Analysis to Improve Efficiency of Drainage Operations Maintenance Crews

The City of Edmonton’s Drainage Operations branch oversees the inspection, maintenance, and repair of the city’s drainage network. This network covers an area of 700.6?km2 to serve the city’s growing population of nearly 1 million people. The activities performed by this division consume a large amount of funding and are, therefore, receptive to improvements in productivity. The study presented in this paper was conducted to develop improved work methods and engineered productivity standards for the various drainage operations. The study focused on six crews, which accounted for over 25% of the total drainage operations budget. The six crews were divided based on their respective duties: (1) cleaning mains by low pressure flushing (LPF); (2) cleaning mains by high pressure flushing; (3) scheduled mechanical cleaning of catch basins (CBC); (4) inspecting mains by televising; (5) commercial establishment investigation; and (6) service-line rodding and televising. The study utilized the concept of work simplification and focused on two crew activities—LPF and CBC work tasks—to improve crew work methods, to develop an established work standard, and to verify the proposed improvements based on the simulation model’s output. These activities are described in greater detail in two case studies. The work measurement concept was implemented to develop engineered productivity standards for the remaining crews in order to improve their productivity as well. This paper describes the application of an industrial engineering philosophy of work measurement—lean production theory—and the technique of simulation analysis to capture current work methods, generate and test alternative methods, and develop new productivity standards for drainage maintenance operations crews.     

Graph-Laplacian features for neural waveform classification

Analysis of extracellular recordings of neural action potentials (known as spikes) is highly dependent upon the accuracy of neural waveform classification, commonly referred to as spike sorting. Feature extraction is an important stage of this process because it can limit the quality of clustering that is performed in the feature space. Principal components analysis (PCA) is the most commonly used feature extraction method employed for neural spike recordings. To improve upon PCA's feature extraction performance for neural spike sorting, we revisit the PCA procedure to analyze its weaknesses and describe an improved feature extraction method. This paper proposes a linear feature extraction technique that we call graph-Laplacian features, which simultaneously minimizes the graph Laplacian and maximizes variance. The algorithm's performance is compared with PCA and a wavelet-coefficient-based feature extraction algorithm on simulated single-electrode neural data. A cluster-quality metric is proposed to quantitatively measure the algorithm performance. The results show that the proposed algorithm produces more compact and well-separated clusters compared to the other approaches.     

Chaotic Interleaving for Robust Image Transmission with LDPC Coded OFDM

To improve the error resilience and video quality over wireless networks, we propose a novel packet-level layer-based interleaving unequal forward error correction (LIU-FEC) method. First, a scalable layer-based interleaving architecture is proposed for improving the efficiency of FEC from successive packet losses in variable channel conditions. The interleaved transmission across different scalable layers can efficiently disperse the consecutive packet losses into different scalable layers. Second, a closed form FEC assignment solution is proposed for minimizing video quality degradation using simple layer-based error propagation metric in hierarchical prediction structure. The simulation results show that the proposed algorithm offers higher PSNR values in various channel status, compared to the conventional FEC algorithm.     

Medical Diagnosis Using Machine Learning: A Statistical Review

Decision making in case of medical diagnosis is a complicated process. A large number of overlapping structures and cases, and distractions, tiredness, and limitations with the human visual system can lead to inappropriate diagnosis. Machine learning (ML) methods have been employed to assist clinicians in overcoming these limitations and in making informed and correct decisions in disease diagnosis. Many academic papers involving the use of machine learning for disease diagnosis have been increasingly getting published. Hence, to determine the use of ML to improve the diagnosis in varied medical disciplines, a systematic review is conducted in this study. To carry out the review, six different databases are selected. Inclusion and exclusion criteria are employed to limit the research. Further, the eligible articles are classified depending on publication year, authors, type of articles, research objective, inputs and outputs, problem and research gaps, and findings and results. Then the selected articles are analyzed to show the impact of ML methods in improving the disease diagnosis. The findings of this study show the most used ML methods and the most common diseases that are focused on by researchers. It also shows the increase in use of machine learning for disease diagnosis over the years. These results will help in focusing on those areas which are neglected and also to determine various ways in which ML methods could be employed to achieve desirable results.