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  Subjects -> ENGINEERING (Total: 2291 journals)
    - CHEMICAL ENGINEERING (192 journals)
    - CIVIL ENGINEERING (187 journals)
    - ELECTRICAL ENGINEERING (105 journals)
    - ENGINEERING (1209 journals)
    - ENGINEERING MECHANICS AND MATERIALS (385 journals)
    - HYDRAULIC ENGINEERING (55 journals)
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    - MECHANICAL ENGINEERING (90 journals)

ENGINEERING (1209 journals)                  1 2 3 4 5 6 7 | Last

Showing 1 - 200 of 1205 Journals sorted alphabetically
3 Biotech     Open Access   (Followers: 7)
3D Research     Hybrid Journal   (Followers: 18)
AAPG Bulletin     Hybrid Journal   (Followers: 7)
AASRI Procedia     Open Access   (Followers: 15)
Abstract and Applied Analysis     Open Access   (Followers: 3)
Aceh International Journal of Science and Technology     Open Access   (Followers: 2)
ACS Nano     Full-text available via subscription   (Followers: 233)
Acta Geotechnica     Hybrid Journal   (Followers: 7)
Acta Metallurgica Sinica (English Letters)     Hybrid Journal   (Followers: 5)
Acta Polytechnica : Journal of Advanced Engineering     Open Access   (Followers: 2)
Acta Scientiarum. Technology     Open Access   (Followers: 3)
Acta Universitatis Cibiniensis. Technical Series     Open Access  
Active and Passive Electronic Components     Open Access   (Followers: 7)
Adaptive Behavior     Hybrid Journal   (Followers: 11)
Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi     Open Access  
Adsorption     Hybrid Journal   (Followers: 4)
Advanced Engineering Forum     Full-text available via subscription   (Followers: 6)
Advanced Science     Open Access   (Followers: 5)
Advanced Science Focus     Free   (Followers: 3)
Advanced Science Letters     Full-text available via subscription   (Followers: 7)
Advanced Science, Engineering and Medicine     Partially Free   (Followers: 8)
Advanced Synthesis & Catalysis     Hybrid Journal   (Followers: 17)
Advances in Calculus of Variations     Hybrid Journal   (Followers: 2)
Advances in Catalysis     Full-text available via subscription   (Followers: 5)
Advances in Complex Systems     Hybrid Journal   (Followers: 7)
Advances in Engineering Software     Hybrid Journal   (Followers: 25)
Advances in Fuel Cells     Full-text available via subscription   (Followers: 16)
Advances in Fuzzy Systems     Open Access   (Followers: 5)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 10)
Advances in Heat Transfer     Full-text available via subscription   (Followers: 22)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 9)
Advances in Natural Sciences: Nanoscience and Nanotechnology     Open Access   (Followers: 29)
Advances in Operations Research     Open Access   (Followers: 11)
Advances in OptoElectronics     Open Access   (Followers: 5)
Advances in Physics Theories and Applications     Open Access   (Followers: 12)
Advances in Polymer Science     Hybrid Journal   (Followers: 41)
Advances in Porous Media     Full-text available via subscription   (Followers: 4)
Advances in Remote Sensing     Open Access   (Followers: 37)
Advances in Science and Research (ASR)     Open Access   (Followers: 6)
Aerobiologia     Hybrid Journal   (Followers: 1)
African Journal of Science, Technology, Innovation and Development     Hybrid Journal   (Followers: 4)
AIChE Journal     Hybrid Journal   (Followers: 30)
Ain Shams Engineering Journal     Open Access   (Followers: 5)
Akademik Platform Mühendislik ve Fen Bilimleri Dergisi     Open Access  
Alexandria Engineering Journal     Open Access   (Followers: 1)
AMB Express     Open Access   (Followers: 1)
American Journal of Applied Sciences     Open Access   (Followers: 28)
American Journal of Engineering and Applied Sciences     Open Access   (Followers: 11)
American Journal of Engineering Education     Open Access   (Followers: 9)
American Journal of Environmental Engineering     Open Access   (Followers: 17)
American Journal of Industrial and Business Management     Open Access   (Followers: 23)
Analele Universitatii Ovidius Constanta - Seria Chimie     Open Access  
Annals of Combinatorics     Hybrid Journal   (Followers: 3)
Annals of Pure and Applied Logic     Open Access   (Followers: 2)
Annals of Regional Science     Hybrid Journal   (Followers: 7)
Annals of Science     Hybrid Journal   (Followers: 7)
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 2)
Applicable Analysis: An International Journal     Hybrid Journal   (Followers: 1)
Applied Catalysis A: General     Hybrid Journal   (Followers: 6)
Applied Catalysis B: Environmental     Hybrid Journal   (Followers: 16)
Applied Clay Science     Hybrid Journal   (Followers: 5)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 12)
Applied Magnetic Resonance     Hybrid Journal   (Followers: 4)
Applied Nanoscience     Open Access   (Followers: 8)
Applied Network Science     Open Access   (Followers: 1)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 5)
Applied Physics Research     Open Access   (Followers: 3)
Applied Sciences     Open Access   (Followers: 2)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 4)
Arabian Journal for Science and Engineering     Hybrid Journal   (Followers: 5)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 4)
Archives of Foundry Engineering     Open Access  
Archives of Thermodynamics     Open Access   (Followers: 7)
Arkiv för Matematik     Hybrid Journal   (Followers: 1)
ASEE Prism     Full-text available via subscription   (Followers: 3)
Asia-Pacific Journal of Science and Technology     Open Access  
Asian Engineering Review     Open Access  
Asian Journal of Applied Science and Engineering     Open Access   (Followers: 1)
Asian Journal of Applied Sciences     Open Access   (Followers: 2)
Asian Journal of Biotechnology     Open Access   (Followers: 8)
Asian Journal of Control     Hybrid Journal  
Asian Journal of Current Engineering & Maths     Open Access  
Asian Journal of Technology Innovation     Hybrid Journal   (Followers: 8)
Assembly Automation     Hybrid Journal   (Followers: 2)
at - Automatisierungstechnik     Hybrid Journal   (Followers: 1)
ATZagenda     Hybrid Journal  
ATZextra worldwide     Hybrid Journal  
Australasian Physical & Engineering Sciences in Medicine     Hybrid Journal   (Followers: 1)
Australian Journal of Multi-Disciplinary Engineering     Full-text available via subscription   (Followers: 2)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 8)
Avances en Ciencias e Ingeniería     Open Access  
Balkan Region Conference on Engineering and Business Education     Open Access   (Followers: 1)
Bangladesh Journal of Scientific and Industrial Research     Open Access  
Basin Research     Hybrid Journal   (Followers: 5)
Batteries     Open Access   (Followers: 4)
Bautechnik     Hybrid Journal   (Followers: 1)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 23)
Beni-Suef University Journal of Basic and Applied Sciences     Open Access   (Followers: 4)
BER : Manufacturing Survey : Full Survey     Full-text available via subscription   (Followers: 2)
BER : Motor Trade Survey     Full-text available via subscription   (Followers: 1)
BER : Retail Sector Survey     Full-text available via subscription   (Followers: 2)
BER : Retail Survey : Full Survey     Full-text available via subscription   (Followers: 2)
BER : Survey of Business Conditions in Manufacturing : An Executive Summary     Full-text available via subscription   (Followers: 3)
BER : Survey of Business Conditions in Retail : An Executive Summary     Full-text available via subscription   (Followers: 3)
Bharatiya Vaigyanik evam Audyogik Anusandhan Patrika (BVAAP)     Open Access   (Followers: 1)
Biofuels Engineering     Open Access  
Biointerphases     Open Access   (Followers: 1)
Biomaterials Science     Full-text available via subscription   (Followers: 10)
Biomedical Engineering     Hybrid Journal   (Followers: 16)
Biomedical Engineering and Computational Biology     Open Access   (Followers: 13)
Biomedical Engineering Letters     Hybrid Journal   (Followers: 5)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 17)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 32)
Biomedical Engineering: Applications, Basis and Communications     Hybrid Journal   (Followers: 5)
Biomedical Microdevices     Hybrid Journal   (Followers: 9)
Biomedical Science and Engineering     Open Access   (Followers: 4)
Biomedizinische Technik - Biomedical Engineering     Hybrid Journal  
Biomicrofluidics     Open Access   (Followers: 4)
BioNanoMaterials     Hybrid Journal   (Followers: 2)
Biotechnology Progress     Hybrid Journal   (Followers: 39)
Boletin Cientifico Tecnico INIMET     Open Access  
Botswana Journal of Technology     Full-text available via subscription   (Followers: 1)
Boundary Value Problems     Open Access   (Followers: 1)
Brazilian Journal of Science and Technology     Open Access   (Followers: 2)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 10)
Bulletin of Canadian Petroleum Geology     Full-text available via subscription   (Followers: 14)
Bulletin of Engineering Geology and the Environment     Hybrid Journal   (Followers: 3)
Bulletin of the Crimean Astrophysical Observatory     Hybrid Journal  
Cahiers, Droit, Sciences et Technologies     Open Access  
Calphad     Hybrid Journal  
Canadian Geotechnical Journal     Hybrid Journal   (Followers: 14)
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 42)
Case Studies in Engineering Failure Analysis     Open Access   (Followers: 8)
Case Studies in Thermal Engineering     Open Access   (Followers: 3)
Catalysis Communications     Hybrid Journal   (Followers: 6)
Catalysis Letters     Hybrid Journal   (Followers: 2)
Catalysis Reviews: Science and Engineering     Hybrid Journal   (Followers: 8)
Catalysis Science and Technology     Free   (Followers: 7)
Catalysis Surveys from Asia     Hybrid Journal   (Followers: 3)
Catalysis Today     Hybrid Journal   (Followers: 8)
CEAS Space Journal     Hybrid Journal  
Cellular and Molecular Neurobiology     Hybrid Journal   (Followers: 3)
Central European Journal of Engineering     Hybrid Journal   (Followers: 1)
CFD Letters     Open Access   (Followers: 6)
Chaos : An Interdisciplinary Journal of Nonlinear Science     Hybrid Journal   (Followers: 2)
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 3)
Chinese Journal of Catalysis     Full-text available via subscription   (Followers: 2)
Chinese Journal of Engineering     Open Access   (Followers: 2)
Chinese Science Bulletin     Open Access   (Followers: 1)
Ciencia e Ingenieria Neogranadina     Open Access  
Ciencia en su PC     Open Access   (Followers: 1)
Ciencias Holguin     Open Access   (Followers: 1)
CienciaUAT     Open Access  
Cientifica     Open Access  
CIRP Annals - Manufacturing Technology     Full-text available via subscription   (Followers: 11)
CIRP Journal of Manufacturing Science and Technology     Full-text available via subscription   (Followers: 14)
City, Culture and Society     Hybrid Journal   (Followers: 22)
Clay Minerals     Full-text available via subscription   (Followers: 10)
Clean Air Journal     Full-text available via subscription   (Followers: 2)
Coal Science and Technology     Full-text available via subscription   (Followers: 3)
Coastal Engineering     Hybrid Journal   (Followers: 11)
Coastal Engineering Journal     Hybrid Journal   (Followers: 5)
Coatings     Open Access   (Followers: 4)
Cogent Engineering     Open Access   (Followers: 2)
Cognitive Computation     Hybrid Journal   (Followers: 4)
Color Research & Application     Hybrid Journal   (Followers: 1)
COMBINATORICA     Hybrid Journal  
Combustion Theory and Modelling     Hybrid Journal   (Followers: 13)
Combustion, Explosion, and Shock Waves     Hybrid Journal   (Followers: 13)
Communications Engineer     Hybrid Journal   (Followers: 1)
Communications in Numerical Methods in Engineering     Hybrid Journal   (Followers: 2)
Components, Packaging and Manufacturing Technology, IEEE Transactions on     Hybrid Journal   (Followers: 26)
Composite Interfaces     Hybrid Journal   (Followers: 6)
Composite Structures     Hybrid Journal   (Followers: 262)
Composites Part A : Applied Science and Manufacturing     Hybrid Journal   (Followers: 183)
Composites Part B : Engineering     Hybrid Journal   (Followers: 281)
Composites Science and Technology     Hybrid Journal   (Followers: 179)
Comptes Rendus Mécanique     Full-text available via subscription   (Followers: 2)
Computation     Open Access  
Computational Geosciences     Hybrid Journal   (Followers: 14)
Computational Optimization and Applications     Hybrid Journal   (Followers: 7)
Computational Science and Discovery     Full-text available via subscription   (Followers: 2)
Computer Applications in Engineering Education     Hybrid Journal   (Followers: 6)
Computer Science and Engineering     Open Access   (Followers: 17)
Computers & Geosciences     Hybrid Journal   (Followers: 28)
Computers & Mathematics with Applications     Full-text available via subscription   (Followers: 5)
Computers and Electronics in Agriculture     Hybrid Journal   (Followers: 4)
Computers and Geotechnics     Hybrid Journal   (Followers: 10)
Computing and Visualization in Science     Hybrid Journal   (Followers: 5)
Computing in Science & Engineering     Full-text available via subscription   (Followers: 30)
Conciencia Tecnologica     Open Access  
Concurrent Engineering     Hybrid Journal   (Followers: 3)
Continuum Mechanics and Thermodynamics     Hybrid Journal   (Followers: 7)
Control and Dynamic Systems     Full-text available via subscription   (Followers: 9)
Control Engineering Practice     Hybrid Journal   (Followers: 42)
Control Theory and Informatics     Open Access   (Followers: 8)
Corrosion Science     Hybrid Journal   (Followers: 25)
CT&F Ciencia, Tecnologia y Futuro     Open Access  
CTheory     Open Access  
Current Applied Physics     Full-text available via subscription   (Followers: 4)

        1 2 3 4 5 6 7 | Last

Journal Cover Biomedical Engineering Letters
  [SJR: 0.339]   [H-I: 8]   [5 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 2093-9868 - ISSN (Online) 2093-985X
   Published by Springer-Verlag Homepage  [2352 journals]
  • MEG and EEG dipole clusters from extended cortical sources
    • Authors: Manfred Fuchs; Jörn Kastner; Reyko Tech; Michael Wagner; Fernando Gasca
      Pages: 185 - 191
      Abstract: Abstract Data from magnetoencephalography (MEG) and electroencephalography (EEG) suffer from a rather limited signal-to-noise-ratio (SNR) due to cortical background activities and other artifacts. In order to study the effect of the SNR on the size and distribution of dipole clusters reconstructed from interictal epileptic spikes, we performed simulations using realistically shaped volume conductor models and extended cortical sources with different sensor configurations. Head models and cortical surfaces were derived from an averaged magnetic resonance image dataset (Montreal Neurological Institute). Extended sources were simulated by spherical patches with Gaussian current distributions on the folded cortical surface. Different patch sizes were used to investigate cancellation effects from opposing walls of sulcal foldings and to estimate corresponding changes in MEG and EEG sensitivity distributions. Finally, white noise was added to the simulated fields and equivalent current dipole reconstructions were performed to determine size and shape of the resulting dipole clusters. Neuronal currents are oriented perpendicular to the local cortical surface and show cancellation effects of source components on opposing sulcal walls. Since these mostly tangential aspects from large cortical patches cancel out, large extended sources exhibit more radial components in the head geometry. This effect has a larger impact on MEG data as compared to EEG, because in a spherical head model radial currents do not yield any magnetic field. Confidence volumes of single reconstructed dipoles from simulated data at different SNRs show a good correlation with the extension of clusters from repeated dipole reconstructions. Size and shape of dipole clusters reconstructed from extended cortical sources do not only depend on spike and timepoint selection, but also strongly on the SNR of the measured interictal MEG or EEG data. In a linear approximation the size of the clusters is proportional to the inverse SNR.
      PubDate: 2017-08-01
      DOI: 10.1007/s13534-017-0019-2
      Issue No: Vol. 7, No. 3 (2017)
       
  • Statistical non-parametric mapping in sensor space
    • Authors: Michael Wagner; Reyko Tech; Manfred Fuchs; Jörn Kastner; Fernando Gasca
      Pages: 193 - 203
      Abstract: Abstract Establishing the significance of observed effects is a preliminary requirement for any meaningful interpretation of clinical and experimental Electroencephalography or Magnetoencephalography (MEG) data. We propose a method to evaluate significance on the level of sensors whilst retaining full temporal or spectral resolution. Input data are multiple realizations of sensor data. In this context, multiple realizations may be the individual epochs obtained in an evoked-response experiment, or group study data, possibly averaged within subject and event type, or spontaneous events such as spikes of different types. In this contribution, we apply Statistical non-Parametric Mapping (SnPM) to MEG sensor data. SnPM is a non-parametric permutation or randomization test that is assumption-free regarding distributional properties of the underlying data. The method, referred to as Maps SnPM, is demonstrated using MEG data from an auditory mismatch negativity paradigm with one frequent and two rare stimuli and validated by comparison with Topographic Analysis of Variance (TANOVA). The result is a time- or frequency-resolved breakdown of sensors that show consistent activity within and/or differ significantly between event or spike types. TANOVA and Maps SnPM were applied to the individual epochs obtained in an evoked-response experiment. The TANOVA analysis established data plausibility and identified latencies-of-interest for further analysis. Maps SnPM, in addition to the above, identified sensors of significantly different activity between stimulus types.
      PubDate: 2017-08-01
      DOI: 10.1007/s13534-017-0015-6
      Issue No: Vol. 7, No. 3 (2017)
       
  • A wearable system for adaptation to left–right reversed audition tested
           in combination with magnetoencephalography
    • Authors: Atsushi Aoyama; Shinya Kuriki
      Pages: 205 - 213
      Abstract: Abstract Exposure of humans to unusual spaces is effective to observe the adaptive strategy for an environment. Though adaptation to such spaces has been typically tested with vision, little has been examined about adaptation to left–right reversed audition, partially due to the apparatus for adaptation. Thus, it is unclear if the adaptive effects reach early auditory processing. Here, we constructed a left–right reversed stereophonic system using only wearable devices and asked two participants to wear it for 4 weeks. Every week, the magnetoencephalographic responses were measured under the selective reaction time task, where they immediately distinguished between sounds delivered to either the left or the right ear with the index finger on the compatible or incompatible side. The constructed system showed high performance in sound localization and achieved gradual reduction of a feeling of strangeness. The N1m intensities for the response-compatible sounds tended to be larger than those for the response-incompatible sounds until the third week but decreased on the fourth week, which correlated with the initially shorter and longer reaction times for the compatible and incompatible conditions, respectively. In the second week, disruption of the auditory-motor connectivity was observed with the largest N1m intensities and the longest reaction times, irrespective of compatibility. In conclusion, we successfully produced a high-quality space of left–right reversed audition using our system. The results suggest that a 4-week exposure to the reversed audition causes optimization of the auditory-motor coordination according to the new rule, which eventually results in the modulation of early auditory processing.
      PubDate: 2017-08-01
      DOI: 10.1007/s13534-017-0026-3
      Issue No: Vol. 7, No. 3 (2017)
       
  • Non-magnetic compliant finger sensor for continuous fine motor movement
           detection
    • Authors: Anterpal Sandhu; Yasong Li; Nicholas Peatfield; Xin Yi Yong; Ryan D’Arcy; Carlo Menon; Teresa P. L. Cheung
      Pages: 215 - 219
      Abstract: Abstract A non-magnetic MEG compatible device has been developed that provides continuous force and velocity information. Combined with MEG, this device may find utility in characterizing brain regions associated with force and velocity relative to individual digits or movement pattern. 15 healthy right-handed participants were given visual cues to perform random finger movements on the prototype finger sensor for 21 s and then rest for 21 s (7 times). Respective finger flexion data were obtained, during 151-channel MEG brain scanning, by feeding the signal from finger sensor into four input Analog to Digital Converter (ADC) channels in the MEG hardware. The source activity was reconstructed in beta band using a Linearly Constrained Minimum Variance (LCMV) beamformer in the beta band. The ADC channels were used as regressors for a continuous time General Linear Model (GLM) and a Region of Interest (ROI) was identified to examine activity. MEG analysis showed bilateral activation in the primary motor cortex region. Because individual digits could be isolated in the ADC data, somatotopy of the fingers were observed consistent with the homunculus except pinky finger. The total span was calculated to be 5.5662 mm. The study confirms that the finger sensor is magnetically compatible with MEG measurements and may potentially provide a means to study complex sensorimotor functions. Improved isolation of individual digit information along with the use of machine learning algorithms can help retrieve more accurate results.
      PubDate: 2017-08-01
      DOI: 10.1007/s13534-017-0031-6
      Issue No: Vol. 7, No. 3 (2017)
       
  • Dimensional contraction by principal component analysis as preprocessing
           for independent component analysis at MCG
    • Authors: M. Iwai; K. Kobayashi
      Pages: 221 - 227
      Abstract: Abstract We propose a noise reduction method for magnetocardiograms (MCGs) based on independent component analysis (ICA). ICA is useful to separate the noise and signal components, but ICA-based automatic noise reduction faces two main difficulties: the dimensional contraction process applied after the principal component analysis (PCA) used for preprocessing, and the component selection applied after ICA. The results of noise reduction vary among people, because these two processes typically depend on personal qualitative evaluations of the obtained components. Therefore, automatic quantitative ICA-based noise reduction is highly desirable. We will focus on the first difficulty, by improving the index used in the dimensional contraction process. The index used for component ordering after PCA affects the accuracy of separation obtained with ICA. The contribution ratio is often used as an index. However, its efficacy is highly dependent on the signal-to-noise ratio (SNR) it unsuitable for automation. We propose a kurtosis-based index, whose efficacy does not depend on SNR. We compare the two decision indexes through simulation. First, we evaluate their preservation rate of the MCG information after dimensional contraction. In addition, we evaluate their effect on the accuracy of the ICA-based noise reduction method. The obtained results show that the kurtosis-based index does preserve the MCG signal information through dimensional contraction, and has a more consistent behavior when the number of components increases. The proposed index performs better than the traditional index, especially in low SNRs. As such, it paves the way for the desired noise reduction process automation.
      PubDate: 2017-08-01
      DOI: 10.1007/s13534-017-0024-5
      Issue No: Vol. 7, No. 3 (2017)
       
  • A bench-top micro-CT capable of simulating head motions
    • Authors: Mohamed A. A. Hegazy; Mohamed Elsayed Eldib; Yang Ji Mun; Myung Hye Cho; Min Hyoung Cho; Soo Yeol Lee
      Pages: 237 - 244
      Abstract: Abstract Computational three-dimensional (3D) models of a dental structure generated from 3D dental computed tomography (CT) images are now widely used in digital dentistry. To generate precise 3D models, high-resolution imaging of the dental structure with a dental CT is required. However, a small head motion of the patient during the dental CT scan could degrade the spatial resolution of CT images to the extent that digital dentistry is no longer possible. A bench-top micro-CT has been built to evaluate the head motion effects on the dental CT images. A micro-CT has been built on an optic table with a micro-focus x-ray source and a flat-panel detector. A rotation stage, placed in between the x-ray source and the detector, is mounted on two-directional goniometers that can rotate the rotation stage in two orthogonal directions while the rotation stage is performing the CT scan. The goniometers can make object motions of an arbitrary waveform to simulate head tilting or head nodding. CT images of a phantom have been taken with and without introducing the motions, and the motion effects on the CT images have been evaluated. Object motions parallel to the detector plane have greater effects on the CT images than those against the detector plane. With the bench-top micro-CT, the motion effects have been visually seen at a tiny rotational motion as small as 0.3°. The bench-top micro-CT can be used to evaluate head motion effects on the dental CT images. The projection data, taken with the motion effects, would be used to develop motion artifact correction methods for a high-resolution dental-CT.
      PubDate: 2017-08-01
      DOI: 10.1007/s13534-017-0023-6
      Issue No: Vol. 7, No. 3 (2017)
       
  • Application of computer-aided approaches to the PUMC classification of
           scoliosis
    • Authors: Junhua Zhang; Hongjian Li; Yufeng Zhang
      Pages: 245 - 251
      Abstract: Abstract Surgical planning for scoliosis relies on the classification of the spinal curve pattern. To improve the reliability of the Peking Union Medical College (PUMC) classification system for scoliotic spinal curves, a computer-aided system is proposed and its reliability is evaluated. First, the reliability of curve measurements was improved by the computer-aided Cobb measurement approach. Second, judgmental errors were reduced by the computer program through the automation of the PUMC classification procedure. Four observers divided into an expert group and a resident group participated in the experiments. The kappa statistic was used to evaluate the variability. Classifications of 65 scoliotic cases by the four observers showed that with the computer’s aid, the average intraobserver and interobserver kappa values were improved from 0.86 to 0.93 and from 0.75 to 0.86, respectively. The results indicate that the proposed computerized system can assist a surgeon in the PUMC classification of scoliosis and is especially useful for inexperienced surgeons.
      PubDate: 2017-08-01
      DOI: 10.1007/s13534-017-0022-7
      Issue No: Vol. 7, No. 3 (2017)
       
  • Primary blast waves induced brain dynamics influenced by head orientations
    • Authors: Yi Hua; Yugang Wang; Linxia Gu
      Pages: 253 - 259
      Abstract: Abstract There is controversy regarding the directional dependence of head responses subjected to blast loading. The goal of this work is to characterize the role of head orientation in the mechanics of blast wave-head interactions as well as the load transmitting to the brain. A three-dimensional human head model with anatomical details was reconstructed from computed tomography images. Three different head orientations with respect to the oncoming blast wave, i.e., front-on with head facing blast, back-on with head facing away from blast, and side-on with right side exposed to blast, were considered. The reflected pressure at the blast wave-head interface positively correlated with the skull curvature. It is evidenced by the maximum reflected pressure occurring at the eye socket with the largest curvature on the skull. The reflected pressure pattern along with the local skull areas could further influence the intracranial pressure distributions within the brain. We did find out that the maximum coup pressure of 1.031 MPa in the side-on case as well as the maximum contrecoup pressure of −0.124 MPa in the back-on case. Moreover, the maximum principal strain (MPS) was also monitored due to its indication to diffuse brain injury. It was observed that the peak MPS located in the frontal cortex region regardless of the head orientation. However, the local peak MPS within each individual function region of the brain depended on the head orientation. The detailed interactions between blast wave and head orientations provided insights for evaluating the brain dynamics, as well as biomechanical factors leading to traumatic brain injury.
      PubDate: 2017-08-01
      DOI: 10.1007/s13534-017-0027-2
      Issue No: Vol. 7, No. 3 (2017)
       
  • Design of a stimulation protocol to predict temperature distribution in
           subcutaneous tissue using the finite element model
    • Authors: Hyoun-Seok Myoung; Dong-Hyun Kim; Han-Sung Kim; Kyoung-Joung Lee
      Pages: 261 - 266
      Abstract: Abstract Moxibustion is a traditional Oriental medicine therapy that treats the symptoms of a disease with thermal stimulation. However, it is difficult to control the strength of the thermal or chemical stimulus generated by the various types and amounts of moxa and to prevent energy loss through the skin. To overcome these problems, we previously developed a method to efficiently provide RF thermal stimulation to subcutaneous tissue. In this paper, we propose a finite element model (FEM) to predict temperature distributions in subcutaneous tissue after radio-frequency thermal stimulation. To evaluate the performance of the developed FEM, temperature distributions were obtained from the FEM, and in vivo experiments were conducted using the RF stimulation system at subcutaneous tissue depths of 5 and 10 mm in the femoral region of a rabbit model. High correlation coefficients between simulated and actual temperature distributions—0.98 at 5 mm and 0.99 at 10 mm—were obtained, despite some slight errors in the temperature distribution at each depth. These results demonstrate that the FEM described here can be used to determine thermal stimulation profiles produced by RF stimulation of subcutaneous tissue.
      PubDate: 2017-08-01
      DOI: 10.1007/s13534-017-0029-0
      Issue No: Vol. 7, No. 3 (2017)
       
  • Modelling side to side intestinal anastomosis
    • Authors: Javier Civit; Fernando de la Portilla; Jose Luis Sevillano; Anton Civit
      Pages: 267 - 271
      Abstract: Abstract Side-to-side intestinal anastomosis is a surgical procedure where an incision is performed between two parallel segments of gut and then they are sutured together. The purpose of this paper is to investigate if the standard surgical practice diameter used in anastomosis leads to undesirable closed circulatory flows which may be harmful to the gut tissue. A finite element model for the chyme flow in a side by side anastomosis with realistic user configurable parameters is developed and solved in a wide range of situations. We analyze the flow crossing the anastomosis, the normalized pressure difference in the gut section and the streamlines that show the presence or absence of closed flow regions for a set of surgically feasible anastomosis diameter values. In contrast with the findings of simpler analytical models, closed flows do not appear in any of these cases. The study shows that the current standard surgical practice where the anastomosis diameter is similar to the gut diameter does not lead to undesirable effects predicted by some simple analytical models.
      PubDate: 2017-08-01
      DOI: 10.1007/s13534-017-0032-5
      Issue No: Vol. 7, No. 3 (2017)
       
  • Performance of machine learning methods in diagnosing Parkinson’s
           disease based on dysphonia measures
    • Authors: Salim Lahmiri; Debra Ann Dawson; Amir Shmuel
      Abstract: Abstract Parkinson’s disease (PD) is a widespread degenerative syndrome that affects the nervous system. Its early appearing symptoms include tremor, rigidity, and vocal impairment (dysphonia). Consequently, speech indicators are important in the identification of PD based on dysphonic signs. In this regard, computer-aided-diagnosis systems based on machine learning can be useful in assisting clinicians in identifying PD patients. In this work, we evaluate the performance of machine learning based techniques for PD diagnosis based on dysphonia symptoms. Several machine learning techniques were considered and trained with a set of twenty-two voice disorder measurements to classify healthy and PD patients. These machine learning methods included linear discriminant analysis (LDA), k nearest-neighbors (k-NN), naïve Bayes (NB), regression trees (RT), radial basis function neural networks (RBFNN), support vector machine (SVM), and Mahalanobis distance classifier. We evaluated the performance of these methods by means of a tenfold cross validation protocol. Experimental results show that the SVM classifier achieved higher average performance than all other classifiers in terms of overall accuracy, G-mean, and area under the curve of the receiver operating characteristic plot. The SVM classifier achieved higher performance measures than the majority of the other classifiers also in terms of sensitivity, specificity, and F-measure statistics. The LDA, k-NN and RT achieved the highest average precision. The RBFNN method yielded the highest F-measure.; however, it performed poorly in terms of other performance metrics. Finally, t tests were performed to evaluate statistical significance of the results, confirming that the SVM outperformed most of the other classifiers on the majority of performance measures. SVM is a promising method for identifying PD patients based on classification of dysphonia measurements.
      PubDate: 2017-10-12
      DOI: 10.1007/s13534-017-0051-2
       
  • Computer-assisted brain tumor type discrimination using magnetic resonance
           imaging features
    • Authors: Sajid Iqbal; M. Usman Ghani Khan; Tanzila Saba; Amjad Rehman
      Abstract: Abstract Medical imaging plays an integral role in the identification, segmentation, and classification of brain tumors. The invention of MRI has opened new horizons for brain-related research. Recently, researchers have shifted their focus towards applying digital image processing techniques to extract, analyze and categorize brain tumors from MRI. Categorization of brain tumors is defined in a hierarchical way moving from major to minor ones. A plethora of work could be seen in literature related to the classification of brain tumors in categories such as benign and malignant. However, there are only a few works reported on the multiclass classification of brain images where each part of the image containing tumor is tagged with major and minor categories. The precise classification is difficult to achieve due to ambiguities in images and overlapping characteristics of different type of tumors. In the current study, a comprehensive review of recent research on brain tumors multiclass classification using MRI is provided. These multiclass classification studies are categorized into two major groups: XX and YY and each group are further divided into three sub-groups. A set of common parameters from the reviewed works is extracted and compared to highlight the merits and demerits of individual works. Based on our analysis, we provide a set of recommendations for researchers and professionals working in the area of brain tumors classification.
      PubDate: 2017-10-04
      DOI: 10.1007/s13534-017-0050-3
       
  • PID controller design to generate pulsatile flow rate for in vitro
           experimental studies of physiological flows
    • Authors: Mohammad Reza Najjari; Michael W. Plesniak
      Abstract: Abstract Producing accurate pulsatile flow rates is essential for many in vitro experimental studies in biofluid dynamics research. A controller system was developed to control a flow loop to produce easily adjustable pulsatile flow rates with sufficient accuracy. An Arduino board is used as a micro-controller to control a pump to produce various pulsatile flow rates, and an open-source proportional-integral-derivative (PID) control algorithm is developed for this purpose. Four non-trivial pulsatile waveforms were produced by the PID controller, as well as an iterative controller, and the performance of both controllers was evaluated. Both the PID and iterative controllers were able to successfully produce slowly-varying signals (single and multi-harmonic low frequency sine waves), but for high frequency signals where the flow has strong acceleration/deceleration (e.g. for physiological waveforms) the iterative controller exhibited significant undershoot. The comparison of PID and iterative controllers suggests that if the desired flow rate is a low frequency, simple waveform then the iterative controller is preferred due to simplicity of implementation. However, if the desired signal is rapidly changing and more complicated then the PID controller achieves better results. This system can be implemented in many flow loops due to its simplicity and low cost, and does not require a mathematical model of the system.
      PubDate: 2017-09-30
      DOI: 10.1007/s13534-017-0049-9
       
  • Gastrointestinal polyp detection in endoscopic images using an improved
           feature extraction method
    • Authors: Mustain Billah; Sajjad Waheed
      Abstract: Abstract Gastrointestinal polyps are treated as the precursors of cancer development. So, possibility of cancers can be reduced at a great extent by early detection and removal of polyps. The most used diagnostic modality for gastrointestinal polyps is video endoscopy. But, as an operator dependant procedure, several human factors can lead to miss detection of polyps. In this peper, an improved computer aided polyp detection method has been proposed. Proposed improved method can reduce polyp miss detection rate and assists doctors in finding the most important regions to pay attention. Color wavelet features and convolutional neural network features are extracted from endoscopic images, which are used for training a support vector machine. Then a target endoscopic image will be given to the classifier as input in order to find whether it contains any polyp or not. If polyp is found, it will be marked automatically. Experiment shows that, color wavelet features and convolutional neural network features together construct a highly representative of endoscopic polyp images. Evaluations on standard public databases show that, proposed system outperforms state-of-the-art methods, gaining accuracy of 98.34%, sensitivity of 98.67% and specificity of 98.23%. In this paper, the strength of color wavelet features and power of convolutional neural network features are combined. Fusion of these two methodology and use of support vector machine results in an improved method for gastrointestinal polyp detection. An analysis of ROC reveals that, proposed method can be used for polyp detection purposes with greater accuracy than state-of-the-art methods.
      PubDate: 2017-09-07
      DOI: 10.1007/s13534-017-0048-x
       
  • Variations in gait features in elderly adults during walking considering
           their balance
    • Authors: Hyuk-Jae Choi; Han-Sung Kim; Jeicheong Ryu; Gyoosuk Kim; Chang-Yong Ko
      Abstract: Abstract The aim of this study was to evaluate the influence of balance on the spatiotemporal features, lower-limb kinematics, and center of mass (COM) of the non-faller elderly during walking. In this study, 20 healthy elderly women (age, 76.2 ± 5.6 years; height, 150.1 ± 3.2 cm; weight, 55.8 ± 9.0 kg) were enrolled. Based on the Berg balance scale (BBS), the elderly were classified into two groups: poor balance (PB; BBS scores <46; n = 10; 43.8 ± 1.8) and good balance (GB; BBS scores ≥46; n = 10; 50.4 ± 2.5). The two groups had no differences in terms of the spatiotemporal features and range of motion (ROM) of the vertical COM (all p > 0.05). The ROM of the mediolateral COM was greater in PB than in GB. Hip transversal movements in the two groups were different. The impairment of the lateral balance function might contribute to an increase in the incidence of fall events in the elderly with poor balance.
      PubDate: 2017-09-04
      DOI: 10.1007/s13534-017-0045-0
       
  • Deep-learning-based automatic computer-aided diagnosis system for diabetic
           retinopathy
    • Authors: Romany F. Mansour
      Abstract: Abstract The high-pace rise in advanced computing and imaging systems has given rise to a new research dimension called computer-aided diagnosis (CAD) system for various biomedical purposes. CAD-based diabetic retinopathy (DR) can be of paramount significance to enable early disease detection and diagnosis decision. Considering the robustness of deep neural networks (DNNs) to solve highly intricate classification problems, in this paper, AlexNet DNN, which functions on the basis of convolutional neural network (CNN), has been applied to enable an optimal DR CAD solution. The DR model applies a multilevel optimization measure that incorporates pre-processing, adaptive-learning-based Gaussian mixture model (GMM)-based concept region segmentation, connected component-analysis-based region of interest (ROI) localization, AlexNet DNN-based highly dimensional feature extraction, principle component analysis (PCA)- and linear discriminant analysis (LDA)-based feature selection, and support-vector-machine-based classification to ensure optimal five-class DR classification. The simulation results with standard KAGGLE fundus datasets reveal that the proposed AlexNet DNN-based DR exhibits a better performance with LDA feature selection, where it exhibits a DR classification accuracy of 97.93% with FC7 features, whereas with PCA, it shows 95.26% accuracy. Comparative analysis with spatial invariant feature transform (SIFT) technique (accuracy—94.40%) based DR feature extraction also confirms that AlexNet DNN-based DR outperforms SIFT-based DR.
      PubDate: 2017-08-31
      DOI: 10.1007/s13534-017-0047-y
       
  • Automatic heart activity diagnosis based on Gram polynomials and
           probabilistic neural networks
    • Authors: Francesco Beritelli; Giacomo Capizzi; Grazia Lo Sciuto; Christian Napoli; Francesco Scaglione
      Abstract: Abstract The paper proposes a new approach to heart activity diagnosis based on Gram polynomials and probabilistic neural networks (PNN). Heart disease recognition is based on the analysis of phonocardiogram (PCG) digital sequences. The PNN provides a powerful tool for proper classification of the input data set. The novelty of the proposed approach lies in a powerful feature extraction based on Gram polynomials and the Fourier transform. The proposed system presents good performance obtaining overall sensitivity of 93%, specificity of 91% and accuracy of 94%, using a public database of over 3000 heart beat sound recordings, classified as normal and abnormal heart sounds. Thus, it can be concluded that Gram polynomials and PNN prove to be a very efficient technique using the PCG signal for characterizing heart diseases.
      PubDate: 2017-08-22
      DOI: 10.1007/s13534-017-0046-z
       
  • The research of sleep staging based on single-lead electrocardiogram and
           deep neural network
    • Authors: Ran Wei; Xinghua Zhang; Jinhai Wang; Xin Dang
      Abstract: Abstract The polysomnogram (PSG) analysis is considered the golden standard for sleep staging under the clinical environment. The electroencephalogram (EEG) signal is the most important signal for classification of sleep stages. However, in-vivo signal recording and analysis of EEG signal presents us with a few technical challenges. Electrocardiogram signals on the other hand, are easier to record, and can provide an attractive alternative for home sleep monitoring. In this paper we describe a method based on deep neural network (DNN), which can be used for the classification of the sleep stages into Wake (W), rapid-eye-movement (REM) and non-rapid-eye-movement (NREM) sleep stage. We apply the sleep stage stacked autoencoder to constitute a 4-layer DNN model. In order to test the accuracy of our method, eighteen PSGs from the MIT-BIH Polysomnographic Database were used. A total of 11 features were extracted from each electrocardiogram recording The experimental design employs cross-validation across subjects, ensuring the independence of the training and the test data. We obtained an accuracy of 77% and a Cohen’s kappa coefficient of about 0.56 for the classification of Wake, REM and NREM.
      PubDate: 2017-08-01
      DOI: 10.1007/s13534-017-0044-1
       
  • Recent advances in biomagnetism and its applications
    • Authors: Chang-Hwan Im; Sung Chan Jun; Kensuke Sekihara
      PubDate: 2017-07-12
      DOI: 10.1007/s13534-017-0042-3
       
  • Unified principles of thalamo-cortical processing: the neural switch
    • Authors: Urs Ribary; S. M. Doesburg; L. M. Ward
      Abstract: Abstract It has been reported that cross-frequency interactions may play an important role in local processing within thalamus and neocortex, as well as information transfer between subcortical and cortico-cortical brain regions. Strong commonalities in rhythmic network properties have been observed across recording techniques and task demands, but strong neuroscientific theories to situate such observations within a unified framework with direct relevance to explain neuropathologies remain scarce. Based on a comprehensive review of animal and human literature, we probe and introduce a neurophysiological framework to explain how coordinated cross-frequency and interregional oscillatory cortical dynamics underlie typical and atypical brain activation, and the formation of distributed functional ensembles supporting cortical networks underpinning perception and cognition. We propose that local regional activation by an external stimulus via a sensory pathway entails (1) attenuated alpha (8–14 Hz) and increased theta (4–8 Hz) and gamma (30–50 Hz) oscillatory activity, and (2) increased interactions among theta and gamma rhythms. These local dynamics also mediate the integration of activated neural populations into large-scale functional assemblies through neuronal synchronization. This comprehensive perspective into the animal and human literature indicates a further thinking beyond synchrony and connectivity and the readiness for more hypothesis-driven research and modeling toward unified principles of thalamo-cortical processing. We further introduced such a possible framework: “The ATG switch”. We also discussed evidence that alpha–theta–gamma dynamics emerging from thalamocortical interactions may be implicated and disrupted in numerous neurological and neuropsychiatric conditions.
      PubDate: 2017-05-02
      DOI: 10.1007/s13534-017-0033-4
       
 
 
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