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	<title>Medicine Archives &#8212; MATLAB Number ONE</title>
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	<link>https://matlab1.com/category/medicine/</link>
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	<title>Medicine Archives &#8212; MATLAB Number ONE</title>
	<link>https://matlab1.com/category/medicine/</link>
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		<title>Main Effect Of The Propagation Of Ultrasonic Waves In Human Tissues</title>
		<link>https://matlab1.com/main-effect-propagation-ultrasonic-waves-human-tissues/</link>
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		<dc:creator><![CDATA[global MATLAB]]></dc:creator>
		<pubDate>Tue, 28 Nov 2017 18:52:15 +0000</pubDate>
				<category><![CDATA[Medicine]]></category>
		<category><![CDATA[10256539]]></category>
		<category><![CDATA[Human Tissues]]></category>
		<category><![CDATA[Main Effect Of The Propagation Of Ultrasonic Waves In Human Tissues]]></category>
		<category><![CDATA[The Propagation Of Ultrasonic Waves In Human Tissues]]></category>
		<guid isPermaLink="false">https://matlab1.com/?p=3893</guid>

					<description><![CDATA[<p>Main Effect Of The Propagation Of Ultrasonic Waves In Human Tissues The most obvious effect is heating. Since a significant portion of the energy is absorbed and converted into heat during ultrasound propagation, this could potentially lead to a temperature increase. As the wave intensity is increased, temperature rises and if the temperature becomes higher [&#8230;]</p>
<p>The post <a href="https://matlab1.com/main-effect-propagation-ultrasonic-waves-human-tissues/">Main Effect Of The Propagation Of Ultrasonic Waves In Human Tissues</a> appeared first on <a href="https://matlab1.com">MATLAB Number ONE</a>.</p>
]]></description>
		
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		<title>Ultrasonic Communications in Human Tissues</title>
		<link>https://matlab1.com/ultrasonic-communications-human-tissues/</link>
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		<dc:creator><![CDATA[global MATLAB]]></dc:creator>
		<pubDate>Tue, 28 Nov 2017 18:46:45 +0000</pubDate>
				<category><![CDATA[Medicine]]></category>
		<category><![CDATA[10256539]]></category>
		<category><![CDATA[Fundamentals of UltrasoundWaves]]></category>
		<category><![CDATA[Ultrasonic Communications in Human Tissues]]></category>
		<category><![CDATA[Ultrasonic Transducers]]></category>
		<guid isPermaLink="false">https://matlab1.com/?p=3867</guid>

					<description><![CDATA[<p>Ultrasonic Communications in Human Tissues Fundamentals of UltrasoundWaves Ultrasounds are mechanical pressure waves with a frequency above the upper limit of human hearing, i.e., 20 kHz. Ultrasounds consist of mechanical vibrations of particles in a material. Even if each particle oscillates around its rest position, the vibration energy propagates as a wave traveling from particle [&#8230;]</p>
<p>The post <a href="https://matlab1.com/ultrasonic-communications-human-tissues/">Ultrasonic Communications in Human Tissues</a> appeared first on <a href="https://matlab1.com">MATLAB Number ONE</a>.</p>
]]></description>
		
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		<item>
		<title>Spiking Model In Vivo Neurophysiology</title>
		<link>https://matlab1.com/spiking-model-vivo-neurophysiology/</link>
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		<dc:creator><![CDATA[global MATLAB]]></dc:creator>
		<pubDate>Tue, 28 Nov 2017 13:33:07 +0000</pubDate>
				<category><![CDATA[data mining]]></category>
		<category><![CDATA[Medicine]]></category>
		<category><![CDATA[10253889]]></category>
		<category><![CDATA[cathodal-leading pulse]]></category>
		<category><![CDATA[microelectrode array]]></category>
		<category><![CDATA[Multichannel recordings]]></category>
		<category><![CDATA[neurophysiological recording]]></category>
		<category><![CDATA[Tucker-Davis Technologies (TDT)]]></category>
		<guid isPermaLink="false">https://matlab1.com/?p=3739</guid>

					<description><![CDATA[<p>Spiking Model In Vivo Neurophysiology The animals employed for neurophysiological recording were adult, male Long-Evans rats acquired at 4 months of age. The animal protocols used were approved by the University of Kansas Medical Center Institutional Animal Care and Use Committee, and the experiments were performed in compliance with the Guide for the Care and [&#8230;]</p>
<p>The post <a href="https://matlab1.com/spiking-model-vivo-neurophysiology/">Spiking Model In Vivo Neurophysiology</a> appeared first on <a href="https://matlab1.com">MATLAB Number ONE</a>.</p>
]]></description>
		
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		<item>
		<title>Noise in MEA recordings</title>
		<link>https://matlab1.com/noise-mea-recordings/</link>
					<comments>https://matlab1.com/noise-mea-recordings/#respond</comments>
		
		<dc:creator><![CDATA[global MATLAB]]></dc:creator>
		<pubDate>Tue, 28 Nov 2017 13:22:53 +0000</pubDate>
				<category><![CDATA[data mining]]></category>
		<category><![CDATA[Medicine]]></category>
		<category><![CDATA[10253889]]></category>
		<category><![CDATA[electrophysiological recordings offline]]></category>
		<category><![CDATA[hyperpolarization period]]></category>
		<category><![CDATA[MEA recordings]]></category>
		<category><![CDATA[multichannel]]></category>
		<category><![CDATA[neuron]]></category>
		<category><![CDATA[SNR]]></category>
		<guid isPermaLink="false">https://matlab1.com/?p=3735</guid>

					<description><![CDATA[<p>Noise in MEA recordings In addition to inherent noise coming from the source, i.e. the neuron, as previously described, there are several other sources of additive noise in an extracellular recording coming from the biological interface, the electrode interface and the device employed for recording. The biological noise contains unwanted electrical signatures including action potentials, [&#8230;]</p>
<p>The post <a href="https://matlab1.com/noise-mea-recordings/">Noise in MEA recordings</a> appeared first on <a href="https://matlab1.com">MATLAB Number ONE</a>.</p>
]]></description>
		
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		<title>Extracellular Recordings In Neuron</title>
		<link>https://matlab1.com/extracellular-recordings-neuron/</link>
					<comments>https://matlab1.com/extracellular-recordings-neuron/#respond</comments>
		
		<dc:creator><![CDATA[global MATLAB]]></dc:creator>
		<pubDate>Mon, 27 Nov 2017 10:48:55 +0000</pubDate>
				<category><![CDATA[data mining]]></category>
		<category><![CDATA[Medicine]]></category>
		<category><![CDATA[10253889]]></category>
		<category><![CDATA[amplified]]></category>
		<category><![CDATA[electrophysiological]]></category>
		<category><![CDATA[Extracellular recordings]]></category>
		<category><![CDATA[Processing pipeline]]></category>
		<category><![CDATA[waveforms]]></category>
		<guid isPermaLink="false">https://matlab1.com/?p=3674</guid>

					<description><![CDATA[<p>Extracellular Recordings In Neuron Most extracellular recording experiments follow the same general process to collect multiunit activity and identify individual neurons in the environment, albeit employing different methodologies for each step in the process, as depicted in Figure 2.1. Raw signals collected by electrodes are recorded and amplified by an electrophysiological recording system. The incoming [&#8230;]</p>
<p>The post <a href="https://matlab1.com/extracellular-recordings-neuron/">Extracellular Recordings In Neuron</a> appeared first on <a href="https://matlab1.com">MATLAB Number ONE</a>.</p>
]]></description>
		
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		<item>
		<title>Extracellular Activity In Neuron</title>
		<link>https://matlab1.com/extracellular-activity-neuron/</link>
					<comments>https://matlab1.com/extracellular-activity-neuron/#respond</comments>
		
		<dc:creator><![CDATA[global MATLAB]]></dc:creator>
		<pubDate>Mon, 27 Nov 2017 10:34:46 +0000</pubDate>
				<category><![CDATA[data mining]]></category>
		<category><![CDATA[Medicine]]></category>
		<category><![CDATA[10253889]]></category>
		<category><![CDATA[biosynthetic]]></category>
		<category><![CDATA[cell body]]></category>
		<category><![CDATA[Extracellular Activity In Neuron]]></category>
		<category><![CDATA[hyper polarization]]></category>
		<guid isPermaLink="false">https://matlab1.com/?p=3670</guid>

					<description><![CDATA[<p>Extracellular Activity In Neuron Neurons are composed of three components: soma, axon, and dendrites. As in any cell, the soma or cell body is responsible for metabolic function, but in neurons it is further specialized to maintain high levels of biosynthetic activity. Inputs are received and integrated by the dendritic arbor (and to a small [&#8230;]</p>
<p>The post <a href="https://matlab1.com/extracellular-activity-neuron/">Extracellular Activity In Neuron</a> appeared first on <a href="https://matlab1.com">MATLAB Number ONE</a>.</p>
]]></description>
		
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		<item>
		<title>Methods to facilitate the study of precise temporal coding in biological and artificial networks</title>
		<link>https://matlab1.com/methods-facilitate-study-precise-temporal-coding-biological-artificial-networks/</link>
					<comments>https://matlab1.com/methods-facilitate-study-precise-temporal-coding-biological-artificial-networks/#respond</comments>
		
		<dc:creator><![CDATA[global MATLAB]]></dc:creator>
		<pubDate>Mon, 27 Nov 2017 10:24:27 +0000</pubDate>
				<category><![CDATA[data mining]]></category>
		<category><![CDATA[Medicine]]></category>
		<category><![CDATA[10253889]]></category>
		<category><![CDATA[electrophysiological]]></category>
		<category><![CDATA[intracortical microstimulation (ICMS)]]></category>
		<category><![CDATA[MEA Recording]]></category>
		<category><![CDATA[Micro-Electrode Array (MEA)]]></category>
		<category><![CDATA[neuromorphic systems]]></category>
		<category><![CDATA[receiver operating characteristics (ROC)]]></category>
		<category><![CDATA[SNR]]></category>
		<category><![CDATA[Spiking Model]]></category>
		<category><![CDATA[waveforms]]></category>
		<guid isPermaLink="false">https://matlab1.com/?p=3667</guid>

					<description><![CDATA[<p>Neuron Model The brain is a network of billions of neurons , which makes the brain orders of magnitude more dense than state-of-the-art silicon-based VLSI electronic systems and thus challenging to map and study with conventional methods . Furthermore, it is estimated that the human brain can process, analyze, and learn with a power consumption [&#8230;]</p>
<p>The post <a href="https://matlab1.com/methods-facilitate-study-precise-temporal-coding-biological-artificial-networks/">Methods to facilitate the study of precise temporal coding in biological and artificial networks</a> appeared first on <a href="https://matlab1.com">MATLAB Number ONE</a>.</p>
]]></description>
		
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		<item>
		<title>Accelerating Parallel Image Reconstruction Using Random Projection</title>
		<link>https://matlab1.com/accelerating-parallel-image-reconstruction-using-random-projection/</link>
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		<dc:creator><![CDATA[global MATLAB]]></dc:creator>
		<pubDate>Sun, 26 Nov 2017 19:14:05 +0000</pubDate>
				<category><![CDATA[Computer]]></category>
		<category><![CDATA[data mining]]></category>
		<category><![CDATA[database of image]]></category>
		<category><![CDATA[image processing]]></category>
		<category><![CDATA[Medicine]]></category>
		<category><![CDATA[10251459]]></category>
		<category><![CDATA[Accelerating Parallel Image]]></category>
		<category><![CDATA[ACS data]]></category>
		<category><![CDATA[Generalized auto-calibrating partially parallel acquisition (GRAPPA)]]></category>
		<category><![CDATA[matrix multiplication]]></category>
		<category><![CDATA[principal component analysis (PCA)]]></category>
		<category><![CDATA[signal-to-noise ratios (SNR)]]></category>
		<guid isPermaLink="false">https://matlab1.com/?p=3652</guid>

					<description><![CDATA[<p>Accelerating Parallel Image Reconstruction Using Random Projection Prologue Random projection has been used for data dimension reduction . The concept of random projection is related to compressed sensing , a topic that has attracted many attentions recently. By projecting the data to lower dimensions using some random matrices with certain properties (e.g., the restricted isometry [&#8230;]</p>
<p>The post <a href="https://matlab1.com/accelerating-parallel-image-reconstruction-using-random-projection/">Accelerating Parallel Image Reconstruction Using Random Projection</a> appeared first on <a href="https://matlab1.com">MATLAB Number ONE</a>.</p>
]]></description>
		
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		<item>
		<title>Compressed Sensing MRI</title>
		<link>https://matlab1.com/compressed-sensing-mri/</link>
					<comments>https://matlab1.com/compressed-sensing-mri/#respond</comments>
		
		<dc:creator><![CDATA[global MATLAB]]></dc:creator>
		<pubDate>Sun, 26 Nov 2017 18:40:51 +0000</pubDate>
				<category><![CDATA[Computer]]></category>
		<category><![CDATA[data mining]]></category>
		<category><![CDATA[database of image]]></category>
		<category><![CDATA[image processing]]></category>
		<category><![CDATA[Medicine]]></category>
		<category><![CDATA[10251459]]></category>
		<category><![CDATA[Compressed Sensing]]></category>
		<category><![CDATA[Compressed Sensing MRI]]></category>
		<category><![CDATA[fundamental electrodynamic principles]]></category>
		<category><![CDATA[JPEG2000]]></category>
		<category><![CDATA[medical imaging]]></category>
		<category><![CDATA[Sparsity]]></category>
		<guid isPermaLink="false">https://matlab1.com/?p=3644</guid>

					<description><![CDATA[<p>Compressed Sensing MRI Parallel imaging has led to revolutionary progress in the field of rapid MRI in the past two decades. However, as discussed in the previous section, the maximum acceleration that can be achieved in parallel imaging is limited by the number and the design of coils, and ultimately by fundamental electrodynamic principles. Compressed [&#8230;]</p>
<p>The post <a href="https://matlab1.com/compressed-sensing-mri/">Compressed Sensing MRI</a> appeared first on <a href="https://matlab1.com">MATLAB Number ONE</a>.</p>
]]></description>
		
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		<title>Parallel MRI</title>
		<link>https://matlab1.com/parallel-mri/</link>
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		<dc:creator><![CDATA[global MATLAB]]></dc:creator>
		<pubDate>Sun, 26 Nov 2017 18:18:53 +0000</pubDate>
				<category><![CDATA[Computer]]></category>
		<category><![CDATA[data mining]]></category>
		<category><![CDATA[database of image]]></category>
		<category><![CDATA[image processing]]></category>
		<category><![CDATA[Medicine]]></category>
		<category><![CDATA[10251459]]></category>
		<category><![CDATA[Coil Arrays]]></category>
		<category><![CDATA[dimensional imaging]]></category>
		<category><![CDATA[Generalized Parallel MRI]]></category>
		<category><![CDATA[k-space data]]></category>
		<category><![CDATA[multicoil MR signal]]></category>
		<category><![CDATA[oise covariance matrix]]></category>
		<category><![CDATA[Speed in MRI]]></category>
		<guid isPermaLink="false">https://matlab1.com/?p=3633</guid>

					<description><![CDATA[<p>The Need for Speed in MRI MR imaging speed is of critical importance in many clinical applications. However, the imaging speed with which gradient-encoded MR images can be acquired is fundamentally limited by the sequential nature of gradient-based MR acquisitions, in which only one k-space line can be acquired per unit time. In order to [&#8230;]</p>
<p>The post <a href="https://matlab1.com/parallel-mri/">Parallel MRI</a> appeared first on <a href="https://matlab1.com">MATLAB Number ONE</a>.</p>
]]></description>
		
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