Ensemble-Empirical-Mode-Decomposition based micro-Doppler signal separation and classification

The target echo signals obtained by Synthetic Aperture Radar (SAR) and Ground Moving Target Indicator (GMTI platforms are mainly composed of two parts, the micro-Doppler signal and the target body part signal. The wheeled vehicle and the track vehicle are classified according to the different character of their micro-Doppler signal. In order to overcome the mode mixing problem in Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD) is employed to decompose the original signal into a number of Intrinsic Mode Functions (IMF). The correlation analysis is then carried out to select IMFs which have a relatively high correlation with the micro-Doppler signal. Thereafter, four discriminative features are extracted and Support Vector Machine (SVM) classifier is applied for classification. The experimental results show that the features extracted after EEMD decomposition are effective, with up 90% success rate for classification using one feature. In addition, these four features are complementary in different target velocity and azimuth angles

Automatisierte Planung von digitalen Hochgeschwindigkeitsnetzen

Der Ausbau von digitalen Hochgeschwindigkeitsnetzen ist gekennzeichnet durch neuartige Anforderungen an den Planungsprozess. Diese Anforderungen erfordern wiederum den Einsatz von neuartigen Paradigmen, die eine effiziente und zugleich genaue Planung von flächendeckenden Glasfasernetzen ermöglichen. Hierbei können wiederkehrende Planungsaufgaben durch eine gezielte computergestützte Automatisierung effizienter und genauer ausgeführt, als es mit bisherigen Planungskonzepten möglich ist. Dieses Arbeitspapier beschreibt die computergestützte Ausführung eines Planungsprozesses auf Basis von fünf grundlegenden, iterativen Planungsschritten und gibt Empfehlungen für eine effiziente und genaue Planung von Glasfasernetzen. Der hier vorgestellte Ansatz ermöglicht es Netzbetreibern und Investoren, den Ausbau beliebiger Siedlungs- und Gewerbegebiete auf der zuverlässigen Basis von belastbarem Faktenwissen wirtschaftlich zu priorisieren

Cycle Threshold (C_T) values for ECs in validation cohort.

<p>Cycle Threshold (C_T) values for ECs in validation cohort.</p

Variation associated with each candidate EC.

<p>Bonferroni confidence intervals for standard deviations. There was a significant difference in variance (p<0.001, Bartletts's test) associated with each candidate EC, indicating differing stabilities.<i>MiR-16</i> showed greater variance than <i>miR-425</i> and <i>U6</i>.</p

Equivalence test for candidate ECs.

<p>Each line represents the difference in logarithmic (log base2) expression between the cancer and control groups. The upper and lower bars of individual candidate ECs represents the upper and lower limits of symmetrical confidence intervals, respectively. Confidence intervals between −1 and +1 corresponded to fold changes of ≤2. No candidate EC displayed a fold change greater than 2. All three candidate ECs were equivalently expressed.</p

GME analysis to determine 10 most stably expressed miRNAs from the microarray dataset.

<p>The top 10 most stably expressed miRNAs following normalization of the microarray data using global mean expression (GME). Nine of these miRNAs have been implicated in breast cancer. <i>MiR-425</i> is the only candidate miRNA with no reported association with breast or any other cancer type.</p

Quantity of each candidate miRNA on microarray analysis.

<p>The quantity of each miRNA or snoRNA (quantification cycle/cycle threshold) was determined by microarray for the cancer group and the control group. There was no significant difference (t-test) in candidate EC expression between the cancer group and the control group.</p

Clinico-pathological data for blood samples derived from breast cancer cases and controls for microarray and RQ-PCR analysis.

<p>Mm, millimeter; UICC, breast tumor staging according to the International Union Against Cancer criteria; HER2/<i>neu</i>, human epidermal growth factor receptor. Control subjects had no personal or family history of breast or ovarian cancer and were clinically well at the time of sampling.</p

Determination of the best combination of ECs for normalization.

<p>Determination of optimum number of candidate ECs for normalization. The GeNorm programme establishes the optimum combination of candidate ECs for normalization, producing the lowest V variable. This factor is calculated using the variable ‘V’ as the pairwise variation (Vn/Vn+1) between two sequential normalization factors (NFs) (NF_n and NF_n+1). The combination of candidate ECs is deemed optimal when the V variable achieves the lowest value. The optimal combination was achieved by combining <i>miR-16</i> and <i>miR-425</i>.</p

Candidate endogenous controls.

<p>mirBase database accession number</p><p>NCBI Gene ID</p