Echo Components Research Articles

Assessing the effectiveness and feasibility of the Experienced Carers Helping Others program in relatives of adolescents with eating disorders using an online application format with individual sessions.

Eating disorders (ED) usually involve hospital admission and a high relapse rate, with the return home being a critical moment for patients and their families. After their return home, they often have trouble incorporating the guidelines they have learned into their daily context. ECHOMANTRA intervention program aims to facilitate this transition by offering psychological strategies that involve both patients and their families and carers. Specifically, the ECHO program is aimed at the relatives of these patients. The present study aimed to analyze the efficacy of adding the ECHO program to the usual treatment (TAU) of relatives through a novel format based on individual intervention and with an online format and to examine the acceptability and feasibility of this new format. The study design was multi-center, randomized, controlled, with a longitudinal design and comparing two parallel groups. A total of 108 family members participated. Results indicated that relatives from both groups, TAU and ECHO + TAU, showed improvements in expressed emotion, accommodation, impact of the ED, emotional well-being, and caregiver skills. However, effect sizes in the ECHO + TAU group were slightly larger than the TAU group. In addition, the changes were greater in depression and caregiver skills when the ECHO component was added. Most caregivers (81.48%) completed the ECHO and indicated a high level of satisfaction with the program. These results suggest the efficacy and the feasibility of adding the ECHO intervention program to the usual treatment in an individual online format.

The factors that influence the diagnostic accuracy and sample adequacy of EUS-guided tissue acquisition for the diagnosis of solid pancreatic lesions.

EUS-guided tissue acquisition (EUS-TA) is the preferred method to acquire pancreatic cancer (PC) tissues. The factors associated with false-negative outcomes and inadequate samples should be explored to gain an understanding of EUS-TA. The patients who underwent EUS-TA for suspected solid PC but whose results were false-negative were analyzed. The PC patients who underwent EUS-TA with true-positive results on the first day of every month during the study period were selected as the control group. The factors influencing diagnostic accuracy and sample adequacy were explored. From November 2017 to January 2022, 184 patients were included in the false-negative group, and 175 patients were included in the control group. Multivariate logistic regression demonstrated that the recent acute pancreatitis [odds ratio (OR): 0.478, 95% confidence interval (CI): 0.250-0.914, P = 0.026] and high echo component within the tumor (OR: 0.103, 95% CI: 0.027-0.400, P = 0.001) were independently associated with false-negative EUS-TA results. Meanwhile, using fine-needle biopsy (FNB) needles (OR: 2.270, 95% CI: 1.277-4.035, P = 0.005), more needle passes (OR: 1.651,95% CI: 1.239-2.199, P = 0.005), large tumor size (OR: 1.053, 95% CI: 1.029-1.077, P < 0.001), and high CA-19-9 level (OR: 1.001, 95% CI: 1.000-1.001, P = 0.019) were independently associated with true-positive EUS-TA outcomes. Three needle passes are needed to achieve optimal EUS-TA outcomes. Tumor location in the body/tail (OR: 1.38, 95% CI: 1.01-1.72; P = 0.04), needle passes ≥3 (OR: 1.90; 95% CI: 1.22-2.56; P < 0.001), and using the FNB needle (OR: 2.10; 95%: 1.48-2.85; P < 0.001) were independently related to sample adequacy. Numerous factors were identified to be associated with the diagnostic accuracy and sample adequacy of EUS-TA.

Micro‐motion signal time‐frequency results inversion of rotor targets under low signal‐to‐noise ratios

AbstractA signal time‐frequency results inversion method is proposed for extracting micro‐motion features of rotor targets under low signal‐to‐noise ratios (SNRs). In the case of low SNRs, the echo's energy of rotor targets is mainly concentrated in the flash's echo component. Conventional micro‐motion feature extraction of rotor targets primarily utilises the sinusoidal modulation feature in time‐frequency results, whose energy is much lower than the flash. Under low SNRs, the sinusoidal modulation in the echo's time‐frequency results will be submerged in the noise, making feature extraction challenging. A deep learning network is used to inverse the time‐frequency results containing sinusoidal modulation based on the flash's features in the time‐frequency results. Based on the inversion time‐frequency results, the GS‐IRadon algorithm is used to extract micro‐motion features, which can significantly reduce the times of IRadon transformations and improve feature extraction speed and accuracy. Through simulation and analysis, a novel method using a deep learning network like UNet can effectively inverse time‐frequency results under low SNRs, providing a new technical approach for micro‐motion feature extraction. Time‐frequency results inversion is a novelty method used to achieve micro‐motion feature extraction of rotor targets.

UHECR echoes from the Council of Giants

ABSTRACT Recent anisotropy studies of UHECR data at energies ≳40 EeV have disclosed a correlation of their angular distribution with the extragalactic local structure, specifically with either local starburst galaxies or AGN. Using Monte Carlo simulations taking into account photodisintegration processes, we further explore a framework in which these UHECRs were accelerated by Centaurus A in a recent powerful outburst before being scattered by magnetic fields associated with local, Council of Giant, extragalactic structure. We find that the observed intermediate-scale anisotropies can be accounted for by the Council of Giant structure imposing a response function on the initial outburst of UHECRs from a single source located at Centaurus A’s position. The presence of these local structures create ‘echoes’ of UHECRs after the initial impulse and focusing effects. The strongest echo wave has a lag of ∼20 Myr, comparable to the age of synchrotron-emitting electrons in the giant Centaurus A lobes. Through consideration of the composition of both the direct and echo wave components, we find that the distribution of the light (1 &amp;lt; ln A &amp;lt; 1.5) component across the sky offers exciting prospects for testing the echo model using future facilities such as Auger prime. Our results demonstrate the potential that UHECR nuclei offer, as ‘composition clocks’, for probing propagation scenarios from local sources.

Adolescent Experiences With Social Media and Suicidality.

Examine social media experiences of the suicidal adolescent presenting to the emergency department with an acute mental health crisis. We used qualitative interviews to obtain in-depth understanding of both negative and positive impacts of social media use on acute adolescent suicidal behavior. A bilingual transcriptionist transcribed audio recordings. Three investigators independently reviewed transcripts to identify themes and develop initial coding scheme through "open coding." Using grounded theory, data collection proceeded along with cultivation of themes until thematic saturation was achieved. Thematic saturation was determined when no new themes were generated from the data. Data were coded in Dedoose software to facilitate reporting of themes and quotes. Techniques to ensure trustworthiness included iterative data collection, use of a coding framework, and multiple coders. Seventeen interviews were conducted from May to October 2020. Median participant age was 15 years. Twenty-four percent were of Hispanic ethnicity and 82% identified as cisgender. Major themes include distraction from negative emotions; facilitated communication resulting in improved social connectedness; metric of connectedness; comparison of self to others; and desensitization and normalization to suicidal acts. Minor theme of increased time on social media is also discussed. These themes echoed components of current suicide theory. Acutely suicidal adolescents report social media experiences that reflect themes of social alienation and learned capacity for suicidal acts. Themes echo components of current suicide theory.Our participants also reported positive uses of social media. These protective experiences should be leveraged to inform strategies to interrupt behaviors leading to acute suicidality.

Full-waveform LiDAR echo decomposition method based on deep learning and sparrow search algorithm

For the full-waveform LiDAR echo, the conventional decomposition method is to judge the number of waveform components in the echo through the IP (inflection point) method or RL (Richardson-Lucy) deconvolution method after filtering and obtain the initial estimation of their parameters, and then use the intelligent optimization algorithm to fit the waveform. However, this preprocessing method has poor accuracy in judging the echo components when the distance between targets is small. In this paper, a highly accurate decomposition method is proposed based on LSTM with SSA (Sparrow Search Algorithm). Firstly, the LSTM network trained by the simulation data sets under three kinds of background noise is used to judge the number of Gaussian components in the full-waveform LiDAR echo, and then SSA is used for waveform fitting. The accuracy rate of each LSTM network is more than 95%. This method is compared with IP, RL and MGD (multi-Gaussian decomposition) method. Within the accuracy of 0.1 m, the minimum decomposition distance of LiDAR echo is shorten from 0.7 m to 0.45 m compared with IP method and from 0.55 m to 0.45 m compared with RL and MGD method. The minimum ranging distance of LiDAR echo is shorten from 0.75 m to 0.65 m compared with IP and RL method and from 0.85 m to 0.65 m compared with MGD method. With proposed method, the accuracy of full-waveform LiDAR echo decomposition is improved and distance limit of decomposition is reduced.

Chaotic Behavior Analysis and Multifractal Feature Learning of Specular Reflection Ionosphere Clutter for Target Detection

Because ionosphere condition is dynamically varying in the Earth’s middle-latitude, the long-range detection performance of operating high frequency surface wave radar (HFSWR) systems is severely restricted by the presence of ionosphere clutter. The development and performance evaluation of target detection under the F-layer specular reflection ionosphere clutter with concentrated power (Fs-concentrated clutter) will be described in this paper. First, this article pays attention to the variable chaotic behavior of Fs-concentrated clutter by using amplitude and phase echo data. Then, this article investigates the multifractal property of the power spectrum between the Fs-concentrated clutter and mixed echo component. The various fractal characteristics of different types of echo component and Doppler scale are analyzed. Next, the singularity intensity correlation function width, the accumulation area of multifractal correlation spectrum and asymmetry parameter are extracted as the learning features. These multifractal feature parameters are fed into the improved AlexNet and Resnet networks for distinguishing the types of echo component. Experimental results using real HFSWR echoes datasets have demonstrated that this proposed method can increase the detection probability by about 20% than the state-of-art detection methods and classical target detection methods under various signal-clutter ratio (SCR) background.

Joint Online Estimation of Early and Late Residual Echo PSD for Residual Echo Suppression

In hands-free telephony and other distant-talking applications, an acoustic echo cancellation system is typically required, where a short adaptive filter is often used in practice to achieve fast convergence at low computational cost. This may result in late residual echo (LRE) remaining due to under-modeling of the echo path and early residual echo (ERE) due to filter misalignment. Both residual echo components can be suppressed using a postfilter in the subband domain, which requires accurate estimates of the power spectral density (PSD) of the ERE and LRE components. State-of-the-art methods estimate the ERE and LRE PSDs independently of each other, where the ERE PSD is estimated by simply multiplying the loudspeaker PSD with a frequency-dependent scalar and the LRE PSD is estimated using a recursive estimator based on frequency-dependent reverberation scaling and decay parameters. In this paper, we propose to extend the ERE PSD estimator from a scalar to a moving average filter on the loudspeaker PSD. In addition, we propose a signal-based method to jointly estimate all model parameters for the ERE and LRE PSD estimators in online mode, and derive two gradient-descent-based algorithms to simultaneously update the model parameters by minimizing the mean squared log error. The proposed method is compared with state-of-the-art methods in terms of estimation accuracy of the model parameters as well as the residual echo PSDs. Simulation results using both artificially generated as well as measured impulse responses show that the proposed method outperforms state-of-the-art methods for all considered scenarios.

Steady-State Free Precession sequences for high and low field NMR spectroscopy in solution: Challenges and opportunities

The receptivity of NMR spectroscopy is low when compared to other techniques. Historically, increasing the strength of the static magnetic field has been the major approach to increase NMR sensitivity. In recent years several polarization transfer protocols have been used to enhance the signal-to-noise ratio (SNR), although they require special accessories and/or sample preparation. In this paper, we consider both the challenges and opportunities of steady-state free precession (SSFP) pulse sequences as a simple and efficient alternative to enhance SNR, in standard high-resolution and benchtop low-resolution NMR spectrometers. The maximum gain in these sequences is obtained with the shortest time between the pulses (Tp). However, when Tp<T2, the SSFP signal contains FID and echo components which lead to phase, intensity, and truncation artifacts on spectra obtained by Fast Fourier transform (FT). Several phase alternation SSFP sequences were used to cancel the echo component and minimize these problems in the FT spectra. Krylov base diagonalization method (KBDM) was used to eliminate the phase and truncation problems in spectra acquired by SSFP pulse sequences and can be a viable alternative to FT. The experiments were performed in high and low resolution (bench top) NMR spectrometers and significant enhancements in SNR of low receptivity nuclei such as 13C and 15N could be achieved. The SSFP sequences were also shown to enhance SNR in nuclei with high receptivity such as 19F and 31P, in very dilute samples, as is common in environmental and biological samples.

Deep Learning Aided Time–Frequency Analysis Filter Framework for Suppressing Ionosphere Clutter

In a heterogeneous environment, the ionosphere is dynamically changing in the Earth’s middle latitude, and backscatter from fast-moving irregularities in the plasma can cause ionosphere clutter to extend. Suppressing varying ionosphere clutter and exploring obscured targets are challenging tasks for high frequency surface wave radar (HFSWR). For responding to these challenges, this research presents a multi-channel deep learning time–frequency feature filter framework (DL-TFF). Firstly, we observed the behavior of the ionosphere clutter for a long period of time before selecting the representative ionosphere clutter. Secondly, different transform techniques are applied to provide a time–frequency representation of the non-stationary echo signals, and representation results of different echo components are collected as a training set for feature learning. Thirdly, we design a multi-channel time–frequency feature learning network (MTF), which is responsible for mining discriminative time–frequency information between targets and different types of ionosphere clutter. Experimental results on real HFSWR data sets have demonstrated that DL-TFF can remove varying ionosphere clutter and simultaneously reveal covered targets. Moreover, its suppression effectiveness is more ideal than the previous classical method.

LiDAR Echo Gaussian Decomposition Algorithm for FPGA Implementation.

As the existing processing algorithms for LiDAR echo decomposition are time-consuming, this paper proposes an FPGA-based improved Gaussian full-waveform decomposition method. The proposed FPGA architecture consists of three modules: (i) a pre-processing module, which is used to pipeline data reading and Gaussian filtering, (ii) the inflection point coordinate solution module, applied to the second-order differential operation and to calculate inflection point coordinates, and (iii) the Gaussian component parameter solution and echo component positioning module, which is utilized to calculate the Gaussian component and echo time parameters. Finally, two LiDAR datasets, covering the Congo and Antarctic regions, are used to verify the accuracy and speed of the proposed method. The experimental results show that (i) the accuracy of the FPGA-based processing is equivalent to that of PC-based processing, and (ii) the processing speed of the FPGA-based processing is 292 times faster than that of PC-based processing.

Time-Division-Multiplexed Online Gauss-Newton-Based Multi-Echo Decomposition Method for Real-Time In-Situ Laser Ranging

In forestry and agriculture investigations, the laser spot of FW-LiDAR may simultaneously illuminate multiple targets, which reflect multiple echoes. To realize the real-time in-situ laser ranging of multiple targets, a time-division-multiplexed online Gauss-Newton-based multi-echo decomposition method (GMOT) is proposed for decomposing the multi-echo in an online manner. In waveform preprocessing, a local-region linear analysis method was employed in GMOT to recognize the echo components of multi-target. Gauss-Newton method was then used to decompose the multi-echo by fitting the waveform. In the waveform fitting, a sparse Jacobi matrix was employed to improve the target extraction ratio. Due to the ability of the parallel computation, a Kintex-7 FPGA was employed as the hardware platform. GMOT was implemented on the embedded FPGA with the parallel-pipeline architecture and time-division multiplexing method to improve the measurement rate for decomposing the multi-echo in real-time. Simulations proved that GMOT achieves a measurement rate of 238.1 kHz, approximately 3.5 times as fast as online Gauss-Newton-based multi-echo decomposition method. An FPGA-based in-situ ranging system was built to evaluate the ranging performance of GMOT by experiments. The results revealed that GMOT increases the target extraction ratio by 11.4%, compared with the Gauss-Newton-based multi-echo post-processing decomposition method (GMP). The ranging error of GMOT is on the same scale of GMP. The proposed method will be used for the real-time multi-target ranging in the forestry survey and intelligent driving.

End-to-End Full-Waveform Echo Decomposition Based on Self-Attention Classification and U-Net Decomposition

Different from conventional decomposition methods which utilize several steps to obtain the final result, a self-attention based neural network, Attention Full-waveform Decomposition Network (AFD-Net), is discussed in this paper for end-to-end full-waveform LiDAR signal decomposition. In existing LiDAR waveform decomposition methods, complicate functional models are used to fit echo components. Thus, the echo decomposition problem can be translated into a function approximation task. Recent studies present great progress in estimating the parameters of fitting models, hence in the final decomposition results. However, the shape of received echo components are always irregular. None of the parametric functional models can fit the received echo components perfectly, which leads to unavoidable errors in the initial step of echo decomposition. In this paper, we propose an end-to-end net work AFD-Net to solve the echo decomposition problem without assuming any parametric functional models. AFD-Net is consisted with two modules, the classification module and the decomposition module. The former module is used to determine the number of echo components in a received waveform. Then the decomposition module is used to output the echo components. By experiments, we have a classification accuracy 96 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> using the first module. The average <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$R^{2}$</tex-math></inline-formula> coefficient for the decomposed echo components is 0.9799. In addition, there are no public datasets for the waveform decomposition task available. Thus, another contribution of our work is to develop a tool to generate synthetic full-waveform LiDAR signals, which can help researchers to construct their own dataset for related works. All of our source codes are available in the following site: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ZedFm/AFD-Net</uri>

Improved Moments Estimation for Ground-Based K-Band Doppler Radar Using Cost Function Method

An improved moments estimation algorithm used in a newly developed K-band wind profiler to obtain accurate 3-D profiles of wind velocity along the altitude is described. The signal-to-noise ratio (SNR) of turbulent echoes detected by millimeter-wave radar is usually low, making it becomes a challenging task to determine accurate Doppler profiles. And if wind profilers can provide quick estimates of wind, they will have greater applicability in automated, real-time environments. The improved method combines the concepts of adaptive Doppler windows and profile chain construction, reduces the search range of prospective spectral peaks by using adaptive Doppler windows, and designs new multiparameter cost functions to determine velocity profiles by studying the wind shear in the surface boundary layer. The algorithm can effectively identify the atmospheric echo components of each range bin even under the condition of low SNR, and the computational time is decreased by at least 18&#x0025; compared with the previous algorithms. The proposed method is tested on the real wind radar datasets to verify its robustness and reliability. The wind information obtained by four different algorithms is compared with the data obtained from the meteorological mast of the Gaoyou wind farm in China. The comparison results indicate that the proposed method shows a nicer match with the data of the mast, and it derives the winds more accurately in the atmosphere of the surface boundary layer.

Acoustic Nonlinearity Parameter Estimation for Exoskeleton Control

The acoustic nonlinearity parameter of skeletal muscles changes with their contractions and has the potential to be utilized for exoskeleton control. In our previous work, a system comprising a wearable ultrasound probe was developed to estimate the acoustic nonlinearity parameter in vivo by measuring amplitudes of second-harmonic ultrasound. The estimating accuracy was found to be significantly affected by the corruption between echoes due to the limited axial resolution of ultrasound. As both frequency components of the single echo and the extent of corruption are unknown, a method based on a bi-directional deconvolution is proposed in this paper to decompose corrupted echoes, and integrated into the measuring system. Eight subjects were recruited for the in vivo measurement of the biceps brachii acoustic nonlinearity parameter, thickness, and elbow torque. Linear regression between the elbow torque and (1) the estimated acoustic nonlinearity parameter, (2) the thickness demonstrated the correlations, with average coefficients of determinations ( $R^{2}$ ) of 0.861 and 0.633 respectively. This work paves a way for estimating the acoustic nonlinearity parameter of skeletal muscles in vivo .

Nontrivial Dynamic Regimes of Small (Nano-Scale) Quantum Systems

Small (but still containing many atoms) quantum systems (traditionally termed nano-systems) are dramatically different from their macroscopic or genuine microscopic (atomic) cousins. Microscopic molecular systems (with a few atoms) obey a regular quantum dynamics (described by time dependent Schrodinger equation), whereas in macroscopic systems with continuous energy spectra, one can expect, also regular, although typically relaxation, dynamic behavior. The topic of our paper is in-between these limits. System behavior becomes non-trivial and manifests a sort of transitions between regular and chaotic dynamics. We show that such dynamic transitions occur when the Loschmidt echo time of life exceeds the typical recurrence cycle period. We illustrate this behavior in the frame work of a few versions of the exactly solvable quantum problem, proposed long ago by Zwanzig. It is based on the study of time evolution of the initially prepared vibrational state coupled to a reservoir with dense spectrum of its vibrational states. In the simplest version of the Zwanzig model, the reservoir has an equidistant spectrum, and the system - reservoir coupling matrix elements are independent of the reservoir states. We generalize the model to include into consideration the coupling of the initially prepared single state to system phonon excitations. The coupling results to temperature dependent broadening and decay of the echo components. Another generalization is to replace a single level by two states coupled to the Zwanzig reservoir. We anticipate that the basic ideas inspiring our work can be applied to a large variety of interesting for the applications nano-systems (e.g., dissipative free propagation of excitations along molecular chains, or as a model for exchange reactions).

Deep learning-based stereophonic acoustic echo suppression without decorrelation.

Traditional stereophonic acoustic echo cancellation algorithms need to estimate acoustic echo paths from stereo loudspeakers to a microphone, which often suffers from the nonuniqueness problem caused by a high correlation between the two far-end signals of these stereo loudspeakers. Many decorrelation methods have already been proposed to mitigate this problem. However, these methods may reduce the audio quality and/or stereophonic spatial perception. This paper proposes to use a convolutional recurrent network (CRN) to suppress the stereophonic echo components by estimating a nonlinear gain, which is then multiplied by the complex spectrum of the microphone signal to obtain the estimated near-end speech without a decorrelation procedure. The CRN includes an encoder-decoder module and two-layer gated recurrent network module, which can take advantage of the feature extraction capability of the convolutional neural networks and temporal modeling capability of recurrent neural networks simultaneously. The magnitude spectra of the two far-end signals are used as input features directly without any decorrelation preprocessing and, thus, both the audio quality and stereophonic spatial perception can be maintained. The experimental results in both the simulated and real acoustic environments show that the proposed algorithm outperforms traditional algorithms such as the normalized least-mean square and Wiener algorithms, especially in situations of low signal-to-echo ratio and high reverberation time RT60.

NON-TRIVIAL DYNAMIC REGIMES OF SMALL (NANO-SCALE) QUANTUM SYSTEMS

Small (but still containing many atoms) quantum systems (traditionally termed nano-systems) are dramatically different from their macroscopic or genuine microscopic (atomic) cousins. Microscopic molecular systems (with a few atoms) obey a regular quantum dynamics (described by time dependent Schrodinger equation), whereas in macroscopic systems with continuous energy spectra, one can expect, also regular, although typically relaxation, dynamic behavior. The topic of our paper is in-between these limits. System behavior becomes non-trivial and manifests a sort of transitions between regular and chaotic dynamics. We show that such dynamic transitions occur when the Loschmidt echo time of life exceeds the typical recurrence cycle period. We illustrate this behavior in the frame work of a few versions of the exactly solvable quantum problem, proposed long ago by Zwanzig. It is based on the study of time evolution of the initially prepared vibrational state coupled to a reservoir with dense spectrum of its vibrational states. In the simplest version of the Zwanzig model, the reservoir has an equidistant spectrum, and the system - reservoir coupling matrix elements are independent of the reservoir states. We generalize the model to include into consideration the coupling of the initially prepared single state to system phonon excitations. The coupling results to temperature dependent broadening and decay of the echo components. Another generalization is to replace a single level by two states coupled to the Zwanzig reservoir. We anticipate that the basic ideas inspiring our work can be applied to a large variety of interesting for the applications nano-systems (e.g., dissipative free propagation of excitations along molecular chains, or as a model for exchange reactions).

Human Target Localization Using Doppler Through-Wall Radar Based on Micro-Doppler Frequency Estimation

Micro-Doppler frequency estimation is an important issue for radar target recognition and localization. In this paper, a novel approach is proposed to accurately estimate the micro-Doppler frequencies of specific target scattering parts, which can be very promising in real-time human sensing applications. For this approach, the Hough transform with a set of Bezier fitting models is first applied to extract the echo component of the human lower leg and accurately estimate their micro-Doppler frequencies. Then, the original echo is demodulated based on the result of micro Doppler frequency estimation, and the echo of leg component is obtained. Finally, the short-time Fourier transform (STFT) algorithm is used to extract the Doppler frequency of the lower leg, and the target trajectory is synthesized by Doppler processing. Compared with the traditional localization algorithm, the proposed approach can greatly improve both the estimation accuracy of the target movement trajectory and the robustness of the Doppler through-wall radar system, and further improve the recognition and tracking targets ability of the system in different situations. Therefore, a series of experiments are conducted to illustrate the performance of the proposed approach.

Promoting evidence-based practices for breast cancer care through web-based collaborative learning

The Knowledge Summaries for Comprehensive Breast Cancer Control (KSBCs) are a series of 14 publications aligned with World Health Organization guidance on evidence-based breast cancer control and accepted frameworks for action. To study utilization of the KSBCs in the development of locally relevant breast cancer control policies and programs in limited resource settings, the National Cancer Institute Center for Global Health, the University of Washington and the Fred Hutchinson Cancer Research Center developed the Project ECHO® for KSBCs (KSBC ECHO). Project ECHO is an online model which employs case-based learning, while promoting multi-directional learning and network-building. The program was evaluated using a pre-post study design to assess if this online collaborative learning platform can be an effective model for dissemination and utilization of the KSBCs to inform breast cancer control programs and policy advocacy in limited resource settings.A total of 28 KSBC ECHO participants (57 %) responded to the baseline and endpoint program evaluation surveys. Across all 28 respondents, analysis of the data indicates that knowledge increase was statistically significant overall: average knowledge gain was 0.77, 95 % CI [0.44–1.08] and p value < 0.0001. A majority of responding team leads reported that the core ECHO components (case/didactic presentations, discussion) contributed to a great extent to strengthening their project proposal/goals.Program evaluation survey responses indicate that utilization of this online platform provided an opportunity for individual knowledge gain, multi-directional information exchange, network-building, and strengthening of the proposed breast cancer control projects based in limited resource settings.