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This paper proposed and implemented the Teager-Kaiser energy operator (TKEO) and envelope spectral analysis techniques for the fault detection of discharge valves of a reciprocating compressor. Based on the extraction of fault features, the instantaneous frequency and amplitude of the signals due to the discharged valve based on energy identification can be effectively characterized by the TKEO that was used to identify the characteristic fault signals accurately. The synthesized signal is processed by envelope spectral analysis and TKEO, which can extract the characteristic signal and eliminate the noise. The experimental design is verified experimentally through different reciprocating compressor gas valve conditions. The simulation results verify the feasibility of the proposed method. The experimental verification is carried out through the measurement signals of the six-cylinder reciprocating compressor under different valve operating conditions. TKEO can remove background noise to obtain reciprocating compressor fault feature signals. Feature extraction is based on TKEO and envelope spectra for fault detection of reciprocating compressors. It is expected to reduce the errors produced by traditional manual fault diagnosis methods and improve the accuracy and efficiency of fault diagnosis. The research results of vibration fault feature extraction using TKEO can be used as the basis for fault diagnosis of the reciprocating compressor system.

The ball screw feed system has been an important part of precision machinery and machine tools. Its preload degradation affects machining accuracy and the issue should be first coped with per manufacturers’ suggestion. The study aims to apply the adaptive order tracking technique and other spectrum analysis schemes as well to differentiate the preload conditions of ball screws. Here varied preload conditions (4 %, 1 % preload and 12um backlash) were considered for monitoring during normal operation. The sideband energy of higher harmonics of ball pass frequency (BPF) are featured to highlight the preload degradation. The control charts together with sideband ratios were established to differentiate preload conditions and single out backlash ball screws.

The study aims to construct an inertial measuring system for the application of amputee subjects wearing a prosthesis. A new computation scheme to process inertial data by installing seven wireless inertial sensors on the lower limbs was implemented and validated by comparing it with an optical motion capture system. We applied this system to amputees to verify its performance for gait analysis. The gait parameters are evaluated to objectively assess the amputees’ prosthesis-wearing status. The Madgwick algorithm was used in the study to correct the angular velocity deviation using acceleration data and convert it to quaternion. Further, the zero-velocity update method was applied to reconstruct patients’ walking trajectories. The combination of computed walking trajectory with pelvic and lower limb joint motion enables sketching the details of motion via a stickman that helps visualize and animate the walk and gait of a test subject. Five participants with above-knee (n = 2) and below-knee (n = 3) amputations were recruited for gait analysis. Kinematic parameters were evaluated during a walking test to assess joint alignment and overall gait characteristics. Our findings support the feasibility of employing simple algorithms to achieve accurate and precise joint angle estimation and gait parameters based on wireless inertial sensor data.

The Near-Infrared (NIR) Diffuse Optical Tomography (DOT) aims to reconstruct optical-property images of tissue and identify and localize breast tumors. This study has developed an efficient and fast DOT reconstruction method based on Deep Learning (DL) algorithm. The DL has already been applied in DOT application with limitations such as a limited dataset and the reconstruction of absorption coefficients only. We solve the problem of a limited dataset by generating a large dataset with multiple phantoms and inclusions at various positions. Moreover, a single Deep Neural Network (DNN) model has been designed to reconstruct absorption coefficients and scattering coefficients. For evaluation of the proposed DNN models, the phantom experimental dataset has been used where the results of the proposed DNN models outperform the results of the Tikhonov Regularization (TR) method and other Artificial Neural Networks (ANN). Moreover, it is shown that the DNN model with batch normalization layer results in improved spatial resolution, based on Contrast-and-Size Detail (CSD) analysis, as compared to DNN models without batch normalization layers.

Imaging tasks today are being increasingly shifted toward deep learning-based solutions. Biomedical imaging problems are no exception toward this tendency. It is appealing to consider deep learning as an alternative to such a complex imaging task. Although research of deep learning-based solutions continues to thrive, challenges still remain that limits the availability of these solutions in clinical practice. Diffuse optical tomography is a particularly challenging field since the problem is both ill-posed and ill-conditioned. To get a reconstructed image, various regularization-based models and procedures have been developed in the last three decades. In this study, a sensor-to-image based neural network for diffuse optical imaging has been developed as an alternative to the existing Tikhonov regularization (TR) method. It also provides a different structure compared to previous neural network approaches. We focus on realizing a complete image reconstruction function approximation (from sensor to image) by combining multiple deep learning architectures known in imaging fields that gives more capability to learn than the fully connected neural networks (FCNN) and/or convolutional neural networks (CNN) architectures. We use the idea of transformation from sensor- to image-domain similarly with AUTOMAP, and use the concept of an encoder, which is to learn a compressed representation of the inputs. Further, a U-net with skip connections to extract features and obtain the contrast image, is proposed and implemented. We designed a branching-like structure of the network that fully supports the ring-scanning measurement system, which means it can deal with various types of experimental data. The output images are obtained by multiplying the contrast images with the background coefficients. Our network is capable of producing attainable performance in both simulation and experiment cases, and is proven to be reliable to reconstruct non-synthesized data. Its apparent superior performance was compared with the results of the TR method and FCNN models. The proposed and implemented model is feasible to localize the inclusions with various conditions. The strategy created in this paper can be a promising alternative solution for clinical breast tumor imaging applications.

We demonstrate a working prototype of an optical breast imaging system involving parallel-plate architecture and a dual-direction scanning scheme designed in combination with a mammography machine; this system was validated in a pilot study to demonstrate its application in imaging healthy and malignant breasts in a clinical environment. The components and modules of the self-developed imaging system are demonstrated and explained, including its measuring architecture, scanning mechanism, and system calibration, and the reconstruction algorithm is presented. Additionally, the evaluation of feature indices that succinctly demonstrate the corresponding transmission measurements may provide insight into the existence of malignant tissue. Moreover, five cases are presented including one subject without disease (a control measure), one benign case, one suspected case, one invasive ductal carcinoma, and one positive case without follow-up treatment. A region-of-interest analysis demonstrated significant differences in absorption between healthy and malignant breasts, revealing the average contrast between the abnormalities and background tissue to exceed 1.4. Except for ringing artifacts, the average scattering property of the structure densities was 0.65–0.85 mm−1.

Breast cancer remains the most frequently diagnosed cancer and is the leading cause of neoplastic disease burden for females worldwide, suggesting that effective therapeutic and/or diagnostic strategies are still urgently needed. In this study, a type of indocyanine green (ICG) and camptothecin (CPT) co-loaded perfluorocarbon double-layer nanocomposite named ICPNC was developed for detection and photochemotherapy of breast cancer. The ICPNCs were designed to be surface modifiable for on-demand cell targeting and can serve as contrast agents for fluorescence diffuse optical tomography (FDOT). Upon near infrared (NIR) irradiation, the ICPNCs can generate a significantly increased production of singlet oxygen compared to free ICG, and offer a comparable cytotoxicity with reduced chemo-drug dosage. Based on the results of animal study, we further demonstrated that the ICPNCs ([ICG]/[CPT] = 40-/7.5-μM) in association with 1-min NIR irradiation (808 nm, 6 W/cm2) can provide an exceptional anticancer effect to the MDA-MB-231 tumor-bearing mice whereby the tumor size was significantly reduced by 80% with neither organ damage nor systemic toxicity after a 21-day treatment. Given a number of aforementioned merits, we anticipate that the developed ICPNC is a versatile theranostic nanoagent which is highly promising to be used in the clinic.

In clinical settings, traditional stroke rehabilitation evaluation methods are subjectively scored by occupational therapists, and the assessment results vary individually. To address this issue, this study aims to develop a stroke rehabilitation assessment system by using inertial measurement units. The inertial signals from the upper extremities were acquired, from which three quantitative indicators were extracted to reflect rehabilitation performance during stroke patients’ movement examination, i.e., shoulder flexion. Both healthy adults and stroke patients were recruited to correlate the proposed quantitative evaluation indices and traditional rehab assessment scales. Especially, as a unique feature of the study the weight for each of three evaluation indicators was estimated by the least squares method. The quantitative results demonstrate the proposed method accurately reflects patients’ recovery from pre-rehabilitation, and confirm the feasibility of applying inertial signals to evaluate rehab performance through feature extraction. The implemented assessment scheme appears to have the potential to overcome some shortcomings of traditional assessment methods and indicates rehab performance correctly.

We propose and implement three-dimensional (3-D) ring-scanning equipment for near-infrared (NIR) diffuse optical imaging to screen breast tumors under prostrating examination. This equipment has the function of the radial, circular, and vertical motion without compression of breast tissue, thereby achieving 3-D scanning; furthermore, a flexible combination of illumination and detection can be configured for the required resolution. Especially, a rotation-sliding-and-moving mechanism was designed for the guidance of source- and detectionchannel motion. Prior to machining and construction of the system, a synthesized image reconstruction was simulated to show the feasibility of this 3-D NIR ring-scanning equipment; finally, this equipment is verified by performing phantom experiments. Rather than the fixed configuration, this addressed screening/diagnosing equipment has the flexibilities of optical-channel expansion for spatial resolution and the dimensional freedom for scanning in reconstructing optical-property images. [ABSTRACT FROM AUTHOR]

The design scheme of the source-and-detector arrangement of a ring-scanning-based near-infrared optical imaging system prior to the mechanical and optical construction is demonstrated. In terms of the effectiveness and efficiency of design, through the computation of image reconstruction for varied imaging configurations, the influences of the source-and-detector arrangement on the resulting images are evaluated and a formula to estimate the scanning time is provided. The basic idea of our design is to divide circular scanning into several zones, each of which includes n sources and l detectors; i.e., m zones and n sources along with l detectors per zone are defined in the design. Comparison is made among different imaging configurations where their contrast-to-noise ratio measures are evaluated and contrast-and-size detail resolution curves are depicted. Results show that the 2Z3S or 3Z3S configuration is the optimal design in terms of the time consumption of a complete scanning and the resolution of reconstructed optical-property images. [ABSTRACT FROM AUTHOR]

Dynamic signals acquired from rotary machines can be characterized by the order tracking (OT) techniques. The extracted order components correspond to the operation of specific machine elements and reflect their current healthy or faulty states. The study extends the angular-velocity Vold-Kalman OT scheme to simultaneously extract multiple order components. Theoretical derivation is illustrated with simulation of processing three synthetic signals to show its merit. Additionally, as an example to validate its effectiveness, the improved OT scheme is used to process pass-by noise emitted from an electric scooter with a planetary-gear-set transmission system. The gear-meshing orders are effectively decoupled from structure-borne resonances. [ABSTRACT FROM AUTHOR]

We attempt to develop a systematic scheme through adopting high-pass filtering (HPF) to well resolve value-preserved images such as medical images. Our approach is derived from the Poisson maximum a posteriori superresolution algorithm employing the HP filters, where four filters are considered such as two low-pass-filter-combination based filters, wavelet filter, and negative-oriented Laplacian HP filter. The proposed approach is incorporated into the procedure of finite-element-method (FEM)-based image reconstruction for diffuse optical tomography in the direct current domain, posterior to each iteration without altering the original FEM modeling. This approach is justified with various HPF for different cases that breast-like phantoms embedded with two or three inclusions that imitate tumors are employed to examine the resolution performances under certain extreme conditions. The proposed approach to enhancing image resolution is evaluated for all tested cases. A qualitative investigation of reconstruction performance for each case is presented. Following this, we define a set of measures on the quantitative evaluation for a range of resolutions including separation, size, contrast, and location, thereby providing a comparable evaluation to the visual quality. The most satisfactory result is obtained by using the wavelet HP filter, and it successfully justifies our proposed scheme. [ABSTRACT FROM AUTHOR]