Automatic generation method for long-focal-length unobscured freeform optical systems with small volume.

The existing design methods for long-focal-length unobscured freeform systems rarely consider the imaging quality requirements and volume constraints simultaneously, causing most of the final designs to not fulfill the requirement of light weight. This study proposes a method to automatically design a long-focal-length unobscured reflective system that satisfies volume constraints while maintaining high imaging quality. First, a method to adaptively set the structural parameter range is proposed, and multiple parameters for different systemic specifications can be effectively calculated within it. Subsequently, the systemic volume and area functions are constructed using the ray tracing method, where the tilt angles, distances between mirrors, and radii of curvature of the mirrors are chosen as the optimization parameters. Third, a comprehensive objective function is jointly established combining ray obscuration and convergence as performance evaluation factors. Then, the structural parameters of a long-focal-length unobscured system with small volume are easily obtained via the simulated annealing method. Finally, the improved W-W method is used to further enhance the imaging quality of the system, and an unobscured freeform reflective optical system with three mirrors is automatically generated. Experimental results demonstrate that our method can automatically calculate the parameter ranges to facilitate the search for structural parameters, and effectively design the long-focal-length unobscured freeform systems with small volume and high imaging quality.

Improved Diffusion Interaction Parameter Measurement to Predict the Viscosity of Concentrated mAb Solutions.

During the developability assessment of therapeutic monoclonal antibody (mAb) candidates, utilization of robust high-throughput predictive assays enables rapid selection of top candidates with low risks for late-stage development. Predicting the viscosities of highly concentrated mAbs using limited materials is an important aspect of developability assessment because high viscosity can complicate manufacturability, stability, and administration. Here, we report a high-throughput assay measuring protein-protein interactions to predict mAb viscosity. The diffusion interaction parameter (kD) measures colloidal self-association in dilute solutions and has been reported to be predictive of the mAb viscosity at high concentrations. However, kD of Amgen early stage IgG1 mAb candidates measured in 10 mM acetate at pH 5.2 containing sucrose and polysorbate (denoted A52SuT) shows only weak correlation to their viscosities at 140 mg/mL in A52SuT. We hypothesize that kD measured in A52SuT reflects primarily long-range electrostatic repulsions because most of these mAb candidates carry strong net positive charges in this low ionic strength formulation with pH (5.2) well below pI values of mAb candidates. However, the viscosities of high concentration mAbs depend heavily on short-range molecular interactions. We propose an improved kD method in which salt is added to suppress charge repulsions and to allow for detection of key short-range interactions in dilute solutions. Salt types and salt concentrations were screened, and an optimal salt condition was identified. This optimized method was further validated using two test mAb sets. Overall, the method improves the Pearson R2 between kD and viscosity (6-230 cP) from 0.24 to 0.80 for a data set consisting of 37 mAbs.

Off-axis three-mirror freeform systems design based on improved W-W differential equations.

Design of an off-axis system using the Wassermann-Wolf (W-W) differential equations can effectively eliminate the spherical aberration and coma problem; however, it is complicated and time consuming to calculate the discrete point coordinates on the freeform mirror surfaces due to multiple numbers of reference system transformation in the design process. This paper presents an improved W-W-differential-equations-based design method for off-axis three-mirror freeform systems. First, to reduce the number of coordinate transformations, a geometric relationship between different optical rays in an off-axis system is established using the distance between the central points of adjacent mirrors. Second, a three-dimensional rotation matrix is used to associate the optical paths passing through adjacent mirrors in different reference coordinate systems, and new simplified W-W differential equations based on the ray vectors are constructed. The experimental results show that our method can easily and effectively design off-axis three-mirror freeform systems with different parameters and structures, and the designed systems have good imaging quality.

A Robust and Low Computational Cost Pitch Estimation Method.

Pitch estimation is widely used in speech and audio signal processing. However, the current methods of modeling harmonic structure used for pitch estimation cannot always match the harmonic distribution of actual signals. Due to the structure of vocal tract, the acoustic nature of musical equipment, and the spectrum leakage issue, speech and audio signals' harmonic frequencies often slightly deviate from the integer multiple of the pitch. This paper starts with the summation of residual harmonics (SRH) method and makes two main modifications. First, the spectral peak position constraint of strict integer multiple is modified to allow slight deviation, which benefits capturing harmonics. Second, a main pitch segment extension scheme with low computational cost feature is proposed to utilize the smooth prior of pitch more efficiently. Besides, the pitch segment extension scheme is also integrated into the SRH method's voiced/unvoiced decision to reduce short-term errors. Accuracy comparison experiments with ten pitch estimation methods show that the proposed method has better overall accuracy and robustness. Time cost experiments show that the time cost of the proposed method reduces to around 1/8 of the state-of-the-art fast NLS method on the experimental computer.

Automatic Speech Recognition Performance Improvement for Mandarin Based on Optimizing Gain Control Strategy.

Automatic speech recognition (ASR) is an essential technique of human-computer interactions; gain control is a commonly used operation in ASR. However, inappropriate gain control strategies can lead to an increase in the word error rate (WER) of ASR. As there is a current lack of sufficient theoretical analyses and proof of the relationship between gain control and WER, various unconstrained gain control strategies have been adopted on realistic ASR systems, and the optimal gain control with respect to the lowest WER, is rarely achieved. A gain control strategy named maximized original signal transmission (MOST) is proposed in this study to minimize the adverse impact of gain control on ASR systems. First, by modeling the gain control strategy, the quantitative relationship between the gain control strategy and the ASR performance was established using the noise figure index. Second, through an analysis of the quantitative relationship, an optimal MOST gain control strategy with minimal performance degradation was theoretically deduced. Finally, comprehensive comparative experiments on a Mandarin dataset show that the proposed MOST gain control strategy can significantly reduce the WER of the experimental ASR system, with a 10% mean absolute WER reduction at -9 dB gain.

An Improvement to Conformer-Based Model for High-Accuracy Speech Feature Extraction and Learning.

Owing to the loss of effective information and incomplete feature extraction caused by the convolution and pooling operations in a convolution subsampling network, the accuracy and speed of current speech processing architectures based on the conformer model are influenced because the shallow features of speech signals are not completely extracted. To solve these problems, in this study, we researched a method that used a capsule network to improve the accuracy of feature extraction in a conformer-based model, and then, we proposed a new end-to-end model architecture for speech recognition. First, to improve the accuracy of speech feature extraction, a capsule network with a dynamic routing mechanism was introduced into the conformer model; thus, the structural information in speech was preserved, and it was input to the conformer blocks via sequestered vectors; the learning ability of the conformed-based model was significantly enhanced using dynamic weight updating. Second, a residual network was added to the capsule blocks, thus, the mapping ability of our model was improved and the training difficulty was reduced. Furthermore, the bi-transformer model was adopted in the decoding network to promote the consistency of the hypotheses in different directions through bidirectional modeling. Finally, the effectiveness and robustness of the proposed model were verified against different types of recognition models by performing multiple sets of experiments. The experimental results demonstrated that our speech recognition model achieved a lower word error rate without a language model because of the higher accuracy of speech feature extraction and learning using our model architecture with a capsule network. Furthermore, our model architecture benefited from the advantage of the capsule network and the conformer encoder, and also has potential for other speech-related applications.

Determination of the SLAMF1 self-association affinity constant with sedimentation velocity ultracentrifugation.

Signaling lymphocytic activating molecule family member 1 (SLAMF1 or CD150) is a cell surface glycoprotein expressed on various immune populations, regulating cell-cell interactions, activation, differentiation, and inflammatory responses and has been suggested as a potential target for inflammatory diseases. Signaling is believed to be mediated by high-affinity homophilic interactions; the recombinant soluble form of SLAMF1 has optimal activity in the range of 20 µg/mL. This contradicts with a rather weak homo-dimerization binding constant (KD) value reported previously; however, the analytical approach and data analysis suffered from various technical limitations at the time and therefore warrants re-examination. To address this apparent discrepancy, we determined the KD of soluble SLAMF1 using sedimentation velocity analytical ultracentrifuge (SV-AUC). A globally fitted monomer-dimer model properly explains the data from a wide concentration range obtained with both UV and fluorescence detection systems. The analysis suggests the dimerization KD value for human SLAMF1 is 0.48 µM. Additionally, our data show that SLAMF1 self-association is not driven by non-specific binding to glycans supporting the view of specific protein-protein interaction. We anticipate antibody biotherapeutics capable of modulating the biological consequences of SLAMF1 interactions will be readily identified.

Global deblurring for continuous out-of-focus images using a depth-varying diffusion model.

The phenomenon of continuous out-of-focus imaging often occurs in high-magnification optical microscopy when observing large-scale targets. Lacking of accurate depth-varying point spread functions (DVPSFs) for blurred regions at different depths, it is difficult to locally reconstruct the clear images of these blurred regions using traditional deblurring methods, making it unreasonable to globally observe the optical features of large-scale targets in high-magnification optical microscopy. This paper proposes a global deblurring method for continuous out-of-focus images of large-scale sphere samples. In this study, first we analyze the energy diffusion characteristics of the optical imaging process, integrating the relationship between high-frequency energy parameters, optical range distance, and depth of field, and we propose a three-dimensional continuous energy diffusion model for optical imaging. Next, we propose an adaptive weight depth calculation method for a continuously changing surface based on the depth varying diffusion model by introducing the sample surface curvature variation and light direction. Finally, we propose a universal method for deblurring continuous out-of-focus images of large-scale sphere samples for the purpose of observing the global optical features in high-magnification optical microscopy. Moreover, we use dynamic microspheres of different sizes to verify the effectiveness of our proposed method. The results prove that our proposed method can accurately calculate the depth of the sample surface and the energy diffusion parameters at each depth, and it can achieve the image deblurring of a continuously changing surface and the global deblurring of multiple samples in a wide field of view.

Developing Biologics Tablets: The Effects of Compression on the Structure and Stability of Bovine Serum Albumin and Lysozyme.

Oral administration is advantageous compared to the commonly used parenteral administration for local therapeutic uses of biologics or mucosal vaccines, since it can specifically target the gastrointestinal (GI) tract. It offers better patient compliance, even though the general use of such a delivery route is often limited by potential drug degradation in the GI tract and poor absorption. Using bovine serum albumin (BSA) and lysozyme as two model proteins, we studied their solid-state properties, mechanical properties, and tabletability as well as effects of compaction pressure, particle size, and humidity on protein degradation. It was found that BSA and lysozyme are highly hygroscopic, and their tablet manufacturability (powder caking, punch sticking, and tablet lamination) is sensitive to the humidity. BSA and lysozyme exhibited high plasticity and excellent tabletability and remained amorphous at high pressure and humidity. As for protein stability, lysozyme was resistant to high pressure (up to 300 MPa) and high humidity (up to 93%). In contrast, BSA underwent aggregation upon compression, an effect that was more pronounced for smaller BSA particles. High humidity accelerated the aggregation of BSA during incubation, but it did not further synergize with mechanical stress to induce protein degradation. Thus, compression can potentially induce protein aggregation, but this effect is protein-dependent. Therefore, strategies (e.g., the use of excipients, optimized manufacturing processes) to inhibit protein degradation should be explored before their tablet dosage form development.

Blind Estimation of the PN Sequence of A DSSS Signal Using A Modified Online Unsupervised Learning Machine.

Direct sequence spread spectrum (DSSS) signals are now widely used in air and underwater acoustic communications. A receiver which does not know the pseudo-random (PN) sequence cannot demodulate the DSSS signal. In this paper, firstly, the principle of principal component analysis (PCA) for PN sequence estimation of the DSSS signal is analyzed, then a modified online unsupervised learning machine (LEAP) is introduced for PCA. Compared with the original LEAP, the modified LEAP has the following improvements: (1) By normalizing the system state transition matrices, the modified LEAP can obtain better robustness when the training errors occur; (2) with using variable learning steps instead of a fixed one, the modified LEAP not only converges faster but also has excellent estimation performance. When the modified LEAP is converging, we can utilize the network connection weights which are the eigenvectors of the autocorrelation matrix of the DSSS signal to estimate the PN sequence. Due to the phase ambiguity of the eigenvectors, a novel approach which is based on the properties of the PN sequence is proposed here to exclude the wrong estimated PN sequences. Simulation results showed that the methods mentioned above can estimate the PN sequence rapidly and robustly, even when the DSSS signal is far below the noise level.

Automatic Modulation Classification of Digital Communication Signals Using SVM Based on Hybrid Features, Cyclostationary, and Information Entropy.

Since digital communication signals are widely used in radio and underwater acoustic systems, the modulation classification of these signals has become increasingly significant in various military and civilian applications. However, due to the adverse channel transmission characteristics and low signal to noise ratio (SNR), the modulation classification of communication signals is extremely challenging. In this paper, a novel method for automatic modulation classification of digital communication signals using a support vector machine (SVM) based on hybrid features, cyclostationary, and information entropy is proposed. In this proposed method, by combining the theory of the cyclostationary and entropy, based on the existing signal features, we propose three other new features to assist the classification of digital communication signals, which are the maximum value of the normalized cyclic spectrum when the cyclic frequency is not zero, the Shannon entropy of the cyclic spectrum, and Renyi entropy of the cyclic spectrum respectively. Because these new features do not require any prior information and have a strong anti-noise ability, they are very suitable for the identification of communication signals. Finally, a one against one SVM is designed as a classifier. Simulation results show that the proposed method outperforms the existing methods in terms of classification performance and noise tolerance.

Label-Free, Direct Measurement of Protein Concentrations in Turbid Solutions with a UV-Visible Integrating Cavity Absorbance Spectrometer.

Protein-particle conjugates and mixtures have been investigated extensively for their diverse applications in biotechnology. However, general methods to measure protein concentration of protein-particle solutions are lacking. Typically, proteins in turbid solutions require separation or staining with another chromophore to quantitate their concentration. Here we demonstrate a label-free, direct approach to measure protein concentrations in turbid solutions using a UV-vis integrating cavity absorbance spectrometer. Three systems are used to test the ability to measure accurate protein concentrations: proteins adsorbed to Alhydrogel, proteins in solution with gold nanoparticles, and proteins encapsulated within polymeric microspheres. Protein concentrations in each of the three protein-particle systems were successfully quantified using a calibration curve created from the absorbance at 280 nm.

Compatibility, Stability, and Efficacy of Vancomycin Combined With Gentamicin or Ethanol in Sodium Citrate as a Catheter Lock Solution.

Background: Indwelling catheters deliver lifesaving medical treatments for many chronically ill patients but are frequently a source of infection. Treatment may include an antimicrobial agent(s) and anticoagulant solution dwelling within the catheter. In vitro determinations of solution compatibility and stability are necessary prior to use in patients. Objective: The aim of this study was to determine the physical compatibility, chemical stability, and antimicrobial activity of vancomycin (5 or 10 mg/mL) with gentamicin (1 mg/mL) or 40% ethanol in 4% sodium citrate lock solution over 72 hours. Methods: All solutions were prepared per manufacturer's instructions. Samples were studied under 4 conditions: (1) 25°C with light, (2) 25°C without light, (3) 37°C with light, and (4) 37°C without light. Physical compatibility and chemical stability were assessed at 0, 24, 48, and 72 hours. Antimicrobial susceptibility testing was conducted at 0 and 72 hours. All studies were carried out in triplicate. Results: All solution combinations under each condition remained patent from baseline to 48 hours. One solution combination of vancomycin (5 mg/mL) and ethanol (40% v/v) in 4% sodium citrate revealed a slight turbidity at 72 hours. Clarity and pH remained stable in all other solutions during the entire study period. Chemical compatibility and antibiotic activity ranged from 95% to 105% and 95% to 106% of initial baseline values, respectively, for all solutions under 4 storage conditions. Conclusions: All antibiotic-anticoagulant lock solutions were found to be physically, chemically, and microbiologically stable during the 72-hour study period except vancomycin (5 mg/mL) and ethanol (40% v/v) in 4% sodium citrate solution which showed slight turbidity at 72 hours.

Effective Waterline Detection of Unmanned Surface Vehicles Based on Optical Images.

Real-time and accurate detection of the sailing or water area will help realize unmanned surface vehicle (USV) systems. Although there are some methods for using optical images in USV-oriented environmental modeling, both the robustness and precision of these published waterline detection methods are comparatively low for a real USV system moving in a complicated environment. This paper proposes an efficient waterline detection method based on structure extraction and texture analysis with respect to optical images and presents a practical application to a USV system for validation. First, the basic principles of local binary patterns (LBPs) and gray level co-occurrence matrix (GLCM) were analyzed, and their advantages were integrated to calculate the texture information of river images. Then, structure extraction was introduced to preprocess the original river images so that the textures resulting from USV motion, wind, and illumination are removed. In the practical application, the waterlines of many images captured by the USV system moving along an inland river were detected with the proposed method, and the results were compared with those of edge detection and super pixel segmentation. The experimental results showed that the proposed algorithm is effective and robust. The average error of the proposed method was 1.84 pixels, and the mean square deviation was 4.57 pixels.

Shape Reconstruction Based on a New Blurring Model at the Micro/Nanometer Scale.

Real-time observation of three-dimensional (3D) information has great significance in nanotechnology. However, normal nanometer scale observation techniques, including transmission electron microscopy (TEM), and scanning probe microscopy (SPM), have some problems to obtain 3D information because they lack non-destructive, intuitive, and fast imaging ability under normal conditions, and optical methods have not widely used in micro/nanometer shape reconstruction due to the practical requirements and the imaging limitations in micro/nano manipulation. In this paper, a high resolution shape reconstruction method based on a new optical blurring model is proposed. Firstly, the heat diffusion physics equation is analyzed and the optical diffraction model is modified to directly explain the basic principles of image blurring resulting from depth variation. Secondly, a blurring imaging model is proposed based on curve fitting of a 4th order polynomial curve. The heat diffusion equations combined with the blurring imaging are introduced, and their solution is transformed into a dynamic optimization problem. Finally, the experiments with a standard nanogrid, an atomic force microscopy (AFM) cantilever and a microlens have been conducted. The experiments prove that the proposed method can reconstruct 3D shapes at the micro/nanometer scale, and the minimal reconstruction error is 3 nm.

Diffusion-based three-dimensional reconstruction of complex surface using monocular vision.

Three-dimensional (3D) reconstruction based on optical diffusion has certain significant advantages, such as its capacity for high-precision depth estimation with a small lens, distant-object depth estimation, a monocular vision basis, and no required camera or scene adjustment. However, few mathematical models to relate the depth information acquired using this technique to the basic principles of intensity distribution during optical diffusion have been proposed. In this paper, the heat diffusion equation of physics is applied in order to construct a mathematical model of the intensity distribution during optical diffusion. Hence, a high-precision 3D reconstruction method with optical diffusion based on the heat diffusion equation is proposed. First, the heat diffusion equation is analyzed and an optical diffusion model is introduced to explain the basic principles of the diffusion imaging process. Second, the novel 3D reconstruction method based on global heat diffusion is proposed, which incorporates the relationship between the depth information and the degree of diffusion. Finally, a simulation involving synthetic images and an experiment using five playing cards are conducted, with the results confirming the effectiveness and feasibility of the proposed method.

Masking the bitter taste of injectable lidocaine HCl formulation for dental procedures.

Several attempts have been made to mask the bitter taste of oral formulations, but none have been made for injectable formulations. This study aims to mask the bitter taste of dental lidocaine HCl (LID) injection using hydroxypropyl-ß-cyclodextrin (HP-ß-CD) and sodium saccharin. Inclusion complexes of LID and HP-ß-CD were prepared by the solution method in 1:1 and 1:2 M ratios. Inclusion complexes in solution were studied using phase solubility in phosphate buffer solutions (pH 8, 9, and 10). Freeze-dried inclusion complexes were characterized using differential scanning calorimetry (DSC), X-ray, Fourier transform infrared (FT-IR), nuclear magnetic resonance (NMR), scanning electron microscopy (SEM), and in vitro release. Injectable formulations were prepared using inclusion complexes and characterized for stability and for taste using an Alpha MOS ASTREE electronic tongue (ETongue). The association constants of HP-ß-CD with lidocaine-free base and its ionized form were found to be 26.23 ± 0.00025 and 0.8694 ± 0.00045 M(-1), respectively. Characterization studies confirmed the formation of stable inclusion complexes of LID and HP-ß-CD. Injectable formulations were found to be stable for up to 6 months at 4°C, 25°C, and 40°C. The taste evaluation study indicated that HP-ß-CD (1:1 and 1:2 M ratios) significantly improved the bitter taste of LID injectable formulation. In conclusion, inclusion complex in the 1:1 M ratio with 0.09% sodium saccharin was considered to be optimum in masking the bitter taste of LID.

Nanoscale depth reconstruction from defocus: within an optical diffraction model.

Depth from defocus (DFD) based on optical methods is an effective method for depth reconstruction from 2D optical images. However, due to optical diffraction, optical path deviation occurs, which results in blurring imaging. Blurring, in turn, results in inaccurate depth reconstructions using DFD. In this paper, a nanoscale depth reconstruction method using defocus with optical diffraction is proposed. A blurring model is proposed by considering optical diffraction, leading to a much higher accuracy in depth reconstruction. Firstly, Fresnel diffraction in an optical system is analyzed, and a relationship between intensity distribution and depth information is developed. Secondly, a blurring imaging model with relative blurring and heat diffusion is developed through curving fitting of a numerical model. In this way, a new DFD method with optical diffraction is proposed. Finally, experimental results show that this new algorithm is more effective for depth reconstruction on the nanoscale.

Three-dimensional modelling of blur property for conventional optical microscopes.

Intensity diffusion caused by optical diffraction limits the imaging resolution of conventional optical microscopes, therefore modelling and measuring the intensity transmission and distribution property of the light sources is a significant research topic in system development and pattern recognition. However, the complicated wave propagation process in optical imaging makes it difficult to provide a direct, analytical and simple mathematical model to measure the relationship between the blur degree and various camera parameters. In this study, an improved intensity transmission and distribution calculation method for conventional optical microscopes was proposed; furthermore, a simple mathematical relation between the blur degree and camera parameters was achieved based on the proposed method. First, the light intensity distribution and propagation characteristics of a conventional optical microscope were modeled based on the property of the Fresnel circular hole diffraction combined with the practical optical parameters. Second, by analyzing the property of intensity distribution and blurring imaging, a quantitative simplified mathematical relationship between the blur degree and camera parameters in optical microscope imaging was obtained, and the three-dimensional (3D) blur property in the optical imaging process was analyzed under different conditions. Third, the connection between diffractive optics and geometric optics was obtained by summarizing and generalizing the 3D blur property curve of each monochromatic light source. Finally, the proposed method was verified through a series of simulations and experiments.

Evaluating the resolution of conventional optical microscopes through point spread function measurement.

In the imaging process of conventional optical microscopy, the primary factor hindering microscope resolution is the energy diffusion of incident light, most directly described by the point spread function (PSF). Therefore, accurate calculation and measurement of PSF are essential for evaluating and enhancing imaging resolution. Currently, there are various methods to obtain PSFs, each with different advantages and disadvantages suitable for different scenarios. To provide a comprehensive analysis of PSF-obtaining methods, this study classifies them into four categories based on different acquisition principles and analyzes their advantages and disadvantages, starting from the propagation property of light in optical physics. Finally, two PSF-obtaining methods are proposed based on mathematical modeling and deep learning, demonstrating their effectiveness through experimental results. This study compares and analyzes these results, highlighting the practical applications of image deblurring.