Beampattern-Based Tracking for Millimeter Wave Communication Systems

We present a tracking algorithm to maintain the communication link between a base station (BS) and a mobile station (MS) in a millimeter wave (mmWave) communication system, where antenna arrays are used for beamforming in both the BS and MS. Downlink transmission is considered, and the tracking is performed at the MS as it moves relative to the BS. Specifically, we consider the case that the MS rotates quickly due to hand movement. The algorithm estimates the angle of arrival (AoA) by using variations in the radiation pattern of the beam as a function of this angle. Numerical results show that the algorithm achieves accurate beam alignment when the MS rotates in a wide range of angular speeds. For example, the algorithm can support angular speeds up to 800 degrees per second when tracking updates are available every 10 ms.Comment: 6 pages, to be published in Proc. IEEE GLOBECOM 2016, Washington, D.C., US

The FruitShell French synthesis system at the Blizzard 2023 Challenge

This paper presents a French text-to-speech synthesis system for the Blizzard Challenge 2023. The challenge consists of two tasks: generating high-quality speech from female speakers and generating speech that closely resembles specific individuals. Regarding the competition data, we conducted a screening process to remove missing or erroneous text data. We organized all symbols except for phonemes and eliminated symbols that had no pronunciation or zero duration. Additionally, we added word boundary and start/end symbols to the text, which we have found to improve speech quality based on our previous experience. For the Spoke task, we performed data augmentation according to the competition rules. We used an open-source G2P model to transcribe the French texts into phonemes. As the G2P model uses the International Phonetic Alphabet (IPA), we applied the same transcription process to the provided competition data for standardization. However, due to compiler limitations in recognizing special symbols from the IPA chart, we followed the rules to convert all phonemes into the phonetic scheme used in the competition data. Finally, we resampled all competition audio to a uniform sampling rate of 16 kHz. We employed a VITS-based acoustic model with the hifigan vocoder. For the Spoke task, we trained a multi-speaker model and incorporated speaker information into the duration predictor, vocoder, and flow layers of the model. The evaluation results of our system showed a quality MOS score of 3.6 for the Hub task and 3.4 for the Spoke task, placing our system at an average level among all participating teams

A Novel Overlap-Time Effect Suppression for Current Source Converter

In order to ensure the continuity of the DC-side inductor current, current source converter (CSC) needs to add overlap time between the drive signals, but the overlap time will introduce low order (mainly fifth and seventh) harmonics to the grid current, which seriously degrade the harmonic performance of grid current. At present, some research has been conducted to theoretically analyze and mitigate the overlap-time effect in CSC, including the use of positive-slope sawtooth wave or negative-slope sawtooth wave as the carrier wave, turning on the switch early or delaying turning it off, and eliminating the deviation effect by compensation algorithms, etc. However, existing overlap-time suppression schemes takes the nearest three vector synthesis reference vector scheme as the object of study, in other words, the effect of overlap time on the non-nearest three-vector synthesis reference vector scheme has not been considered. To address these issues, this paper takes the non-nearest three-vector synthesis reference vector scheme as the object of study to analyze the effect of overlap time on the driving signal and establishes the quantitative relationship between the current harmonics introduced in the grid current and overlap time through Fourier decomposition. Then, the design process of the proposed improved space vector modulation by constructing freewheeling channels to replace the overlap time is presented in detail. Finally, simulation and experimental results verify that the overlap-time suppression effect of the proposed scheme is about 100%

Analysis and prediction on the cutting process of constrained damping boring bars based on PSO-BP neural network model

Firstly, this paper computed the static and dynamic characteristics of common boring bars and constrained damping boring bars respectively, and the correctness of the computational model in time-frequency domain was also validated by experiments. Modal frequencies of constrained damping boring bars were obviously more than those of common boring bars, which could effectively avoid structural resonance in low frequency and had an obvious advantage in improving anti-vibration performance of boring bars. The absolute value of the maximum vibration acceleration of common boring bars was 13.1 m/s2, while the absolute value of the maximum vibration acceleration of constrained damping boring bars was 9.1 m/s2. The maximum vibration acceleration decreased by 30.5 %. The maximum vibration displacement of common boring bars was 5.2 mm and corresponding frequency was 201 Hz. The maximum vibration displacement of constrained damping boring bars was 2.3 mm and corresponding frequency was 235 Hz. When the analyzed frequency was lower than the frequency with the maximum vibration displacement, the displacement spectrum of common boring bars had more peak values. Thus, it was clear that constrained damping boring bars had an obvious advantage in improving vibration characteristics. The impact of cutting speed, feed rate and back cutting depth on vibration characteristics was studied respectively. Results showed that the vibration of constrained damping boring bars gradually decreased with the increase of cutting speed and gradually increased with the increase of feed rate and back cutting depth. In addition, the amplitude and frequency of vibration displacement spectrum of boring bars were basically unchanged no matter how cutting parameters changed. In order to quickly predict the vibration characteristic, BP neural network and PSO-BP neural network were respectively used to predict the cutting process of boring bars. When the iteration number of BP neural network was 300, iterative error was 0.00015 which was far more than the set target error. When the iteration number of PSO-BP neural network was 215, iterative error was converged to the set target error. Therefore, PSO-BP neural network had an obvious advantage in predicting the cutting process of boring bars. In addition, the predicted result of PSO-BP neural network was consistent with the experimental result, which showed that the neural network model in this paper was effective

Learning to Behave Like Clean Speech: Dual-Branch Knowledge Distillation for Noise-Robust Fake Audio Detection

Most research in fake audio detection (FAD) focuses on improving performance on standard noise-free datasets. However, in actual situations, there is usually noise interference, which will cause significant performance degradation in FAD systems. To improve the noise robustness, we propose a dual-branch knowledge distillation fake audio detection (DKDFAD) method. Specifically, a parallel data flow of the clean teacher branch and the noisy student branch is designed, and interactive fusion and response-based teacher-student paradigms are proposed to guide the training of noisy data from the data distribution and decision-making perspectives. In the noise branch, speech enhancement is first introduced for denoising, which reduces the interference of strong noise. The proposed interactive fusion combines denoising features and noise features to reduce the impact of speech distortion and seek consistency with the data distribution of clean branch. The teacher-student paradigm maps the student's decision space to the teacher's decision space, making noisy speech behave as clean. In addition, a joint training method is used to optimize the two branches to achieve global optimality. Experimental results based on multiple datasets show that the proposed method performs well in noisy environments and maintains performance in cross-dataset experiments

Preparation of modified whey protein isolate with gum acacia by ultrasound maillard reaction

peer-reviewedEffect of ultrasound treatment on whey protein isolate (WPI)-gum Acacia (GA) conjugation via Maillard reaction was investigated. And the physicochemical properties of the conjugates obtained by ultrasound treatment were compared with those obtained by classical heating. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis, high-performance size exclusion chromatography and fourier transform infrared spectroscopy provided evidence on the formation of the Maillard type conjugation. Compared with classical heating, ultrasound treatment could accelerate the glycation reaction between WPI and GA. A degree of graft of 11.20% was reached by classical heating for 48 h, whereas only 20 min was required by ultrasound treatment. Structural analyses suggested that the conjugates obtained by ultrasound treatment had less α-helix content, higher surface hydrophobicity and fluorescence intensity than those obtained by classical heating. Significantly lower level of browning intensity and significantly higher (p < 0.05) level of solubility (under alkaline conditions), thermal stability, emulsifying activity and emulsifying stability were observed for the conjugates obtained by ultrasound treatment as compared with those obtained by classical heating

A novel model based on necroptosis to assess progression for polycystic ovary syndrome and identification of potential therapeutic drugs

BackgroundPolycystic ovary syndrome (PCOS), a common endocrine and reproductive disorder, lacks precise diagnostic strategies. Necroptosis was found to be crucial in reproductive and endocrine disorders, but its function in PCOS remains unclear. We aimed to identify differentially diagnostic genes for necroptosis (NDDGs), construct a diagnostic model to assess the progression of PCOS and explore the potential therapeutic drugs.MethodsGene expression datasets were combined with weighted gene co-expression network analysis (WGCNA) and necroptosis gene sets to screen the differentially expressed genes for PCOS. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a necroptosis-related gene signatures. Independent risk analyses were performed using nomograms. Pathway enrichment of NDDGs was conducted with the GeneMANIA database and gene set enrichment analysis (GSEA). Immune microenvironment analysis was estimated based on ssGSEA algorithm analysis. The Comparative Toxicogenomics Database (CTD) was used to explore potential therapeutic drugs for NDDGs. The expression of NDDGs was validated in GSE84958, mouse model and clinical samples.ResultsFour necroptosis-related signature genes, IL33, TNFSF10, BCL2 and PYGM, were identified to define necroptosis for PCOS. The areas under curve (AUC) of receiver operating characteristic curve (ROC) for training set and validation in diagnostic risk model were 0.940 and 0.788, respectively. Enrichment analysis showed that NDDGs were enriched in immune-related signaling pathways such as B cells, T cells, and natural killer cells. Immune microenvironment analysis revealed that NDDGs were significantly correlated with 13 markedly different immune cells. A nomogram was constructed based on features that would benefit patients clinically. Several compounds, such as resveratrol, tretinoin, quercetin, curcumin, etc., were mined as therapeutic drugs for PCOS. The expression of the NDDGs in the validated set, animal model and clinical samples was consistent with the results of the training sets.ConclusionIn this study, 4 NDDGs were identified to be highly effective in assessing the progression and prognosis of PCOS and exploring potential targets for PCOS treatment

Optimization of macroporous resin adsorption process of enzymatic hydrolysis of Pueraria protein and its antioxidant activity in vitro by response surface method

Objective： This study aims to optimize the adsorption of enzymatic hydrolysis of Pueraria protein by macroporous resin and maximize the antioxidant properties in vitro. Methods: The Box-Behnken response surface method was used to determine the optimization of adsorption of enzymatic hydrolysis of Pueraria protein. Using VC as a control, the antioxidant activity of Kase was determined before and after purification under the optimal purification process. Results： The optimal adsorption-desorption process of macroporous resin is: the mass concentration of the sample solution was 10.0 mg/mL, the flow rate of the eluent was 2.6 mL/min, and the volume fraction of ethanol in the eluent was 74%. After adsorption, the content of enzymatic hydrolysis of Pueraria protein increased to 37.19%. The scavenging rate of DPPH, ABTS+, and hydroxyl radicals of Pueraria Mirifica protease after adsorption was stronger than before adsorption. Conclusion： This study shows that enzymatic hydrolysis of Pueraria protein after adsorption of macroporous resin has good antioxidant effect, which can be used as a potential protein polypeptide in food, providing data reference for further research of enzymatic hydrolysis of Pueraria protein