Automatic modulation recognition of radio fuzes using a DR2D-based adaptive denoising method and textural feature extraction

The identification of intercepted radio fuze modulation types is a prerequisite for decision-making in interference systems.However,the electromagnetic environment of modern battlefields is complex,and the signal-to-noise ratio(SNR)of such environments is usually low,which makes it difficult to implement accurate recognition of radio fuzes.To solve the above problem,a radio fuze automatic modulation recognition(AMR)method for low-SNR environments is proposed.First,an adaptive denoising algorithm based on data rearrangement and the two-dimensional(2D)fast Fourier transform(FFT)(DR2D)is used to reduce the noise of the intercepted radio fuze intermediate frequency(IF)signal.Then,the textural features of the denoised IF signal rearranged data matrix are extracted from the statistical indicator vectors of gray-level cooccurrence matrices(GLCMs),and support vector machines(SVMs)are used for classification.The DR2D-based adaptive denoising algorithm achieves an average correlation coefficient of more than 0.76 for ten fuze types under SNRs of-10 d B and above,which is higher than that of other typical algorithms.The trained SVM classification model achieves an average recognition accuracy of more than 96%on seven modulation types and recognition accuracies of more than 94%on each modulation type under SNRs of-12 d B and above,which represents a good AMR performance of radio fuzes under low SNRs.

Deep learning-based activity recognition and fine motor identification using 2D skeletons of cynomolgus monkeys

Video-based action recognition is becoming a vital tool in clinical research and neuroscientific study for disorder detection and prediction.However,action recognition currently used in non-human primate(NHP)research relies heavily on intense manual labor and lacks standardized assessment.In this work,we established two standard benchmark datasets of NHPs in the laboratory:Monkeyin Lab(Mi L),which includes 13 categories of actions and postures,and MiL2D,which includes sequences of two-dimensional(2D)skeleton features.Furthermore,based on recent methodological advances in deep learning and skeleton visualization,we introduced the Monkey Monitor Kit(Mon Kit)toolbox for automatic action recognition,posture estimation,and identification of fine motor activity in monkeys.Using the datasets and Mon Kit,we evaluated the daily behaviors of wild-type cynomolgus monkeys within their home cages and experimental environments and compared these observations with the behaviors exhibited by cynomolgus monkeys possessing mutations in the MECP2 gene as a disease model of Rett syndrome(RTT).Mon Kit was used to assess motor function,stereotyped behaviors,and depressive phenotypes,with the outcomes compared with human manual detection.Mon Kit established consistent criteria for identifying behavior in NHPs with high accuracy and efficiency,thus providing a novel and comprehensive tool for assessing phenotypic behavior in monkeys.

Using Speaker-Specific Emotion Representations in Wav2vec 2.0-Based Modules for Speech Emotion Recognition

Speech emotion recognition is essential for frictionless human-machine interaction,where machines respond to human instructions with context-aware actions.The properties of individuals’voices vary with culture,language,gender,and personality.These variations in speaker-specific properties may hamper the performance of standard representations in downstream tasks such as speech emotion recognition(SER).This study demonstrates the significance of speaker-specific speech characteristics and how considering them can be leveraged to improve the performance of SER models.In the proposed approach,two wav2vec-based modules(a speaker-identification network and an emotion classification network)are trained with the Arcface loss.The speaker-identification network has a single attention block to encode an input audio waveform into a speaker-specific representation.The emotion classification network uses a wav2vec 2.0-backbone as well as four attention blocks to encode the same input audio waveform into an emotion representation.These two representations are then fused into a single vector representation containing emotion and speaker-specific information.Experimental results showed that the use of speaker-specific characteristics improves SER performance.Additionally,combining these with an angular marginal loss such as the Arcface loss improves intra-class compactness while increasing inter-class separability,as demonstrated by the plots of t-distributed stochastic neighbor embeddings(t-SNE).The proposed approach outperforms previous methods using similar training strategies,with a weighted accuracy(WA)of 72.14%and unweighted accuracy(UA)of 72.97%on the Interactive Emotional Dynamic Motion Capture(IEMOCAP)dataset.This demonstrates its effectiveness and potential to enhance human-machine interaction through more accurate emotion recognition in speech.

An Activity Recognition System at Home Based on CO2 Sensors

Activity recognition of indoor occupants using indirect sensing with less privacy violation is one of the hot research topics. This paper proposes a CO2 sensor-based indoor occupant activity monitoring system. Using the IoT sensor node that contains CO2 sensors, the measured CO2 concentrations in three locations (laboratory, office, and bedroom) were stored in a cloud server for up to 35 days starting July 1, 2023. The CO2 measurements stored at 30-second intervals were statistically processed to produce a heat-mapped display of the hourly average or maximum CO2 concentration. From the heatmap visualizations of CO2 concentration, the proposed system estimated meeting, heating water using a portable stove, and sleep for the occupants’ activity recognition.

2DPCA versus PCA for face recognition

Dimensionality reduction methods play an important role in face recognition. Principal component analysis(PCA) and two-dimensional principal component analysis(2DPCA) are two kinds of important methods in this field. Recent research seems like that 2DPCA method is superior to PCA method. To prove if this conclusion is always true, a comprehensive comparison study between PCA and 2DPCA methods was carried out. A novel concept, called column-image difference(CID), was proposed to analyze the difference between PCA and 2DPCA methods in theory. It is found that there exist some restrictive conditions when2 DPCA outperforms PCA. After theoretical analysis, the experiments were conducted on four famous face image databases. The experiment results confirm the validity of theoretical claim.

Synthesis,Crystal Structure and Recognition Properties of a New Benzothiazole Derivative：C28H24N4O2S

A benzothiazole-based compound 1, C28H24N4O2S, has been synthesized and characterized by single-crystal X-ray diffraction. It crystallizes in monoclinic, space group P21/c with a = 9.6309（14）, b = 15.230（2）, c = 17.197（3）A, β = 105.222（2）°, V = 2433.9（6） A^3, Z = 4, F（000） = 1008, Dc = 1.311 Mg/m^3, Mr = 480.57, μ = 0.166 mm^-1, the final R = 0.0509 and wR = 0.1481 for 6643 observed reflections with I 〉 2σ（I）. The crystal structure of compound 1 is stabilized by C–H…O, N–H…N, N–H…O, O–H…N and C–H…N hydrogen bonds. The spectroscopic studies of the title compound toward various metal ions were also investigated in 25%（V/V） ethanol aqueous solution, and the result showed that it can selectively recognize Cu^2＋ with fluorescence quenching.

4-Tert-butyl-N′-((2-Hydroxynaphthalen-1-yl)methylene)Benzohydrazide:Synthesis,Crystal Structure and Recognition Properties

A new naphthol-based compound 1, C22 H22 N2 O2, has been designed and synthesized. The structure of the title compound 1 was confirmed by IR, 1 H NMR, 13 C NMR, H RMS, and X-ray single-crystal diffraction. The crystal belongs to the monoclinic system, space group P21/c, with a = 12.888（9）, b = 15.543（10）, c = 9.119（6） ？, β = 94.05（3）°, V = 1822（2） ？3, Z = 4, Dc = 1.263 g/cm3, Mr = 346.41, μ = 0.081 mm-1, F（000） = 736.0, the final R = 0.0452 and wR = 0.1142 for 3404 observed reflections with（I 〉 2σ（I））. The crystal structure of 1 is stabilized by O–H···N, N–H···O, C–H···O hydrogen bonds and π-π interactions. The spectroscopic studies of 1 toward various metal ions were also investigated in 25%（V/V） ethanol aqueous solution, and the result showed that it can selectively recognize Zn2＋ with fluorescence enhancement.

基于长短期记忆网络的CO_(2)气层识别方法

CO_(2)监测是油气开采中的关键环节,传统的CO_(2)监测方法面临很多挑战,在人工智能逐渐兴起的当下,深度学习技术被广泛应用于地球物理测井。珠江口盆地恩平凹陷深层CO_(2)气藏发育,传统测井方法无法准确评价储层流体。构建了基于长短期记忆网络(LSTM)的CO_(2)气层识别模型,采用m×2正则化交叉验证优选CO_(2)敏感测井参数,并对模型进行训练。利用该模型对珠江口盆地恩平凹陷L2井CO_(2)气层进行识别,并与支持向量机和K近邻算法识别结果进行对比。结果表明,3种深度学习算法对CO_(2)气层的识别效果良好,其中LSTM算法对CO_(2)气层的识别效果最好,准确度达93.4%,为深层CO_(2)气层识别工作提供了新思路。

Synthesis, Structure Characterization, and Cu^(2+) Recognition of 3-{[3-(Phenylsulfonamido)benzoyl]methylidene}-3,4-dihydroquinoxaline-2(1H)-one

Aryl diketo acid derivatives are one of the most promising HIV-1 integrase（IN） inhibitors. With a view to substitute the critical diketo acid pharmacophore with the diketo benzimidazole unit, the coupling reaction of compound 4 with o-phenylenediamine was carried out. However, the reaction product, compound 5, was confirmed to be 3-{ [ 3- （phenylsulfonamido） benzoyl] methylidene t -3,4-dihydroquinoxaline-2 （1H） -one rather than the 2-benzimidazole derivative by using X-ray diffraction. Owing to its low solubility in water, the evaluation of the anti-HIV IN activity of the synthesized compound 5 could not be carried out. Consequently, the ion-binding properties of compound 5 in the absence of HIV-1 IN were investigated with UV-Vis spectroscopy in organic solvents. The results show that such a compound can selectively recognize Cu^2＋.

5-磷酸吡哆醛缩合罗丹明B席夫碱合成及对Cu^(2+)的识别研究

合成了吡哆醛-5-磷酸罗丹明B席夫碱.通过紫外-可见光光谱法研究了该化合物对Cu^(2+)的识别能力:在丙酮-水(体积比1∶1)混合体系中,加入Cu^(2+)后,在526 nm处出现了一个新的吸收峰,并且随着Cu^(2+)的加入,吸收强度逐渐增强.在(0.25~10)×10^(-6) mol/L的浓度范围内,检测结果呈现出良好的线性关系(R^(2)=0.9993),检测限为2.30×10^(-7) mol/L.

PATTERN RECOGNITION APLLIED TO STABILITY OF FILLED Ti_2Ni PHASES

Pattern recognition method is used for the investigation of stability,region of filled Ti_2Ni phases in multi-dimensional bond-parameter space.The filling of C,N and O atoms into T_6 octahedra consisting of atoms of earhy-transition elements makes the expansion of the stability region of Ti_2Ni phase,and the relative stability of AI_2Cu and MoSi_2 type com- pounds decreases after the introduction of non-metallic elements such as C,N and O.

2D spiral pattern recognition based on neural network covering algorithm

The main aim for a 2D spiral recognition algorithm is to learn to discriminate between data distributed on two distinct strands in the x-y plane.This problem is of critical importance since it incorporates temporal characteristics often found in real-time applications.Previous work with this benchmark has witnessed poor results with statistical methods such as discriminant analysis and tedious procedures for better results with neural networks.This paper presents a max-density covering learning algorithm based on constructive neural networks which is efficient in terms of the recognition rate and the speed of recognition.The results show that it is possible to solve the spiral problem instantaneously(up to 100% correct classification on the test set).

Research on Face Recognition Algorithm Based on Robust 2DPCA

As a new dimension reduction method, the two-dimensional principal component (2DPCA) can be well applied in face recognition, but it is susceptible to outliers. Therefore, this paper proposes a new 2DPCA algorithm based on angel-2DPCA. To reduce the reconstruction error and maximize the variance simultaneously, we choose F norm as the measure and propose the Fp-2DPCA algorithm. Considering that the image has two dimensions, we offer the Fp-2DPCA algorithm based on bilateral. Experiments show that, compared with other algorithms, the Fp-2DPCA algorithm has a better dimensionality reduction effect and better robustness to outliers.

Novel Method Fusing （2D）^2 LDA with Multichannel Model for Face Recognition

A fusion method of Gabor features and (2D)~2LDA for face feature extraction is proposed in this paper. Gabor filters are utilized to extract multi-direction and multi-scale features from facial image to employ its robust performance for illumination,expressional variability and other factors. The extracted features have the defect of high dimension and redundancy data.(2D)~2LDA is implemented to reduce the dimension of Gabor features and select effective feature data. Finally, the nearest neighbor classifier is used to classify characteristics and complete face recognition. The experiments are implemented by using ORL database and Yale database respectively. The experimental results show that the proposed method significantly reduces the dimension of Gabor features and decrease the influence of other factors. The proposed method acquires excellent recognition accuracy and has light architectures as well.

Action Recognition Using Multi-Scale Temporal Shift Module and Temporal Feature Difference Extraction Based on 2D CNN

Convolutional neural networks, which have achieved outstanding performance in image recognition, have been extensively applied to action recognition. The mainstream approaches to video understanding can be categorized into two-dimensional and three-dimensional convolutional neural networks. Although three-dimensional convolutional filters can learn the temporal correlation between different frames by extracting the features of multiple frames simultaneously, it results in an explosive number of parameters and calculation cost. Methods based on two-dimensional convolutional neural networks use fewer parameters;they often incorporate optical flow to compensate for their inability to learn temporal relationships. However, calculating the corresponding optical flow results in additional calculation cost;further, it necessitates the use of another model to learn the features of optical flow. We proposed an action recognition framework based on the two-dimensional convolutional neural network;therefore, it was necessary to resolve the lack of temporal relationships. To expand the temporal receptive field, we proposed a multi-scale temporal shift module, which was then combined with a temporal feature difference extraction module to extract the difference between the features of different frames. Finally, the model was compressed to make it more compact. We evaluated our method on two major action recognition benchmarks: the HMDB51 and UCF-101 datasets. Before compression, the proposed method achieved an accuracy of 72.83% on the HMDB51 dataset and 96.25% on the UCF-101 dataset. Following compression, the accuracy was still impressive, at 95.57% and 72.19% on each dataset. The final model was more compact than most related works.

TiO_(2-x) 纳米线基光电忆阻突触器件性能的优化

金属氧化物基光电忆阻器能够同时实现光信号的采集、存储和处理功能,被认为是构筑神经形态视觉系统的理想选择之一.然而,由于金属氧化物材料持续光电导效应下电子和空穴的快速复合,会导致器件电导变化线性度低,限制了其在高精度图像识别方面的发展.实验基于水热法制备了TiO_(2-x)纳米线构筑光电忆阻器,通过等离子体处理的方式优化器件性能,提升器件的电导变化线性度,实现了短时可塑性和长时可塑性的光电忆阻行为及高精度图像识别功能.实验结果表明:等离子体处理能够在TiO_(2-x)纳米线中引入氧空位缺陷,增强器件的持续光电导效应.

Traffic sign recognition algorithm based on shape signature and dual-tree complex wavelet transform

A novel traffic sign recognition system is presented in this work. Firstly, the color segmentation and shape classifier based on signature feature of region are used to detect traffic signs in input video sequences. Secondly, traffic sign color-image is preprocessed with gray scaling, and normalized to 64×64 size. Then, image features could be obtained by four levels DT-CWT images. Thirdly, 2DICA and nearest neighbor classifier are united to recognize traffic signs. The whole recognition algorithm is implemented for classification of 50 categories of traffic signs and its recognition accuracy reaches 90%. Comparing image representation DT-CWT with the well-established image representation like template, Gabor, and 2DICA with feature selection techniques such as PCA, LPP, 2DPCA at the same time, the results show that combination method of DT-CWT and 2DICA is useful in traffic signs recognition. Experimental results indicate that the proposed algorithm is robust, effective and accurate.

连续识别Zn^(2+)和草甘膦荧光探针的合成与应用

以2-巯基苯并噻唑为原料,设计合成了一种结构简单的苯并噻唑类荧光探针2-[2-(苯并噻吩-2-基亚甲基)肼基]苯并噻唑(简称NSS),并通过FTIR、HRMS、^(1)HNMR、^(13)CNMR对其结构进行了表征。荧光光谱表明,在二甲基亚砜中,探针NSS实现了Zn^(2+)的“关-开”型检测,具有响应时间短(30 s)、特异性强、抗干扰性强等优点。探针NSS荧光强度与Zn^(2+)浓度(0~11μmol/L)呈现良好的线性关系,检出限达19.1 nmol/L,并与Zn^(2+)形成物质的量比为1∶1的络合物。同时,络合物NSS-Zn^(2+)对草甘膦呈现特异性的荧光猝灭响应,猝灭率达99.4%,检出限16.0 nmol/L(2.71 ng/mL),且不受其他有机磷农药的干扰。

基于SE-R(2+1)D网络的自然环境下的奶牛行为识别

智能行为识别对于奶牛健康的自动诊断和精准养殖具有重要意义。由于接触式传感器会损害动物福利,对奶牛产生应激反应。因此,本文设计了R(2+1)D模型对奶牛进行行为识别。3D网络作为1种时空卷积网络,可以有效识别奶牛的基本时序行为,但该模型针对奶牛的进食行为与反刍行为不易区分,因此对残差网络中的残差映射部分进行改进,在残差网络中添加注意力机制,将SE模块加入到残差映射部分。首先,利用Kinect相机采集奶牛的行为视频;其次,将采集到的奶牛视频分解成连续帧输入到改进后的模型中,连续帧经过二维空间特征和一维时间特征提取,经过残差网络的注意力模块,忽略一些无关的特征;最后,经过模型的Softmax层进行行为分类。实验证明,和原模型比较,准确率提高了2.36%。本文方法实现了精准的奶牛行为识别,可为智慧畜牧业的发展提供技术支持。

Face Recognition Systems Using Relevance Weighted Two Dimensional Linear Discriminant Analysis Algorithm

Low-dimensional feature representation with enhanced discriminatory power of paramount importance to face recognition systems. Most of traditional linear discriminant analysis (LDA)-based methods suffer from the disadvantage that their optimality criteria are not directly related to the classification ability of the obtained feature representation. Moreover, their classification accuracy is affected by the “small sample size” (SSS) problem which is often encountered in face recognition tasks. In this paper, we propose a new technique coined Relevance-Weighted Two Dimensional Linear Discriminant Analysis (RW2DLDA). Its over comes the singularity problem implicitly, while achieving efficiency. Moreover, a weight discriminant hyper plane is used in the between class scatter matrix, and RW method is used in the within class scatter matrix to weigh the information to resolve confusable data in these classes. Experiments on two well known facial databases show the effectiveness of the proposed method. Comparisons with other LDA-based methods show that our method improves the LDA classification performance.