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Background: Tokyo, the capital of Japan, is a densely populated city of >13 million people, so the population is at high risk of epidemic severe acute respiratory coronavirus 2 (SARS-CoV-2) infection. A serologic survey of anti–SARS-CoV-2 IgG would provide valuable data for assessing the city’s SARS-CoV-2 infection status. Therefore, this cross-sectional study estimated the anti–SARS-CoV-2 IgG seroprevalence in Tokyo. Methods: Leftover serum of 23,234 hospital visitors was tested for antibodies against SARS-CoV-2 using an iFlash 3000 chemiluminescence immunoassay analyzer (Shenzhen YHLO Biotech, Shenzhen, China) with an iFlash–SARS-CoV-2 IgG kit (YHLO) and iFlash–SARS-CoV-2 IgG-S1 kit (YHLO). Serum samples with a positive result (≥10 AU/mL) in either of these assays were considered seropositive for anti–SARS-CoV-2 IgG. Participants were randomly selected from patients visiting 14 Tokyo hospitals between September 1, 2020 and March 31, 2021. No participants were diagnosed with coronavirus disease 2019 (COVID-19), and none exhibited COVID-19-related symptoms at the time of blood collection. Results: The overall anti–SARS-CoV-2 IgG seroprevalence among all participants was 1.83% (95% confidence interval [CI], 1.66–2.01%). The seroprevalence in March 2021, the most recent month of this study, was 2.70% (95% CI, 2.16–3.34%). After adjusting for population age, sex, and region, the estimated seroprevalence in Tokyo was 3.40%, indicating that 470,778 individuals had a history of SARS-CoV-2 infection. Conclusions: The estimated number of individuals in Tokyo with a history of SARS-CoV-2 infection was 3.9-fold higher than the number of confirmed cases. Our study enhances understanding of the SARS-CoV-2 epidemic in Tokyo.

ObjectivesTo compare the temporal changes of IgM, IgG, and IgA antibodies against the SARS-CoV-2 nucleoprotein, S1 subunit, and receptor binding domain and neutralizing antibodies (NAbs) against SARS-CoV-2 in patients with COVID-19.MethodsA total of five patients in Nissan Tamagawa Hospital, Tokyo, Japan confirmed COVID-19 from August 8, 2020 to August 14, 2020 were investigated. Serum samples were acquired multiple times from 0 to 76 days after symptom onset. Using a fully automated CLIA analyzer, we measured the levels of IgG, IgA, and IgM against the SARS-CoV-2 N, S1, and RBD and NAbs against SARS-CoV-2.ResultsThe levels of IgG antibodies against SARS-CoV-2 structural proteins increased over time in all cases but IgM and IgA levels against SARS-CoV-2 showed different increasing trends among individuals in the early stage. In particular, we observed IgA antibodies increasing before IgG and IgM in 3/5 cases. The NAb levels against SARS-CoV-2 increased and kept above 10 AU/mL more than around 70 days after symptom onset in all cases. Furthermore, in the early stage, NAb levels were more than cut off value in 4/5 COVID-19 patients some of whose antibodies against RBD didn’t exceed 10 AU/mL.ConclusionsOur findings indicate that patients with COVID-19 should be examined for IgG, IgA and IgM antibodies against SARS-CoV-2 structural proteins and NAbs against SARS-CoV-2 in addition to conventional antibody testing methods for SARS-CoV-2 (IgG and IgM kits) to analyze the diversity of patients’ immune mechanisms.

The thesis is described into 5 chapters:Chapter 1 is general abstract.In chapter 2, dibenzo-24-crown-8 (DB24C8)-bearing copolymers were synthesized by radical copolymerization using a DB24C8-carrying acrylamide derivative, acrylamide and N-isopropylacrylamide monomers. The cloud points of the resulting copolymers changed in aqueous solution in the presence of Cs+. In addition, the 1H NMR signals of DB24C8-bearing copolymers were more shifted following the addition of Cs+ compared to Rb+, K+, Na+, and Li+ ions due to recognition of the Cs+ ion by DB24C8. These findings suggest that DB24C8-bearing polymers hold promise for adsorbing and removing cesium from the environment.In chapter 3, polymer gels containing DB24C8 moieties were synthesized by radical copolymerization of a DB24C8-containing monomer and acrylamide in the presence of a cross-linker. The synthesized polymer gels containing DB24C8 moieties can effectively remove Cs+ from an aqueous environment without nonspecific adsorption in a selective manner, according to competition tests performed in the presence of Na+ and K+. These results imply that DB24C8-containing polymer gels hold promise for selective cesium removal from aqueous environments and will have contribution for treating radioactive wastes.In chapter 4, chemical modification of chitosan with DB24C8 moieties was attempted by three methods, i.e., the reactions of DB24C8 having a COOH group with either 1-(3-Dimethylaminopropyl)-3-ethylcarbodiimide hydro- chloride (EDC)-1-hydroxybenzotriazole (HOBt) or EDC-N-hydroxysuccinimide (NHS) and the reaction using the succinimide-activated ester of DB24C8, were compared on chitosan. The use of the activated ester of DB24C8 resulted in a higher degree of substitution (15.2%) at amino groups on chitosan when compared with the EDC-HOBt or EDC-NHS systems. This result implies that the use of activated ester derivatives is efficient for the modification of chitosan in aqueous solution.Chapter 5 is general conclusion.

Time delay estimation (TDE) is a hot research topic in wireless location technology. Compressed sensing (CS) theory has been widely applied to image reconstruction and direction of arrival estimation since it was proposed in 2004. The sparse model can be constructed in time domain for estimating the time delay by using the CS theory. The measurement matrix plays a crucial role in the processing of signal reconstruction which is the core problem of CS theory. Therefore the research in the measurement matrix has becomes a hotspot in recent years. The existing measurement matrix is mainly divided into two categories, i.e., random measurement matrix and deterministic measurement matrix. The performance of random measurement matrix has bottlenecks. Firstly, because of the redundant measurement matrix data, the generation and storage of the random number put forward a high requirement for hardware. Secondly the random matrix can only satisfy the restricted isometry property in a statistical sense. The research of the deterministic measurement matrix is of great value under this background. The parity check matrix of low density parity check (LDPC) code has good performance in CS theory. However, the method of randomly selecting non-zero element position has a certain probability to generate a measurement matrix with a short loop structure during generating LDPC code measurement matrix. The robustness of the reconstruction performance decreases with the increase of iteration times. A novel quasi-cyclic CS algorithm based on progressive edge-growth is constructed to estimate the time delay. The purpose of this article is to deal with the need to store a large number of data in existing measurement matrix during time delay, by using the CS theory. The algorithm presented here can achieve TDE in a high precision. First, the theoretical bridge between CS and the maximum likelihood decoding is established. And the design criterion of measurement matrix based on the LDPC code is derived. The sparse measurement matrix with quasi-cyclic structure is constructed by introducing the idea of progressive edge-growth. Finally, the orthogonal matching pursuit algorithm is used to estimate the time delay. Furthermore, the computational complexity of the algorithm and the data storage of the measurement matrix are analyzed theoretically. Simulations show that the correct reconstruction probability of the proposed approach is higher than those of the Gauss random matrix and random LDPC matrix under the same dimension. Compared with the random LDPC matrix, the proposed method can improve performance at the expense of less complexity under the condition of the same data storage.

提出了一种基于混沌激光多位量化的高速物理随机数实时产生方法.利用外腔反馈混沌半导体激光器作为物理熵源，通过时钟速率为7 GHz的多位模数转换器对其采样量化，生成6位有效位的二进制随机比特，然后利用现场可编程软件抽取低2位有效位的随机序列并进行自延迟异或处理，获得了实时速率为14 Gb/s的物理随机数.该随机数具有良好的统计随机性，可成功通过随机数行业测试标准（NIST SP 800-22）.

Wireless target location technology has been widely used in civil and military fields. In the two-step localization algorithms, the signal measurements, such as thetime of arrival, theangle of arrival, the frequency difference of arrival, etc., should be extracted first from the receivedsource signal. Then the target position is identified by calculating the location equation. Compared with the two-step localization algorithms, the direct position determination (DPD) method, which need not estimate the signal parameters and calculate the position step by step, but obtainsthe source position from the received signals directly based on the maximum likelihood criterion, has been shown to have a goodestimation accuracy and robustness, especially under low signal-to-noise ratio (SNR) conditions. So it has been widely studied in recent years and has made remarkable achievements in academic research. However, the DPD algorithm of wideband signals emitters is not performing well with moving receivers in the joint positioning based on time delay and Doppler shift under the low SNRs. To obtaina better positioning performance, in this paper we present a DPD algorithm with variable velocity receivers based on coherent summation of short-time signal segments, and derive the source position Cramer-Rao lower bound (CRLB). The algorithm designs a positioning model in which the multiple variable velocity receivers are usedtoobtain the source signal, then the signal received at the same receiver is patitioned into multiple non-overlapping short-time signal segments, based on which, an approximate maximum likelihood estimator for the new DPD algorithm is developed. The algorithm makes full use of the location information contained in the coherency among the signals segments, while extra target position information is acquired through the speed variability in the positioning model, and thus the problem of location ambiguity is solved. The simulation results show thatthe algorithm proposed in this paper further improves the positioning performance, and outperforms the traditional DPD algorithms with more accurate results. Especially in the low SNR, it is closer to the CRLB.

2008 International Congress on Membranes and Membrane Processes (ICOM 2008). Poster Session II, July 12-18, 2008, Honolulu, Hawaii, available, unlimited, public

The time delay estimation is widely used in wireless location field, and is the research emphasis in complex environment of this field. The current delay estimation algorithms can be classified as five methods of correlation, high-order statistics, self-adaption, maximum likelihood and subspace. However, the existing algorithms can hardly achieve an ideal performance in small sample（single snapshot) and low signal-to-noise ratio environment during wireless location. In order to solve the problem about the insufficiency of the current algorithms in the above conditions, many new methods have been introduced into the delay estimation problem. The compressed sensing sparse reconstruction method has been applied to the signal processing field as a newly-proposed algorithm in recent years. The delay estimation is realized by using the method of sparse reconstruction, in which the sparse representation of the signal is the premise. The rational construction of the measurement matrix and the design of the signal reconstruction algorithm are the core of correct estimation.The purpose of this article is to deal with the lack of measurement data in small sample（single snapshot) and low signal-to-noise ratio environment during wireless location. In the model of wireless location, the signal can be represented as a sparse matrix form by selecting suitable sparse representation matrix. The wireless multi-channel is measured in the time domain, the propagation delay varies with channel and the delay representation in the time domain is sparse, so that it can be directly constructed into the form of sparse signal. Since the necessary and the sufficient condition of the coefficient sparse matrix successfully reconstructed by the measurement matrix are the measurement matrix meeting the restricted isometry property（RIP). The orthogonal measurement matrix based on the steering vector by the method of Gram-Schmidt is proven to achieve the RIP. A novel sparse reconstruction algorithm based on backtracking filter is constructed to estimate the time delay. In order to guarantee that the first selection includes the optimal atom, several atoms are selected. And then the backtracking mechanism is introduced, and the selected atoms are approached by the method of the minimum square to sequence the obtained signals and select the optimal atom. Therefore, this method can be used to guarantee that the optimal atom is selected. The presented algorithm can achieve the delay estimation by using the corresponding relation between the time delay and the measurement matrix in a high precision. Furthermore, the Cramer-Rao bound（CRB) of this model is derived. Finally, simulations show that the proposed approach is suitable for small sample（single snapshot) and low signal-to-noise ratio environment. The proposed method can achieve a higher precision than Root-Music and improve performance at low complexity cost compared with OMP algorithm. The simulation result proves that the algorithm is stable and reliable.

In a passive target tracking system, the position and velocity of a target can be estimated based on time difference of arrival (TDOA) and frequency difference of arrival (FDOA) received by different stations. But TDOA and FDOA equations are nonlinear, which make the target tracking become a nonlinear estimation problem. To solve the nonlinear estimation problem, the most extensive research algorithms are those of extended Kalman filter (EKF), particle filter (PF), unscented Kalman filter (UKF), quadrature Kalman filter (QKF), and cubature Kalman filter (CKF). But the existing algorithms all come up with shortcoming in some way. EKF only retains the first order of the nonlinear function by Taylor series expansion, which will bring large error. PF has to face the degeneracy phenomenon and the problem of large computational complexity. The standard UKF is easy to become divergence in a high dimensional state estimation. QKF is sensitive to the dimension of state, and the calculation is of exponential growth with the growth of dimension. Although CKF can effectively improve the shortcomings, the discarded error is proportional to the state dimension, which may be large in high dimensional state. In view of the above problems, this paper presents an orthogonal cubature Kalman filter (OCKF) algorithm. This algorithm reduces the sampling error by introducing special orthogonal matrix to change the method of cubature sampling based on CKF. It eliminates the dimension impact on the sampling error. In the absence of additional computation, it effectively improves the tracking precision. Simulation results show that, based on the TDOA and FDOA, compared with the EKF and CKF algorithms, OCKF algorithm can improve the tracking performance significantly.