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Passive sonar systems are used to detect the acoustic signals that are radiated from marine objects (e.g., surface ships, submarines, etc.), and an accurate estimation of the frequency components is crucial to the target detection. In this paper, we introduce sparse Bayesian learning (SBL) for the frequency analysis after the corresponding linear system is established. Many algorithms, such as fast Fourier transform (FFT), estimate signal parameters via rotational invariance techniques (ESPRIT), and multiple signal classification (RMUSIC) has been proposed for frequency detection. However, these algorithms have limitations of low estimation resolution by insufficient signal length (FFT), required knowledge of the signal frequency component number, and performance degradation at low signal to noise ratio (ESPRIT and RMUSIC). The SBL, which reconstructs a sparse solution from the linear system using the Bayesian framework, has an advantage in frequency detection owing to high resolution from the solution sparsity. Furthermore, in order to improve the robustness of the SBL-based frequency analysis, we exploit multiple measurements over time and space domains that share common frequency components. We compare the estimation results from FFT, ESPRIT, RMUSIC, and SBL using synthetic data, which displays the superior performance of the SBL that has lower estimation errors with a higher recovery ratio. We also apply the SBL to the in-situ data with other schemes and the frequency components from the SBL are revealed as the most effective. In particular, the SBL estimation is remarkably enhanced by the multiple measurements from both space and time domains owing to remaining consistent signal frequency components while diminishing random noise frequency components.

An enhanced smoothed l0-norm algorithm for the passive phased array system, which uses the covariance matrix of the received signal, is proposed in this paper. The SL0 (smoothed l0-norm) algorithm is a fast compressive-sensing-based DOA (direction-of-arrival) estimation algorithm that uses a single snapshot from the received signal. In the conventional SL0 algorithm, there are limitations in the resolution and the DOA estimation performance, since a single sample is used. If multiple snapshots are used, the conventional SL0 algorithm can improve performance in terms of the DOA estimation. In this paper, a covariance-fitting-based SL0 algorithm is proposed to further reduce the number of optimization variables when using multiple snapshots of the received signal. A cost function and a new null-space projection term of the sparse recovery for the proposed scheme are presented. In order to verify the performance of the proposed algorithm, we present the simulation results and the experimental results based on the measured data.

We design a simulator in order to seek a surface warship’s survivability against a torpedo attack using MATLAB GUI. Assuming that a torpedo chases a warship in an acoustic-homing way, we mo...

In this paper, a compressive sensing-based data fitting direction-of-departure/direction-of-arrival (DOD/DOA) estimation algorithm is proposed to apply the superior performance of compressive sensing method to the bistatic MIMO sonar systems. The algorithm proposed in this paper optimizes the output data via convex optimization-based sparse recovery, so that it is possible to estimate the DOD and the DOA for each target accurately. In order to minimize the amount of computation, the cost function with constraint condition is implemented in this paper. Furthermore, the constraint condition parameter of the cost function is analytically derived. Through various simulations, it is shown that the superior DOD and DOA estimation performance of the proposed algorithm and that the analytical derivation of the constraint condition parameter is useful for determination of regularization parameter.

This paper presents a conceptual model design for entity-level combat simulation to enhance model composability and reusability. For conceptual modeling, we first describe the following three probl...

Circulating tumor cells (CTCs) are recognized as promising biomarkers for diagnosis and indication of the prognosis of several epithelial cancers. However, at present, CTC monitoring is available only for advanced-stage patients rather than for those at an early stage of cancer. This is because of the extraordinary rarity of CTCs and the limited sensitivity of current methods. Herein, we report the development of multifunctional magnetic nanowires for the efficient isolation and detection of CTCs from the blood of patients, especially those with non-metastatic early-stage cancer. The nanowires, which are equipped with a high density of magnetic nanoparticles and five different types of antibodies (Ab mixture_mPpyNWs), offer a significant improvement in cell-isolation efficiency, even from very small amounts of blood (250 μL-1 mL). Notably, CTCs were isolated and identified in 29 out of 29 patients (100%) with non-metastatic early breast cancer, indicating that this procedure allowed detection of CTCs with greater accuracy, sensitivity, and specificity. In addition, we demonstrated in situ "naked eye" identification of the captured cancer cells via a simple colorimetric immunoassay. Our results show that antibody-functionalized magnetic nanowires offer great potential for a broad range of practical clinical applications, including early detection, diagnosis, and treatment of cancer.

In the present study, we describe a reusable electrochemical immunosensor for the repeated detection of cancer biomarkers using a single platform. The integration of a temperature-responsive polymer on the electrode surface enables easy manipulation of the biological sensing interface (i.e., addition of biotin, streptavidin, and antibody), thus allowing for temperature-induced regeneration and disruption of the interface architecture of the electrode surface. Using our immunosensor, we demonstrate sequential amperometric detection of three tumor markers: CA125, CEA, and PSA. Interestingly, greatly amplified signals are achieved by immersing the immunosensor in a solution of horseradish peroxidase (HRP) and antibody-labeled nanoparticles, resulting in a linear range of 0.0064 to 256 U/mL, 1 pg/mL to 100 ng/mL, and 10 pg/mL to 10 ng/mL with a detection limit of 0.007 U/mL, 0.7 pg/mL, and 0.9 pg/mL for CA125, CEA, and PSA, respectively. By alternating temperature, the immunosensor adsorbs and desorbs the biological elements without damage. Our proposed methodology can be expanded to measure other relevant biological species by repeated detection and thus has enormous potential for industrial and clinical applications.

Narrow band signal estimation and broad band signal estimation can be used to detect the ship-radiated noise. The broad band signal estimation method to detect the ship-radiated noise is called DEMON (Detection of Envelop Modulation On Noise). This paper proposes a new DEMON algorithm applying compressed sensing algorithm to filter bank and autocorrelation. We show the proposed algorithm estimates the hidden period in the wide band signal better than the conventional DEMON algorithm a nd the recently proposed filter-bank based DEMON algorithm. Especially we show that the proposed algorithm needs shorter data length than the conventional DEMON algorithm.

A covariance fitting algorithm for the estimation of direction-of-arrivals of multiple incident signals is addressed in this paper. The scheme takes advantage of the fact that the incident signals are spatially sparse. A previous study has presented the regularization parameters of the covariance fitting for a very large number of snapshots. In this paper, a strategy on how to determine the regularization constant of the covariance fitting for a general number of snapshots is presented. The strategy essentially exploits the norm of the noise covariance matrix. The proposed algorithm has been validated via numerical simulations.

When operating active sonar systems, target-like signals including clutter are detrimental to detecting target signals and induce an increment of false alarms. In this work, various approaches including machine learning and deep learning techniques are applied to in-situ data to investigate their performance. First, one of conventional schemes, constant false alarm rate (CFAR) detector is used to find target signals in acoustic data. It detects target signals with high SNR, but it also captures high-intensity clutter signals, which leads to many false alarms. Subsequently, data-driven decision rules from machine learning of support vector machine (SVM) and deep learning of convolutional neural network (CNN) are applied. Both of SVM using aural features and CNN with spectrogram show better accuracy. In particular, false alarms from the CFAR detector are extremely decreased owing to the data-driven decision rules not exploiting the signal intensity for target detection.

The frequency estimation of tonal signals in passive sonar systems is crucial to the identification of the marine object. In this study, we introduce the frequency estimation of tonal signals using the multiple measurement vector atomic norm minimization(MMV-ANM) technique based on compressive sensing (CS) and sparse Bayesian learning (SBL) technique based on Bayesian Inference. CS techniques have been employed in identifying the tonal frequency components. It is now well-known from the theory of CS that a high-dimensional signal can be recovered from a relatively small number of measurements as long as the desired signal is sparsely represented. MMV-ANM, an extended version of CS technique for solving the basis mismatch problem, has been proposed and shown better performance in terms of resolution and stability. On the other hand, tonal signals at passive sonar systems are also estimated with sparse Bayesian learning (SBL) using MMV. The prior source amplitudes are assumed independent zero-mean complex Gaussian distribution with the unknown variances (hyperparameters). The hyperparameters are derived by maximizing the probability of given passive data, which leads to sparse tonal signal frequencies. We compare performance of SBL and MMV-ANM in estimation of passive tonal signals with synthetic and in situ data. [*corresponding author Youngmin Choo]

This paper focuses on target motion analysis by fusion of information from two moving acoustic sensors. These two sensors may obtain small measurements in order to make quick analysis of moving targets. In this situation, conventional approaches often fail to find accurate motion of targets. In this paper, a fusion algorithm for target motion analysis designed to handle this situation is proposed. First, optimization based PSO is applied in order to find accurate initial motion of targets. Second, the target trajectory is estimated via a sequential fusion algorithm based on UKF. According to the various simulated results, the effectiveness of the proposed method is then verified.

Narrow band signal estimation and broad band signal estimation can be used to detect the ship-radiated noise. The broad band signal estimation method to detect the ship-radiated noise is called DEMON (Detection of Envelop Modulation On Noise). This paper proposes a new DEMON algorithm using filter bank and autocorrelation. We show the proposed algorithm estimates the hidden period in the wide band signal better than the conventional DEMON algorithm and the recently proposed filter-bank based DEMON algorithm.

Triple-negative breast cancers (TNBCs) are known to be intrinsically resistant to inhibitors for epidermal growth factor receptor (EGFR). Until now, clinical trials for TNBCs using EGFR inhibitors (EGFRis) as single agents have yielded disappointing results. Here, we report that combinatorial treatment using EGFRis, such as gefitinib or erlotinib, with PI3K/AKT pathway inhibitors (PI3K/AKTis) demonstrated a synergistic, anti-proliferative effect in cell lines of the basal-like (BL) subtype, a subtype of TNBC. Western blot analysis revealed that the gefitinib/PI-103 combination significantly reduced the level of both phospho-AKT and phospho-ERK in two susceptible BL subtype cell lines, SUM149PT and MDA-MB-468, whereas it had little or no effect on the level of phospho-ERK in two non-susceptible cell lines (HS578T and MDA-MB-231) of mesenchymal stem-like (MSL) TNBC subtype. The gefitinib/PI-103 combination also significantly induced caspase-3/7-mediated PARP cleavage and reduced two anti-apoptotic proteins, XIAP and Bcl-2 in the susceptible cell lines. In addition, the level of myeloid cell leukemia 1 (Mcl-1) protein was markedly decreased by gefitinib/PI-103 combination in the BL TNBC cells, but showed no significant change by this combination in MSL subtype cells. These results suggest that pharmacological inhibition of EGFR used in combination of PI3K/AKTis is a potential therapeutic approach to treat a subtype of TNBCs.

Amyloid plaques and neurofibrillary tangles are the pathological hallmarks of Alzheimer's disease. However, there has been a long-standing discussion on the dynamic relations between Aβ and tau proteins, partially due to the lack of a tool to track protein dynamics in individual live neurons at the early stage of Aβ generation and tau phosphorylation. Here, we developed nanoplasmonic fiber tip probe (nFTP) technology to simultaneously monitor Aβ42 generation and tau phosphorylation (at serine 262) in living, single neuroblastoma cells over 12 h. We observed that Aβ42 generation, under clinically relevant anesthetic treatment, preceded tau phosphorylation, which then facilitated Aβ42 generation. This observation is also supported by measuring proteins in cell lysates using the ultrasensitive label-free photonic crystal nanosensors. nFTP therefore provides an advanced method to investigate protein expression and post-translational modification in live cells and determine outcomes of intervention of Alzheimer's disease and other neurodegenerative disorders.

Amyloid plaques and neurofibrillary tangles are the pathological hallmarks of Alzheimer's disease. However, there has been a long-standing discussion on the dynamic relations between Aβ and tau proteins, partially due to the lack of a tool to track protein dynamics in individual live neurons at the early stage of Aβ generation and tau phosphorylation. Here, we developed nanoplasmonic fiber tip probe (nFTP) technology to simultaneously monitor Aβ42 generation and tau phosphorylation (at serine 262) in living, single neuroblastoma cells over 12 h. We observed that Aβ42 generation, under clinically relevant anesthetic treatment, preceded tau phosphorylation, which then facilitated Aβ42 generation. This observation is also supported by measuring proteins in cell lysates using the ultrasensitive label-free photonic crystal nanosensors. nFTP therefore provides an advanced method to investigate protein expression and post-translational modification in live cells and determine outcomes of intervention of Alzheimer's disease and other neurodegenerative disorders.

We developed a nanoroughened, biotin-doped polypyrrole immunosensor for the detection of tumor markers through dual-signal (electrochemical and colorimetric) channels, electrochemical and colorimetric, that demonstrates remarkable analytical performance. A rapid, one-step electric field-mediated method was employed to fabricate the immunosensor with nanoscale roughness by simply modulating the applied electrical potential. We demonstrated the successful detection of three tumor markers (CA125, CEA, and PSA) via the double enzymatic signal amplifications in the presence of a target antigen, ultimately leading to desired diagnostic accuracy and reliability. The addition of multiple horseradish peroxidase (HRP)- and antibody-labeled nanoparticles greatly amplified the signal and simplified the measurement of cancer biomarker proteins by sequentially magnifying electrochemical and colorimetric signals in a single platform. The two parallel assays performed using the proposed immunosensor have yielded highly consistent and reproducible results. Additionally, for the analysis of plasma samples in a clinical setting, the values obtained with our immunosensor were validated by correlating the results with those of a standard radioimmunoassay (RIA), which obtained very similar clinically valid responses.

In the development phase of a torpedo, the effectiveness analysis is carried out to predict the performance and to learn how to use the torpedo. In order to obtain reliable data, it is required to model the tactical situation closely to the actual one. Because the submarine is a target of a lightweight torpedo, the anti-torpedo countermeasures of a submarine such as evasive maneuvering and TACM (Torpedo Acoustic Counter Measure) should be modeled in detail. In this paper, the evasive maneuvering is modeled reflecting the movement characteristics of the submarine. Furthermore various TACMs such as a floating-type decoy, a self-propelled decoy and jammers are also modeled. Then, effectiveness of a lightweight torpedo is measured and analyzed using the simulation program which is developed through the above modeling procedure.

This paper deals with the consensus problem for multi-agent linear dynamic systems with directed communication topology. All the agents are identical high-order linear systems and their state information is exchanged through a communication network with directed graph. It is shown that a consensus is achieved if there exists a feasible solution to a set of linear matrix inequalities obtained for a simultaneous stabilization problem for multiple systems. Examples are presented to show the effectiveness of the proposed method.

In Y-shaped passive linear array sonar (PLAS) systems composed of 3 sensor legs with consecutively missed detections in a full scan, the complex ghosts that occur due to bearing ambiguity seriously deteriorate the target tracking performance. A solution to the ghost problem is proposed which exploits the track information and geometrical relationship between each PLAS leg and the target. The region of possible ghost targets is first established by making use of the geometrical relationship of the PLAS with respect to the target. The ghost targets are then eliminated by means of the updated track information refined via a combination of 3-D assignment with gating and judicial track management. In the track management and gating procedure, we propose an optimal measurement selection criterion based on maximum joint probability data association (MJPDA), which combines both the soft and hard association techniques. The established tracks are updated by the Kalman filter with MJPDA and are terminated with a logic-based procedure. Representative simulations demonstrate the effectiveness of the proposed approach.

The performance comparison of target localization for active sonar and effective fusion algorithms for target localization of multistatic sonar is investigated. Active sonar can be categorized into monostatic, bistatic, and multistatic, depending on the number of receiver elements. Target localization performance depends on the system configuration. The target localization performance of the monostatic, bistatic, and multistatic sonar systems is compared assuming that each element can receive both range and azimuth information of the target. In addition, we propose the weighted least square (WLS) algorithm, which incorporates judicial weighting to the conventional least square (LS) method, and an efficient sensor arrangement rule for target localization in the multistatic sonar system. The representative experimental results demonstrate that the target localization performance of multistatic sonar configuration is superior in terms of root-mean-square error (RMSE), to monostatic sonar and bistatic sonar by 35.98% and 37.45%, respectively, while the proposed WLS algorithm showed an improvement of 2.27% compared with the LS method.

An electroresponsive drug release system based on polypyrrole (Ppy) nanowires was developed to induce the local delivery of anticancer drug, doxorubicin (DOX), according to the applied electric field. DOX-conjugated Ppy nanowire (NW) (DOX/Ppy NW) array was initially prepared by electrochemical deposition of a mixture of pyrrole monomers and biotin as dopants in the anodic alumina oxide membrane as a sacrificial template. Morphological observation by scanning electron microscopy revealed free-standing and 3D nanotopographical features with large surface area and high density. In addition, we investigated the antitumor efficacy of DOX released from DOX/Ppy NW array in response to the external electric field using two kinds of cancer cell lines, human oral squamous carcinoma cells (KB cells) and human breast cancer cells (MCF7 cells). Meanwhile, strong photothermal effect as a result of a near-infrared absorbing ability of Ppy synergistically maximizes the chemotherapeutic efficacy. Our results suggested that the proposed multifunctional Ppy platform possessing several beneficial features is very promising for many therapeutic applications including cancer.

Single-crystal diamond, with its unique optical, mechanical and thermal properties, has emerged as a promising material with applications in classical and quantum optics. However, the lack of heteroepitaxial growth and scalable fabrication techniques remains the major limiting factors preventing more wide-spread development and application of diamond photonics. In this work, we overcome this difficulty by adapting angled-etching techniques, previously developed for realization of diamond nanomechanical resonators, to fabricate racetrack resonators and photonic crystal cavities in bulk single-crystal diamond. Our devices feature large optical quality factors, in excess of 105, and operate over a wide wavelength range, spanning visible and telecom. These newly developed high-Q diamond optical nanocavities open the door for a wealth of applications, ranging from nonlinear optics and chemical sensing, to quantum information processing and cavity optomechanics.