Target Acquisition Research Articles

Model for selecting the Earth observation satellite systems by object recognition probability

Model for selecting the Earth observation satellite systems by object recognition probability

Effect of Subthalamic Nucleus Deep Brain Stimulation (STN-DBS) on balance performance in Parkinson's disease.

To study the effect of STN-DBS on balance performance of Parkinson's disease. 16 idiopathic PD patients treated with bilateral STN-DBS (DBS Group) and 20 PD patients treated with Levodopa (Medicine group) were included in the study. Clinical material including Levodopa Equivalent Daily Dose (LEDD, mg/day), life quality (PDQ-39) were collected. For DBS group and Medicine group, The motor disability (Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale Ⅲ, MDS-UPDRSIII) and balance performance (MDS-UPDRS 3.12, Berg Balance Scale BBS) and the Limits of Stability (LoS) (target acquisition percentage, trunk swing angle standard deviation, time) in state of Med-Off/Med-On at preoperation, postoperation, 6 months postoperation and 12 months postoperation were evaluated. Repeated ANOVA was used to analyze the effect of STN-DBS on balance performance. The Clinical material (age, gender, duration, LEDD preoperation, PDQ39), motor disability (Med-on/Med-Off), balance performance (Med-on/Med-Off) and LoS preoperation had no differences in DBS-group and Medical-group (P>0.05). During the follow up, LEDD, PDQ39, Motor disability (MDS-UPDRSIII), balance performance (MDS-UPDRS 3.12, BBS) in Medicine-group had no significant changes in both Med-Off and Med-On. For DBS-group, immediately improvement of motor disability (MDS-UPDRSIII), LoS (target acquisition percentage, trunk swing angle standard deviation, time) and LEDD were observed postoperation (P<0.05); PDQ39, balance performance (MDS-UPDRS 3.12, BBS) began to improve at 6 months and 12 months postoperation. Repeated ANOVA showed that DBS could significantly improve the motor disability, balance performance and LoS in PD. STN-DBS could improve the balance performance of PD patients in H&Y3.

Two-stage sparse representation of NSCT features with application to SAR target classification

To improve synthetic aperture radar (SAR) target classification performance, both the feature extraction and classification algorithms are considered. The nonsubsampled contourlet transform (NSCT) is employed to decompose SAR images to generate multi-layer components, which provide more discriminative descriptions for the targets. During the classification, the two-stage sparse representations are developed. Each NSCT component is classified using the sparse representation-based classification (SRC). Afterwards, the joint sparse representation (JSR) is adopted to represent the selected NSCT components from the first stage, which are assumed to be highly discriminative. Based on the two decisions, the target label of the test sample is determined. In the experiment, the moving and stationary target acquisition and recognition (MSTAR) dataset is used to set up scenarios for performance evaluation including the standard operating condition (SOC) and several extended operating conditions (EOCs). The results show the superior performance of the proposed methodover some current methods.

Deep convolutional neural network structure design for remote sensing image scene classification based on transfer learning

To obtain an ideal scene classification effect when applying a deep convolutional neural network (DCNN) to remote sensing images, a DCNN named ConNet-3F based on transfer learning was constructed. Firstly, the initial 13 layers of VGG16 were used as the main architecture, and then 6 layers were added to the end of it to extract deeper features. The pruned model of VGG16 acted as the feature extractor to extract features from remote sensing images. Subsequently, the parameters trained on the ImageNet were set as initialization. Finally, parameters of all layers were trained on the remote sensing data to obtain the favorable model. The results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset demonstrate that this method achieves higher classification accuracy compared with other mentioned methods. Especially, even if the ratio of training data was reduced on the NWPU-RESISC45 dataset, classification accuracies of the proposed method always stay above 90%.

Characterization of FIREFLY, an Imaging Spectrometer Designed for Remote Sensing of Solar Induced Fluorescence.

Solar induced fluorescence (SIF) is an ecological variable of interest to remote sensing retrievals, as it is directly related to vegetation composition and condition. FIREFLY (fluorescence imaging of red and far-red light yield) is a high performance spectrometer for estimating SIF. FIREFLY was flown in conjunction with NASA Goddard’s lidar, hyperspectral, and thermal (G-LiHT) instrument package in 2017, as a technology demonstration for airborne retrievals of SIF. Attributes of FIREFLY relevant to SIF retrieval, including detector response and linearity; full-width at half maximum (FWHM); stray light; dark current; and shot noise were characterized with a combination of observations from Goddard’s laser for absolute measurement of radiance calibration facility; an integrating sphere; controlled acquisitions of known targets; in-flight acquisitions; and forward modelling. FWHM, stray light, and dark current were found to be of acceptable magnitude, and characterized to within acceptable limits for SIF retrieval. FIREFLY observations were found to represent oxygen absorption features, along with a large number of solar absorption features. Shot noise was acceptable for direct SIF retrievals at native resolution, but indirect SIF retrievals from absorption features would require spatial aggregation, or repeated observations of targets.

ON DEVELOPMENT OF AVIATION EQUIPMENT’SRELIABILITY MODELS (ON THE EXAMPLE OF HELICOPTERS)

On the example of helicopters, the topical issues of systematization and mathematical processing of statistical information taking from fault log sheetsto assess the level of reliability and flight safety of state aviation equipment are considered.&#x0D; The reliability of all helicopter systems must meet the pressing needs of defense capabilities and support the capabilities of the state aviation of Ukraine to carry out combat missions at the current level. Improving preparedness for assigned tasks will facilitate participation in combined operations with peacekeeping and NATO forces. The considered model of the system of aviation equipment on the example of helicopters consists of interdependent, jointly functioning, united by a single purpose of five subsystems of the corresponding type of specialty: “helicopter and engine”, “aircraft armament”,“aircraftequipment”, “avionics equipment”,“navigation and target acquisition system”.&#x0D; Realization and methodical approaches to the development of complex failuremodels of the elements of “helicopter and engine” system by types of helicopters are substantiated. The paper proposes to address on the example of helicopters the lines of approach the problem of assessing the real level of operational reliability and flight safety of aircraft to maintain their serviceable condition.&#x0D; When putting military aviation assets into operation according to the technical condition, using statistical models of reliability and flight safety, the important task of increasing resource indicators is solved, which requires improving the system of collecting, processing and analyzing information from fault log sheets.

Design and Configuration of Folded Platonic Strapdowns of Biaxial MEMS Accelerometers

Abstract The authors report on the design, configure, and test of isotropic accelerometer strapdowns for high-precision inertial measurement unit (IMU) and folded MEMS configuration. The biaxial low-g MEMS accelerometers are based on the Platonic solids. A Platonic strapdown is integrated into an embedded system for acceleration-array signal acquisition targeting rigid-body pose-and-twist estimation. The electromechanical properties for dynamic sensitivity are tested on a haptic manipulator, which shows that the position estimation matches reasonably well the encoder readouts. The Platonic strapdown is promising in folded MEMS IMU with chip-level miniaturization and high estimation precision.

A semi-greedy neural network CAE-HL-CNN for SAR target recognition with limited training data

ABSTRACT Synthetic aperture radar (SAR) automatic target recognition (ATR) based on convolutional neural network (CNN) is a research hotspot in recent years. However, CNN is data-driven, and severe overfitting occurs when training data is scarce. To solve this problem, we first introduce a non-greedy CNN network. But when a CNN structure with a non-greedy classifier is used to handle the SAR ATR in the case of scarce training data, the feature extraction capability of the network degrades. To balance the feature extraction and anti-overfitting capabilities of the network, a semi-greedy network called transfer learning with convolutional auto-encoders (CAE) and hinge loss CNN (HL-CNN), namely CAE-HL-CNN, is proposed in this paper. First, the CAE-HL-CNN introduces a non-greedy network which uses a hinge loss classifier in the CNN structure to enhance the network’s generalization performance. It retains the hierarchical feature extraction structure of CNN and has the same anti-overfitting capability as support vector machine. Then, by combining CAE with the HL-CNN through transfer learning, the CAE-HL-CNN extracts a complete feature representation to compensate for the degradation in feature extraction capability in a greedy way. Experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset show that in the case of scarce training data, the proposed network can improve the recognition performance of CNN, which achieves higher classification accuracy and performs more equably on each category, and it extracts sparser feature maps than the compared methods.

Mechanisms of Fritillariae Thunbergii Flos in lung cancer treatment from a systems pharmacology perspective

Ethnopharmacological relevanceFritillariae Thunbergii Flos (FTF) included in the Chinese Pharmacopoeia (1977 Edition) is a Chinese medicinal herb traditionally used to treat bronchitis. In recent years, it has been applied in the treatment of lung cancer. However, the molecular mechanism remains largely unknown. MethodsThe screening of bioactive compounds, acquisition of drug targets, network construction, and experimental validation in vivo were combined to explored the mechanism of FTF in the treatment of lung carcinoma with regards to systems pharmacology. ResultsThe network Lung Cancer Pathway consisted of 114 nodes (44 compounds and 70 potential targets) and 361 edges, as well as modules that included inflammatory response, angiogenesis, negative regulation of the apoptotic process, and positive regulation of cell proliferation and migration. It was examined by conducting experiments that involved the administration of ethanol-based extracts of FTF in Lewis lung carcinoma mice. The extracts exerted excellent anti-lung cancer effects in vivo by significantly inhibiting tumor proliferation, thereby extending the survival period of tumor-bearing mice. Moreover, FTF induced the downregulation of PIK3CG, Bcl-2, eNOS, VEGF, p-STAT3, and STAT3 genes in tumor-bearing mice. ConclusionsThe findings of the present study verify the therapeutic effects and mechanism of FTF on lung cancer and provide a theoretical basis to support the comprehensive utilization of FTF resources.

Wireless sensor network monitoring algorithm for partial discharge in smart grid

With the continuous improvement of power grid intelligent requirements, the Power Internet of Things develops rapidly. Substation is an important part of the power grid. The real-time monitoring of its equipment is conducive to the timely detection and removal of power supply faults. In this paper, a monitoring network is built for partial discharge (PD) of high voltage (HV) equipment. According to the three-dimensional characteristics of the equipment, the deployment strategy of ultrasonic sensors for PD monitoring is discussed. A virtual force algorithm based on adaptive angle adjustment is proposed. It is proved that the application of that algorithm can cover more than 94% of target in the range of controllable convergence. According to the target triggering characteristics of PD, the networking method of wireless sensor network is discussed. A sensor cooperation scheme based on the standing guard protocol is proposed, which verifies its energy saving and time delay shortening effect on the basis of ensuring the target acquisition rate. This paper describes the application of dynamic alliance routing protocol in PD information transmission, designs the process and steps of the protocol. Its performance is better than LEACH and Opportunity Routing protocol.

The Effects of Vocal Stereotypy on Secondary Target Acquisition.

Instructive feedback (IF) is a strategy for increasing the efficiency of targeted instruction. Previous research has demonstrated the success of IF with learners with autism spectrum disorder (ASD), but limited research exists regarding the efficacy of the intervention with individuals with ASD who engage in vocal stereotypy. The effects of 2 forms of IF were examined with a learner with ASD who engaged in vocal stereotypy. In Study 1, no intervention for vocal stereotypy was implemented. In Study 2, response interruption and redirection (RIRD) was implemented contingent on vocal stereotypy. IF before the praise statement resulted in faster acquisition of secondary targets, but only when RIRD was implemented. These results extend the IF literature by providing evidence that individuals who engage in vocal stereotypy may require concurrent intervention to increase the opportunity for indiscriminable contingencies to be established and the acquisition of secondary targets via IF.

Block Sparse Bayesian Learning over Local Dictionary for Robust SAR Target Recognition

This paper applied block sparse Bayesian learning (BSBL) to synthetic aperture radar (SAR) target recognition. The traditional sparse representation-based classification (SRC) operates on the global dictionary collaborated by different classes. Afterwards, the similarities between the test sample and various classes are evaluated by the reconstruction errors. This paper reconstructs the test sample based on local dictionaries formed by individual classes. Considering the azimuthal sensitivity of SAR images, the linear coefficients on the local dictionary are sparse ones with block structure. Therefore, to solve the sparse coefficients, the BSBL is employed. The proposed method can better exploit the representation capability of each class, thus benefiting the recognition performance. Based on the experimental results on the moving and stationary target acquisition and recognition (MSTAR) dataset, the effectiveness and robustness of the proposed method is confirmed.

Round-Number Bidding as an M&A Strategy

Round number initial offers are pervasive in many bargaining situations. We examine the prevalence and outcome of such offers in the market for merger and acquisition targets. We find that the practice of making initial offers at round transaction values is associated with shorter contest duration and lower probability of encountering challenging bids. Our results suggest that round numbers are chosen strategically as a signal by impatient bidders who are willing to take a slight drive-up of price to secure a timely purchase and prevent competing bidders from entering the game.

Value Drivers from Prior Alliances with Targets in Mergers and Acquisitions

Acquisitions are often associated with insipid performance returns for the acquirer for many reasons, the most prominent of which is information asymmetry. Prior alliances with the target firm can attenuate losses from asymmetric information and yield higher returns for the acquirer. Using learning, network, and property rights theories, we study acquisitions of alliance targets and calibrate the extent of value enhancement as a function of three sets of moderating factors – alliance-, network-, and market. Alliance-level factors include quantity, duration, and diversity. Network-level factors include network centrality, efficiency, and reputation. Market-level factors include demand uncertainty, competition, and technology intensity. Analysis of 1,499 acquisitions that occurred between 1990 and 2015 indicates that while alliance partner acquisitions are associated with higher stock returns for the acquiring firm, the aforementioned moderators affect the extent of returns. We find evidence of non-linear returns from alliance-level moderators, quantity and duration, and positive returns from market-level moderators. Some of the network-level benefits accrue at the time of the alliance announcement itself, leaving little residual value gains at the time of the acquisition. Our study contributes to a richer understanding of the drivers of value from prior alliances with the acquisition target.

Robot as a Member of Combat UnitA Utopia or Reality for Ground Forces?

There is a perceptible scepticism around armed robot in combat units - the question whether to use it or not. The EOD robots (EOD - Explosive Ordnance Disposal) won their way to war theatre already, but other support assets on UGV platforms (UGV - Unmanned Ground Vehicles) need to be trusted in future, too. One of the reasons for distrust could be a discussion gap between researchers and infantry units on the lowest tactical level. The article tries to set simple demands throughout infantryman-eyes perspective and it outlines needs for the UGV in the mechanized / infantry units in a first part. The second part outlines basic technical and tactical specification as a challenging list for development teams. Finally, the third part proposes two notional AAR (After Action Report) reports of units using UGV for support. The article sets basic questions of themes as the autonomy and controls of UGVs and UGV in ISTAR systems (ISTAR - Intelligence Surveillance Target Acquisition Reconnaissance).

Real-Time Visual Tracking of Moving Targets Using a Low-Cost Unmanned Aerial Vehicle with a 3-Axis Stabilized Gimbal System

Unmanned Aerial Vehicles (UAVs) have recently shown great performance collecting visual data through autonomous exploration and mapping, which are widely used in reconnaissance, surveillance, and target acquisition (RSTA) applications. In this paper, we present an onboard vision-based system for low-cost UAVs to autonomously track a moving target. Real-time visual tracking is achieved by using an object detection algorithm based on the Kernelized Correlation Filter (KCF) tracker. A 3-axis gimbaled camera with separate Inertial Measurement Unit (IMU) is used to aim at the selected target during flights. The flight control algorithm for tracking tasks is implemented on a customized quadrotor equipped with an onboard computer and a microcontroller. The proposed system is experimentally validated by successfully chasing a ground and aerial target in an outdoor environment, which has proven its reliability and efficiency.

Heterogeneity in the information content of 8-K disclosures about private targets: Acquirer size and target significance

In this paper, we empirically analyze the disclosures required by the Securities Exchange Commission (SEC) for acquisitions of privately held target firms by public acquirers. We find that 8-K disclosures filed by public acquirers within a week after the announcement date of the takeover of a privately held target firm materially affect the pricing and the trading of the acquirers' shares around the event date, but only for large acquiring firms. This impact is economically significant even for targets classified as “insignificant” by the SEC, but again, only for large acquirers. Our results suggest that it may be optimal to further reduce the disclosure costs faced by smaller acquirers in acquisitions of private targets.

Effects of aerosol modulation transfer function on target identification

Atmospheric aerosol effects are often overlooked in target acquisition studies. Typically, performance models only consider extinction and turbulence within the prediction processes. The aerosol modulation transfer function (MTF) is included in range acquisition algorithms to determine how scattering and absorption effects change the target identification predictions. We modeled the aerosols as monodisperse water droplets comparable to a tenuous fog or mist. Integrating the aerosol MTF into the system MTF gives the opportunity to utilize the night vision integrated performance model to predict the target identification range with aerosol contributions. The aerosol MTF is a function of range, water droplet composition, wavelength, and aperture size. The analysis focuses on these variables with an emphasis on wavelength dependence to characterize mid-wave and long-wave performance. Results show that the mid-wave systems have a substantial diffraction advantage over long-wave systems. Only in the limit of increasing optical depths do the mid-wave and long-wave performance models begin to converge, verifying that the aerosols can be the limiting factor for target identification.

Shadow detection in SAR images based on greyscale distribution, a saliency model, and geometrical matching

ABSTRACT Shadow information has been widely used in synthetic aperture radar interpretation, but corresponding shadow detection technology has not been given much attention in past studies. In this paper, we propose a hierarchical architecture based on greyscale distribution, a saliency model, and geometrical matching for shadow detection in SAR images. We find that the greyscale distribution of one Gaussian filtered image might contain a ‘distortion’ in its uphill part, which is the effect of shadow existence. Based on this distortion, a global threshold can be determined and then be used to segment candidate shadows. If there is no obvious distortion, a shadow saliency model is proposed as a substitute to extract such candidate areas. Usually, these candidate areas may contain some non-shadow components. According to the geometric relationships between shadow and object, we design a matching strategy to eliminate non-shadow parts from candidate regions. The remained areas are final shadow detection results. Experiments on two real datasets, Moving and Stationary Target Acquisition Recognition and MiniSAR, show that our method performs much better than two other published methods. The results demonstrate the effectiveness and feasibility of our proposed algorithm in practical SAR shadow detection tasks.

Target recognition of SAR images using fused deep feature by multiset canonical correlations analysis

A target recognition method of synthetic aperture radar (SAR) image is developed based on deep features learned from convolutional neural networks (CNN). The learned feature maps by convolution operations are combined as one unified feature vector through multiset canonical correlations analysis (MCCA). Afterwards, the sparse representation-based classification (SRC) is adopted as the classifier for target recognition. The proposed method could effectively combine the discriminability of different feature maps. In addition, the advantages of CNN and SRC can be integrated simultaneously. In the experiments, several conditions are setup based on the moving and stationary target acquisition and recognition (MSTAR) dataset for the validation. According to the results, the proposed method keeps high effectiveness and robustness under standard operating condition (SOC) and extended operating conditions (EOC).