U.S. Army Command, Control, Communications, Computers, Cyber, Intelligence, Surveillance and Reconnaissance Center

A novel reversible data hiding algorithm, which can recover the original image without any distortion from the marked image after the hidden data have been extracted, is presented in this paper. This algorithm utilizes the zero or the minimum points of the histogram of an image and slightly modifies the pixel grayscale values to embed data into the image. It can embed more data than many of the existing reversible data hiding algorithms. It is proved analytically and shown experimentally that the peak signal-to-noise ratio (PSNR) of the marked image generated by this method versus the original image is guaranteed to be above 48 dB. This lower bound of PSNR is much higher than that of all reversible data hiding techniques reported in the literature. The computational complexity of our proposed technique is low and the execution time is short. The algorithm has been successfully applied to a wide range of images, including commonly used images, medical images, texture images, aerial images and all of the 1096 images in CorelDraw database. Experimental results and performance comparison with other reversible data hiding schemes are presented to demonstrate the validity of the proposed algorithm.

Recently, among various data hiding techniques, a new subset, lossless data hiding, has received increasing interest. Most of the existing lossless data hiding algorithms are, however, fragile in the sense that the hidden data cannot be extracted out correctly after compression or other incidental alteration has been applied to the stego-image. The only existing semi-fragile (referred to as robust in this paper) lossless data hiding technique, which is robust against high-quality JPEG compression, is based on modulo-256 addition to achieve losslessness. In this paper, we first point out that this technique has suffered from the annoying salt-and-pepper noise caused by using modulo-256 addition to prevent overflow/underflow. We then propose a novel robust lossless data hiding technique, which does not generate salt-and-pepper noise. By identifying a robust statistical quantity based on the patchwork theory and employing it to embed data, differentiating the bit-embedding process based on the pixel group's distribution characteristics, and using error correction codes and permutation scheme, this technique has achieved both losslessness and robustness. It has been successfully applied to many images, thus demonstrating its generality. The experimental results show that the high visual quality of stego-images, the data embedding capacity, and the robustness of the proposed lossless data hiding scheme against compression are acceptable for many applications, including semi-fragile image authentication. Specifically, it has been successfully applied to authenticate losslessly compressed JPEG2000 images, followed by possible transcoding. It is expected that this new robust lossless data hiding algorithm can be readily applied in the medical field, law enforcement, remote sensing and other areas, where the recovery of original images is desired.

A mid-IR supercontinuum (SC) fiber laser based on a thulium-doped fiber amplifier (TDFA) is demonstrated. A continuous spectrum extending from ∼1.9 to 4.5 μm is generated with ∼0.7 W time-average power in wavelengths beyond 3.8 μm. The laser outputs a total average power of up to ∼2.6 W from ∼8.5 m length of ZrF4─BaF2─LaF3─AlF3─NaF (ZBLAN) fiber, with an optical conversion efficiency of ∼9% from the TDFA pump to the mid-IR SC. Optimal efficiency in generating wavelengths beyond 3.8 μm is achieved by reducing the losses in the TDFA stage and optimizing the ZBLAN fiber length. We demonstrate a novel (to our knowledge) approach of generating modulation instability-initiated SC starting from 1.55 μm by splitting the spectral shifting process into two steps. In the first step, amplified approximately nanosecond-long 1.55 μm laser diode pulses with ∼2.5 kW peak power generate a SC extending beyond 2.1 μm in ∼25 m length of standard single-mode fiber (SMF). The ∼2 μm wavelength components at the standard SMF output are amplified in a TDFA and coupled into ZBLAN fiber leading to mid-IR SC generation. Up to ∼270 nm SC long wavelength edge extension and ∼2.5× higher optical conversion efficiency to wavelengths beyond 3.8 μm are achieved by switching an Er:Yb-based power amplifier stage with a TDFA. The laser also demonstrates scalability in the average output power with respect to the pulse repetition rate and the amplifier pump power. Numerical simulations are performed by solving the generalized nonlinear Schrödinger equation, which show the long wavelength edge of the SC to be limited by the loss in ZBLAN.

To secure a mobile ad hoc network (MANET) in adversarial environments, a particularly challenging problem is how to feasibly detect and defend possible attacks on routing protocols, particularly internal attacks, such as a Byzantine attack. In this paper, we propose a novel algorithm that detects internal attacks by using both message and route redundancy during route discovery. The route-discovery messages are protected by pairwise secret keys between a source and destination and some intermediate nodes along a route established by using public key cryptographic mechanisms. We also propose an optimal routing algorithm with routing metric combining both requirements on a node's trustworthiness and performance. A node builds up the trustworthiness on its neighboring nodes based on its observations on the behaviors of the neighbor nodes. Both of the proposed algorithms can be integrated into existing routing protocols for MANETs, such as ad hoc on-demand distance vector routing (AODV) and dynamic source routing (DSR). As an example, we present such an integrated protocol called secure routing against collusion (SRAC), in which a node makes a routing decision based on its trust of its neighboring nodes and the performance provided by them. The simulation results have demonstrated the significant advantages of the proposed attack detection and routing algorithm over some known protocols.

Automatic modulation recognition (AMR)-based software-defined radio (SDR) is a research challenge in developing third-generation (3G) and fourth-generation (4G) wireless communications with adaptive modulation capability. However, the existing AMR technology does not satisfy the seamless demodulation requirement of the SDR. A novel design of the AMR method with reduced computational complexity and fast processing speed is needed. This paper describes a discrete likelihood-ratio test (DLRT)-based rapid-estimation approach to identifying the modulation schemes blindly for uninterrupted data demodulation in real time. The statistical performance of the fast AMR associated with its implementation using the SDR is presented.

Lithium garnet Li7La3Zr2O12 (LLZO), with high ionic conductivity and chemical stability against a Li metal anode, is considered one of the most promising solid electrolytes for lithium–sulfur batteries. However, an infinite charge time resulting in low capacity has been observed in Li–S cells using Ta-doped LLZO (Ta-LLZO) as a solid electrolyte. It was observed that this cell failure is correlated with lanthanum segregation to the surface of Ta-LLZO that reacts with a sulfur cathode. We demonstrated this correlation by using lanthanum excess and lanthanum deficient Ta-LLZO as the solid electrolyte in Li–S cells. To resolve this challenge, we physically separated the sulfur cathode and LLZO using a poly(ethylene oxide) (PEO)-based buffer interlayer. With a thin bilayer of LLZO and the stabilized sulfur cathode/LLZO interface, the hybridized Li–S batteries achieved a high initial discharge capacity of 1307 mA h/g corresponding to an energy density of 639 W h/L and 134 W h/kg under a high current density of 0.2 mA/cm2 at room temperature without any indication of a polysulfide shuttle. By simply reducing the LLZO dense layer thickness to 10 μm as we have demonstrated before, a significantly higher energy density of 1308 W h/L and 257 W h/kg is achievable. X-ray diffraction and X-ray photoelectron spectroscopy indicate that the PEO-based interlayer, which physically separates the sulfur cathode and LLZO, is both chemically and electrochemically stable with LLZO. In addition, the PEO-based interlayer can adapt to the stress/strain associated with sulfur volume expansion during lithiation.

To prevent economic losses, maintain social order, and protect the well-being of the populace during public safety and crisis recovery scenarios, such as man-made and natural disasters, the efficient and effective delivery of time-critical information to first responders and victims plays a key role. Nonetheless, too often, the communication infrastructures that enable time-critical information delivery become dysfunctional, due to traffic overloads or physical damage. Thus, the user-side solution [e.g., device-to-device (D2D) communications] and the network-side solution [e.g., dynamic wireless networks (DWNs)] are essential communication techniques that can enhance or restore communication for responders and victims in the harsh environment associated with public safety scenes. While D2D has been widely studied and investigated in legacy/commercial communication networks, as well as DWN, little work has been done toward adapting D2D and DWN from a public safety perspective. In this survey, we first design a layered structure, consisting of the public safety service layer, time-critical information delivery layer, and physical object layer, from which to consider the public safety system and its key components. We then extensively review research efforts on both D2D and DWN as complimentary user-side and network-side communication techniques toward effective public safety communications. Particularly, we investigate the approaches and standardization progress of D2D and DWN for public safety communications. Finally, we provide insights into challenges and potential solutions regarding D2D, DWN, security and resilience, and performance evaluation of public safety communication, as well as the integration of state-of-the-art communication and computing technologies to further improve time-critical information delivery in various public safety scenarios.

Automatic modulation classification (AMC) has been intensively studied to enhance the successful classification rate, particularly for overcoming the physical limit that deals with weak signals received in a noncooperative communication environment. A wireless sensor network (WSN) has multiple geometrically distributed sensors to work cooperatively. The distributed signal sensing and classification performed by collaborated sensors is proven to be beneficial to increasing the modulation classification reliability. In this paper, we apply the likelihood ratio-based distributed detection fusion technique to address the issues of general binary modulation classifications. The data fusion algorithm performed in the primary node is presented. Its numerical performance with simulation results is demonstrated.

Automatic modulation classification (AMC) is deployed, as the intermediate step between signal detection and demodulation, to identify modulation schemes automatically. Modulation classification is a challenging task, especially in a non-cooperative environment, owing to the lack of prior information on the transmitted signal at the receiver; the problem will be more challenging in the multipath fading channel. The proposed AMC method based on optimal decision fusion by using wireless sensor networks provides a more accurate classification result than any one of the individual signal alone. Wireless sensor networks offer increased reliability and optimal decision fusion provides huge gains in overall classification performance as compared to that of the single sensor. Thus, optimal decision fusion based AMC by using wireless sensor networks greatly enhances classification performance of weak signals in non-cooperative communication environment. Classification performances of optimal decision fusion based AMC by using wireless sensor networks in the multipath fading channel are investigated and evaluated in terms of correct classification probability. Through Monte Carlo simulations, we demonstrate that the proposed AMC algorithm can greatly outperform that of single sensor in multipath fading channel.

In this paper, we consider the problem of automatic modulation classification with multiple sensors in the presence of unknown time offset, phase offset and received signal amplitude. We develop a novel hybrid maximum likelihood (HML) classification scheme based on a generalized expectation maximization (GEM) algorithm. GEM is capable of finding ML estimates numerically that are extremely hard to obtain otherwise. Assuming a good initialization technique is available for GEM, we show that the classification performance (in terms of the probability of error) can be greatly improved with multiple sensors compared to that with a single sensor, especially when the signal-to-noise ratio (SNR) is low. We further demonstrate the superior performance of our approach when simulated annealing (SA) with uniform as well as nonuniform grids is employed for initialization of GEM in low SNR regions. The proposed GEM based approach employs only a small number of samples (in the order of hundreds) at a given sensor node to perform both time and phase synchronization, signal power estimation, followed by modulation classification. We provide simulation results to show the efficiency and effectiveness of the proposed algorithm.

Due to their unique microstructure, buckypaper-supported platinum (Pt) catalysts derived from carbon nanotube and carbon nanofiber have demonstrated a high Pt utilization in proton exchange membrane fuel cells (PEMFCs). [SWNT means single-walled carbon nanotube.] The durability of a buckypaper-supported Pt catalyst was investigated using an accelerated degradation test (ADT) in a mimic cathode environment of PEMFC. Compared to commercial carbon black-supported Pt, Pt/buckypaper showed a better catalyst durability after holding at 1.2 V for 400 h; specifically, almost 80% of the Pt electrochemical surface area was lost for Pt/carbon black, with only a 43% loss for Pt/buckypaper. Transmission electron microscopy and cyclic voltammetry were used to study the Pt degradation mechanism. It was concluded that Pt coarsening and Pt detachment from buckypaper support due to carbon corrosion make the major contribution to the Pt surface area loss under this condition. The Pt loss via detachment from supports after the ADT was calculated as 18% in Pt/buckypaper, while the Pt loss was 69% in Pt/C. It is supposedly due to the higher corrosion resistance of buckypaper because of its high graphitization degree, which is indicated by a slower formation rate of surface oxides in buckypaper than in carbon black. Further durability improvement of the Pt/buckypaper is expected by improving the dispersion of Pt on the buckypaper to reduce Pt sintering.

The practical applications of rechargeable zinc metal batteries are prevented by poor Zn reversibility, which induces both inferior Coulombic efficiency (CE) and zinc dendrite growth that worsens at low temperatures because of deteriorated kinetics in both charge and mass transfer. Herein, a liquefied gas electrolyte based on a mixture of fluoromethane and difluoromethane is demonstrated, which displays an excellent conductivity (>3.4 mS cm–1) across a broad temperature range (−60 to +20 °C) and enables highly reversible Zn cycling with no evidence of shorting behavior at both room temperature and −20 °C for over 200 cycles (>400 h) with an average CE of >99.3% and 20% Zn utilization per cycle. Density functional theory calculations showed that such improvements benefited from a ZnF2-enriched interphase formed on the anode because of decomposition of the liquefied gas electrolyte. This electrolyte was verified in a Zn||Na2V6O16·1.63H2O cell with stable performance, where a similar ZnF2-rich interphase was also confirmed.

Infrared transmittance and hemispherical-directional reflectance data from 2.5 to 25 microm on microstructured silicon surfaces have been measured, and spectral emissivity has been calculated for this wavelength range. Hemispherical-total emissivity is calculated for the samples and found to be 0.84 before a measurement-induced annealing and 0.65 after the measurement for the sulfur-doped sample. Secondary samples lack a measurement-induced anneal, and reasons for this discrepancy are presented. Emissivity numbers are plotted and compared with a silicon substrate, and Aeroglaze Z306 black paint. Use of microstructured silicon as a blackbody or microbolometer surface is modeled and presented, respectively.

Recent research has examined several novel radio frequency (RF) remote probing techniques. These new results are joined with the prior art leading to a unified understanding of RF remote probing that articulates the fundamental principles involved and allows extension to the analysis of additional “new” RF probing techniques. Two significant aspects in RF probing are emphasized: 1) information, coding, and signal theory considerations are identified as these establish bounds on probe performance against a specific information task and 2) building upon the well-known radar equation, the object cross section is generalized to be an interaction term describing a much broader range of interactions than simply reflection. These results are significant in enabling the rapid analysis and evaluation of “new” RF sensing techniques proposed to accomplish a variety of functions in the detection and identification of both natural and manmade objects.

The Internet of Things (IoT) has emerged as the key networking paradigm for supporting the connectivity of massively distributed objects and numerous simultaneous applications. The constrained application protocol (CoAP) is designed to meet the requirements for IoT data transmission among constrained nodes. The lean design of CoAP enables it to additionally meet the needs of the data transmission in dynamic network environments. In this paper, we conduct an emulation-based quantitative performance assessment of CoAP in comparison with HTTP, assessing data transmission based on key characteristics of dynamic network environments and the designed scenarios. We also designed scenarios and evaluate the performance of investigated protocols using real-world IoT datasets. Our experimental results demonstrate that CoAP performs better than HTTP for data transmission in the dynamic network environments with respect to delivery rate, delay, and overhead. In addition, we analyze the impact of features in dynamic network environments on the performance of data transmission protocols with respect to success rate, delay and overhead, as well as discuss some further extensions for future research.

This paper proposes a sensor network framework for sensing and exploiting unknown signals. It includes distributed decision fusion, distributed processing, centralized synchronous signal sensing, centralized asynchronous signal sensing, and system of systems single sensing. Data fusion is applied to enhance the SNR of weak signals and to eliminate channel distortion. Statistical estimation and modulation classification techniques are discussed.

Chemical sensors play an important role in a variety of civilian and military domains. In these contexts, the ability to accurately and quickly identify chemical agents is of utmost importance. In practice, constraints on physical footprint, power consumption, ease of use, and time required for accurate detection often restrict the utility of sensors, particularly in remote and isolated regions. One solution to address this problem is the engineering of advanced signal processing techniques, which decrease the time required for accurate detection. This allows software to facilitate the construction of hardware that meet stringent power and concept of operations guidelines. In this paper, we propose the Kalman filter as a preprocessing technique applicable to chemical sensor time series data for downstream machine learning. Using data collected from a sensor array of multiple unique polymer-graphene nanoplatelet coated electrodes, we show accurate and early detection of both organophosphates and interferents is improved when the Kalman filter is used as a preprocessing technique. In particular, within two seconds of analyte exposure to the sensor array, classification using Kalman filtered first derivative estimates achieve an error of less than 10%. By comparison, the non-Kalman filtered data set has a classification error rate above 40% within this time. An advantage of our approach is classification does not depend on a set parameter, such as maximum resistance change, or a pre-determined exposure time, and which allows rapid classification immediately after analyte introduction.

This paper describes research on the measurement of the 50% probability of identification cycle criteria (N50,V50) for a set of hand-held objects normally held or used in a single hand. These cycle criteria are used to calibrate the Night Vision Electronic Sensors Directorate (NVESD) target acquisition models. The target set consists of 12 objects, from innocuous to potentially lethal. Objects are imaged in the visible, midwave infrared (MWIR), and long-wave infrared (LWIR) spectrum at 12 different aspects. Two human perception experiments are performed. The first experiment simulates an incremental constriction of the imaging systems modulation transfer function (MTF). The N50, and V50 calibration criteria are measured from this perception experiment. The second experiment not only simulates an incremental constriction of the system MTF but also down samples the imagery to simulate the objects at various ranges. The N50 and V50 values are used in NVTherm 2002 and NVThermIP, respectively, to generate range prediction curves for both the LWIR and MWIR sensors. The range predictions from both NVTherm versions are then compared with the observer results from the second perception experiment. The comparison between the results of the second experiment and the model predictions provides a verification of measured cycle criteria.

The problem addressed is source localization via time-difference-of-arrival estimation in a multipath channel. Solving this localization problem typically implies cross-correlating the noisy signals received at pairs of sensors deployed within reception range of the source. Correlation-based localization is severely degraded by the presence of multipath. The proposed method exploits the sparsity of the multipath channel for estimation of the line-of-sight component. The time-delay estimation problem is formulated as an ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -regularization problem, where the ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -norm is used as a channel sparsity constraint. The proposed method requires knowledge of the pulse shape of the transmitted signal, but it is blind in the sense that information on the specific transmitted symbols is not required at the sensors. Simulation results show that the proposed method delivers higher accuracy and robustness to noise compared to conventional or even super-resolution MUSIC time-difference-of-arrival source localization methods.

A novel Bayesian modulation classification scheme is proposed for a single-antenna system over frequency-selective fading channels. The method is based on Gibbs sampling as applied to a latent Dirichlet Bayesian network (BN). The use of the proposed latent Dirichlet BN provides a systematic solution to the convergence problem encountered by the conventional Gibbs sampling approach for modulation classification. The method generalizes, and is shown to improve upon, the state of the art.