This paper addresses the challenges posed by the proliferation of Internet-of-Things (IoT) based smart grids in modern power systems, which can threaten the stability and security of next-generation smart microgrids. Sharing sensitive information about suppliers and consumers to maintain communication with the primary grid can lead to breaches in confidentiality or availability, resulting in significant economic damage, loss of life, or national security threats. To reduce the risk of sensitive information exposure, the paper presents a secure federated learning framework that only allows microgrids to exchange their encrypted learned models that predict the grid's stability. The communication channel between the server and clients (microgrids) is authenticated using Transport Layer Security (TLS) protocols, and a Tree-based Group Diffie-Hellman (TGDH) group encryption scheme is employed to encrypt the model updates between the server and clients, ensuring the security of the data-link layer. Finally, the paper presents a comparative analysis to determine the impact of data sharing on the accuracy of stability predictions for each microgrid.

This paper proposes a hierarchical deep neural network (HDNN) for diagnosing the faults on the Tennessee-Eastman process (TEP). The TEP process is a benchmark simulation model for evaluating process control and monitoring method. A supervisory deep neural network is trained to categorize the whole faults into a few groups. For each group of faults, a special deep neural network which is trained for the particular group is triggered for further diagnosis. The training and test data is generated from the Tennessee Eastman process simulation. The performance of the proposed method is evaluated and compared to single neural network (SNN) and duty-oriented hierarchical artificial neural network (DOHANN) methods. The results of experiment demonstrate that our method outperforms the SNN and DOHANN methods.

Recently, sparse representation has been applied to visual tracking to find the target with the minimum reconstruction error from a target template subspace. Though effective, these L1 trackers require high computational costs due to numerous calculations for l1 minimization. In addition, the inherent occlusion insensitivity of the l1 minimization has not been fully characterized. In this paper, we propose an efficient L1 tracker, named bounded particle resampling (BPR)-L1 tracker, with a minimum error bound and occlusion detection. First, the minimum error bound is calculated from a linear least squares equation and serves as a guide for particle resampling in a particle filter (PF) framework. Most of the insignificant samples are removed before solving the computationally expensive l1 minimization in a two-step testing. The first step, named τ testing, compares the sample observation likelihood to an ordered set of thresholds to remove insignificant samples without loss of resampling precision. The second step, named max testing, identifies the largest sample probability relative to the target to further remove insignificant samples without altering the tracking result of the current frame. Though sacrificing minimal precision during resampling, max testing achieves significant speed up on top of τ testing. The BPR-L1 technique can also be beneficial to other trackers that have minimum error bounds in a PF framework, especially for trackers based on sparse representations. After the error-bound calculation, BPR-L1 performs occlusion detection by investigating the trivial coefficients in the l1 minimization. These coefficients, by design, contain rich information about image corruptions, including occlusion. Detected occlusions are then used to enhance the template updating. For evaluation, we conduct experiments on three video applications: biometrics (head movement, hand holding object, singers on stage), pedestrians (urban travel, hallway monitoring), and cars in traffic (wide area motion imagery, ground-mounted perspectives). The proposed BPR-L1 method demonstrates an excellent performance as compared with nine state-of-the-art trackers on eleven challenging benchmark sequences.

Personal Health Records are starting to get more attention, as they have the potential to improve the quality of care and reduce healthcare costs. In this paper, the concepts behind PHRs and the advantages of using PHRs compared to other methods of medical record keeping are discussed. In particular, platform style PHRs that allow the easy incorporation of third party tools are examined, as they have the potential to allow medical data to be used in novel ways.

Remote obstetrics care monitoring is currently being used in many different countries to improve the quality of prenatal care, with promising results. The next generation of remote monitoring systems take advantage of improvements in wireless communications and mobile phone technologies to incorporate off-the-shelf equipment, such as Android smartphones, into their design. This not only reduces the overall cost, but also allows for greater flexibility, since the patient can perform monitoring in the comfort of their home. However, our analysis suggests that recently proposed systems have inadequate security protections needed to meet HIPAA requirements for health data. We also proposed recommendations to improve the security of these emerging systems.

The recently proposed covariance region descriptor has been proven robust and versatile for a modest computational cost. The covariance matrix enables efficient fusion of different types of features, where the spatial and statistical properties, as well as their correlation, are characterized. The similarity between two covariance descriptors is measured on Riemannian manifolds. Based on the same metric but with a probabilistic framework, we propose a novel tracking approach on Riemannian manifolds with a novel incremental covariance tensor learning (ICTL). To address the appearance variations, ICTL incrementally learns a low-dimensional covariance tensor representation and efficiently adapts online to appearance changes of the target with only O(1) computational complexity, resulting in a real-time performance. The covariance-based representation and the ICTL are then combined with the particle filter framework to allow better handling of background clutter, as well as the temporary occlusions. We test the proposed probabilistic ICTL tracker on numerous benchmark sequences involving different types of challenges including occlusions and variations in illumination, scale, and pose. The proposed approach demonstrates excellent real-time performance, both qualitatively and quantitatively, in comparison with several previously proposed trackers.

Dance expresses and translates human experience into meaningful body forms, transcending cultural barriers and language. Ideas and concepts can be portrayed through each dancer's movement on the stage. Mixing cutting edge technologies into contemporary fine arts creates opportunities for human imagination and advances development of futuristic technologies. Augmented Reality (AR) is this technology, and we envision the use of AR in avantgarde dance, interactive media, and story telling. Dance and Engineering in Augmented Reality (DEAR) is a collaborative effort between artists and engineers. Through this project, we will realize multiple creative works that take advantage of AR in an integral and non trivial manner. As part of this paper, we describe our application of the technology in dance and propose a simple projector camera calibration method for projection mapping installations. With an easier setup, we hope enable and inspire future AR pieces from talented artists, computer engineers and computer scientists.

Recently, sparse representation has been applied to visual tracking to find the target with the minimum reconstruction error from the target template subspace. Though effective, these L1 trackers require high computational costs due to numerous calculations for ℓ1minimization. In addition, the inherent occlusion insensitivity of the ℓ1minimization has not been fully utilized. In this paper, we propose an efficient L1 tracker with minimum error bound and occlusion detection which we call Bounded Particle Resampling (BPR)-L1 tracker. First, the minimum error bound is quickly calculated from a linear least squares equation, and serves as a guide for particle resampling in a particle filter framework. Without loss of precision during resampling, most insignificant samples are removed before solving the computationally expensive ℓ1minimization function. The BPR technique enables us to speed up the L1 tracker without sacrificing accuracy. Second, we perform occlusion detection by investigating the trivial coefficients in the ℓ1minimization. These coefficients, by design, contain rich information about image corruptions including occlusion. Detected occlusions enhance the template updates to effectively reduce the drifting problem. The proposed method shows good performance as compared with several state-of-the-art trackers on challenging benchmark sequences.

Large-scale systems refer to systems that consist of many interdependent local systems. Conventional centralized control schemes are not suitable for such large-scale systems due to global dynamic behavior complexity and computational difficulties. This paper introduces a general framework of agent based federated control motivated by the political structure where partially self-governing states are united by a federal government. Likewise, a multi-agent based federated control system is composed of local autonomous subsystems that cooperate to provide an overall system behavior. Formation stability and trajectory tracking control of a group of autonomous agents is designed based on this concept. Decentralized cooperative federated controllers are developed for each individual agent. Simulations show that the overall system is globally asymptotically stable to track a time-varying trajectory with a pre-designated formation for a group of nonlinear dynamic agents.

We propose to develop a design for a reliable distributed relational database management system to safeguard sensitive information. Each day, increasingly more highly sophisticated computer attacks are occurring in the information storage systems of financial institutions, eCommerce businesses, universities, hospitals and government agencies. We urgently need to develop a secure and dependable way to safeguard our information repositories and storage systems. In this research, we propose to develop a highly robust, dependable and secure relational database management system to prevent sensitive information from being lost, stolen or corrupted. The basic idea is to (i) include a (k, n) threshold-based secret sharing scheme (k ≤ n) to provide privacy and durability in order to prevent sensitive information from being lost or stolen, (ii) incorporate an efficient distributed database management design to enhance system performance and minimize interfered accesses contentions, and (iii) integrate private information storage (PIS) schemes to reduce communication overhead and improve robustness of the system.

In this paper, we use reliability theory to determine the answer of an important question about how often we need to update a piece of secure information. For example, one may need to determine an appropriate time for updating a new password. To generalize, we compute an efficient updating time for a dependable secure computing (DSC) system which stores secret information in a reliable and secure fashion among n servers. The secret information can be reconstructed by successfully connecting to any k servers (k ¿ n), but cannot be revealed by using (k - 1) or fewer servers. Generally, the secret information has to be updated in the system in order to prevent adversaries from learning secret information by breaking into k servers in a DSC system. There are serval proposed procedures to update secret information, but few has been able to determine what an appropriate updating time on a DSC system is. To achieve the most reliable way to safeguard the secret information, we developed an interesting approach to compute the updating time on a DSC system from the reliability point of view.

This paper presents a hardware implementation of a secure and reliable k-out-of-n threshold based secret image sharing method. The secret image is divided into n image shares so that any k image shares are sufficient to reconstruct the secret image in a lossless manner, but (k-1) or fewer image shares cannot reveal anything about the secret image. This secret sharing method comprises multiple independent computations which are conducive to parallel processing architectures. Fine-grained field programmable gate array (FPGA) architectures are the near optimal hardware platform for performing parallel processing. This paper illustrates the design and implementation of the secret image sharing method for 8-bit grayscale images on an FPGA which enhances execution time. On average, it was found that the FPGA executes image sharing and reconstruction approximately 300 times faster than a microprocessor operating on the same image.

Many communication systems require a two-way, or three-way handshaking process to improve their dependability & authenticity in order to achieve a more successful operation. In this paper, we present a new two-way handshaking reliability model based upon threshold-based cryptography systems. Such systems require a two-way handshaking process to i) establish a group of participated servers in the first handshaking process, and ii) calculate a cipher with successfully connected servers collaboratively in the second handshaking process. When the servers are attempted, each server has three known connection probabilities in the following three states: i) successful, ii) breakdown, and iii) congested. These connection probabilities are unchanged in both handshaking processes. During the first handshaking process, we establish connections that more than servers are willing to participate. For the second handshaking process, the system becomes successful as soon as we can connect these servers successfully again. Because we need to connect servers successfully in the second handshaking process, we would rather connect additional servers besides the servers required to be connected successfully in the first handshaking process. This preference will minimize the chance that the system breaks down when fewer than servers can be reconnected successfully in the second handshaking process. We refer to this system as a Two-Way Handshaking Circular Sequential-out-of-Congestion (TWHCSknC) system. In this paper, we derived analytical formulas for the system's successful probability & average stop length, and we showed that the TWHCSknC system is a communication system with an efficient two-way handshaking process.

In this paper, we investigate how to compute an updating time for a dependable secure computing (DSC) system from a survivability assessment perspective. Generally speaking, to prevent adversaries from learning secret information in a DSC system, the secret information has to be updated in the system. The proposed method is based on a novel concept of survivability of reconfigurable systems presented earlier in [1]. It is shown that secret updating time increases with k, which is the number of secret shares for reconstructing the secret. The proposed method presents a solution for an important problem in the DSC system which is how to determine an appropriate updating time.

Distributed image steganography (DIS) [8] is a new method of concealing secret information in several host images, leaving smaller traces than conventional steganographic techniques, and requiring a collection of affected images for secret information retrieval. Fusion system designs of the future will require enhanced security measures for distributed data communication. DIS, compared to other conventional steganographic techniques, can improve security and information hiding capacity because DIS leaves reduced signatures of hidden information in host images. The open literature does not offer effective detection methods and countermeasures for DIS, indicating that it can be potentially usable to criminals for unchallenged covert communication over the Internet and fusion architectures. In this paper, we explore a new information extraction method for both detecting and reversing DIS method by considering images as pseudo-random processes. The key idea is to estimate secret image as a random process, which is corrupted by a noise source (i.e. host image). The secret images may be nonlinear, non-Gaussian and nonstationary in nature, and can be disclosed by using some estimation techniques such as Kalman filtering. Our proposed method demonstrates great promise to reveal a secret image. Consequently, it is useful for intelligence gathering and information extraction in steganographic - images produced by DIS.

This paper presents a new probabilistic approach to determine survivability of reconfigurable systems as a system-level performance metric in an operational environment. In contrast to known methods of estimating survivability in terms of susceptibility and vulnerability, the proposed method (1) includes directional threats on various subsystems into the analysis and (2) provides a framework for operational information fusion processes to better sustain unpredictable or hostile environmental disturbances. In this paper, we distinguish the survivability and reliability metrics, where we demonstrate the importance of survivability metric in a dynamic information fusion process for an operational environment. We present our main result using a piping system of fluid flow; however, the concept easily extends to other flow systems, such as power networks, computer communication networks, and military reconfigurable information systems, etc. Survivability of these large scale reconfigurable networks depend on their capability of assessing directional threats, situation awareness, and their ability to dynamically adapt to new configurations. The proposed survivability method embedded in an information fusion environment can be used for real time dynamic reconfiguration of large scale systems, optimization and routing of data and information, and detect and mitigate hardware and software threats.

The k-out-of-n secret sharing schemes are effective, reliable, and secure methods to prevent a secret or secrets from being lost, stolen, or corrupted. The circular sequential k-out-of-n congestion (CSknC) system , based upon this type of secret sharing scheme, is presented for reconstructing secret(s) from any k servers among n servers in circular, sequential order. When a server is connected successfully, it will not be reconnected in later rounds until the CSknC system has k distinct, successfully connected servers. An optimal server arrangement in a CSknC system is determined in where n servers have known network connection probabilities for two states, i.e., congested, and successful. In this paper, we present: i) a generalized access structure congestion (GGammaC) system that is based upon the generalized secret sharing scheme, and ii) an efficient connection procedure for the GGammaC system in terms of the minimal number of server connection attempts. The k-out-of-n secret sharing schemes are considered as simple cases of the generalized secret sharing schemes. It implies that the GGammaC system is a more general system than the CSknC system. We established an iterative connection procedure for the new system. Simulation results are used to demonstrate that the iterative connection procedure is more efficient in terms of minimizing the number of connection attempts

A ternary state circular sequential k-out-of-n congestion (TSCSknC) system is presented. The system is an extension of the circular sequential k-out-of-n congestion (CSknC) system which consists of two connection states: a) congestion (server busy), and b) successful. In contrast, a TSCSknC system considers three connection states: i) congestion, ii) break down, and iii) successful. It finds applications in some reliable systems to prevent single-point failures, such as the ones used in (k,n) secret key sharing systems. The system further assumes that each of the n servers has known connection probabilities in congestion, break-down, and successful states. These n servers are arranged in a circle, and are made with connection attempts sequentially round after round. If a server is not congested, the connection can be either successful, or a failure. Previously connected servers are blocked from reconnecting if they were in either states ii), or iii). Congested servers are attempted repeatedly until k servers are connected successfully, or (n-k+1) servers have a break-down status. In other words, the system works when k servers are successfully connected, but fails when (n-k+1) servers are in the break-down state. In this paper, we present the recursive, and marginal formulas for the system successful probability, the system failure probability, as well as the average stop length, i.e. number of connections needed to terminate the system to a successful or failure state, and its computational complexity.

This paper presents a reliable image secret sharing method which incorporates two k-out-of-n secret sharing schemes: i) Shamir's secret sharing scheme and ii) matrix projection secret sharing scheme. The technique allows a colored secret image to be divided as n image shares so that: i) any k image shares (k les n) are sufficient to reconstruct the secret image in the lossless manner and ii) any (k - 1) or fewer image shares cannot get enough information to reveal the secret image. It is an effective, reliable and secure method to prevent the secret image from being lost, stolen or corrupted. In comparison with other image secret sharing methods, this approach's advantages are its large compression rate on the size of the image shares, its strong protection of the secret image and its ability for the realtime processing

This paper presents an image secret sharing method which essentially incorporates two k-out-of-n secret sharing schemes: i) Shamir's secret sharing scheme and ii) matrix projection secret sharing scheme. The technique allows a secret image to be divided as n image shares so that: i) any k image shares (klesn) can be used to reconstruct the secret image in lossless manner and ii) any (k-1) or fewer image shares cannot get sufficient information to reveal the secret image. It is an effective, reliable and secure method to prevent the secret image from being lost, stolen or corrupted. In comparison with other image secret sharing methods, this approach's advantages are its large compression rate on the size of the image shares, its strong protection of the secret image and its ability for realtime processing

This paper studies the asymptotic performance of signal detection for large random multiple-input multiple-output (MIMO) systems. Two iterative receivers are considered: unconditional MMSE receiver with soft interference cancellation (U-MMSE-SIC) and block-iterative generalized decision feedback equalizer (BI-GDFE). We prove that for the limiting case, the output SINRs at each iteration for both receivers converge in probability to their respective deterministic limits. We also show that for U-MMSE-SIC, the limiting SINR is determined by the statistics of the soft estimates; while for BI-GDFE, the limit is determined by the statistical reliability of the hard decisions. Numerical results have shown that the U-MMSE-SIC receiver has faster convergence than the BI-GDFE receiver with the price of higher computational complexity in each iteration due to the use of soft interference cancellation.

This paper presents a strong (k,n) threshold-based ramp secret sharing scheme with k access levels. The secrets are the elements represented in a square matrix S. The secret matrix S can be shared among n different participants using a matrix projection technique where: i) any subset of k participants can collaborate together to reconstruct the secret, and ii) any subset of (k-1) or fewer participants cannot partially discover the secret matrix. The primary advantages are its large compression rate on the size of the shares and its strong protection of the secrets

A circular sequential k-out-of-n congestion (CSknC) model is presented. This model finds use in reliable systems to prevent single-point failure, such as (k,n) secret key sharing systems. The model assumes that each of the n servers has a known congestion probability. These n servers are arranged in a circle, and are connected sequentially. A server is connected successfully if it is not congested. Previously connected servers are blocked from reconnecting. Congested servers have to be reconnected until k servers are connected successfully. We present the recursive, and marginal formulas for the probability of success, as well as the average number of connection attempts needed with k successfully connected servers. The optimal arrangement of servers is specified to minimize the average number of connection attempts. These formulas can be used to rearrange the servers adaptively when the initial congestion probabilities are not known. Compared to the circular CknG systems, the CSknC systems are shown to have a higher success probability, and require less connection attempts with k successfully connected servers.

High resolution direction-of-arrival (DOA) estimation is one of the most challenging problems in array signal processing. In this paper, a variation of the improved polynomial rooting (IPR) method is proposed for DOA estimation of multiple targets by a sensor array. The variation, unitary IPR (UIPR), transforms the complex-valued covariance matrix of the sensor signals to a real-valued matrix using unitary transformations. Then the IPR method is applied to determine the DOA of the targets. Simulation results indicate the potential improvement provided by our approach compared with MUSIC, Root-MUSIC, ESPRIT, and IPR.

A secure email protocol (SEP) is introduced to provide a decentralized means of revoking trust access and preventing false repudiation. Verification of the exchange of a message between sender and recipient is accomplished through an identification cookie (IC) and an encryption key (EK) handled by Verification and Authentication Agents (VAAs). The communicated narrative is encrypted by the EK. The augmented message is then encrypted again using one of the widely available encryption algorithms, such as the Pretty Good Privacy (PGP) scheme, and sent to the addressee. The extracted IC serves as a label which the addressee uses to obtain the EK from the sender's VAA. Once the addressee possesses the message's EK, s/he is able to decrypt the narrative, verify that the IC is correct, and the process is complete. Quick revocation of access privileges and cancellation of old messages is achieved by deleting ICs not requested by the addressee. The structure of the message body is written in the eXtensive Markup Language (XML) to take advantage of the language's extendibility and compatibility features.