Statistical Performance Analysis of MDL Source Enumeration in Array Processing

In this correspondence, we focus on the performance analysis of the widely-used minimum description length (MDL) source enumeration technique in array processing. Unfortunately, available theoretical analysis exhibit deviation from the simulation results. We present an accurate and insightful performance analysis for the probability of missed detection. We also show that the statistical performance of the MDL is approximately the same under both deterministic and stochastic signal models. Simulation results show the superiority of the proposed analysis over available results.Comment: Accepted for publication in IEEE Transactions on Signal Processing, April 200

A Multi-stage Secret Sharing Scheme Using All-or-Nothing Transform Approach

A multi-stage secret sharing (MSS) scheme is a method of sharing a number of secrets among a set of participants, such that any authorized subset of participants could recover one secret in every stage. The first MSS scheme was proposed by He and Dawson in 1994, based on Shamir’s well-known secret sharing scheme and one-way functions. Several other schemes based on different methods have been proposed since then. In this paper, the authors propose an MSS scheme using All-Or-Nothing Transform (AONT) approach. An AONT is an invertible map with the property that having “almost all” bits of its output, one could not obtain any information about the input. This characteristic is employed in the proposed MSS scheme in order to reduce the total size of secret shadows, assigned to each participant. The resulted MSS scheme is computationally secure. Furthermore, it does not impose any constraint on the order of secret reconstructions. A comparison between the proposed MSS scheme and that of He and Dawson indicates that the new scheme provides more security features, while preserving the order of public values and the computational complexity

An Efficient Multistage Secret Sharing Scheme Using Linear One-way Functions and Bilinear Maps

In a Multistage Secret Sharing (MSSS) scheme, the authorized subsets of participants could reconstruct a number of secrets in consecutive stages. A One-Stage Multisecret Sharing (OSMSS) scheme is a special case of MSSS schemes that all secrets are recovered simultaneously. In these schemes, in addition to the individual shares, the dealer should provide the participants with a number of public values related to the secrets. The less the number of public values, the more efficient the scheme. It is desired that MSSS and OSMSS schemes provide the computational security; however, we show in this paper that OSMSS schemes do not fulfill the promise. Furthermore, by introducing a new multi-use MSSS scheme based on linear one-way functions, we show that the previous schemes can be improved in the number of public values. Compared to the previous MSSS schemes, the proposed scheme has less complexity in the process of share distribution. Finally, using bilinear maps, the participants are provided with the ability of verifying the released shares from other participants. To the best of our knowledge, this is the first verifiable MSSS scheme in which the number of public values linearly depends on the number of the participants and the secrets and which does not require secure communication channels

The July-December 2022 earthquake sequence in the southeastern Fars arc of Zagros mountains, Iran

Within two hours on 01 July 2023, three earthquakes of Mw 5.8-6.0 hit the SE Fars arc, Iran. In the following months, the region characterized by the collision of the Iranian and the Arabian plate, thrust faulting, and salt diapirism was stroke by more than 120 aftershocks of mL 3.1-5.2, of which two of the largest events occurred within one minute on 23 July 2023 in spatial vicinity to each other. We analyzed both the large mainshocks and aftershocks using different techniques, such as the inversion of seismic and satellite deformation data in a joint process and aftershock relocation. Our results indicate the activation of thrust faults within the lower sedimentary cover of the region along with high aftershock activity in significantly larger depth, supporting the controversial model of a crustal strain decoupling during the collision in the Fars Arc. We resolved a magnitude difference of >0.2 magnitude units between seismic and joint seismic and satellite deformation inversions probably caused by afterslip, thereby allowing to bridge between results from international agencies and earlier studies. We also find evidence for an event doublet and triplet activating the same or adjacent faults within the sedimentary cover and the basemen

Radiobiological effects of wound fluid on breast cancer cell lines and human-derived tumor spheroids in 2D and microfluidic culture

Intraoperative radiotherapy (IORT) could abrogate cancer recurrences, but the underlying mechanisms are unclear. To clarify the effects of IORT-induced wound fluid on tumor progression, we treated breast cancer cell lines and human-derived tumor spheroids in 2D and microfluidic cell culture systems, respectively. The viability, migration, and invasion of the cells under treatment of IORT-induced wound fluid (WF-RT) and the cells under surgery-induced wound fluid (WF) were compared. Our findings showed that cell viability was increased in spheroids under both WF treatments, whereas viability of the cell lines depended on the type of cells and incubation times. Both WFs significantly increased sub-G1 and arrested the cells in G0/G1 phases associated with increased P16 and P21 expression levels. The expression level of Caspase 3 in both cell culture systems and for both WF-treated groups was significantly increased. Furthermore, our results revealed that although the migration was increased in both systems of WF-treated cells compared to cell culture media-treated cells, E-cadherin expression was significantly increased only in the WF-RT group. In conclusion, WF-RT could not effectively inhibit tumor progression in an ex vivo tumor-on-chip model. Moreover, our data suggest that a microfluidic system could be a suitable 3D system to mimic in vivo tumor conditions than 2D cell culture

A New Achievable Rate and the Capacity of Some Classes of Multilevel Relay Network

Abstract A new achievable rate based on a partial decoding scheme is proposed for the multilevel relay network. A novel application of regular encoding and backward decoding is presented to implement the proposed rate. In our scheme, the relays are arranged in feed-forward structure from the source to the destination. Each relay in the network decodes only part of the transmitted message by the previous relay. The proposed scheme differs from general parity forwarding scheme in which each relay selects some relays in the network but decodes all messages of the selected relays. It is also shown that in some cases higher rates can be achieved by the proposed scheme than previously known by Xie and Kumar. For the classes of semideterministic and orthogonal relay networks, the proposed achievable rate is shown to be the exact capacity. The application of the defined networks is very well understood in wireless networking scenarios.</p

A Secure and efficient elliptic curve based authentication and key agreement protocol suitable for WSN

Authentication and key agreement protocols play an important role in wireless sensor communication networks. Recently Xue et al\u27. suggested a key agreement protocols for WSN which in this paper we show that the protocol has some security flaws. Also we introduce an enhanced authentication and key agreement protocol for WSN satisfying all the security requirements

Line-line fault detection and classification for photovoltaic systems using ensemble learning model based on I-V characteristics

The fault diagnosis of photovoltaic (PV) arrays aims to increase the reliability and service life of PV systems. Line-Line (LL) faults may remain undetected under low mismatch level and high impedance due to low currents of faults, resulting in power losses and fire potential disaster. This paper proposes a novel and intelligent fault diagnosis method based on an ensemble learning model and Current-Voltage (I-V) characteristics to detect and classify LL faults at the DC side of PV systems. For this purpose, first, the key features are extracted via analyzing I-V characteristics under various LL fault events and normal operation. Second, a feature selection algorithm has been applied to select the best features for each learning algorithm in order to reduce the amount of data required for the learning process. Third, an ensemble learning model is developed that combines several learning algorithms based on the probabilistic strategy to achieve superior diagnostic performance. Here, we find an excellent agreement between simulation and experimental results that the proposed method can obtain higher accuracy in detecting and classifying the LL faults, even under low mismatch levels and high fault impedances. In addition, the comparison results demonstrate that the performance of the proposed method is better than individual machine learning algorithms, so that the proposed method precisely detects and classifies LL faults on PV systems under the different conditions with an average accuracy of 99% and 99.5%, respectively