Analysis of the Security of BB84 by Model Checking

Quantum Cryptography or Quantum key distribution (QKD) is a technique that allows the secure distribution of a bit string, used as key in cryptographic protocols. When it was noted that quantum computers could break public key cryptosystems based on number theory extensive studies have been undertaken on QKD. Based on quantum mechanics, QKD offers unconditionally secure communication. Now, the progress of research in this field allows the anticipation of QKD to be available outside of laboratories within the next few years. Efforts are made to improve the performance and reliability of the implemented technologies. But several challenges remain despite this big progress. The task of how to test the apparatuses of QKD For example did not yet receive enough attention. These devises become complex and demand a big verification effort. In this paper we are interested in an approach based on the technique of probabilistic model checking for studying quantum information. Precisely, we use the PRISM tool to analyze the security of BB84 protocol and we are focused on the specific security property of eavesdropping detection. We show that this property is affected by the parameters of quantum channel and the power of eavesdropper.Comment: 12 Pages, IJNS

IMPACT ANALYSIS OF BLACK HOLE ATTACKS ON MOBILE AD HOC NETWORKS PERFORMANCE

A Mobile Ad hoc Network (MANET) is a collection of mobile stations with wireless interfaces which form a temporary network without using any central administration. MANETs are more vulnerable to attacks because they have some specific characteristics as complexity of wireless communication and lack of infrastructure. Hence security is an important requirement in mobile ad hoc networks. One of the attacks against network integrity in MANETs is the Black Hole Attack. In this type of attack all data packets are absorbed by malicious node, hence data loss occurs. In this paper we investigated the impacts of Black Hole attacks on the network performance. We have simulated black hole attacks using Network Simulator 2 (NS-2) and have measured the packet loss in the network without and with a black hole attacks. Also, we measured the packet loss when the number of black hole attacks increases

Artificial Intelligence Techniques in Medical Imaging: A Systematic Review

This scientific review presents a comprehensive overview of medical imaging modalities and their diverse applications in artificial intelligence (AI)-based disease classification and segmentation. The paper begins by explaining the fundamental concepts of AI, machine learning (ML), and deep learning (DL). It provides a summary of their different types to establish a solid foundation for the subsequent analysis. The prmary focus of this study is to conduct a systematic review of research articles that examine disease classification and segmentation in different anatomical regions using AI methodologies. The analysis includes a thorough examination of the results reported in each article, extracting important insights and identifying emerging trends. Moreover, the paper critically discusses the challenges encountered during these studies, including issues related to data availability and quality, model generalization, and interpretability. The aim is to provide guidance for optimizing technique selection. The analysis highlights the prominence of hybrid approaches, which seamlessly integrate ML and DL techniques, in achieving effective and relevant results across various disease types. The promising potential of these hybrid models opens up new opportunities for future research in the field of medical diagnosis. Additionally, addressing the challenges posed by the limited availability of annotated medical images through the incorporation of medical image synthesis and transfer learning techniques is identified as a crucial focus for future research efforts

Location selection of agricultural-residuals particleboard industry through group decision: The case study of northern Iran

This paper presents a framework for locating agricultural-residuals particleboard industry in the northern provinces of Iran. Particleboard industry is the only Iranian wood and paper industry with an export potential and the use of agricultural residuals as the raw material can help with increasing the production in this industry, while reducing the damage to forest resources. The northern provinces of Iran are agricultural centers with ample amounts of agricultural residues. These provinces are, therefore, preferable to other provinces as the construction sites of particleboard plants. In the location selection model presented in this paper, the Analytical Hierarchy Process (AHP) method is used and the results indicate that the criterion of ‘material and production’ and the sub-criterion of ‘reliability of supply’ have the highest priorities, and that Golestan province is the best alternative

Covérification des systèmes intégrés

Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal

CMOS-MEMS Scanning Microwave Microscopy

This thesis presents the design, fabrication and experimental validation of an integrated dual-mode scanning microwave microscopy (SMM)/Atomic Force Microscopy (AFM) system that does not require the use of a conventional laser-based AFM or external scanners. Microfabricated SMM probes are collocated with strain-based piezoresistive AFM probes in a CMOS-MEMS process, and are actuated by integrated electrothermal scanners. Integration of AFM enables dual-mode imaging (topography and electrical properties); it also enables control over tip-sample distance, which is crucial for accurate SMM imaging. The SMM (also known as Scanning Near-field Microwave Microscope and Scanning Evanescent Microwave Microscope) is the most well-known type of Scanning Probe Microscopes (SPM) that can quantify local dielectric and conductivity of materials. It has emerged as the most promising means for the fast, non-contact, and non-destructive study of materials and semiconductor devices. The CMOS-MEMS SMM devices are fabricated by using a standard foundry CMOS process, followed by an in-house mask-less post-processing technique to release them. Single-chip SMM/AFM devices with integrated 1-D and 3-D actuation are introduced. The CMOS-MEMS fabrication process allows external bulky scanners to be replaced with integrated MEMS actuators that are small and immune to vibration and drift. In this work, electrothermal MEMS actuators are utilized to scan the tip over the sample in 3 degrees of freedom, over a 13 µm x 13 µm x 10 µm scan range in the x, y, and z directions, respectively. Furthermore, the availability of polysilicon layers on the CMOS processes allows for on-chip integrated piezoresistive position sensing that obviates the need for the laser system. Vertical tip-sample distance control of a few nanometers is achieved with the integrated piezoresistive position sensors. These devices are used to modulate the tip-sample separation to underlying samples with a periodic signal, improving immunity to long-term system drifts. To improve the sensitivity of the CMOS-MEMS SMM, different types of matching networks for SMMs are thoroughly analyzed and closed form formulas are presented for each type. Based on the analyses, the stub matching method is selected to match the high tip-to-sample impedance to the 50 ohm characteristic impedance of the system. After that, with the help of lumped models and EM simulations, different sections of the CMOS-MEMS SMM system are analyzed and suggestions for selecting the best micro-transmission line and bonding-pad transmission lines are given. A measurement circuit for SMM is then presented and explained, showing how this measurement system can improve the output-signal-to-noise ratio and hence the sensitivity of microwave imaging. Calculations for the entire SMM system indicate that sub-attofarad tip-sample impedance can be measured. It is noteworthy that most of the analyses and suggestions given in this thesis can be applied to any Scanning Microwave Microscopes or, even more generally, to any microwave system that needs to sense a small signal. Finally, the measurement results for the fabricated CMOS-MEMS SMM are presented to verify the proposed methods. Several samples with sub-micron and nanometer feature sizes are imaged. A special test sample with no topography but with buried dielectric materials in grid and stripes is also designed and measured

Proteins expression clustering of Alzheimer disease in rat hippocampus proteome

Because of the huge amounts of proteomic data and demand for new methods of laboratory analysis results, proteins collective analysis, in addition to taking less time, biostatistician assist at identification of new patterns in the data set. In this study, rat hippocampus proteome in normal and Alzheimer's disease (AD) were analyzed by using proteomic techniques and bioinformatics’ analysis. Protein extracts from normal and Alzheimer's rats were separated by using two-dimensional electrophoresis (2DE). The silver staining method was used for detecting spots. Bioinformatics analysis of proteome were performed by progensis same spots software. Bioinformatics and statistical analysis of 2DE gel techniques obtained 760 protein spots were detected in both normal and AD rats.  Comparisons between controls and Alzheimer gel containing 20 common proteins were expressed significantly differences. 16 new proteins were expressed in AD, while 36 proteins were suppressed. Proteins clustering by using correlation analysis evaluated 3 clusters in the proteome; Principal component analysis also confirmed the results of clustering. Finally, we can conclude that a significant expression of Alzheimer changes in the hippocampus proteome which are associated with specific biological processes summarized in 3 main clusters indicated 3 principal biological pathways of AD.

Measurement-based Methodology for Modeling the Energy Consumption of Mobile Devices

International audienceEnergy consumption is the result of interactions between hardware, software, users, and the application environment. Optimization of energy consumption has become crucial, and energy is considered a critical resource, so it is important to know and understand both how energy is measured and consumed on mobile devices. An accurate knowledge will allow us to develop efficient solutions to reduce energy consumption in order to improve the user experience. In this paper we propose an experimental methodology to build a model of the energy consumption of mobile applications. Based on precise measurements, we elaborate predictive models of energy consumption for both unconnected and connected applications

Unsupervised clustering for 5G network planning assisted by real data

The fifth-generation (5G) of networks is being deployed to provide a wide range of new services and to manage the accelerated traffic load of the existing networks. In the present-day networks, data has become more noteworthy than ever to infer about the traffic load and existing network infrastructure to minimize the cost of new 5G deployments. Identifying the region of highest traffic density in megabyte (MB) per km2 has an important implication in minimizing the cost per bit for the mobile network operators (MNOs). In this study, we propose a base station (BS) clustering framework based on unsupervised learning to identify the target area known as the highest traffic cluster (HTC) for 5G deployments. We propose a novel approach assisted by real data to determine the appropriate number of clusters k and to identify the HTC. The algorithm, named as NetClustering, determines the HTC and appropriate value of k by fulfilling MNO's requirements on the highest traffic density MB/km2 and the target deployment area in km2. To compare the appropriate value of k and other performance parameters, we use the Elbow heuristic as a benchmark. The simulation results show that the proposed algorithm fulfills the MNO's requirements on the target deployment area in km2 and highest traffic density MB/km2 with significant cost savings and achieves higher network utilization compared to the Elbow heuristic. In brief, the proposed algorithm provides a more meaningful interpretation of the underlying data in the context of clustering performed for network planningThis work was supported by the Spanish National Project IRENE-EARTH (PID2020-115323RB-C33/AEI/10.13039/501100011033