Simulink behavioral modeling of a 10-bit pipelined ADC

The increasing architecture complexity of data converters makes it necessary to use behavioral models to simulate their electrical performance and to determine their relevant data features. For this purpose, a specific data converter simulation environment has been developed which allows designers to perform time-domain behavioral simulations of pipelined analog to digital converters (ADCs). All the necessary blocks of this specific simulation environment have been implemented using the popular Matlab simulink environment. The purpose of this paper is to present the behavioral models of these blocks taking into account most of the pipelined ADC non-idealities, such as sampling jitter, noise, and operational amplifier parameters (white noise, finite DC gain, finite bandwidth, slew rate, and saturation voltages). Simulations, using a 10-bit pipelined ADC as a design example, show that in addition to the limits analysis and the electrical features extraction, designers can determine the specifications of the basic blocks in order to meet the given data converter requirements.     

Dynamic array PIN:A novel approach to secure NFC electronic payment between ATM and smartphone

ABSTRACT

Near Field Communication (NFC) technology has been used recently for electronic payment between an Automated Teller Machine (ATM) and a Smartphone. It is threatened by several attacks that can steal the user personal data like the password or the Personal Identification Number (PIN). In this paper, we present Dynamic Array PIN (DAP), a novel approach for user authentication on a Smartphone that uses NFC electronic payment with an ATM. Our analysis and experimentation prove that this technique protects against thirteen different attacks and is cost-effective in terms of required hardware, authentication time, computing power and storage space.     

Optimal coverage of an infrastructure network using sensors with distance-decaying sensing quality

Motivated by recent applications of wireless sensor networks in monitoring infrastructure networks, we address the problem of optimal coverage of infrastructure networks using sensors whose sensing performance decays with distance. We show that this problem can be formulated as a continuous p-median problem on networks. The literature has addressed the discrete p-median problem   on networks and in continuum domains, and the continuous p$p$-median problem in continuum domains extensively. However, in-depth analysis of the continuous p$p$-median problem on networks has been lacking. With the sensing performance model that decays with distance, each sensor covers a region equivalent to its Voronoi partition on the network in terms of the shortest path distance metric. Using Voronoi partitions, we define a directional partial derivative of the coverage metric with respect to a sensor’s location. We then propose a gradient descent algorithm to obtain a locally optimal solution with guaranteed convergence. The quality of an optimal solution depends on the choice of the initial configuration of sensors. We obtain an initial configuration using two approaches: by solving the discrete p$p$-median problem on a lumped   network and by random sampling. We consider two methods of random sampling: uniform sampling and D²$D^2$-sampling. The first approach with the initial solution of the discrete p$p$-median problem leads to the best coverage performance for large networks, but at the cost of high running time. We also observe that the gradient descent on the initial solution with the D²$D^2$-sampling method yields a solution that is within at most 7% of the previous solution and with much shorter running time.     

Distributed Bayesian diagnosis for modular assembly systems—A case study

The growing interest in modular and distributed approaches for the design and control of intelligent manufacturing systems gives rise to new challenges. One of the major challenges that have not yet been well addressed is monitoring and diagnosis in distributed manufacturing systems. In this paper we propose the use of a multi-agent Bayesian framework known as Multiply Sectioned Bayesian Networks (MSBNs) as the basis for multi-agent distributed diagnosis in modular assembly systems. We use a close-to-industry case study to demonstrate how MSBNs can be used to build component-based Bayesian sub-models, how to verify the resultant models, and how to compile the multi-agent models into runtime structures to allow consistent multi-agent belief update and inference.     

Precision design aspects for friction actuation with error compensation

Some key aspects in the design of actuators for ultra precision systems will require particular attention as actuators have to comply with ultra precision positioning and related tight specifications. The actuation is an important sub-group within the overall system to be designed. Any design failure in this sub-group will degrade drastically the machine performance. An interface analysis with immediate surrounding sub-groups is important to secure better generation of motion. In this paper, a design procedure for an actuator dedicated to generate high precision motion is presented with a discussion of some important aspects encountered when designing a friction drive. The procedure encompasses design concepts, axis stiffness, control, system dynamics, and, error compensation using a dedicated control strategy compensating for errors. A case study is discussed.     

Self-tuning dynamic models of HVAC system components

A great majority of modern buildings are equipped with Energy Management and Control Systems (EMCS) which monitor and collect operating data from different components of heating ventilating and air conditioning (HVAC) systems. Models derived and tuned by using the collected data can be incorporated into the EMCS for online prediction of the system performance. To that end, HVAC component models with self-tuning parameters were developed and validated in this paper. The model parameters were tuned online by using a genetic algorithm which minimizes the error between measured and estimated performance data. The developed models included: a zone temperature model, return air enthalpy/humidity and CO₂ concentration models, a cooling and heating coil model, and a fan model. The study also includes tools for estimating the thermal and ventilation loads. The models were validated against real data gathered from an existing HVAC system. The validation results show that the component models augmented with an online parameter tuner, significantly improved the accuracy of predicted outputs. The use of such models offers several advantages such as designing better real-time control, optimization of overall system performance, and online fault detection.     

Design of survivable WDM networks using pre-configured protection structures with unrestricted shapes

We propose a novel protection approach for the design of link-protection schemes in survivable Wavelength Division Multiplexing mesh networks by merging the well-known p-cycle- and p-tree-protection structures. So doing, we aim at gathering the advantages of p-cycles in terms of protection capabilities, and of p-trees in terms of protection flexibilities (local re-routing, scalability) in a single protection scheme. As opposed to existing protection schemes based on protection structures with a pre-defined shape, the building blocks of the new scheme are protection structures with unrestricted shapes. Thus, they allow more flexibility in provisioning spare capacity, and provide higher capacity efficiency when compared to the shaped-protection schemes that have been proposed so far. In order to cope with the size of the solution space which includes all the possible protection structures, we propose an efficient and scalable optimization technique in large-scale systems named column generation (CG). In our CG-based optimization approach, the shape of a candidate protection structure is dynamically decided during the optimization process according to a link spare capacity budget. Experimental results on different network instances show that the protection plan resulting from the merging of p-cycle and p-tree structures is, on average, ~15% less capacity redundant and ~15% more reliable than the pure p-cycle one. It also requires, on average, ~30% less protection structures. In addition, those structures provide backup paths ~30% smaller than those of the p-cycle-based scheme.     

An Expeditious Copper‐Catalyzed Access to 3‐Aminoquinolinones, 3‐Aminocoumarins and Anilines using Sodium Azide

An efficient copper‐catalyzed in situ C(sp²) NH₂ bond formation to provide a range of 3‐aminoquinolin‐2(1H)‐ones and 3‐aminocoumarins from 3‐bromoquinolinones and 3‐bromocoumarins, respectively, has been achieved. The reaction conditions involve the use of copper powder as the catalyst, eco‐friendly ethanol as the solvent in the presence of pipecolinic acid as the ligand and ascorbic acid as the additive. The efficiency of this practical method was demonstrated in the synthesis of various anilines.