Analog Performance Prediction Based on Archimedean Copulas Generation Algorithm

International audienceTesting analog circuits is a complex and very time consuming task. In contrary to digital circuits, testing analog circuits needs different configurations, each of them targets a certain set of output parameters which are the performances and the test measures. One of the solutions to simplify the test task and optimize test time is the reduction of the number of to-be-tested performances by eliminating redundant ones. However, the main problem with such a solution is the identification of redundant performances. Traditional methods based on calculation of the correlation between different performances or on the defect level are shown to be not sufficient. This paper presents a new method based on the Archimedean copula generation algorithm. It predicts the performance value from each output parameter value based on the dependence (copula) between the two values. Therefore, different performances can be represented by a single output parameter; as a result, less test configurations are required. To validate the proposed approach, a CMOS imager with two performances and one test measure is used. The simulation results show that the two performances can be replaced by a single test measure. Industrial results are also reported to prove the superiority of the proposed approach

On Fault Diagnosis using Bayesian Networks ; A Case Study of Combinational Adders.

In this paper, we use Bayesian networks to reduce the set of vectors for the test and the diagnosis of combinational circuits. We are able to integrate any fault model (such as bit-flip and stuck-at models) and consider either single or multiple faults. We apply our method to adders and obtain a minimum set of vectors for a complete diagnosis in the case of the bit-flip model. A very good diagnosis coverage for the stuck-at fault model is found with a minimum set of test vectors and a complete diagnosis by adding few vectors

A New Method for Estimation of Missing Data Based on Sampling Methods for Data Mining

International audienceToday we collect large amounts of data and we receive more than we can handle, the accumulated data are often raw and far from being of good quality they contain Missing Values and noise. The presence of Missing Values in data are major disadvantages for most Datamining algorithms. Intuitively, the pertinent information is embedded in many attributes and its extraction is only possible if the original data are cleaned and pre-treated. In this paper we propose a new technique for preprocessing data that aims to estimate the Missing Values, in order to obtain representative Samples of good quality, and also to assure that the information extracted is more safe and reliable

Online Inference for Adaptive Diagnosis via Arithmetic Circuit Compilation of Bayesian Networks

International audienceConsidering technology and complexity evolution the design of fully reliable embedded systems will be prohibitively complex and costly. Onboard diagnosis is a first solution that can be achieved by means of Bayesian networks. An efficient compilation of Bayesian inference is proposed using Arithmetic Circuits (AC). ACs can be efficiently implemented in hardware to get very fast response time. This approach has been recently experimented in Software Health Management of aircrafts or UAVs. However, there are two kinds of obstacles that must be addressed. First, the tree complexity can lead to intractable solutions and second, an offline static analysis cannot capture the dynamic behaviour of a system that can have multiple configurations and applications. In this paper, we present our direction to solve these issues. Our approach relies on an adaptive version of the diagnosis computation for different kinds of applications/missions of UAVs. In particular, we consider an incremental generation of the AC structure. This adaptive diagnosis can be implemented using dynamic reconfiguration of FPGA circuits

New techniques for selecting test frequencies for linear analog circuits

International audienceIn this paper we show that the problem of minimizing the number of test frequencies necessary to detect all possible faults in a multi-frequency test approach for linear analog circuits can be modeled as a set covering problem. We will show in particular, that under some conditions on the considered faults, the coefficient matrix of the problem has the strong consecutive-ones property and hence the corresponding set covering problem can be solved in polynomial time. For an efficient solution of the problem, an interval graph formulation is also used and a polynomial algorithm using the interval graph structure is suggested. The optimization of test frequencies for a case-study biquadratic filter is presented for illustration purposes. Numerical simulations with a set of randomly generated problem instances demonstrate two different implementation approaches to solve the optimization problem very fast, with a good time complexity

New techniques for selecting test frequencies for linear analog circuits

International audienceIn this paper we show that the problem of minimizing the number of test frequencies necessary to detect all possible faults in a multi-frequency test approach for linear analog circuits can be modeled as a set covering problem. We will show in particular, that under some conditions on the considered faults, the coefficient matrix of the problem has the strong consecutive-ones property and hence the corresponding set covering problem can be solved in polynomial time. For an efficient solution of the problem, an interval graph formulation is also used and a polynomial algorithm using the interval graph structure is suggested. The optimization of test frequencies for a case-study biquadratic filter is presented for illustration purposes. Numerical simulations with a set of randomly generated problem instances demonstrate two different implementation approaches to solve the optimization problem very fast, with a good time complexity

Intelligent Data Mining Techniques for Emergency Detection in Wireless Sensor Networks

2nd IEEE International Conference on Cloud and Big Data Computing (CBDCom 2016), Toulouse, France, 18-21 JulyEvent detection is an important part in many Wireless Sensor Network (WSN) applications such as forest fire and environmental pollution. In this kind of applications, the event must be detected early in order to reduce the threats and damages. In this paper, we propose a new approach for early forest fire detection, which is based on the integration of Data Mining techniques into sensor nodes. The idea is to partition the node set into clusters so that each node can individually detect fires using classification techniques. Once a fire is detected, the corresponding node will send an alert to its cluster-head. This alert will then be routed via gateways and other cluster-heads to the sink in order to inform the firefighters. The approach is validated using the CupCarbon simulator. The results show that our approach can provide a fast reaction to forest fires with efficient energy consumption.French National Research Agency (ANR

A compute and wait in pow (Cw-pow) consensus algorithm for preserving energy consumption

Several trusted tasks use consensus algorithms to solve agreement challenges. Usually, consensus agreements are used to ensure data integrity and reliability in untrusted environments. In many distributed networking fields, the Proof of Work (PoW) consensus algorithm is commonly used. However, the standard PoW mechanism has two main limitations, where the first is the high power consumption and the second is the 51 % attack vulnerability. In this paper, we look to improve the PoW consensus protocol by introducing several proof rounds. Any given consensus node should resolve the game of the current round Roundi before participating in the next round Roundi+1 . Any node that resolves the game of Roundi can only pass to the next round if a predetermined number of solutions has been found by other nodes. The obtained evaluation results of this technique show significant improvements in terms of energy consumption and robustness against the 51 % and Sybil attacks. By fixing the number of processes, we obtained an energy gain rate of 15.63 % with five rounds and a gain rate of 19.91 % with ten rounds

An optimized scalable multi-ant colony system for multi-depot vehicle routing problems using a reactive multi-agent system

The multi-depot vehicle routing problem is a variant of the vehicle routing problem that tries to minimize the total cost of providing the service from several depots to satisfy several client demands. This paper presents a multi-ant colony system to solve the multi-depot vehicle routing problem using a reactive agent-based approach. This approach is designed to effectively solve the problem, in which each reactive agent is inspired by modeling the behavior of the ant. We define two types of reactive agents whose behavior differs in the use of two kinds of pheromone trail. In order to refer to the two phases of the execution process, i.e., the assignment phase and the routing phase, every reactive agent cooperates with others to provide a scalable solution for the overall problem. The solution of the multi-depot vehicle routing problem is beneficial and helpful for many real applications. The performance evaluation of the proposed approach is done using instances from the literature, and the results obtained demonstrate good performance when compared with other approaches

A History of Drug Discovery for Treatment of Nausea and Vomiting and the Implications for Future Research.

The origins of the major classes of current anti-emetics are examined. Serendipity is a recurrent theme in discovery of their anti-emetic properties and repurposing from one indication to another is a continuing trend. Notably, the discoveries have occurred against a background of company mergers and changing anti-emetic requirements. Major drug classes include: (i) Muscarinic receptor antagonists-originated from historical accounts of plant extracts containing atropine and hyoscine with development stimulated by the need to prevent sea-sickness among soldiers during beach landings; (ii) Histamine receptor antagonists-searching for replacements for the anti-malaria drug quinine, in short supply because of wartime shipping blockade, facilitated the discovery of histamine (H1) antagonists (e.g., dimenhydrinate), followed by serendipitous discovery of anti-emetic activity against motion sickness in a patient undergoing treatment for urticaria; (iii) Phenothiazines and dopamine receptor antagonists-investigations of their pharmacology as "sedatives" (e.g., chlorpromazine) implicated dopamine receptors in emesis, leading to development of selective dopamine (D2) receptor antagonists (e.g., domperidone with poor ability to penetrate the blood-brain barrier) as anti-emetics in chemotherapy and surgery; (iv) Metoclopramide and selective 5-hydroxytryptamine3(5-HT3) receptor antagonists-metoclopramide was initially assumed to act only via D2 receptor antagonism but subsequently its gastric motility stimulant effect (proposed to contribute to the anti-emetic action) was shown to be due to 5-hydroxytryptamine4 receptor agonism. Pre-clinical studies showed that anti-emetic efficacy against the newly-introduced, highly emetic, chemotherapeutic agent cisplatin was due to antagonism at 5-HT3 receptors. The latter led to identification of selective 5-HT3 receptor antagonists (e.g., granisetron), a major breakthrough in treatment of chemotherapy-induced emesis; (v) Neurokinin1receptor antagonists-antagonists of the actions of substance P were developed as analgesics but pre-clinical studies identified broad-spectrum anti-emetic effects; clinical studies showed particular efficacy in the delayed phase of chemotherapy-induced emesis. Finally, the repurposing of different drugs for treatment of nausea and vomiting is examined, particularly during palliative care, and also the challenges in identifying novel anti-emetic drugs, particularly for treatment of nausea as compared to vomiting. We consider the lessons from the past for the future and ask why there has not been a major breakthrough in the last 20 years