Ines Kammoun

A parking management system using Wireless Sensor Networks

In this paper, we conceive and develop a prototype of a car parking management system. The system gives a real-time snapshot of the monitored parking equipped by a Wireless Sensor Network (WSN). The developed application consists of two parts; one embedded in sensor nodes and another one executed at the pc containing interfaces. Moreover, this system enables to develop a Web application which allows the driver to reserve a free place in the parking or to guide him to localize a place using the parking map with all details of the way to reach it.

A reappraisal of small- and large-fiber damage in carpal tunnel syndrome: New insights into the value of the EMLA test for improving diagnostic sensitivity

OBJECTIVES To reappraise the respective involvement of small- and large-fiber damage in carpal tunnel syndrome (CTS) and to determine the diagnostic sensitivity of autonomic tests compared to conventional nerve conduction study (NCS). METHODS Thirty-two manual workers complaining of at least unilateral CTS were enrolled. They underwent clinical interview and completed the symptom severity scale of the Boston CTS Questionnaire (sssBCTQ) and the Neuropathic Pain Symptom Inventory (NPSI). In addition, transcarpal NCS was performed to investigate large sensory and motor fibers of the median nerve, while small autonomic fibers were assessed by recording sympathetic skin reflexes (SSR) at the palm and by grading skin wrinkling in response to eutectic mixture of local anesthetic (EMLA) cream application at the pulp of the index finger. For each neurophysiological variable, sensitivity and specificity values for the diagnosis of CTS were calculated and clinical correlations were studied. RESULTS Among 64 hands examined, 36 were clinically symptomatic, while 22 were clinically asymptomatic and served as controls. Among all the neurophysiological variables studied, only the values of transcarpal sensory nerve conduction velocity and the EMLA test grade were found to be more altered in clinically symptomatic hands, with also a trend towards prolonged distal motor latency. Overall, for the diagnosis of clinically symptomatic CTS, NCS, SSR, and the EMLA test had a sensitivity of 66.7%, 22.2%, and 69.4%, respectively, and a specificity of 72.7%, 90.9%, and 50%, respectively. Combining NCS and the EMLA test led to a sensitivity of 88.9% and a specificity of 45.4%. The sssBCTQ (r=-0.34, P=0.009) and the total NPSI score (r=-0.41, P=0.001) correlated to a more altered EMLA test grade, but not to any NCS or SSR variables. In symptomatic hands, burning sensation was associated with more severe small-fiber lesion, while other pain and sensory symptoms were rather found to be reduced in case of large-fiber damage, evidenced by NCS alteration. CONCLUSIONS This study confirms the discrepancy between conventional NCS results and clinical presentation of CTS, but still suggests a major involvement of Aβ fibers in the positive sensory symptoms of CTS, excepting burning sensation. On the other hand, the EMLA test was found to correlate with clinical data and to be able to improve sensitivity of neurophysiological investigation in diagnosing CTS.

Efficient User Identification and Semi-Blind Channel Estimation for MU-MIMO Systems

In this letter, we consider an uncoordinated access to the uplink of MU-MIMO cellular networks. Based on randomly rotated modulation constellations, we propose an efficient approach to blind identification of active users and estimation of all involved channels, up to a discrete phase ambiguity. To eliminate this ambiguity, we use short orthogonal pilot sequences. To improve the obtained channel estimates, we also rely on a combination of pilot-based and blind channel estimates. Moreover, since no coordination is assumed with the base station the problem of pilot contamination can occur when some active users use the same pilot sequence. As a solution for this problem, we propose an efficient semi-blind recovery of all contaminated channel estimates.

Cooperative beamforming in multi-cells multi-users networks with quantized feedback

In this paper, we consider a downlink coordinated multi-cells multi-users network, in which each User Equipment (UE) knows its Channel State Information (CSI) perfectly and feeds back its quantized composite channel to the distributed Base stations (BSs) through a limited feedback. We propose a cooperative beamforming design, considering quantized composite channels, that reduces the inter-cell interference for both cases of perfect and outdated quantized CSI and an optimal power allocation that maximizes the system sum rate. Furthermore, we propose a bit labelling of codebooks, used for channel quantization, that outperforms the random labelling under channel feedback errors and delay. We analyze also the sum rate loss due to the outdated and quantized channel knowledge at the transmitter.

Primary users identification in underlay cognitive radio

In this paper, we consider an underlay cognitive radio system where secondary users can coexist with primary users in the same spectrum and region. To guarantee no interference to the primary network, the secondary base station (BS-S) needs to identify the active primary users (PUs) and to have a perfect or partial knowledge of the PU channels. For this aim, we assume that the BS-S is equipped with multiple antennas and not have any prior information about the channel from the PUs. We assume also that each PU uses a randomly rotated constellation for each transmitted symbol. Based on the received signals at the BS-S antennas, we propose a very efficient and reliable method for a blind identification of active primary users, without any energy or radio resource losses. Numerical results demonstrate the advantages of the proposed method.

A Fuzzy Based Energy Aware Unequal Clustering for Wireless Sensor Networks

One of the most important issues in wireless sensor networks is energy autonomy. Thereby wireless communication leads to excessive demands of energy. In this paper, a fuzzy based energy aware unequal clustering algorithm is presented. The network is partitioned into certain number of rings. An energy analysis model is proposed to measure the optimal radius of each cluster. This enables to vary the size of clusters from one ring to another, which ensures the load balance of the network. Then, a fuzzy logic system is employed to select suitable cluster head. The fuzzy set relies only on three parameters; residual energy, number of neighboring nodes and centrality of node among its neighbors. The proposed algorithm outperforms other clustering approaches, like LEACH, DUCF and MCFL in terms of energy efficiency and network lifetime.

Embedded primary users identification and channel estimation for underlay cognitive radio network based on compressive sensing

Because of its spectrum-sharing nature, a CR network inevitably operates in interference-intensive environments. One challenge is how to maintain the interferences generated by the cognitive transmissions to the primary network below an acceptable threshold level. In this paper, we firstly propose an efficient primary users identification, using compressive sensing (CS). Our focus is on the angular sparsity of the received signal given an unknown number of primary user source signals impinging upon the antenna array from different directions of arrival (DOA). Given multiple snapshots, multiple measurement vectors (MMV) are available at the secondary base station and considered for primary channel detection over the angular domain using the regularized M-FOCUSS algorithm. Then, we develop novel methods for paths separation and primary channels estimation based on their autocorrelation matrix properties. Through simulations, we show that the recovery performance of the proposed approach in terms of false alarm and average minimum square error (MES) between the true and the estimated primary channel, is better than the conventional maximum to minimum eigenvalue (MME) detector.

Downlink Beamforming and Power Allocation in Cognitive Radio Network with Outdated and Limited Feedback of Channel Estimates

In this paper, we consider a downlink Multiple-Input-Multiple-Output (MIMO) cognitive radio network where Secondary Users (SUs) are allowed to transmit concurrently with Primary users (PUs) under an interference limit. We assume that the Channel State Information (CSI) required at the Secondary Base Station (SBS) is available through a limited feedback channel and that it can be disturbed by estimation errors and feedback delay. Our aim is to improve the secondary system performance under the interference power constraint at PUs and the total transmit power constraint, taking into account the channel estimation errors and feedback delay. For this aim, we propose a beamforming matrix design that minimizes theinterference at the PUs and between the SUs. Furthermore, westudy the sum rate maximization problem for secondary systemunder total transmit power and analyze the average interferencecaused to the PUs. Using simulations results, we demonstrate thatour proposed beamforming design with optimal power allocationcan improve the sum rate of the secondary system, in the presenceof channel estimation errors and limited feedback delay.