Blind frequency-offset estimator for OFDM systems transmitting constant-modulus symbols

We address the problem of carrier frequency offset (CFO) synchronization in OFDM communications systems in the context of frequency-selective fading channels. We consider the case where the transmitted symbols have constant modulus, i.e., PSK constellations. A novel blind CFO estimation algorithm is developed. The new algorithm is shown to greatly outperform a recently published blind technique that exploits the fact that practical OFDM systems are not fully loaded. Further, the proposed algorithm is consistent even when the system is fully loaded. Finally, the proposed CFO estimator is obtained via a one-dimensional search, the same as with the existing virtual subcarrier-based estimator, but achieves a substantial gain in performance (10-dB SNR or one order of magnitude in CFO MSE)

Channel estimation and symbol detection for block transmission using data-dependent superimposed training

We address the problem of frequency-selective channel estimation and symbol detection using superimposed training. The superimposed training consists of the sum of a known sequence and a data-dependent sequence that is unknown to the receiver. The data-dependent sequence cancels the effects of the unknown data on channel estimation. The performance of the proposed approach is shown to significantly outperform existing methods based on superimposed training (ST)

Reanalysis of a Vacuum Distillation Unit Foundation

This paper deals with the reanalysis of the foundation of an already existing Vaccum Distillation Unit in an oil refinery. The reanalysis is required to suggest if the existing foundation of the unit would be capable enough to withstand an additional pressure intensity to which it would be subjected due to proposed expansion of the unit. The paper therefore discusses the geotechnical investigation program undertaken for evaluating the relevant design parameters and the methodology adopted to compute the sustainable pressure of the foundation

Study of validity of risk of malignancy index in perimenopausal women with ovarian mass in a tertiary care hospital in India

Background: Ovarian carcinoma is a silent killer because it presents in advanced stage. In India, it ranks third after carcinoma cervix and breast. Incidence of ovarian cancer is 5.4-8.0 per 100,000 in India. Various versions of risk of malignancy index have been studied to show its validity in different settings. We have studied third version of risk of malignancy index in a resource poor setting in India.Methods: In this prospective observational study 74 perimenopausal and postmenopausal women with ovarian mass were recruited. Menopausal score (M), Ultrasonography score (U) and CA-125 are components of Risk of Malignancy Index 3. Patients underwent preoperative ultrasonography and CA-125 level was assessed. Scores of M 1-3, U 1-3 and absolute value of CA-125 was multiplied. This product was value of Risk of Malignancy Index 3. If it is less than 250 it suggests absence of malignancy and more than 250 strongly suggests malignancy. Results were confirmed by histopathology.Results: Fifty six percent women were cancer positive. Ovarian malignancy was more common in postmenopausal age group. Ultrasonography and CA-125 had high sensitivity of 90% but poor specificity. Risk of malignancy index 3 had a sensitivity, specificity, positive predictive value and negative predictive value of 90%, 91%, 78% and 96% respectively at a cut off value of 250.Conclusions: Risk of malignancy index was concluded to be a multimodal approach with better diagnostic scoring index in preoperative stage in women of ovarian masses. It is simple and easily applicable clinical tool in resource poor setting

Analytic thinking reduces belief in conspiracy theories

Belief in conspiracy theories has been associated with a range of negative health, civic, and social outcomes, requiring reliable methods of reducing such belief. Thinking dispositions have been highlighted as one possible factor associated with belief in conspiracy theories, but actual relationships have only been infrequently studied. In Study 1, we examined associations between belief in conspiracy theories and a range of measures of thinking dispositions in a British sample (N = 990). Results indicated that a stronger belief in conspiracy theories was significantly associated with lower analytic thinking and open-mindedness and greater intuitive thinking. In Studies 2–4, we examined the causational role played by analytic thinking in relation to conspiracist ideation. In Study 2 (N = 112), we showed that a verbal fluency task that elicited analytic thinking reduced belief in conspiracy theories. In Study 3 (N = 189), we found that an alternative method of eliciting analytic thinking, which related to cognitive disfluency, was effective at reducing conspiracist ideation in a student sample. In Study 4, we replicated the results of Study 3 among a general population sample (N = 140) in relation to generic conspiracist ideation and belief in conspiracy theories about the July 7, 2005, bombings in London. Our results highlight the potential utility of supporting attempts to promote analytic thinking as a means of countering the widespread acceptance of conspiracy theories

Adapting End Host Congestion Control for Mobility

Network layer mobility allows transport protocols to maintain connection state, despite changes in a node's physical location and point of network connectivity. However, some congestion-controlled transport protocols are not designed to deal with these rapid and potentially significant path changes. In this paper we demonstrate several distinct problems that mobility-induced path changes can create for TCP performance. Our premise is that mobility events indicate path changes that require re-initialization of congestion control state at both connection end points. We present the application of this idea to TCP in the form of a simple solution (the Lightweight Mobility Detection and Response algorithm, that has been proposed in the IETF), and examine its effectiveness. In general, we find that the deficiencies presented are both relatively easily and painlessly fixed using this solution. We also find that this solution has the counter-intuitive property of being both more friendly to competing traffic, and simultaneously more aggressive in utilizing newly available capacity than unmodified TCP

Energy Harvesting Empowered Cognitive Metro-cellular Networks

Harvesting energy from natural (solar, wind, vibration etc.) and synthesized (microwave power transfer) sources is envisioned as a key enabler for realizing green wireless networks. Energy efficient scheduling is one of the prime objectives of cognitive radio platforms. To that end, in this article, we present a comprehensive analytical framework to characterize the performance of a cognitive metro-cellular network empowered by solar energy harvesting. The proposed model considers both spatial and temporal dynamics of the energy field and the mobile user traffic. Channel uncertainties are also captured in terms of large scale path-loss and small-scale Rayleigh fading. A new metric called `energy outage probability' which characterizes the self-sustainable operation of the base stations under energy harvesting is proposed and quantified. It is shown that the energy outage probability is strongly coupled with the path-loss exponent, required quality-of-service, base station and user density. Moreover, the energy outage probability varies both on daily and yearly basis depending on the solar geometry. It is shown that even in winter time BSs can run for 10-15 hours without any purchase of energy from the power grid