A New MCMC Sampling Based Segment Model for Radar Target Recognition

One of the main tools in radar target recognition is high resolution range profile (HRRP)‎. ‎However‎, ‎it is very sensitive to the aspect angle‎. ‎One solution to this problem is to assume the consecutive samples of HRRP identically independently distributed (IID) in small frames of aspect angles‎, ‎an assumption which is not true in reality‎. ‎However, b‎‎ased on this assumption‎, ‎some models have been developed to characterize the sequential information contained in the multi-aspect radar echoes‎. ‎Therefore‎, ‎they only consider the short dependency between consecutive samples‎. ‎Here‎, ‎we propose an alternative model‎, ‎the segment model‎, ‎to address the shortcomings of these assumptions‎. ‎In addition‎, ‎using a Markov chain Monte-Carlo (MCMC) based Gibbs sampler as an iterative approach to estimate the parameters of the segment model‎, ‎we will show that the proposed method is able to estimate the parameters with quite satisfying accuracy and computational load‎

Radar HRRP Modeling using Dynamic System for Radar Target Recognition

High resolution range profile (HRRP) is being known as one of the most powerful tools for radar target recognition. The main problem with range profile for radar target recognition is its sensitivity to aspect angle. To overcome this problem, consecutive samples of HRRP were assumed to be identically independently distributed (IID) in small frames of aspect angles in most of the related works. Here, considering the physical circumstances of maneuver of an aerial target, we have proposed dynamic system which models the short dependency between consecutive samples of HRRP in segments of the whole HRRP sequence. Dynamic system (DS) is used to model the sequence of PCA (principal component analysis) coefficients extracted from the sequence of HRRPs. Considering this we have proposed a model called PCA+DS. We have also proposed a segmentation algorithm which segments the HRRP sequence reliably. Akaike information criterion (AIC) used to evaluate the quality of data modeling showed that our PCA+DS model outperforms factor analysis (FA) model. In addition, target recognition results using simulated data showed that our method based on PCA+DS achieves better recognition rates compared to the method based on FA

The impacts of timing constraints on virtual channels multiplexing in interconnect networks

Interconnect networks employing wormhole-switching play a critical role in shared memory multiprocessor systems-on-chip (MPSoC) designs, multicomputer systems and system area networks. Virtual channels greatly improve the performance of wormhole-switched networks because they reduce blocking by acting as "bypass" lanes for non-blocked messages. Capturing the effects of virtual channel multiplexing has always been a crucial issue for any analytical model proposed for wormhole-switched networks. Dally has developed a model to investigate the behaviour of this multiplexing which have been widely employed in the subsequent analytical models of most routing algorithms suggested in the literature. It is indispensable to modify Dally's model in order to evaluate the performance of channel multiplexing in more general networks where restrictions such as timing constraints of input arrivals and finite buffer size of queues are common. In this paper we consider timing constraints of input arrivals to investigate the virtual channel multiplexing problem inherent in most current networks. The analysis that we propose is completely general and therefore can be used with any interconnect networks employing virtual channels. The validity of the proposed equations has been verified through simulation experiments under different working conditions

The effect of 12 weeks Anethum graveolens (dill) on metabolic markers in patients with metabolic syndrome; A randomized double blind controlled trial

Background: The clustering of metabolic abnormalities defined as metabolic syndrome is now both a public health and a clinical problem .While interest in herbal medicine has greatly increased, lack of human evidence to support efficacies shown in animals does exist. This clinical trial study designed to investigate whether herbal medicine, Anethum graveolens (dill) extract, could improve metabolic components in patients with metabolic syndrome. Methods: A double-blind, randomized, placebo-controlled trial using a parallel design was conducted. 24 subjects who had metabolic syndrome diagnostic criteria (update of ATP III) were randomly assigned to either dill extract (n = 12) or placebo (n = 12) for 3 months. Results: Across lipid component of metabolic syndrome, no significant differences in triglyceride (TG) concentration and high density lipoprotein cholesterol were seen between the two groups. However TG improved significantly from baseline (257.0 vs. 201.5p = 0.01) with dill treatment but such a significant effect was not observed in placebo group. Moreover, no significant differences in waist circumference, blood pressure and fasting blood sugar were seen between two groups after 3 months follow up period. Conclusion: In this small clinical trial in patients with metabolic syndrome, 12 weeks of dill extract treatment had a beneficial effect in terms of reducing TG from baseline. However dill treatment was not associated with a significant improvement in metabolic syndrome related markers compared to control group. Larger studies might be required to prove the efficacy and safety of long-Term administration of dill to resolve metabolic syndrome components. © 2012 Mansouri et al.; licensee BioMed Central Ltd

Causal impact analysis for app releases in google play

App developers would like to understand the impact of their own and their competitors' software releases. To address this we introduce Causal Impact Release Analysis for app stores, and our tool, CIRA, that implements this analysis. We mined 38,858 popular Google Play apps, over a period of 12 months. For these apps, we identified 26,339 releases for which there was adequate prior and posterior time series data to facilitate causal impact analysis. We found that 33% of these releases caused a statistically significant change in user ratings. We use our approach to reveal important characteristics that distinguish causal significance in Google Play. To explore the actionability of causal impact analysis, we elicited the opinions of app developers: 56 companies responded, 78% concurred with the causal assessment, of which 33% claimed that their company would consider changing its app release strategy as a result of our findings

Spontaneous Regeneration of the Mandible after Hemimandibulectomy: Report of a Case

Mandibular defects may result from many conditions such as trauma, inflammatory diseases and tumors. There are rare cases reported in the literature that have demonstrated spontaneous bone regeneration after resection of the mandible. Several factors such as age, preservation of the periosteum and genetics seem to influence spontaneous bone regeneration capacity in individuals. Evaluation of these factors may lead to a better understanding of the mechanism of spontaneous bone regeneration and also help to create new methods for bone reconstruction. The purpose of this article was to describe the spontaneous regeneration of the hemi-mandible with a well shaped condyle and coronoid after resecting a mandibular pathologic lesion in a young man

Impact of loganin on pro-inflammatory cytokines and depression- and anxiety-like behaviors in male diabetic rats

Behavioral disturbances are observed in most patients suffering from diabetes. According to some evidence, pro-inflammatory cytokines have a key role both in diabetes and behavioral disorders, such as anxiety and depression. In this study, the effect of chronic administration of loganin, as a bioflavonoid, was investigated on pro-inflammatory cytokines and depression- and anxiety-like behaviors in streptozotocin-induced diabetes in male Wistar rats. Blood levels of interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α) were assessed by enzyme-linked immunosorbent assay method. Depression- and anxiety-like behaviors were evaluated by forced swimming test (FST), elevated plus maze (EPM), and open field test (OFT), respectively. Body weight was also measured before the interventions and after the experiments in all groups. Our findings show that loganin-treated animals had significantly lower serum concentrations of IL-6 and TNF-α compared with the diabetic group. In the EPM test, loganin treatment significantly increased the percentage of the open arm time and open arm entries. Moreover, loganin treatment significantly decreased the grooming time and restored distance traveled and center crossing in the OFT. However, it decreased immobility time in the FST. Loganin treatment also significantly restored body weight gain and attenuated blood glucose changes in the diabetic rats. These results indicate that loganin possibly alleviates depression- and anxiety-like behaviors associated with diabetes through lowering the blood glucose and pro-inflammatory cytokine levels. More research is required to show the exact mechanism of antidepressant and anxiolytic effects of loganin in diabetes