Blind estimation of multipath channel parameters: a modal analysisapproach

We propose a novel approach to efficiently estimate multipath channel parameters, which is particularly useful in sparse multipath channels. Conventional methods do not fully exploit the inherent structure present in the combined channel response; the excess number of parameters to be estimated by conventional methods makes the identification difficult. By utilizing a priori knowledge of the transmission data pulse, the channel identification problem is transformed into the mode estimation problem. Then, the parameters directly related to the multipath propagation are extracted in the the modal analysis framework, and hence, the number of estimation parameters are significantly reduced. Finally, the multipath channel parameters are obtained by inverse-transforming the mode parameters. Simulation results show significant improvement in the normalized mean square error over existing approaches     

Automated Deep Learning Empowered Breast Cancer Diagnosis Using Biomedical Mammogram Images

Biomedical image processing is a hot research topic which helps to majorly assist the disease diagnostic process. At the same time, breast cancer becomes the deadliest disease among women and can be detected by the use of different imaging techniques. Digital mammograms can be used for the earlier identification and diagnostic of breast cancer to minimize the death rate. But the proper identification of breast cancer has mainly relied on the mammography findings and results to increased false positives. For resolving the issues of false positives of breast cancer diagnosis, this paper presents an automated deep learning based breast cancer diagnosis (ADL-BCD) model using digital mammograms. The goal of the ADL-BCD technique is to properly detect the existence of breast lesions using digital mammograms. The proposed model involves Gaussian filter based pre-processing and Tsallis entropy based image segmentation. In addition, Deep Convolutional Neural Network based Residual Network (ResNet 34) is applied for feature extraction purposes. Specifically, a hyper parameter tuning process using chimp optimization algorithm (COA) is applied to tune the parameters involved in ResNet 34 model. The wavelet neural network (WNN) is used for the classification of digital mammograms for the detection of breast cancer. The ADL-BCD method is evaluated using a benchmark dataset and the results are analyzed under several performance measures. The simulation outcome indicated that the ADL-BCD model outperforms the state of art methods in terms of different measures.     

Excitonic Mechanism of Local Phase Transformations by Optical Pumping

Transformations of cooperative electronic states by impacts of optical pumping and/or electrostatic doping is a new mainstream in physics of correlated systems. Here we present a semi-phenomenological modeling of spatio-temporal effects in a system where the light absorption goes through a channel creating the excitons—intra-molecular ones or bound electron–hole pairs—and finally the condensate of optical excitons feeds and stimulates phase transformations. Interacting with a near-critical order parameter and deformations, the excitons are subject to self-trapping. That locally enhances their density which can surpass a critical value to trigger the phase transformation, even if the mean density is below the required threshold. The model can be used e.g. as a simplified version of optically induced neutral-ionic transitions in organic chain compounds.     

On a decidable generalized quantifier logic corresponding to a decidable fragment of first-order logic

Van Lambalgen (1990) proposed a translation from a language containing a generalized quantifierQ into a first-order language enriched with a family of predicatesR _i, for every arityi (or an infinitary predicateR) which takesQxg(x, y₁,..., y_n) to x(R(x, y₁,..., y₁) (x,y₁,...,y_n)) (y ₁,...,y_n are precisely the free variables ofQx). The logic ofQ (without ordinary quantifiers) corresponds therefore to the fragment of first-order logic which contains only specially restricted quantification. We prove that it is decidable using the method of analytic tableaux. Related results were obtained by Andréka and Németi (1994) using the methods of algebraic logic.     

Sea-level rise and its possible impacts given a 'beyond 4°C world' in the twenty-first century

The range of future climate-induced sea-level rise remains highly uncertain with continued concern that large increases in the twenty-first century cannot be ruled out. The biggest source of uncertainty is the response of the large ice sheets of Greenland and west Antarctica. Based on our analysis, a pragmatic estimate of sea-level rise by 2100, for a temperature rise of 4°C or more over the same time frame, is between 0.5 m and 2 m--the probability of rises at the high end is judged to be very low, but of unquantifiable probability. However, if realized, an indicative analysis shows that the impact potential is severe, with the real risk of the forced displacement of up to 187 million people over the century (up to 2.4% of global population). This is potentially avoidable by widespread upgrade of protection, albeit rather costly with up to 0.02 per cent of global domestic product needed, and much higher in certain nations. The likelihood of protection being successfully implemented varies between regions, and is lowest in small islands, Africa and parts of Asia, and hence these regions are the most likely to see coastal abandonment. To respond to these challenges, a multi-track approach is required, which would also be appropriate if a temperature rise of less than 4°C was expected. Firstly, we should monitor sea level to detect any significant accelerations in the rate of rise in a timely manner. Secondly, we need to improve our understanding of the climate-induced processes that could contribute to rapid sea-level rise, especially the role of the two major ice sheets, to produce better models that quantify the likely future rise more precisely. Finally, responses need to be carefully considered via a combination of climate mitigation to reduce the rise and adaptation for the residual rise in sea level. In particular, long-term strategic adaptation plans for the full range of possible sea-level rise (and other change) need to be widely developed.     

SA-Mediated Regulation and Control of Abiotic Stress Tolerance in Rice

Environmental or abiotic stresses are a common threat that remains a constant and common challenge to all plants. These threats whether singular or in combination can have devastating effects on plants. As a semiaquatic plant, rice succumbs to the same threats. Here we systematically look into the involvement of salicylic acid (SA) in the regulation of abiotic stress in rice. Studies have shown that the level of endogenous salicylic acid (SA) is high in rice compared to any other plant species. The reason behind this elevated level and the contribution of this molecule towards abiotic stress management and other underlying mechanisms remains poorly understood in rice. In this review we will address various abiotic stresses that affect the biochemistry and physiology of rice and the role played by SA in its regulation. Further, this review will elucidate the potential mechanisms that control SA-mediated stress tolerance in rice, leading to future prospects and direction for investigation.     

Embedded nanoFeCu for sewage treatment: Laboratory-scale and pilot studies

Bimetallic nanoparticles have been widely studied for wastewater treatment, but the study of nanoFeCu for sewage treatment is minimal. In the previous work, ammonia was removed by nanoFeCu via an oxidation reaction, and nitrogen gas was released. However, the performance and reusability of nanoFeCu in treating industrial wastewater have not been reported elsewhere. This study revealed the performance of nanoFeCu for sewage treatment on both laboratory-scale and pilot-scale for the first time. A varied mass of embedded nanoFeCu (eFeCu4) was exposed to sewage water, and the quality of the effluent was measured in terms of ammonia, biological oxygen demand (BOD), and chemical oxygen demand (COD) removal. Fe²⁺ and Cu²⁺ concentrations were measured to determine the stability of eFeCu4 in nine reuse cycles. Results showed that the laboratory-scale experiment removed 20%–30% ammonia from sewage. A similar removal rate was reported in all nine cycles of reuse, which confirmed the usability and reliability of eFeCu4. In the pilot-scale study, ammonia was removed from ~22.3 to ~4.8 mg/L, while BOD and COD were reduced from ~204 to ~56 mg/L and ~71 to ~39.7 mg/L, respectively. The treated effluent quality complies with the effluent discharge standard of Malaysia, and it is also comparable with the effluent quality at sewage treatment plants in Malaysia and overseas. In conclusion, nanoFeCu could be an alternative method for sewage treatment due to its stability and pollutant removal performance. A sustainability and cost-effectiveness study should be conducted to determine the feasibility of a full-scale application.     

Knowing when not to use the Internet: Shyness and adolescents’ on-line and off-line interactions with friends

The goal of the study was to explore the content of on-line and off-line peer interactions among shy and non-shy adolescents. Participants were 148 ten-to-eighteen year old adolescents in Rome, Italy (n = 98) and Ottawa, Canada (n = 50). Participants completed self reports of shyness and loneliness and web logs of their interactions with friends both in person and on-line. Among the results, there was little general difference in the general content and emotion expressed during the two modalities of interaction with friends, both of which were used in a wide variety of ways. Importantly, shy participants used the on-line modality more extensively than their non-shy counterparts to express negative emotions and to convey content regarding negative exchanges with peers. Such use of electronic communication may be an important contributor to their loneliness.     

Bayesian decision feedback for segmentation of binary images

We present real-time algorithms for the segmentation of binary images modeled by Markov mesh random fields (MMRFs) and corrupted by independent noise. The goal is to find a recursive algorithm to compute the maximum a posteriori (MAP) estimate of each pixel of the scene using a fixed lookahead of D rows and D columns of the observations. First, this MAP fixed-lag estimation problem is set up and the corresponding optimal recursive (but computationally complex) estimator is derived. Then, both hard and soft (conditional) decision feedbacks are introduced at appropriate stages of the optimal estimator to reduce the complexity. The algorithm is applied to several synthetic and real images. The results demonstrate the viability of the algorithm both complexity-wise and performance-wise, and show its subjective relevance to the image segmentation problem.