Improved Signal Detection for Ambient Backscatter Communications

In ambient backscatter communication (AmBC) systems, passive tags connect to a reader by reflecting an ambient radio frequency (RF) signal. However, the reader may not know the channel states and RF source parameters and can experience interference. The traditional energy detector (TED) appears to be an ideal solution. However, it performs poorly under these conditions. To address this, we propose two new detectors: (1) A joint correlation-energy detector (JCED) based on the first-order correlation of the received samples and (2) An improved energy detector (IED) based on the p-th norm of the received signal vector. We compare the performance of the IED and TED under generalized noise modeled using the McLeish distribution and derive a general analytical formula for the area under the receiver operating characteristic (ROC) curves. Based on our results, both detectors outperform TED. For example, the probability of detection with a false alarm rate of 1% for JCED and IED is 14% and 5% higher, respectively, compared to TED. These gains are even higher using the direct interference cancellation (DIC) technique, with increases of 16% and 7%, respectively. Overall, our proposed detectors offer better performance than the TED, making them useful tools for improving AmBC system performance.Comment: This paper has got Major Revision by IEEE TGC

Enhancing AmBC Systems with Deep Learning for Joint Channel Estimation and Signal Detection

The era of ubiquitous, affordable wireless connectivity has opened doors to countless practical applications. In this context, ambient backscatter communication (AmBC) stands out, utilizing passive tags to establish connections with readers by harnessing reflected ambient radio frequency (RF) signals. However, conventional data detectors face limitations due to their inadequate knowledge of channel and RF-source parameters. To address this challenge, we propose an innovative approach using a deep neural network (DNN) for channel state estimation (CSI) and signal detection within AmBC systems. Unlike traditional methods that separate CSI estimation and data detection, our approach leverages a DNN to implicitly estimate CSI and simultaneously detect data. The DNN model, trained offline using simulated data derived from channel statistics, excels in online data recovery, ensuring robust performance in practical scenarios. Comprehensive evaluations validate the superiority of our proposed DNN method over traditional detectors, particularly in terms of bit error rate (BER). In high signal-to-noise ratio (SNR) conditions, our method exhibits an impressive approximately 20% improvement in BER performance compared to the maximum likelihood (ML) approach. These results underscore the effectiveness of our developed approach for AmBC channel estimation and signal detection. In summary, our method outperforms traditional detectors, bolstering the reliability and efficiency of AmBC systems, even in challenging channel conditions.Comment: Accepted for publication in the IEEE Transactions on Communication

A robust domain partitioning intrusion detection method

The capacity for data mining algorithms to learn rules from data is influenced by, inter-alia, the random nature of training and test data as well as by the diversity of domain partitioning models. Isolating normal from malicious data traffic across networks is one regular task that is naturally affected by that randomness and diversity. We propose a robust algorithm Sample-Measure-Assess (SMA) that detects intrusion based on rules learnt from multiple samples. We adapt data obtained from a set of simulations, capturing data attributes identifiable by number of bytes, destination and source of packets, protocol and nature of data flows (normal and abnormal) as well IP addresses. A fixed sample of 82,332 observations on 27 variables was drawn from a superset of 2.54 million observations on 49 variables and multiple samples were then repeatedly extracted from the former and used to train and test multiple versions of classifiers, via the algorithm. With two class labels–binary and multi-class, the dataset presents a classic example of masked and spurious groupings, making an ideal case for concept learning. The algorithm learns a model for the underlying distributions of the samples and it provides mechanics for model assessment. The settings account for our method’s novelty–i.e., ability to learn concept rules from highly masked to highly spurious cases while observing model robustness. A comparative analysis of Random Forests and individually grown trees show that we can circumvent the former’s dependence on multicollinearity of the trees and their individual strength in the forest by proceeding from dimensional reduction to classification using individual trees. Given data of similar structure, the algorithm can order the models in terms of optimality which, means our work can contribute towards understanding the concept of normal and malicious flows across tools. The algorithm yields results that are less sensitive to violated distributional assumptions and, hence, it yields robust parameters and provides a generalisation that can be monitored and adapted to specific low levels of variability. We discuss its potential for deployment with other classifiers and potential for extension into other applications, simply by adapting the objectives to specific conditions

Visualizing the vibration effect on the tandem-pulsed gas metal arc welding in the presence of surface tension active elements

In this study, a three-dimensional model of the tandem-pulsed gas metal arc welding process is simulated to investigate the heat transfer and material flow in the presence of vibration and the surface tension active elements. The simulation results are in agreement with optical microscopy images of weld cross-section obtained with different conditions, including with and without vibration-assisted welding. The material flow is visualized using 2D and 3D streamlines on the temperature contour maps. It is found that during the operation of pulsed welding, the heat follows a very stable pattern, although the fluid streams continuously change in the rear region of the weld pool, which is responsible for the final geometry of penetration. Consideration of the effect of surface tension active elements on the Marangoni force improves the simulation results noticeably. A novel approach addresses the effect of sulfur content that comes from both workpiece and filler material. Applying the vibration leads to lower heat input by affecting the free surface behavior and plays an important role in the penetration shape change. © 202

Alteration in CD8+ T cell subsets in enterovirus-infected patients: An alarming factor for type 1 diabetes mellitus

Type 1 diabetes is a multi-factorial disease that can develop due to the combination of genetic and environmental factors. Viruses, particularly enteroviruses, are major environmental candidates in the pathogenesis of type 1 diabetes, even though the mechanisms of pathogenicity of these viruses and their effects on the immune system have not been understood very well yet. Previous studies show that any imbalance in the population of different lymphocyte subsets could develop autoimmune diseases. Our theory is that enteroviral infection causes an impairment in the distribution of lymphocyte subtypes and consequently results in the diabetes onset in some individuals. Therefore, in this project, we evaluated the distribution of T CD8+ lymphocytes and their subsets in type 1 diabetes patients. This study was conducted to investigate the relationship between enteroviral infection and type 1 diabetes mellitus in an Iranian population, and suggestion a predicting approach for susceptible subjects

Evaluacija svojstava vezanja sluzi sjemenki biljke Plantago psyllium

Mucilage extracted from Plantago psyllium seeds was evaluated for inertness and safety parameters. The suitability of psyllium mucilage for a pharmaceutical binder was assessed in paracetamol tablets. Properties of the granules prepared using different concentrations of psyllium mucilage was compared with PVP and tragacanth. Psyllium mucilage at 5 % (m⁄m) level was found to be comparable with 3 % (m⁄m) of PVP. Investigated paracetamol tablets indicated that psyllium mucilage can retard the drug release.U radu je ispitivana neškodljivost i sigurnost uporabe sluzi ekstrahirane iz sjemenki biljke Plantago psyllium. Primjenjivost te sluzi kao veziva u farmaceutskim pripravcima ispitana je na tabletama paracetamola. Granule pripravljene s različitim koncentracijama sluzi uspoređene su s granulama s PVP-om i tragakantom. Sluz s udjelom 5 % (m/m) usporediva je s otopinom PVP-a masenog udjela 3 %. Pripravljene tablete paracetamola ukazuju na to da ispitivana sluz može usporiti oslobađanje lijeka

A statistical downscaling framework for environmental mapping

In recent years, knowledge extraction from data has become increasingly popular, with many numerical forecasting models, mainly falling into two major categories—chemical transport models (CTMs) and conventional statistical methods. However, due to data and model variability, data-driven knowledge extraction from high-dimensional, multifaceted data in such applications require generalisations of global to regional or local conditions. Typically, generalisation is achieved via mapping global conditions to local ecosystems and human habitats which amounts to tracking and monitoring environmental dynamics in various geographical areas and their regional and global implications on human livelihood. Statistical downscaling techniques have been widely used to extract high-resolution information from regional-scale variables produced by CTMs in climate model. Conventional applications of these methods are predominantly dimensional reduction in nature, designed to reduce spatial dimension of gridded model outputs without loss of essential spatial information. Their downside is twofold—complete dependence on unlabelled design matrix and reliance on underlying distributional assumptions. We propose a novel statistical downscaling framework for dealing with data and model variability. Its power derives from training and testing multiple models on multiple samples, narrowing down global environmental phenomena to regional discordance through dimensional reduction and visualisation. Hourly ground-level ozone observations were obtained from various environmental stations maintained by the US Environmental Protection Agency, covering the summer period (June–August 2005). Regional patterns of ozone are related to local observations via repeated runs and performance assessment of multiple versions of empirical orthogonal functions or principal components and principal fitted components via an algorithm with fully adaptable parameters. We demonstrate how the algorithm can be extended to weather-dependent and other applications with inherent data randomness and model variability via its built-in interdisciplinary computational power that connects data sources with end-users