Low-Complexity Detection/Equalization in Large-Dimension MIMO-ISI Channels Using Graphical Models

In this paper, we deal with low-complexity near-optimal detection/equalization in large-dimension multiple-input multiple-output inter-symbol interference (MIMO-ISI) channels using message passing on graphical models. A key contribution in the paper is the demonstration that near-optimal performance in MIMO-ISI channels with large dimensions can be achieved at low complexities through simple yet effective simplifications/approximations, although the graphical models that represent MIMO-ISI channels are fully/densely connected (loopy graphs). These include 1) use of Markov Random Field (MRF) based graphical model with pairwise interaction, in conjunction with {\em message/belief damping}, and 2) use of Factor Graph (FG) based graphical model with {\em Gaussian approximation of interference} (GAI). The per-symbol complexities are $O(K^2n_t^2)$ and $O(Kn_t)$ for the MRF and the FG with GAI approaches, respectively, where $K$ and $n_t$ denote the number of channel uses per frame, and number of transmit antennas, respectively. These low-complexities are quite attractive for large dimensions, i.e., for large $Kn_t$. From a performance perspective, these algorithms are even more interesting in large-dimensions since they achieve increasingly closer to optimum detection performance for increasing $Kn_t$. Also, we show that these message passing algorithms can be used in an iterative manner with local neighborhood search algorithms to improve the reliability/performance of $M$-QAM symbol detection

Demarcation of Ground Water Potential Zones using Remote Sensing and GIS Applications

Now-a-days, due to the high demand of water for the human needs, groundwater sources are drastically extracted and causing to least the source. The entire Yearly furnish is contributing from the utmost resource called Groundwater. Globally, groundwater is extracting primarily for the purpose of agricultural fields, domestic and for industrial water supply. Majority of the surface water is in the form of saline water which is not useful for the needs of human beings for their daily needs. Very less amount of fresh surface water is existing on the ground surface. To compensate the needs, it is essential to identify, extract and manage the groundwater which is available at different levels at different areas of the globe. Proper planning is required for the extraction of groundwater using updated technologies for using and maintaining of natural resources like water resources. The prime strive of the selected project area is to map out potential groundwater regions in the Pendlimarri Mandal of Kadapa District by using Geospatial Technology. The main impartial target of the work is to select appropriate methods and assessment criteria of the technology to identify the potential underground demarcations in geographic information system environment with help of ArcGIS software. To demarcate zones of groundwater potential, various key parameters called geology, lineament density, LU / LC, geomorphology, groundwater depths, slope and drainage pattern were prepared by utilizing remote sensing data and secondary data which can collect from concern departments. The thematic layers are to be finally integrated by using weighted overlay analysis of spatial analyst tools of data management tools of ArcMap software to delineate underground water prospects regions output layout of the project. Disparate groundwater prospects levels were categorized, from the range excellent to poor including very good, good and moderate in between. At last, decided that that the applications of geoinformatics are essential and effectively applied for the demarcation of potential zones of groundwater

Female Audit Partners and Extended Audit Reporting: UK Evidence

This study investigates whether audit partner gender is associated with the extent of auditor disclosure and the communication style regarding risks of material misstatements that are classified as key audit matters (KAMs). Using a sample of UK firms during the 2013–2017 period, our results suggest that female audit partners are more likely than male audit partners to disclose more KAMs with more details after controlling for both client and audit firm attributes. Furthermore, female audit partners are found to use a less optimistic tone and provide less readable audit reports, compared to their male counterparts, suggesting that behavioural variances between female and male audit partners may have significant implications on their writing style. Therefore, this study offers new insights on the role of audit partner gender in extended audit reporting. Our findings have important implications for audit firms, investors, policymakers and governments in relation to the development, implementation and enforcement of gender diversity

Molecular Imaging of Pulmonary Tuberculosis in an Ex-Vivo Mouse Model Using Spectral Photon-Counting Computed Tomography and Micro-CT

Assessment of disease burden and drug efficacy is achieved preclinically using high resolution micro computed tomography (CT). However, micro-CT is not applicable to clinical human imaging due to operating at high dose. In addition, the technology differences between micro-CT and standard clinical CT prevent direct translation of preclinical applications. The current proof-of-concept study presents spectral photon-counting CT as a clinically translatable, molecular imaging tool by assessing contrast uptake in an ex-vivo mouse model of pulmonary tuberculosis (TB). Iodine, a common contrast used in clinical CT imaging, was introduced into a murine model of TB. The excised mouse lungs were imaged using a standard micro-CT subsystem (SuperArgus) and the contrast enhanced TB lesions quantified. The same lungs were imaged using a spectral photoncounting CT system (MARS small-bore scanner). Iodine and soft tissues (water and lipid) were materially separated, and iodine uptake quantified. The volume of the TB infection quantified by spectral CT and micro-CT was found to be 2.96 mm(3) and 2.83 mm(3), respectively. This proof-of-concept study showed that spectral photon-counting CT could be used as a predictive preclinical imaging tool for the purpose of facilitating drug discovery and development. Also, as this imaging modality is available for human trials, all applications are translatable to human imaging. In conclusion, spectral photon-counting CT could accelerate a deeper understanding of infectious lung diseases using targeted pharmaceuticals and intrinsic markers, and ultimately improve the efficacy of therapies by measuring drug delivery and response to treatment in animal models and later in humans

A Hybrid RTS-BP Algorithm for Improved Detection of Large-MIMO M-QAM Signals

Abstract—Low-complexity near-optimal detection of large-MI-MO signals has attracted recent research. Recently, we proposed a local neighborhood search algorithm, namely reactive tabu search (RTS) algorithm, as well as a factor-graph based belief propagation (BP) algorithm for low-complexity large-MIMO detection. The motivation for the present work arises from the following two observations on the above two algorithms

Layered Tabu Search Algorithm for Large-MIMO Detection and a Lower Bound on ML Performance

Abstract—In this paper, we are concerned with low-complexity detection in large multiple-input multiple-output (MIMO) systems with tens of transmit/receive antennas. Our new contributions in this paper are two-fold. First, we propose a lowcomplexity algorithm for large-MIMO detection based on a layered low-complexity local neighborhood search. Second, we obtain a lower bound on the maximum-likelihood (ML) bit error performance using the local neighborhood search. The advantages of the proposed ML lower bound are i) it is easily obtained for MIMO systems with large number of antennas because of the inherent low complexity of the search algorithm, ii) it is tight at moderate-to-high SNRs, and iii) it can be tightened at low SNRs by increasing the number of symbols in the neighborhood definition. Interestingly, the proposed detection algorithm based on the layered local search achieves bit error performances which are quite close to this lower bound for large number of antennas and higher-order QAM.Fore.g.,ina32 × 32 V-BLAST MIMO system, the proposed detection algorithm performs close to within 1.7 dB of the proposed ML lower bound at 10 −3 BER for 16-QAM (128 bps/Hz), and close to within 4.5 dB of the bound for 64-QAM (192 bps/Hz). Keywords – Large-MIMO detection, local neighborhood search, QR decomposition, ML lower bound, higher-order QAM, high spectral efficiency. I

Harnessing Learn Rate Schedule for Adaptive Deep Learning in LoRaWAN-IoT Localization

The learning rate is one of the most crucial hyper-parameters to regulate during the training of the Deep Learning (DL) models and optimizers. Adaptive learning rate algorithms try to automate the time-consuming process of manually setting a suitable learning rate, which is still exhausting. This research uses the learn rate schedule mechanism for training DL models. The learn rate schedule mechanism updates the learning rate for each step or iteration in DL models and optimizers for problem-solving. This paper implements a learn rate schedule mechanism and hybrid learn rate schedule mechanism like piecewise, exponential decay, polynomial time, reciprocal time and cosine annealing decay as adaptive learning rate mechanisms for DL models and optimizers like Adadelta, Adam, RMSprop and Stochastic Gradient Descent with Momentum (SGDM) to improve the accuracy of Received Signal Strength Indicator (RSSI)-based localization in LoRaWAN (Long Range Wide Area Networks) based Internet of Things (IoT) networks. These techniques aim to automate the process of determining suitable learning rates that dynamically update the learning rate for each step or iteration for optimizers and deep learning models. This technique improves the model’s performance by introducing adaptability into the learning process and departing from conventional set learning rates. The mathematical model of the learning rate schedule is derived and formulated with adaptive deep learning rate models to map with the LoRaWAN RSSI-based localization datasets for accessing the performance parameters. The learn rate schedule for different types of localization datasets is also analyzed. The results were compared for all the learning rate schedule mechanisms with the default parameter settings of DL models, and it gives a better accuracy of 98.98%, which is higher than the existing models