Evaluation of Linear Precoding Schemes for Cooperative Multi-Cell MU MIMO in Future Mobile Communication Systems

In Mobile Communication Systems, inter-cell interference becomes one of the challenges that degrade the system’s performance, especially in the region with massive mobile users. The linear precoding schemes were proposed to mitigate interferences between the base stations (inter-cell). These schemes are categorized into linear and non-linear;this study focused on linear precoding schemes, which are grounded into three types, namely Zero Forcing (ZF), Block Diagonalization (BD), and Signal Leakage Noise Ratio (SLNR). The study included the Cooperative Multi-cell Multi Input Multi Output (MIMO) System, whereby each Base Station serves more than one mobile station and all Base Stations on the system are assisted by each other by shared the Channel State Information (CSI). Based on the Multi-Cell Multiuser MIMO system, each Base Station on the cell is intended to maximize the data transmission rate by its mobile users by increasing the Signal Interference to Noise Ratio after the interference has been mitigated due to the usefully of linear precoding schemes on the transmitter. Moreover, these schemes used different approaches to mitigate interference. This study mainly concentrates on evaluating the performance of these schemes through the channel distribution models such as Ray-leigh and Rician included in the presence of noise errors. The results show that the SLNR scheme outperforms ZF and BD schemes overall scenario. This implied that when the value of SNR increased the performance of SLNR increased by 21.4% and 45.7% for ZF and BD respectively.

An Application of Machine Learning Methods to Detect Mango Varieties

The mango, a fruit of immense economic and dietary significance in numerous tropical and subtropical regions, plays a pivotal role in our agricultural landscape. Accurate identification is not just a necessity, but a crucial step for effective classification, sorting, and marketing. This study delves into the potential of machine learning for this task, comparing the performance of four models: MobileNetV2, Xception, VGG16, and ResNet50V2. These models were trained on a dataset of annotated mango images, and their performance was evaluated using precision, accuracy, F1 score, and recall, which are standard metrics for image classification. The Xception model, with its exceptional performance, outshone the other models on all performance indicators. It achieved a staggering accuracy of 99.47%, an F1 score of 99.43%, and a recall of 99.43%, showcasing its remarkable ability to accurately identify mango varieties. MobileNetV2 followed closely with performances of 98.95% accuracy, 98.85% F1 score, and 98.86% recall. ResNet50V2 also delivered satisfactory results with 97.39% accuracy, 97.08% F1 score, and 97.17% recall. VGG16, however, was the least effective, with a precision rate of 83.25%, an F1 score of 83.25%, and a recall of 85.47%. These results confirm the superiority of the Xception model in detecting mango varieties. Its advanced architecture allows it to capture more distinguishing features of mango images, leading to greater precision and reliability. Xception’s robustness in identifying true positives is another advantage, minimizing false positives and contributing to more accurate classification. This study highlights the promising potential of machine learning, particularly the Xception model, for accurately identifying mango varieties.

Learning Hand Latent Features for Unsupervised 3D Hand Pose Estimation

Recent hand pose estimation methods require large numbers of annotated training data to extract the dynamic information from a hand representation.Nevertheless,precise and dense annotation on the real data is difficult to come by and the amount of information passed to the training algorithm is significantly higher.This paper presents an approach to developing a hand pose estimation system which can accurately regress a 3D pose in an unsupervised manner.The whole process is performed in three stages.Firstly,the hand is modelled by a novel latent tree dependency model (LTDM) which transforms internal joints location to an explicit representation.Secondly,we perform predictive coding of image sequences of hand poses in order to capture latent features underlying a given image without supervision.A mapping is then performed between an image depth and a generated representation.Thirdly,the hand joints are regressed using convolutional neural networks to finally estimate the latent pose given some depth map.Finally,an unsupervised error term which is a part of the recurrent architecture ensures smooth estimation of the final pose.To demonstrate the performance of the proposed system,a complete experiment was conducted on three challenging public datasets,ICVL,MSRA,and NYU.The empirical results show the significant performance of our method which is comparable or better than the state-of-the-art approaches.

Citizen sensors or extreme publics? Transparency and accountability interventions on the mobile geoweb

Transparency and Accountability(T&A)interventions are emergent social technologies in middle and low-income countries.They bring together citizen sensors,mobile communications,geo-browsers and social organization to raise public awareness on the extent of governance deficits,and monitor government’s(in)action.Due to their novelty,almost all we know about the effectiveness of T&A interventions comes from gray literature.Can citizen sensors radically increase the transparency of the state,or are changes brought about by T&A interventions more likely to be incremental?We review the literature on transparency policies and describe their drivers,characteristics and supply-demand dynamics.We discuss promising cases of T&A interventions in East Africa,the empirical focus of an on-going collaborative research program.We conclude that the effect of T&A interventions is more likely to be incremental and mediated by existing organizations and professional users who populate the space between the state and citizens.Two elements at the interface between supply and demand seem rather crucial for designers of T&A interventions:accountability-relevant data and extreme publics.

Vegetation dynamics in response to human and climatic factors in the Tanzanian Coast

This study of vegetation dynamics in the coastal region of Tanzania provides a fundamental basis to better understand the nature of the factors that underlie observed changes.The Tanzanian coast,rich in biodiversity,is economically and environmentally important although the understanding of the nature and causes of vegetation change is very limited.This paper presents an investigation of the relationship between vegetation dynamics in response to climate variations and human activities using Moderate Resolution Imaging Spectro-radiometer(MODIS),Normalized Difference Vegetation Index(NDVI),meteorological,and Globeland30 Landsat data sets.Spatio-temporal trends and the relationship of NDVI to selected meteorological variables were statistically analyzed for the period 2000-2018 using the Mann-Kendall test and Pearson correlation respectively.The results reveal a significant positive trend in temperature(/?>0,Z=2.87)and a non-significant trend in precipitation(|Z|<1.96).A positive relationship between NDVI and precipitation is observed.Coastal Tanzania has therefore experienced increased temperatures and variable moisture conditions which threaten natural vegetation and ecosystems at large.Classified land cover maps obtained from GlobeLand30 were analyzed to identify the nature and scale of human impact on the land.The analysis of land use and land cover in the region reveals an increase in cultivated land,shrubland,grassland,built-up land and bare land,while forests,wetland and water all decreased between 2000 and 2020.The decrease in forest vegetation is attributable to the fact that most livelihoods in the region are dependent on agriculture and harvesting of forest products(firewood,timber,charcoal).The findings of this study highlight the need for appropriate land-use planning and sustainable utilization of forest resources.

Gene Expression Data Classification Using Consensus Independent Component Analysis

We propose a new method for tumor classification from gene expression data, which mainly contains three steps. Firstly, the original DNA microarray gene expression data are modeled by independent component analysis （ICA）. Secondly, the most discriminant eigenassays extracted by ICA are selected by the sequential floating forward selection technique. Finally, support vector machine is used to classify the modeling data. To show the validity of the proposed method, we applied it to classify three DNA microarray datasets involving various human normal and tumor tissue samples. The experimental results show that the method is efficient and feasible.