Surge of the Innovative Quest in the first lockdown period due to the Pandemic Effect

When the entire world is reeling under the COVID 19 pandemic effect and the tensed human race is struggling to return back to the normalcy of life, the one thing which has become very active is the grey cells of our brain. The pandemic effect has cut down our physical limits due to the movement constraints. But it is thankfully unable to restrict the ticking of the grey cells of the human brain. As is said, “Necessity is the mother of the invention”. Sure enough!! We can be extremely pleased to know that the innovative surge in science and technology continues unabated in this lockdown period. The prime requirement of the pandemic effect is social distancing, less physical contact and keeping ourselves away from infection by corona virus. Keeping this necessity in mind, the doctors, the engineers, the researchers as well as the students’ community are keeping themselves busy in pumping out the solutions to the currently faced problems. The outputs include automatic masks machines, low cost PPE’s, automatic wash basins, suitable ventilators, sanitizer tunnels etc. This review paper looks into the innovative surge already made and what more can be churned out for the effective social safety in this tensed pandemic effect. The most awaited news as of now is the successful implementation of an effective vaccine and cost effective drugs which can help the human beings breathe easy. The pandemic effect has also showed us the way for a cleaner and greener nature. It is now a challenge to the intellectual world to come up with inexpensive, innovative and smart solutions which will make our beautiful planet safer, greener, cleaner and worthier to live in.

IoT Serverless Computing at the Edge: Open Issues and Research Direction

Novel computing paradigms aim to enable better hardware utilization, allowing a greater number of applications to be executed on the same physical resources. Serverless computing is one example of such an emerging paradigm, enabling faster development, more efficient resource usage, as well as no requirements for infrastructure management by end users. Recently, efforts have been made to utilize serverless computing at the network edge, primarily focusing on supporting Internet of Things (IoT) workloads. This study explores open issues, outlines current progress, and summarizes existing research findings about serverless edge computing for IoT by analyzing 67 relevant papers published between 01.01.2015 and 01.09.2021. We discuss the state-of-the-art research in 8 subject areas relevant to the use of serverless at the network edge, derived through the analysis of the selected articles. Results show that even though there is a noticeable interest for this topic, further work is needed to adapt serverless to the resource constrained environment of the edge.

Fault-Tolerant Placement of Additional Mesh Nodes in Rural Wireless Mesh Networks: A Minimum Steiner Tree Based Centre of Mass With Bounded Edge Length

Wireless mesh networks are presented as an attractive solution to reduce the digital divide between rural and developed areas. In a multi-hop fashion, they can cover larger spaces. However, their planning is subject to many constraints including robustness. In fact, the failure of a node may result in the partitioning of the network. The robustness of the network is therefore achieved by carefully placing additional nodes. This work tackles the problem of additional nodes minimization when planning bi and tri-connectivity from a given network. We propose a vertex augmentation approach inspired by the placement of Steiner points. The idea is to incrementally determine cut vertices and bridges in the network and to carefully place additional nodes to ensure connectivity, bi and tri-connectivity. The approach relies on an algorithm using the centre of mass of the blocks derived after the partitioning of the network. The proposed approach has been compared to a modified version of a former approach based on the Minimum Steiner Tree. The different experiments carried out show the competitiveness of the proposed approach to connect, bi-connect, and tri-connect the wireless mesh networks.

Prediction of Breast Cancer images Classification Using Bidirectional Long Short Term Memory and Two-Dimensional Convolutional Neural network

Breast cancer is most prevalent among women around the world and Nigeria is no exception in this menace. The increased in survival rate is due to the dramatic advancement in the screening methods, early diagnosis, and discovery in cancer treatments. There is an improvement in different strategies of breast cancer classification. A model for training deep neural networks for classification of breast cancer in histopathological images was developed in this study. However, this images are affected by data unbalance with the support of active learning. The output of the neural network on unlabeled samples was used to calculate weighted information entropy. It is utilized as uncertainty score for automatic selecting both samples with high and low confidence. A threshold that decays over iteration number is used to decide which high confidence samples should be concatenated with manually labeled samples and then used infine-tuning of convolutional neural network. The neural network was optionally trained using weighted cross-entropy loss to better cope with bias towards the majority class. The developed model was compared with the existing model. The accuracy level of 98.3% was achieved for the developed model while the existing model 93.97%. The accuracy gain of 4.33%. was achieved as performance in the prediction of breast cancer .

Assessment of RCS-specific SNR and Loglikelihood Function in Detecting Low-observable Targets and Drones Illuminated by a Low Probability of Intercept Radar Operating in Littoral Regions

The objective of this study is to deduce signal-to-noise ratio (SNR) based loglikelihood function involved in detecting low-observable targets (LoTs) including drones Illuminated by a low probability of intercept (LPI) radar operating in littoral regions. Detecting obscure targets and drones and tracking them in near-shore ambient require ascertaining signal-related track-scores determined as a function of radar cross section (RCS) of the target. The stochastic aspects of the RCS depend on non-kinetic features of radar echoes due to target-specific (geometry and material) characteristics; as well as, the associated radar signals signify randomly-implied, kinetic signatures inasmuch as, the spatial aspects of the targets fluctuate significantly as a result of random aspect-angle variations caused by self-maneuvering and/or by remote manipulations (as in drones). Hence, the resulting mean RCS value would decide the SNR and loglikelihood ratio (LR) of radar signals gathered from the echoes and relevant track-scores decide the performance capabilities of the radar. A specific study proposed here thereof refers to developing computationally- tractable algorithm(s) towards detecting and tracking hostile LoTs and/or drones flying at low altitudes over the sea (at a given range, R) in littoral regions by an LPI radar. Estimation of relevant detection-theoretic parameters and decide track-scores in terms of maximum likelihood (ML) estimates are presented and discussed.

Sensors and wrong values

In the world of IoT and BigData, sensor based data collection is a really important domain. Using these tools it is possible to stow large amounts of data collection sensors in a factory or in nature in harsh environments. However, in order to obtain valuable information from these tools, it is important that potentially wrong data is discovered and handled. Automated exploration of wrong data is not a trivial task, even if similar measurements are performed in parallel with spatial differences. We present the difficulties of revealing defected data and suggest easy-to-implement procedures for detecting and handing them. We also draw attention to the potential disadvantages of these methods based on the given results.

Conceiving Inferential Prototypes of MIMO Channel Models via Buckingham’s Similitude Principle for 30+ GHz through THz Spectrum

Facilitating newer bands of ‘unused’ segments (windows) of RF spectrum falling in the mm-wave range (above 30+ GHz) and seeking usable stretches across unallocated THz spectrum, could viably be considered for Multiple Input Multiple Output (MIMO) communications. This could accommodate the growing needs of multigigabit 3G/4G applications in outdoor-based backhauls in picocellular networks and in indoor-specific multimedia networking. However, in contrast with cellular and Wi-Fi, wireless systems supporting sub-mm wavelength transreceive communications in the outdoor electromagnetic (EM) ambient could face “drastically different propagation geometry”; also, in indoor contexts, envisaging pertinent spatial-multiplexing with directional, MIMO links could pose grossly diverse propagation geometry across a number of multipaths; as such, channel-models based on stochastic features of diverse MIMO-specific links in the desired test spectrum of mm-wave/THz band are sparsely known and almost non-existent. To alleviate this niche, a method is proposed here to infer sub-mm band MIMO channel-models (termed as “prototypes”) by judiciously sharing “similarity” of details available already pertinent to traditional “models” of lower-side EM spectrum, (namely, VLF through micro-/mm-wave). Relevant method proposed here relies on the “principle of similitude” due to Edgar Buckingham. Exemplar set of “model-to-(inferential)-prototype” transformations are derived and prescribed for an exhaustive set of fading channel models as well as, towards estimating path-loss of various channel statistics in the high-end test spectrum.

Towards a Description Synthesis of the Entanglement of the Substrate with the Interconnection Network, for Fast Modeling of 3D RF circuits

3D chip stacking is considered known to overcome conventional 2D-IC issues, using Trough Silicon Via (TSVs) to ensure vertical signal transmission between data. If the electrical behaviour of 3D interconnections (redistribution metal lines and through silicon vias) used in 3D IC stack technologies are to be explored in this paper, the substrate itself is of interest, via Green Kernels by solving Poisson's equation analytically. Using this technique, the substrate coupling and loss in IC's can be analysed. We implement our algorithms in MATLAB. This method has been already used; but, it permits to extract impedances for a stacked uniform layers substrate. We have extended for any numbers of embedded contacts, of any shape. On a second hand, we grasp the background noise between any two points, in the bulk, or at the surface, from a transfer impedance extraction technique. With an analog algorithm, a strength of this work, we calculate unsteady solutions of the heat equation, using a spreading resistance concept. This method has been adapted to stacked layers. With this general tool of impedance field, we investigate on the problems encountered by interconnects, especially the vias, the substrate, and their entanglement. A calculation of thermal mechanical stresses and their effects on substrate crack (max and min stresses), devices (i.e: transistors) and hotspots, are made to track the performance. But, to well understand the interconnection incidence on 3D system performances, it is important to consider the whole electrical context; it seems relevant to consider the possible couplings between vias, not only by the electromagnetic field, but also by any possible energy transfer between interconnects; more generally, one of actual problem is to determine where the energy is really confined in such 3D circuits, before find solutions to limit pollutions coming from electro-magneto -thermal phenomena or background noises.

Estimating Query Timings in Elasticsearch

In a shared Elasticsearch environment it can be useful to know how long a particular query will take to execute. This information can be used to enforce rate limiting or distribute requests equitably among multiple clusters. Elasticsearch uses multiple Lucene instances on multiple hosts as an underlying search engine implementation, but this abstraction makes it difficult to predict execution with previously known predictors such as the number of postings. This research investigates the ability of different pre-retrieval statistics, available through Elasticsearch, to accurately predict the execution time of queries on a typical Elasticsearch cluster. The number of terms in a query and the Total Term Frequency (TTF) from Elasticsearch’s API are found to significantly predict execution time. Regression models are then built and compared to find the most accurate method for predicting query time.

Interpretable machine learning approach for predicting COVID-19 risk status of an individual

This study aimed to ascertained using Statistical feature selection methods and interpretable Machine learning models, the best features that predict risk status (“Low”, “Medium”, “High”) to COVID-19 infection. This study utilizes a publicly available dataset obtained via; online web-based risk assessment calculator to ascertain the risk status of COVID-19 infection. 57 out of 59 features were first filtered for multicollinearity using the Pearson correlation coefficient and further shrunk to 55 features by the LASSO GLM approach. SMOTE resampling technique was used to incur the problem of imbalanced class distribution. The interpretable ML algorithms were implored during the classification phase. The best classifier predictions were saved as a new instance and perturbed using a single Decision tree classifier. To further build trust and explainability of the best model, the XGBoost classifier was used as a global surrogate model to train predictions of the best model. The XGBoost individual’s explanation was done using the SHAP explainable AI-framework. Random Forest classifier with a validation accuracy score of 96.35 % from 55 features reduced by feature selection emerged as the best classifier model. The decision tree classifier approximated the best classifier correctly with a prediction accuracy score of 92.23 % and Matthew’s correlation coefficient of 0.8960. The XGBoost classifier approximated the best classifier model with a prediction score of 99.7 %. This study identified COVID-19 positive, COVID-19 contacts, COVID-19 symptoms, Health workers, and Public transport count as the five most consistent features that predict an individual’s risk exposure to COVID-19.