A Differential Turbo Detection Aided Sphere Packing Modulated Space-Time Coding Scheme

A signal construction method that combines orthogonal design with sphere packing has recently shown useful performance improvements over the conventional orthogonal design. In this contribution, we extend this concept and propose a novel Sphere Packing (SP) modulated differential Space-Time Block Coded (DSTBC) scheme, referred to here as (DSTBC-SP), which shows performance advantages over conventional DSTBC schemes. We also demonstrate that the performance of DSTBC-SP systems can be further improved by concatenating sphere packing aided modulation with channel coding and performing SP-symbol-to bit demapping as well as channel decoding iteratively. We also investigate the convergence behaviour of this concatenated scheme with the aid of Extrinsic Information Transfer (EXIT) Charts. The proposed turbo-detected DSTBC-SP scheme exhibits a ’turbo-cliff’ at Eb/N0 = 6dB and provides Eb/N0 gains of 23.7dB and 1.7dB at a BER of 10?5 over an equivalent-throughput uncoded DSTBC-SP scheme and a turbo-detected QPSK modulated DSTBC scheme, respectively

Layanan Bimbingan Kelompok Dengan Teknik Self Management Untuk Mengurangi Perilaku Terlambat Masuk Sekolah (Studi Pada Siswa Kelas X SMA 1 Gebog Tahun 2014/2015)

Penelitian ini dilatarbelakangi oleh fenomena keterlambatan siswaKelas X SMA 1 Gebog yang semakin sering terjadi. Tujuan penelitianini adalah mengetahui efektifitas layanan bimbingan kelompok dengan teknik self management untuk mengurangi terlambat masuksekolah pada siswa Kelas X SMA 1 Gebog. Ada 8 siswa sebagaisubjek penelitian yang dipilih berdasarkan frekuensi keterlambatanmasuk sekolah. Penelitian ini dirancang dalam dua siklus dan dimasing-masing siklus terdapat 3 kali pertemuan. Hasil penelitianmenunjukkan bahwa pada pra siklus skor rata-rata adalah 41 menurunpada siklus I menjadi 28,63 dengan kategori cukup, dan pada siklus IImenjadi 13,13 atau sangat rendah dengan kategori sangat baik. Adapenuruanan dari siklus I ke siklus II yaitu sebesar 15,5 atau secarakeseluruhan 27,88. Sehingga hipotesis tindakan dapat diterima, karenaada peningkatan dari indikator keberhasilan

Contextual relabelling of detected objects

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordContextual information, such as the co-occurrence of objects and the spatial and relative size among objects provides deep and complex information about scenes. It also can play an important role in improving object detection. In this work, we present two contextual models (rescoring and re-labeling models) that leverage contextual information (16 contextual relationships are applied in this paper) to enhance the state-of-the-art RCNN-based object detection (Faster RCNN). We experimentally demonstrate that our models lead to enhancement in detection performance using the most common dataset used in this field (MSCOCO)

Drought Vulnerability Assessment Using Geospatial Techniques in Southern Queensland, Australia.

In Australia, droughts are recurring events that tremendously affect environmental, agricultural and socio-economic activities. Southern Queensland is one of the most drought-prone regions in Australia. Consequently, a comprehensive drought vulnerability mapping is essential to generate a drought vulnerability map that can help develop and implement drought mitigation strategies. The study aimed to prepare a comprehensive drought vulnerability map that combines drought categories using geospatial techniques and to assess the spatial extent of the vulnerability of droughts in southern Queensland. A total of 14 drought-influencing criteria were selected for three drought categories, specifically, meteorological, hydrological and agricultural. The specific criteria spatial layers were prepared and weighted using the fuzzy analytical hierarchy process. Individual categories of drought vulnerability maps were prepared from their specific indices. Finally, the overall drought vulnerability map was generated by combining the indices using spatial analysis. Results revealed that approximately 79.60% of the southern Queensland region is moderately to extremely vulnerable to drought. The findings of this study were validated successfully through the receiver operating characteristics curve (ROC) and the area under the curve (AUC) approach using previous historical drought records. Results can be helpful for decision makers to develop and apply proactive drought mitigation strategies

IoT-Based Geotechnical Monitoring of Unstable Slopes for Landslide Early Warning in the Darjeeling Himalayas.

In hilly areas across the world, landslides have been an increasing menace, causing loss of lives and properties. The damages instigated by landslides in the recent past call for attention from authorities for disaster risk reduction measures. Development of an effective landslide early warning system (LEWS) is an important risk reduction approach by which the authorities and public in general can be presaged about future landslide events. The Indian Himalayas are among the most landslide-prone areas in the world, and attempts have been made to determine the rainfall thresholds for possible occurrence of landslides in the region. The established thresholds proved to be effective in predicting most of the landslide events and the major drawback observed is the increased number of false alarms. For an LEWS to be successfully operational, it is obligatory to reduce the number of false alarms using physical monitoring. Therefore, to improve the efficiency of the LEWS and to make the thresholds serviceable, the slopes are monitored using a sensor network. In this study, micro-electro-mechanical systems (MEMS)-based tilt sensors and volumetric water content sensors were used to monitor the active slopes in Chibo, in the Darjeeling Himalayas. The Internet of Things (IoT)-based network uses wireless modules for communication between individual sensors to the data logger and from the data logger to an internet database. The slopes are on the banks of mountain rivulets (jhoras) known as the sinking zones of Kalimpong. The locality is highly affected by surface displacements in the monsoon season due to incessant rains and improper drainage. Real-time field monitoring for the study area is being conducted for the first time to evaluate the applicability of tilt sensors in the region. The sensors are embedded within the soil to measure the tilting angles and moisture content at shallow depths. The slopes were monitored continuously during three monsoon seasons (2017-2019), and the data from the sensors were compared with the field observations and rainfall data for the evaluation. The relationship between change in tilt rate, volumetric water content, and rainfall are explored in the study, and the records prove the significance of considering long-term rainfall conditions rather than immediate rainfall events in developing rainfall thresholds for the region

Optimized Load Balancing based Task Scheduling in Cloud Environment

The fundamental issue of Task scheduling is one important factor to load balance between the virtual machines in a Cloud Computing network. However, the optimal broadcast methods which have been proposed so far focus only on cluster or grid environment. In this paper, task scheduling strategy based on load balancing Quantum Particles Swarm algorithm (BLQPSO) was proposed. The fitness function based minimizing the makespan and data transmission cost. In addition, the salient feature of this algorithm is to optimize node available throughput dynamically using MatLab10A software. Furthermore, the performance of proposed algorithm had been compared with existing PSO and shows their effectiveness in balancing the load

Customized physical and structural features of phosphate-based glass-ceramics: role of ag nanoparticles and ho3+ impurities

The effects of silver nanoparticles (Ag NPs) embedment on the physical and structural characteristics of the holmium ions (Ho3+) activated phosphate-based glass-ceramics were assessed. Two series of such glass-ceramics were prepared using the melt-quenching and characterized. In the first series, the Ag NPs were nucleated from the incorporated AgCl via the redox process. In the second series, the pure Ag nanopowder was directly added. The overall properties of these glass-ceramics were strongly sensitive to the cooling procedure and NPs addition strategies, leading to different density and refractive index modifications in the two series. The recorded O1s XPS peaks were exploited to determine the bridging to non-bridging oxygen ratios in the studied glass-ceramics network that enabled to unfold the differences in the observed inferences. A compelling correlation among various attributes in the achieved glass-ceramics was established. Briefly, the overall traits of the proposed glass-ceramics were tailored by regulating the preparation conditions

Deep learning and explainable artificial intelligence techniques applied for detecting money laundering – a critical review

Money laundering has been a global issue for decades, which is one of the major threat for economy and society. Government, regulatory and financial institutions are combating it together in their respective capacity, however still billions of dollars in fines by authorities make the headlines in the news. High-speed internet services have enabled financial institutions to deliver better customer experience through multi-channel engagements, which has led to exponential growth in transactions and new avenues for laundering the money for fraudsters. Literature shows the usage of statistical methods, data mining and Machine Learning (ML) techniques for money laundering detection, but limited research on Deep Learning (DL) techniques, primarily due to lack of model interpretability and explainability of the decisions made. Several studies are conducted on application of ML for Anti-Money Laundering (AML), and Explainable Artificial Intelligence (XAI) techniques in general, but lacks the study on usage of DL techniques together with XAI. This paper aims to review the current state-of-the-art literature on DL together with XAI for identifying suspicious money laundering transactions and identify future research areas. Key findings of the review are, researchers have preferred variants of Convolutional Neural Networks, and AutoEncoder; graph deep learning together with natural language processing is emerging as an important technology for AML; XAI use is not seen in AML domain; 51% ML methods used in AML are non-interpretable, 58% studies used sample of old real data; key challenges for researchers are access to recent real transaction data and scarcity of labelled training data; and data being highly imbalanced. Future research directions are, application of XAI techniques to bring-out explainability, graph deep learning using natural language processing (NLP), unsupervised and reinforcement learning to handle lack of labelled data; and joint research programs between research community and industry to benefit from domain knowledge and controlled access to data

Forecasting of landslides using rainfall severity and soil wetness: A probabilistic approach for Darjeeling Himalayas

© 2020 by the authors. Rainfall induced landslides are creating havoc in hilly areas and have become an important concern for the stakeholders and public. Many approaches have been proposed to derive rainfall thresholds to identify the critical conditions that can initiate landslides. Most of the empirical methods are defined in such a way that it does not depend upon any of the in situ conditions. Soil moisture plays a key role in the initiation of landslides as the pore pressure increase and loss in shear strength of soil result in sliding of soil mass, which in turn are termed as landslides. Hence this study focuses on a Bayesian analysis, to calculate the probability of occurrence of landslides, based on different combinations of severity of rainfall and antecedent soil moisture content. A hydrological model, called Systeme Hydrologique Europeen Transport (SHETRAN) is used for the simulation of soil moisture during the study period and event rainfall-duration (ED) thresholds of various exceedance probabilities were used to characterize the severity of a rainfall event. The approach was used to define two-dimensional Bayesian probabilities for occurrence of landslides in Kalimpong (India), which is a highly landslide susceptible zone in the Darjeeling Himalayas. The study proves the applicability of SHETRAN model for simulating moisture conditions for the study area and delivers an effective approach to enhance the prediction capability of empirical thresholds defined for the region

Effect of spatial resolution and data splitting on landslide susceptibility mapping using different machine learning algorithms

With the increasing computational facilities and data availability, machine learning (ML) models are gaining wide attention in landslide modeling. This study evaluates the effect of spatial resolution and data splitting, using five different ML algorithms (naïve bayes (NB), K nearest neighbors (KNN), logistic regression (LR), random forest (RF) and support vector machines (SVM)). The maps were developed using twelve landslide conditioning factors at two different resolutions, 12.5 m and 30 m. To identify the effect of data splitting on model performance, 2162 landslide points and an equal number of non-landslide points were used for training and testing the models using k-fold cross-validation, by varying the number of folds from two to ten. Results indicated that the spatial resolution of the dataset affects the performance of all the algorithms considered, while the effect of data splitting is significant in KNN and RF algorithms. All the algorithms yielded better performance while using the dataset with 12.5 m resolution for the same number of folds. It was also observed that the accuracy and area-under-the-curve values of 7, 8, 9, and 10-fold cross-validations with 30 m resolution was better than 2 and 3-fold cross-validations using 12.5 m resolution, in the case of RF algorithm