Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient IOT Based Smart Bin for Clean Environment

Dustbins are containers used for collecting household waste all around the world. In our day to day life, we dispose variety of waste materials categorized as industrial waste, sewage wastes, domestic wastes etc. Dustbins are used for collecting the domestic waste materials. Indoor dustbins are used to collect wastes from household, which are then disposed into the outdoor dustbins maintained by the Corporation or Municipality. Indoor dustbins are smaller in size, whereas municipal dustbins present outdoors are so big in size since it has to accommodate all the wastes from many household users in that area. Hence our main focus is on the dustbins placed outside every corner in the streets in order to keep the environment clean. Road side dustbins are not monitored and cleaned properly most of the times. In this paper we propose a new system for managing garbage within Smart Cities. This Efficient Waste disposal or Management System is considered as an essential for Modern Smart Cities (MSC). Internet of Things (IoT) can be implemented both in IS and MSC creating an highly developed proposal for future Operations. Special methods can be applied to enhance technology used for high Quality of Service (QoS) in our waste management system. Specifically, IoT components like sensors, detectors, and actuators are integrated into Intelligent System (IS) and Inspection systems for efficient waste management. We recommend a sophisticated IS for efficient waste management in Smart Cities. The proposed system is an automated alert based smart bin or garbage collection system and to alert the authorities like corporation or local waste disposal team. Using this, we can monitor the complete waste disposal in an efficient way.
Computational Study About the Active Power and Energy Losses of a 40 MVA 110/6 kV Transformer

It is known that both the transport as well as the distribution of electricity are accompanied by power losses (active and reactive) and implicitly by losses of electricity in all the elements of the given electrical network. The level of energy losses in the networks oscillates in the range between $10\div 15{\%}$ of the energy produced in the power plants, depending on the structure of the electrical network, the operating conditions, etc. Most of that energy is lost in transformers. Starting with a new computational method for active power assessment and finishing with measurements on the real transformer, this paper presents a simple algorithm which could easily be applied for each specified transformer.
Low Cost Satellite Constellation Design Using Walker's Method for a Specified Launch Station

Satellite constellation is IRNSS, consists of 7 satellites which provides regional coverage only. The objective of this work is to design a satellite constellation for worldwide coverage, from a specified launch station and schedule the launch date and time of the satellites. Satellite constellations were developed using the Walker's method, by varying the height, inclination, orbital plane, phasing parameter using the System Tool Kit (STK) software and select the suitable one based on the parameter Geometric Dilution of Precision (GDOP). The value of GDOP should be minimum for better constellation. Satellite constellation design at a height of 23000 km, gives an arrangement of 30 satellites in 10 orbital plane, with phasing parameter 6 and inclination of 45 degree. In Walker's method, satellites are arranged uniformly at an equal altitude and inclination. Compared to polar constellation method coverage using Walker constellation method is more in the equatorial plane, also the number of satellites required per plane is low. The proposed satellite constellation gives GDOP value 2.8, hence satellite constellation is better.
[Research Paper] Semantics-Based Code Search Using Input/Output Examples

As the quality and quantity of open source code increase, semantics-based code search has become an emerging need for software developers to retrieve and reuse existing source code. We present an approach of semantics-based code search using input/output examples for the Java language. Our approach encodes Java methods in code repositories into path constraints via symbolic analysis and leverages SMT solvers to find the methods whose path constraints can satisfy the given input/output examples. Our approach extends the applicability of the semantics-based search technology to more general Java code compared with existing methods. To evaluate our approach, we encoded 1228 methods from GitHub and applied semantics-based code search on 35 queries extracted from Stack Overflow. Correct method code for 29 queries was obtained during the search and the average search time was just about 48 seconds.
Over-the-Air Computation for Cooperative Wideband Spectrum Sensing and Performance Analysis

For sensor network aided cognitive radio, cooperative wideband spectrum sensing can distribute the sampling and computing pressure of spectrum sensing to multiple sensor nodes (SNs). However, this may incur high latency due to distributed data fusion, especially when the number of SNs is large. In this paper, we propose a novel cooperative wideband spectrum sensing scheme using over-the-air computation. Its key idea is to utilize the superposition property of wireless channel to implement the summation of Fourier transform. This avoids distributed data fusion by computing the target function directly. The performance of the proposed scheme is analyzed with imperfect synchronization between different SNs considered. Furthermore, a synchronization phase offset estimation and equalization method is proposed. The corresponding performance after equalization is also derived. Both a working prototype based on universal software radio periphera and Monte Carlo simulations are provided to verify the performance of the proposed scheme.
A Constructive Machine Translation System for English to Odia Translation

India is a country of many languages, diverse regional languages are spoken in different states of India but not all the Indians are the multilingual person. There are a total of 10 prominent scripts and 18 legitimate languages. Many Indians, rural people i.e. distant villagers to be exact, can neither write nor read and can't even understand English, thus employing an effective linguistic interpreter is needed. Machine translation systems, that converts the text of any language to another language, will improve the educated public of Indian without any linguistic barricade. Odia is spoken by most of the people in Odisha & outskirts of Odisha and English, being the official language of India we propose a machine translating system from English to Odia. In this paper, many methodologies of Machine Translation is also defined.
Parameterized Synthetic Image Data Set for Fisheye Lens

Based on different projection geometry, a fisheye image can be presented as a parameterized non-rectilinear image. Deep neural networks(DNN) is one of the solutions for extracting the parameters for fisheye image feature expression. However, a number of images are required for training a reasonable prediction model for DNN. In this paper, we propose to extend the scale of the dataset using parameterized synthetic images which effectively boost the diversity of samples and avoid the limitation on the scale. To simulate different viewing angles and distances, we adopt controllable parameterized projection processes on transformation. The reliability of the proposed method is tested with the image captured by a fisheye camera. The synthetic fisheye image dataset is the first dataset that is developed by existing labeled perspective images. It is accessible via: http://www2.leuphana. de/misl/fisheye-data-set/.
Simulation and Analysis of CMOS Based Micro Thermoelectric Power Generator

This paper presents a Complementary Metal Oxide Semiconductor (CMOS) based Thermoelectric Power Generator (TPG) to harvest unused heat and convert it to usable electrical current. Detailed simulation and analysis of various model has been shown for thermoelectric power generator (TPG) to achieve improved performance. Thermal analysis using the ANSYS software has been done for various models to show the increase in output current of the thermocouple. Different techniques like change in heat sink material, formation of cavities, change in contact materials, change in thermocouple length and width to improve the temperature difference between hot and cold junction and the output current. Comparison has been made for different devices by changing parameters to select the best model to deliver more electrical current. Large Seebeck effects are found in doped Poly-Silicon, which makes it a suitable choice for CMOS based thermoelectric devices. Thin film of poly-Si is deposited and patterned to form thermocouples, and an array of thermocouples, i.e., a thermopile, could be arranged in a tiny area. For a device in the size of $500\mu \text{m}^{2}$ with effective length (L) of each thermocouple is $5\ \mu \text{m}$, an output current of O.4mA is obtained with a 5K temperature difference across two sides.
Sensitive period for cognitive repurposing of human visual cortex

Studies of sensory loss are a model for understanding the functional flexibility of human cortex. In congenital blindness, subsets of visual cortex are recruited during higher-cognitive tasks, such as language and math tasks. Is such dramatic functional repurposing possible throughout the lifespan or restricted to sensitive periods in development? We compared visual cortex function in individuals who lost their vision as adults (after age 17) to congenitally blind and sighted blindfolded adults. Participants took part in resting-state and task-based fMRI scans during which they solved math equations of varying difficulty and judged the meanings of sentences. Blindness at any age caused “visual” cortices to synchronize with specific fronto-parietal networks at rest. However, in task-based data, visual cortices showed regional specialization for math and language and load-dependent activity only in congenital blindness. Thus, despite the presence of long-range functional connectivity, cognitive repurposing of human cortex is limited by sensitive periods.
Deducing putative ancestral forms of GNRA/receptor interactions from the ribosome

Abstract Stable RNAs rely on a vast repertoire of long-range interactions to assist in the folding of complex cellular machineries such as the ribosome. The universally conserved L39/H89 interaction is a long-range GNRA-like/receptor interaction localized in proximity to the peptidyl transferase center of the large subunit of the ribosome. Because of its central location, L39/H89 likely originated at an early evolutionary stage of the ribosome and played a significant role in its early function. However, L39/H89 self-assembly is impaired outside the ribosomal context. Herein, we demonstrate that structural modularity principles can be used to re-engineer L39/H89 to self-assemble in vitro. The new versions of L39/H89 improve affinity and loop selectivity by several orders of magnitude and retain the structural and functional features of their natural counterparts. These versions of L39/H89 are proposed to be ancestral forms of L39/H89 that were capable of assembling and folding independently from proteins and post-transcriptional modifications. This work demonstrates that novel RNA modules can be rationally designed by taking advantage of the modular syntax of RNA. It offers the prospect of creating new biochemical models of the ancestral ribosome and increases the tool kit for RNA nanotechnology and synthetic biology.
Inverse Sets in Pattern Recognition

No matter how efficient indexing-based Internet search engines could be, indexing or inverse representations of data, is not in the mainstream of pattern recognition. One reason for a lack of interest in indexing methods on the part of pattern recognition community is instability of results due to a use of noise-prone measurements as features, rather than key words. The paper suggests a multidimensional numerical data indexing method that opens a path to accurate indexing-based pattern recognition systems that inherit from their search engines predecessors the ability to efficiently deal with large amounts of data.
Machine Intelligence Techniques for Protein Classification

A Lot of large scale biological projects and experiments like Human Genome Project have been carried in recent past which have resulted in generation of mammoth biological data mostly DNA and Protein sequences. It is extremely difficult to manage such a huge amount of data by using traditional methods hence lot of biological databases related to protein families and sequences have come into existence. Many databases contain information about proteins and their families. It is important and necessary to know the family of protein (unknown) hence we need to classify proteins. Using laboratory way for doing this is an arduous task, hence in this research we will use the advanced Machine Intelligence computing techniques like Artificial Neural Networks, Naïve Bayes Classifier, Decision Trees etc.
Glutathione S-Transferases in the Biosynthesis of Sulfur-Containing Secondary Metabolites in Brassicaceae Plants

Plants in the Brassicaceae family have evolved the capacity to produce numerous unique and structurally diverse sulfur-containing secondary metabolites, including constitutively present thio-glucosides, also known as glucosinolates, and indole-type phytoalexins, which are induced upon pathogen recognition. Studies on the glucosinolate and phytoalexin biosynthetic pathways in the model plant Arabidopsis thaliana have shown that glutathione donates the sulfur atoms that are present in these compounds, and this further suggests that specialized glutathione S-transferases (GSTs) are involved in the biosynthesis of glucosinolates and sulfur-containing phytoalexins. In addition, experimental evidence has shown that GSTs also participate in glucosinolate catabolism. Several candidate GSTs have been suggested based on co-expression analysis, however, the function of only a few of these enzymes have been validated by enzymatic assays or with phenotypes of respective mutant plants. Thus, it remains to be determined whether biosynthesis of sulfur-containing metabolites in Brassicaceae plants requires specific or nonspecific GSTs.
Implementing Augmented Reality Using Microsoft Kinect

This project was created to provide a spectacular and interactive way of teaching geography to the new generation, whose members are often visual learners. The system, consisting of a Microsoft Kinect sensor, a projector, a computer, and a box filled with sand, can visualize topography and it can show in real time, that how changing the terrain affects the map created by it. From the surface of the sand, a topographical map is created with the well know colours and contour lines of these maps, and then it is projected back to the surface, reacting to any change of the sand's structure in real time. With the help of different computer algorithms, the system is configurable to create the output by the needs of the operator.
Image Inpainting Based on Image Mapping and Object Removal Using Semi-Automatic Method

The image inpainting process selects the object manually. We are dealing with semi-automatic image inpainting algorithm. In semi-automatic image inpainting method user manually gives an outline of an object which we want to remove or the damaged region which we want to recover and an inpainting algorithm will automatically fill that region. In the proposed method, segmentation is used for removing unwanted regions from the image. As the hole in the image to be inpainted is an unendorsed method, we are totaling the pixel in the holes by means of spatial contextual correspondences. The main importance of our approach is to utilize association of a pixel with its neighbors to get precise smoothness over the inpainted image. The region to be inpainted can be chosen initially by performing the segmentation approach. The region growing method is applied thus segmenting the regions. The multiple and single region selection is also applicable. Now the region to be inpainted is termed as a hole in the given image which is removed from the image. The omitted region can be packed using the correlation made by the information supplied by the neighbors and its information.
g-Mellin Transform

One generalization of the Mellin integral transform in terms of pseudo-analysis is presented in the paper. Basic properties of this type of integral transform and one example are given. Both the g- Mellin convolution and the inverse g- Mellin transform are defined. The connections among the g- Mellin transform, the g- Mellin convolution and the inverse g- Mellin transform and the Mellin transform, as well as the connection between the Mellin convolution and the inverse Mellin transform are presented.
Using Simulation to Design Scalable and Cost-Efficient Archival Storage Systems

The need for reliable and cost-effective data storage grows as digital information becomes increasingly ubiquitous. Archival systems must store valuable data for years while adapting to changing user needs, capacity, and performance requirements. Storage devices differ in terms of performance, capacity, reliability, acquisition cost, power consumption, and the rates at which their features change over time. As a result, choosing the best storage technology to use for an archive has become increasingly challenging with the proliferation of new technologies alongside existing ones. We have designed a simulator that models the capacity, performance, acquisition cost, and power cost of an archival system using the characteristics of the drives and media that comprise it. We simulate and compare four storage technologies that exhibit different cost and performance characteristics: tape, optical disc, hard disk, and NAND flash SSD. We evaluate the total cost of ownership for each storage technology within an archival system, and we explore the effect that prospective technological advancements and growth rates over time may have on the relative cost and viability of each storage technology for archival systems. We show that the lifecycle and upgrade cost of drives are significant cost factors for removable media archives. We observe that increasing performance requires adding more drives to an archival system, and the cost of each drive dominates the cost to increase performance. We compare trends in storage technologies to suggest developments that could minimize the long-term total cost of ownership for archival systems. We show that hard disks and flash could become cost-competitive with tape-based archives by adopting new designs to minimize infrastructure and electricity costs.
Research On Face Image Clustering Based On Integrating Som And Spectral Clustering Algorithm

With the rapid development of neural network and face recognition, the Convolution Neural Networks(CNN) is more and more widely applied. Considering the shortcomings of traditional clustering methods, spectral clustering and self-organizing feature mapping neural network (SOM), a variety of new clustering methods based on deep learning are constantly created. In this paper, firstly a convolutional neural network is used in automatically feature extraction. After extracting features with CNN, S-SOM, an integrated algorithm of SOM and spectral clustering, is proposed to cluster face image data. Finally, to verify the effectiveness of the method, an experiment is employed. Meanwhile, we also analyze the effect of some related parameters of S-SOM on face clustering for further research.
Analyzing the Performance of Machine Learning Algorithms in Anomaly Network Intrusion Detection Systems

With the deployment of numerous networked devices over the internet, the protection of organizational and personal computer networks has become vital owing to new malicious attacks which are rapidly increasing. Network intrusion detection systems (NIDS) are among the most known and reputed network security tools. Maintaining security, data confidentiality, and data integrity are the primary goals of the NIDS. In this way, this paper investigates the application and performance of machine learning algorithms in NIDS. Four algorithms namely, Random Forest, Decision Stump, Naive Bayes, Stochastic Gradient Descent (SGD) combined with different feature selection techniques (Correlation Ranking Filter and Gain Ratio Feature Evaluator) are applied to implement the NIDS models using the NSL-KDD dataset which is the new version of KDD-Cup99. The comparative analysis conducted based on the performance of these algorithms reveals that the Random Forest performs better than the other algorithms regarding the predicted accuracy and detection error.
Modeling and Analysis of a New Voltage Regulation Method for Surface-Mounted Permanent Magnet Synchronous Generator

The current trend for future aircraft is the adoption of the More Electric Aircraft (MEA) concept. The electrical based starter-generator (S/G) system is one of the core ideas from the MEA concept. Usually, the starter-generator produces constant torque as a starter and constant power as a generator. Surface-mounted permanent magnet synchronous generator (SPMSG) is suitable for a high speed operation, which can improve the power density of electrical system. But it is difficult for SPMSG to keep producing constant power for a wide speed operation. This paper proposes a new voltage regulation method of SPMSG for a wide-speed-range operation. After analyzing the stability of two types of single current regulator (SCR), the $\pmb{q}$ -axis SCR is applied to enhance the field weakening ability of SPMSG, which means that the speed operation range of SPMSG is extended. The proposed voltage regulation method has been implemented in simulation over a speed range of 10,000∼24,000rpm. The results indicate that the system with this proposed method can respond quickly and remain stable, and the dynamic performance improved significantly in sudden change of the load.
Prometheus: Coherent Exploration of Hardware and Software Optimizations Using Aspen

With the dramatic increase in scale expected for Exascale computing, there is a dire need for tuning of hardware configurations and software optimizations such that they are in unison. However, the expected increase in tunable hardware parameters makes searching through the design space for optimal hardware-and-software configurations much more challenging. Towards this end, we propose a composable hardware-software optimization framework called Prometheus. Prometheus uses a combination of analytical and machine-learning techniques to capture application characteristics and subsequently determine the hardware-software configuration for near-optimal performance. We evaluate Prometheus for its efficacy using two widely used proxy applications: LULESH and CoMD. We demonstrate that Prometheus identifies near-optimal hardware-software configurations and verify the results via brute-force search of the design space.