Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of Decoder Using Ternary Inverter

For the conventional binary logic, ternary logic is the best alternative which is trivalent logic. Energy efficiency and reduction in chip area as well as complexity plays an important role in ternary. Nowadays CNTFET device is more significant than the other due to higher channel mobility and also enhanced the ate capacitance. In CMOS, the complexity of an interconnect and chip area is increased as the number of function increases. This paper proposes an approach of using CNTFET to get low power consumption and the high-speed ternary decoder. The basic building blocks for designing of the ternary decoder will be PTI (positive ternary inverter) and NTI (negative ternary inverter). The simulation will show the low power, the high-speed ternary decoder.
A Study of the Main Factors Affecting Wireless Sensor Networks

In the last years, the enormous advance in wireless technologies and the proliferation of MEMS (MicroElectroMechanical) systems technology have facilitated the development of smart sensors at low cost and low energy consumption. This development has led to a major shift in wireless sensor network (WSN) technology. The wireless sensor network is considered an exciting and economical technology for widespread use in various areas of life, including military, agriculture, environment, medical, and industry, for multiple reasons, such as monitoring, detection, and tracking. However, this type of network poses a range of problems such as security, deployment, architecture, location, and energy consumption. This paper describes the main factors affecting wireless sensor networks.
On Road Intelligent Vehicle Path Predication and Clustering using Machine Learning Approach

This paper presents a vehicle path predication and clustering using vehicle trajectory-based data. Vehicles provided by the GPS navigator, contributes in-vehicle driving path for drivers to move in the proper direction. In order to scale down the hazards on road, the vehicle GPS sensor gathers the information on the movement of vehicles. Machine learning has the great intelligence to identify the destined direction of vehicle at different intersection, therefore in our paper presents the path prediction method based on two ensemble learning algorithms, are being made use and they are random forests and AdaBoost for model training. This paper also presents the significance of vehicle trajectory in the task of collision avoidance, road hazard informing, scheduling and routing, and efficient method of movement in smart vehicle identification. The path prediction and clustering are completely based on road geometry and kinematic mechanism. The work also includes the future estimation of uncertainty of vehicles using a machine learning model and predicted trajectories are used to estimate the time to collision and collision warning along with the quality of service by routing and scheduling the vehicle routes.
Measurement and Analysis on Local Magnetization Properties of RE-123 Coated Conductor With DC Transport Current and External Magnetic Field

It has been recognized as a significant issue that the magnetization of RE-123 coated conductors affects the spatial homogeneity and the time variation of the magnetic field of the magnets for MRI, NMR, and accelerators. Therefore, the understanding and the modelling of the magnetization of a coated conductor are crucial for the quantitative estimation and the compensation of its influence on a magnet. On the other hand, the magnetization of the coated conductor has been usually measured and analyzed as a global value; then it is difficult to clarify the local electromagnetic behavior governing such a global performance. Furthermore, such behavior should be investigated under the condition not only with external magnetic field but also with DC transport current as is exposed in a magnet. In this study, the magnetization of a coated conductor was characterized by a spatially-resolved measurement based on the scanning Hall-probe microscopy (SHPM). The magnitude and the time variation of the magnetization were clarified from the visualized magnetization current distribution and its time variation. In particular, they were modeled successfully including the influence of the transport current. Furthermore, taking account of the findings, the experimental results were successfully reconstructed by a numerical analysis based on finite element method (FEM). This will contribute to the quantitative estimation and the compensation of the magnetization problem for the magnets comprising RE-123 coated conductors.
Performance comparative of OpenCV Template Matching method on Jetson TX2 and Jetson Nano developer kits

Template Matching is a widely used method for object detection in digital images, it requires great processing power since it is an exhaustive method that compares the intensity levels of a source image pixel-to-pixel with a template image that contains the object to identify. Nowadays there are dedicated embedded systems that provide high processing capabilities, such as the NVIDIA Jetson family. This work presents the experimentation and the performance comparison between the Jetson Nano and Jetson TX2 development kits, when implementing the Template Matching method, in order to get an evaluation criterion to select one of them in image processing projects. It was carried out to six images with different sizes and two variants in terms of the size of the template image. The processing times for the sequential implementation using the CPUs and the parallel implementation with the GPUs were obtained quantitatively. It was observed that the processing times using the parallel versions on average doubled those of the sequential versions and that the Jetson TX2 exceeded the Jetson Nano in execution speeds.
The organization and Evaluation of Digital Logic Projects

This Research to Practice WIP presents the analysis of the project organization and evaluation in Digital Logic course. How to reasonably organize the team, analyze the feasibility of the task, and correctly evaluate each member in the team is the problem in the teaching process. The project is aimed to mobilize the team members, let them play their roles in the project, and master the curriculum knowledge. In this paper, we use different team organization, feasibility analysis of the task, and use comprehensive evaluation for the project members in the team. Through the improvement of the organization, feasibility analysis of every projects and evaluation of the team in project, students reflect well and the teaching efficiency is improved.
Agent based modeling of the process for initiation of Germinal Centers

The biological immune system is the progressive Complex Adaptive Systems (CASs) that consists of inhomogeneous and adaptive agents. It is an important defense mechanism in the human beings that generates an intricate cellular response against the foreign disturbances. It also exhibits prominent properties such as emergence and self-organization. Bone marrow, thymus are primary and spleen, lymph nodes, tonsils, the adenoids, and MALT (Mucosa-associated lymphoid tissue) are secondary lymphoid organs that work with the immune system. After immunization or infection, due to intricate cellular dynamics Germinal Centers (GCs) are formed in the lymph nodes. There is an immediate need to understand intricate cellular dynamics during the process of initiation and formation of mature GC. There is an immediate need of immune system modeling to understand its intricate inbuilt mechanisms in the GC. Various mathematical and computational simulation approaches have been proposed for the modeling of the intricate dynamics of the complex biological immune system. There are two kinds of modeling techniques that are used to simulate the immune system: equation-based modeling and agent-based modeling. Due to some drawbacks of the equation-based simulation, agent-based modeling technique is used. In this paper, we proposed an agent-based model of the process of initiation GC. This model helps researchers to understand the structure of the immune cells emergence during the GC formation and verify the hypotheses. This model can be easily employed as an educational tool in academics and a research tool in scientific disciplines for developing medicines that can keep a disease under control.
Continuous Sliding-Mode Control for a Class of Underactuated Systems

This paper deals with the design of sliding-mode controllers for the stabilization of a class of underactuated systems. The proposed method takes advantage of the mechanical properties of a class of underactuated systems instead of using nonlinear transformations. In order to design the controller, a series of sliding variables with relative degree one and two are introduced. The theoretical differences between these approaches are discussed and some simulation results show some practical differences.
A distributionally robust joint chance constraint approach to smart charging of plug-in electric vehicles

In this paper, the problem of optimal charging of plug-in electric vehicles in a parking lot is addressed. The parking time is assumed to be uncertain and characterized by a statistical distribution with fixed first and second order moments. The energy price during the day is assumed to be known on a day ahead basis. In this context, the problem of maximizing the profit of a charging station is formulated in a receding horizon framework, whose solution provides the optimal charging policy. To accommodate stochastic uncertainty affecting the parking time, a distributionally robust joint chance constraint approach is adopted when formulating the overall optimization problem. The optimal solution guarantees a customer satisfaction criterion expressed as a probabilistic confidence level. Simulation results on a case study show effectiveness and computational feasibility of the proposed approach.
Diet and Health Advisory Mechanism from Facial Features Using Machine Learning

People are seemingly becoming more and more health conscious. They are watching their weight, cholesterol, respiratory status and a lot of other body parameters at every instant. With this newfound concern for the physical well-being we have a macroscopic section of the society who are in constant connection with doctors, dermatologists, dietitians and so on in order to gain necessary insights. This takes a lot of efforts and money to which is why we have another large proportion of society that is reluctant to visit health specialists due to cost and several other factors. In order to curb these efforts and minimize them in a way that such services are taken only when actually needed, this paper identifies fat and adiposity measures and subsequently prescribes diet. This advisory can be periodic, routinely and focused on the inculcation of a new diet plan which would be beneficial to the user in the long run. The diet and health advisory system mentioned in this paper uses the relation between facial adiposity and Body Mass Index(BMI) to obtain an accuracy of 60% in making patient health prediction and diet planning.
Rule-based design for Anaphora Resolution of Marathi Sentence

Understanding the natural language is difficult for the computers because natural language is coherently ambiguous. Now-a-days resolving that ambiguity is the main task for the researcher. And one of such ambiguity is the anaphora resolution. Anaphora occurs frequently in the discourse. Anaphora resolution is a complex problem for the researcher. In the application of natural language processing like question answering system, text summarization, natural language generation and in many more application anaphora resolution is a must task.This paper presents the anaphora resolution for Marathi language. The work on anaphora resolution is performed on many Indian languages like, Hindi, Tamil, Telugu, Bengali, and Urdu etc. but not done on the Marathi language. This paper focuses on the Marathi demonstrative pronouns specifically for the name of the persons and trying to resolve the difficulty of the anaphora and the antecedent. For the resolution of anaphora, rule based structure is designed by taking into account the context of Marathi grammar.
Optimizing Mixed Size & Large Scale Block Placement Using Greedy Approach

In very large scale integration (VLSI) of chip, placement is an important stage where cells or blocks are placed within chip optimally minimizing their interconnect wire-length. Analytical placement approach solves the placement problem in three stages; global, legal and detailed placements. Global placement tries to place cells in chip with some overlaps among cells, which are removed at legal and detailed placement stages. In this work a greedy algorithm for overlap removal among cells has been proposed after global placement, so that the tasks rest on legal and detailed placements can be minimized. Subsequently the proposed algorithm can also be used by analytical placers to expedite the legal and detailed placements . Experimental results for proposed algorithm on random instance of placements reduce overlap by 5% in average as compared to the conventional algorithm.
Palm-print recognition based on scale invariant features

Over the past few years, palm-print recognition has proved to be one of the extensively used technology for human identification/verification in many aspects. This paper presents the implementation of five feature extraction algorithms such as Mean, AAD (Average Absolute Deviation), GMF (Gaussian Membership Function) along with SIFT (Scale Invariant Feature Transform) and SURF (Speeded Up Robust Feature) for effective recognition. SVM (Support Vector Machine) and KNN (K-Nearest Neighbor) are the machine learning algorithms used for the classification of data. Experimentations are carried out on the PolyU and IIT-Delhi palm-print databases. The scale invariant features of SURF provides the best performance with Correct Recognition Rate (CRR) of 99.56% and 97.95% for IIT-Delhi and PolyU palm-print database respectively.
Floorplan Based Performance Estimation of Network-on-Chips using Regression Techniques

An intra-communication problem between the Intellectual Properties(IPs) caused by the growth of a number of cores on single chips in System-on-Chip(SoC) gave rise to new a system architecture called Network-on-Chip(NoC). The early stages of designing NoC can be done using cycle-accurate NoC simulators, but they become slow as the architecture size of NoC increases. Hence a machine learning framework is being proposed by considering two scenarios i,e. A fixed delay between the components and floorplan based delay among the components of NoC. This framework is modeled using distinct Machine Learning(ML) regression algorithms to predict performance parameters of NoCs considering uniform random and transpose traffic patterns. Complete performance analysis of Mesh NoC architecture can be done by using the proposed ML framework. Booksim simulator results are used to verify effectiveness of proposed framework and it showed an overall speedup of 2000× to 2500×.
Coding Programmable Metasurfaces Based on Deep Learning Techniques

Programmable metasurfaces have recently been proposed to dynamically manipulate electromagnetic (EM) waves in both temporal and spatial dimensions. With active components integrated into unit cells of the metasurface, states of the unit cells can be adjusted by digital codes. The metasurface can then construct complex spatial and temporal electromagnetic beams. Given the main parameters of the beam, the optimal codes can be computed by nonlinear optimization algorithms, such as genetic algorithm, particle swarm optimization, etc. The high computational complexity of these algorithms makes it very challenging to compute the codes in real time. In this study, we applied deep learning techniques to compute the codes. A deep convolutional neural network is designed and trained to compute the required element codes in milliseconds, given the requirement of the waveform. The average accuracy of the prediction reaches more than 94 percent. This scheme is validated on a 1-bit programmable metasurface and both experimental and numerical results agree with each other well. This study shows that machines may “learn” the physics of modulating electromagnetic waves with the help of the good generalization ability in deep convolutional neural networks. The proposed scheme may provide us with a possible solution for real-time complex beamforming in antenna arrays, such as the programmable metasurface.
Estimation-Based Quadratic Iterative Learning Control for Trajectory Tracking of Robotic Manipulator With Uncertain Parameters

In this paper, we consider iterative learning control for trajectory tacking of robotic manipulator with uncertainty. An improved quadratic-criterion-based iterative learning control approach (Q-ILC) is proposed to obtain better trajectory tracking performance for the robotic manipulator. Besides of the position error information, which has been used in existing Q-ILC methods for robotic control, the velocity error information is also taken into consideration such that a new norm-optimal objective function is constructed. Convergence and error sensitivity properties for the proposed method are also analyzed. To deal with uncertainty, the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are incorporated for estimation of uncertain parameters by constructing extended system states. The performances between the two filters are also compared. Simulations on a 2DOF Robot manipulator demonstrate that the improved Q-ILC with parameter estimators can achieve faster convergence and better transient performance compared to the original Q-ILC, in the presence of measurement noise and model uncertainty.
Modeling Power Consumption of The Code Execution Using Performance Counters Statistics

This paper presents an empirical model to classify the programs according to their power consumption by using the performance counter statistics. The programs with similar power consumption are put into the same group. The difference in power data between two adjacent groups is 5 watts. A power model is generated based on the performance data that the program generated. Discriminant analysis is adopted to generate the power consumption model upon the data from the performance counter statistics. We use discriminant analysis to determine the power category (i.e., the number of the group) that is derived from the independent variable. By using the performance counter variables as the input to the power model, we can predict the level of power consumption of the code, that is, the group that this code belongs to. The experiment results in modeling and validation show that this power model can predict power group membership of a code with an accuracy of more than 96.5%, with the difference of original and predicted group numbers being smaller than 2.
Compositional Verification of Large-Scale Stochastic Systems via Relaxed Small-Gain Conditions

In this paper, we provide a compositional framework for the construction of finite abstractions (a.k.a. finite Markov chains (MCs)) for networks of not necessarily stable discrete-time stochastic systems. The proposed scheme is based on a notion of finite-step stochastic simulation functions, using which one can employ an abstract system as a substitution of the original one in the verification process with guaranteed error bounds. To this end, we first develop a new type of smallgain conditions which are less conservative than the existing ones in compositionally quantifying the probabilistic distance between the interconnection of stochastic subsystems and that of their finite abstractions. We then propose an approach to construct finite MCs together with their corresponding finitestep simulation functions for discrete-time nonlinear stochastic systems satisfying a finite-step version of an incremental inputto-state stability (δ-ISS) property. We also construct finite MCs for a particular class of nonlinear stochastic systems whose 1-step δ-ISS can be easily verified using the semidefinite programming. Finally, we demonstrate the effectiveness of the proposed results to a network of four subsystems such that one of them is not stable.
Heart Disease Prediction System Using Model Of Machine Learning and Sequential Backward Selection Algorithm for Features Selection

Detection of Heart Disease (HD) by using models of machine learning (ML) is very effective in early stages. The HD treatment and recovery is effective if detected the disease at initial stages. HD identification by machine learning (ML) techniques has been developed to assist the physicians. In this study we proposed an Identification system by using ML models to classify the HD and healthy subjects. Sequential backward selection of feature algorithm was used to select more appropriate features to increase the classification accuracy and reduced the computational time of predictive system. Cleveland heart disease dataset was for evaluation of the system. The dataset 70% used for training and remaining for validation. The proposed system performances have been measured by using evaluation metrics. The experimental results shows that Sequential Backward Selection (SBS) algorithms choose appropriate features and these features increase the accuracy using K-Nearest Neighbor supervised machine learning classifier. The good accuracy of this study suggests that the proposed model will effectively identify the HD and healthy subjects.
PartMitosis: A Partially Supervised Deep Learning Framework for Mitosis Detection in Breast Cancer Histopathology Images

Detection of mitotic tumor cells per tissue area is one of the critical markers of breast cancer prognosis. The aim of this paper is to develop a method for the automatic detection of mitotic figures from breast cancer histological slides using a partially supervised deep learning framework. Unlike the previous literature, which has focused on solving the problem of mitosis detection in the weakly annotated datasets using centroid pixel labels (weak labels) only without taking advantage of the available pixel-level labels (strong labels) of other datasets, in this paper, we design a novel partially supervised framework based on two parallel deep fully convolutional networks. One of them is trained using weak labels and the other is trained using strong labels, together with a weight transfer function. In the detection phase, we fuse the segmentation maps produced by the two networks to obtain the final mitosis detections. Our system exploits the available large sets of mitosis detection samples with mitosis centroid annotation, such as the 2014 ICPR dataset and the AMIDA13 dataset, and only a small set of samples with the annotation of all mitosis pixels, such as the 2012 ICPR dataset, to perform a more accurate mitosis detection on weakly labeled data. This enables us to outperform all previous mitosis detection systems by achieving $F$ -scores of 0.575 and 0.698 on the 2014 ICPR dataset and the AMIDA13 dataset respectively.