Alzheimer's disease is an age-related, progressive and chronic neurodegenerative disorder caused by neuronal cell death and attributed to memory loss. Recent studies have shown that glutamate has an important role in learning, memory formation and synaptic plasticity. A disruption to glutamatergic neurotransmission has been suggested to lead to Alzheimer's Dementia. As a main objective, this study computational models the glutamate pathway to relate molecular mechanisms of how glutamate links to neural activity and predict the involvement in Alzheimer's disease. Simulations was by numerical solving ordinary differential equations as rate equations based on biochemical systems theory. Results suggest in normal or control condition there was an increase in astrocyte calcium levels. Our simulations showed a lower level of GluN2B level in control suggesting a reduced production of amyloid beta compared to the diseased condition. Simulating diseased state, increased IP3 levels lead to the release of $\text{TNF}\alpha$, released glutamine synthase that converted glutamine to glutamate. The results also demonstrated the elevation in extracellular glutamate concentration that activated NMDAR. Through modeling, we show that increased production of amyloid beta that forms amyloid plaques elucidating the main component of neurodegeneration occurred in Alzheimer's disease.
A Multiplicative Spanner is a spanning sub-graph H(V, E ' ) of a graph G(V, E) such that where distance(u, v, G) is the shortest distance between the vertices u and v in G. The parameter t is called the multiplicative stretch of the spanner. When the size of the graph is reduced to construct a spanner, the shortest distance between the vertices increases, consequently the stretch factor also increases. It is known that the construction of spanners with optimum size-stretch is hard. Many researchers proposed efficient algorithms that yield proven near optimal results. In this paper we propose a quadratic time algorithm to construct multiplicative t-spanners with a bound on the stretch factor.
Feature detection is most crucial stage in image identification in computer vision, which helps computer recognizing the image. This work is part of a research for the Indian currency recognition for blind people. Each country has its own currencies with unique features, colors, denominations and international value. As we move towards first quarter of 21st Century, world is facing various issues like terror-funding, smuggling and that has lead to the printing of fake currencies. Due to this, many a times a person would never be able to know that the currency which one is holding is genuine or fake. This can only be decided if one knows all the features of the currency. However, for a common man, it is not possible to remember the features of the currency; especially for blind person it is not at all possible. Though the denomination can easily be recognized for a currency but it becomes difficult to identify a counterfeit currency from the real one. This paper proposes an ACO based novel concept for feature detection using ORB feature detector, named ACORB. It has been tested thoroughly to check its effectiveness and the concept seems promising based on its results. The main motive of this work is to design and develop an algorithm for Indian currency recognition in the regional languages to help the visually challenged people to recognize the currency denomination and to check if the currency is fake or genuine.
This paper presents the rational modeling of a transmission line interconnect system from the Scattering-Parameter data using minimum-phase-all-pass (MPAP) decomposition and a system identification algorithm. Vector Fitting Algorithm (VFA) is used for system identification and it is found that delay extraction before application of VFA enables us to reduce model identification errors. The efficiency of the algorithms has been tested through examples of several lengths of lossy and lossless transmission lines under matched and mismatched terminations.
In this paper, we address the problem of gathering a group of autonomous, homogeneous mobile robots having limited visibility (sensing) ranges. The robots execute a cycle look-compute-move in an asynchronous manner. They do not retain any data from the previous cycle. The robots do not have any kind of communication between themselves through message passing. The robots have been considered opaque. Thus a robot can obstruct the visibility of other robots if it comes between other two robots. This phenomena is known as obstructed visibility in literature. The robots neither have have any common coordinate system or chirality nor have any knowledge about others coordinate systems. However they agree on a direction, i. e., Y axis for this paper. Considering obstructed and limited visibility this paper is the first attempt to write a algorithm for gathering under one axis agreement. The algorithm it is an collision avoidance algorithm by nature.
This work presents the application of precision agriculture based on cyber physical systems (PACPS). We implemented and tested the essential components of PACPS, namely, moisture sensing, actuating a solenoid valve based on a lower threshold value of sensor data as well and generation of actuation signals. Further, we have also implemented, transmission of data to a remote server. We have observed that PACPS is more efficient compared to traditional agricultural practices in terms of physical labour and the total cost. Our laboratory experimentation and subsequent observations also reveal that the system ensures consistent nourishment to the roots of the crops and therefore reduces soil erosion compared to traditional methods.
Transportation in urban cities is a complicated issue that is worsening with the over-population of major cities around the world. Being part of the second most populous country in the world, Indian cities face an exaggerated form of the issue. To solve this issue, major advancements in urban transportation are required. The mobility of a city must be understood thoroughly, to make changes that will have the most prominent effect. Building infrastructure to analyze traffic is very expensive and thus not an option. Synthetically generated data is one of the solutions in this case. A mobility model of the city is needed for this purpose. Traffic demand needed to create the model was collected by using the raw GPS traces hosted by OpenStreetMap. Using the model, traffic demand is scaled up to mimic vehicle population of the city. Running this newly generated traffic demand through SUMO (Simulator for Urban Mobility) gives us highly detailed data on the microscopic behaviour of traffic.
The small and medium scale enterprises (SMEs) are the prime factor for economic growth and job creation in developing countries. The literature shows that only a small number of SMEs are successful in achieving exceptional performance and sustainable growth. Therefore, it is paramount to determine the socioeconomic factors that hinder their growth. Incorporation of machine learning and statistical methods for solving business problems has gained substantial interest in recent years due to an exponential rise in consumer data. However, processing and interpreting this data to support business decision making is demanding, thereby leaving the scope for advancement. Therefore, in this paper, we design a novel performance framework with four modules, each having different functionality and contemplates machine learning methods. The fundamental objective is to predict the impact of strategic planning on SME's performance so that it can sustain in current competitive markets. For the sake of validating the framework, an experimental case study is conducted for a particular module, i.e., PMM Module. The prediction results for PMM module are compared in terms of RMSE concerning RNN, GBT, and RF methods.
To store and analyze Big Data, Hadoop is the most common tool for the researchers and scientists. The storage of huge amount of data in Hadoop is done using Hadoop Distributed File System (HDFS). HDFS uses block placement policy to split a very large file into blocks and place them across the cluster in a distributed manner. Basically, Hadoop and HDFS have been designed in such a way that it works efficiently on the homogeneous cluster. But in this era of networking, we cannot imagine having a cluster of homogeneous nodes only. So, there is the need of storage policy that can work efficiently on both homogeneous as well as the heterogeneous cluster. Thus, the needs of applications that can be executed time-efficiently based on homogeneous as well as the heterogeneous environment can be sufficed. Data locality in Hadoop maps the data block to process in the same node, but often when you're dealing with Big Data, it is required to map the data block to the processes across multiple nodes. To deal with this Hadoop has functionality to copy the data block where mappers are running. This creates a lot of performance degradation especially on heterogeneous cluster due to I/O delay or network congestions. Here we present a Novel algorithm to balance the data blocks on specific nodes (i.e. custom block placement) only by dividing total nodes among two categories like: homogeneous vs. heterogeneous or high performing nodes vs. low performing nodes. This policy helps to achieve better load rearrangement among the nodes and we can put data blocks actually where we want our data to be placed for the processing.
In this work, we have proposed novel deep CNN framework architectures that effectively represent complex image characteristics which performs feature extraction in just two convolution layers and has successfully proved to be an reliable biometric verification system on employment of physiological traits face and iris for our system development. Extensive experiments in configuring the CNN hyper parameters such as number of convolution layers required, filters and its size in each layer, batch size, epochs, iterations and learning rate is a paramount, determining these factors truly depends on the nature of data and its size. Our work has relinquished our novel idea and has obtained 99% of GAR in unimodal biometric verification system itself and definitely the approach has rendered great results when compared with conventional feature extraction and classification techniques.
Visual impairment is one of the disabilities of a human being. To date, numerous methods have been proposed to enhance the life style of visually impaired and blind people. Still, purchasing products in the supermarket without others support is tricky one for them. In this work, a Smart Store Assistor for visually impaired people is proposed. Three major concerns such as locating the product, identifying product and buying the product are addressed in this work. The System consists of 3 modules namely Product Identifier, Smart Glove and Smart Trolley. Radio Frequency Identification Technology (RFID) is used to identify the products. In Product Identifier module, a buzzer will ring whenever the required product is located. Smart Glove module is used to scan the product where an audio instruction will detail about the product specifications to the buyer. Further, when the product is placed in the cart, an automated billing system will provide final bill of the purchased products. Design and hardware implementation of the working prototype model has been demonstrated in the proposed work.
In the world of technology, change is the only constant. As soon as people start adapting to a new invention, another innovation starts paving its way into people's lives. Each change in technology brings in new and improved devices. Old devices are replaced and abandoned. Such Electronic and Electrical Equipments (EEEs) that are discarded by users are termed as e-waste. Management of e-waste includes proper collection, segregation and recycling of discarded devices in an environment friendly manner. The volume of EEEs that is produced throughout the world, has led governments in various countries to create strict policies, to ensure efficient disposal of the generated e-waste. The Indian government also upgraded its E-Waste Management (EWM) laws in 2016 and 2018. These laws are a step towards environment friendly disposal of e-waste but their compliance is still at a nascent stage. Even today, e-waste collection and recycling in India is largely dominated by the unorganized sector, which makes the enforcement of such laws very difficult. In this paper, we propose a novel approach for EWM using blockchain based smart contracts. Blockchain is the technology that enables us to write smart contracts. Smart contracts are self-executing computer codes that take specified actions when certain conditions are met in the real world. EWM using smart contracts will bring more coordination among producers, importers, retailers and recyclers of EEEs. It will enable the government to regulate e-waste collection and recycling. It will also reduce the imbalance between the organized and unorganized sectors which will lead to increased transparency throughout the process.
We present a novel system for the identification of illegal garbage dumping from moving vehicles through the use of video analytics. The proposed system analyses a video sequence to detect garbage disposal from moving vehicles and identifies the owner of the vehicle based on the license plate of the vehicle. The system then moves to intimate the perpetrator of the act through a website. The offender may then pay the fine required through a payment portal available on the website. The proposed system comprises of modules for each of the stated tasks. The Vehicle Detection and Tracking module uses a Haar Cascade Classifier and Background Subtraction. The Garbage Identification module uses contouring and blob creation. The License Plate Reading module utilizes the OpenALPR library. The database interface accesses a Registration Certificate Book database and updates the website with relevant information. Due to the novelty of our problem statement, it was necessary to create our own dataset of video sequences featuring garbage of various sizes being thrown out of moving vehicles. The system successfully detects garbage in videos captured under versatile conditions, indicating that our approach is robust to changes in environment and illumination.
It is quite evident that everyone has certain objectives to be completed and quite a few expectations to be met. Accomplishments of these targets require proper planning in addition to an individual's abilities. Smartphones have seen substantial growth over the past decade and there is perceptible evidence of people relying on them. Although there are solutions in the form of to-do list applications to mitigate the scheduling problem, they provide little insight regarding an individual's time organization. Most of the existing applications just provide the feature of recording the tasks and users themselves need to set the time at which these tasks have to be executed. In our application focus is laid on learning the leisure time of the user by indirectly monitoring day to day activities through the app. Free time as predicted by the model and the list of tasks to be performed together form the input to the scheduling algorithm. The scheduling algorithm then allocates time for each task ensuring load balancing by evenly distributing the tasks across the week and the user is notified of their personalized final schedule.
Vehicle Routing Problem is renowned distribution problem in supply chain management. Because of its complex nature, it is categorized as non-polynomial deterministic (NP) hard problem. In the past decades, many researchers had applied various optimization techniques to solve the problem. In this paper, modification to a recently developed metaheuristic called Firefly Algorithm for specialization of Vehicle Routing problem called Vehicle Routing Problem with Time Windows (VRPTW) is suggested. The adaptation and implementation of improved Firefly Algorithm to VRPTW is explained and corresponding numerical experiments to justify the adequacy of the strategy are provided. Experimental outcomes demonstrate that the proposed modification procedures are effective and can get near optimal results for the VRPTW.
The Least Slack Time First (LST) algorithm is a dynamic scheduling algorithm and also known as Least Laxity First. It decides the dynamic priority of the task based on slack time; The task having minimum slack time will be considered the highest priority. It is the most suitable algorithm for scheduling of tasks in soft Real-Time Operating System (RTOS). The Shortest Job First (SJF) algorithm is a static scheduling algorithm and decides the priority of the task based on execution time required for a given task. Task which has minimum execution time considered as the highest priority task in SJF. It is not directly used for scheduling Soft Real-Time system. In this paper, we have implemented the LST and SJF for the soft real-time operating system. These algorithms have been executed on periodic task set, and observations are gathered. We have observed Success Ratio & Effective CPU Utilization and compared both the algorithm in the same conditions. It is noted that the LST algorithm performs well in underload scenario but not well in an overload situation. SJF not able to schedule specific task even in underload situation but it comparatively performs well in an overload situation. Practical experiments have been conducted on a large dataset. Data Set contains 7500 task set, and each task set includes 1 to 9 processes. CPU load for each process set varies from 0.5 to 5. It has been tested on 500-time unit to validate the correctness of both algorithms.
India is a multi-lingual and multi-content nation containing eighteen authority dialects, Kannada is one among them. A few works have been improved the situation of the recognition of transcribed Kannada characters. The objective of the proposed system is to identify the handwritten Kannada character correctly and convert it to speech to help the children with learning disabilities, like Dyslexia. In this paper, the image features are extracted using contour feature extraction method and k-NN is used for classification. The dataset collected is constrained and has all the basic characters along with compound and complex characters. A total of 5100 basic handwritten Kannada character's (includes Swaras and Vyanjanas) sample images are considered. The total size of dataset comes up to 3 lakh image samples. Pre-processing techniques have been applied to the images. The contour feature extraction method is used for feature extraction. A feature consists of 35 attributes for each image in the dataset. Then these feature vectors are passed to the k-NN classifier for recognition. Each test sample has been recognized as one class based on the nearest neighbors. Once the classes for test samples have been recognized, it is then converted into speech. The proposed algorithm works for all basic Kannada characters. The recognition accuracy is 84.728 % considering only moments in the features of test image sample. The recognized character is then converted into speech.
Nowadays, integrated circuits (ICs) security grown as a primary responsibility at every stage of IC supply chain due to the globalized design, fabrication, test, deployment and monitoring of an IC. In this regard, Advanced Encryption Standard (AES) is widely accepted and supported in both the domain, software as well as hardware. To diminish the effect and cause of different threats, researchers identified that the hardware obfuscation based AES is a promising technique and a solution towards piracy and reverse engineering. This paper discusses about the design and simulation of 128-bit AES algorithm using active hardware obfuscation techniques. 128-bit AES is designed and simulated using Xilinx Vivado 2016.2. In this paper simulation results of 128-bit AES algorithm are analyzed with and without obfuscation techniques. Results shows that with obfuscation techniques 128 bit AES algorithm offer higher level of security and implementation flexibility with small area overhead and power overhead. The robustness of proposed algorithm is also analyzed in the form of throughput and efficiency. The authors also presented the power consumption and analyzed the area overhead of proposed algorithm with different xilinx 7 -series FPGAs.
With the rapid development of the internet financial era, automatic financial transaction equipment has become the indispensable tools for life. Aiming at the defects and deficiencies in the recognition function of folded paper currency of automatic financial transaction equipment, this paper proposes that the convolution neural network should be applied to the identification of folded paper currency, reference to banknote authenticity identification and damage criteria, specification the angle of folded banknote, manual collection of Ukrainian banknotes folded image, damaged image and normal image as experimental dataset. using the OpenCV library to preprocess banknote images, using the Keras framework to construct the nine-layer convolution neural network, compared with the seven-layer LeNet-5 network, the recognition accuracy of the nine-layer convolution neural network is better than that of the seven-layer network, reaching 96.46%.
Good quality of sleep is very much important for keeping oneself fit mentally and physically. If sleep is disturbed it affects daytime functionality of a person and leads to several diseases like heart attack, stress, etc., In this work, we have collected data related to sleep patterns of people, analysed the data and provided suggestions to improve the sleep, health and lifestyle. We have used Miband to collect data, performed feature extraction and classification using logistic regression model. The proposed method resulted in encouraging accuracy and this web application can be developed into a mobile application in future.
