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High-speed vehicles and reckless driving have caused quite frequent accidents by intoxicated people. Alcoholism is one of the key causes contributing to reckless driving. We used an alcohol sensor in this work to detect the driver's condition and take the appropriate action. Because the flow of information is very critical in case of incidents, the device also integrates on board pressure and MEMS sensor to identify the effects. The camera takes the pictures, and the GPS location information is sent to registered mail via the Raspberry pi3 board's TWILIO app mounted in the car, sending the alert to the registered mobile phone. In addition, FMCW radar is used for anti-collision purposes because of its precise measurements of short range.

A practical exhibition of the consequences of the 3D IC in heat management by adding the fin and heat spreaders to thermal through silicon via (TTSV) and IC respectively is proposed in this paper. Thermal cooling and its distribution of burnt potentials at various conditions are the different possessions which can be simulated using FEM simulator. This paper illustrate materials like Silicon nitride (Si3N4), Aluminium oxide (Al2O3), Silicon dioxide (SiO2) as they have the high thermal conductivity and high temperature capabilities which leads to the thermal management in the 3D IC design. Currently, semiconductor industries has cognizance of the metrics such as low density, high thermal strength, good facture of toughness, excellent wear resistance of Silicon nitride and it also shows better capabilities of high temperature, oxidation resistance and high retention strength compared to other metals. Besides silicon nitride has good resistance to thermal shock due to low coefficient of thermal expansion. Aluminium oxide is known for an better electrical insulator but has high thermal conductivity and which can be utilized as the substrate for integrated circuits. The high temperature furnace insulations and electrical insulations are designed by using the aluminium oxides as it has high boiling points and melting points. This makes the rapid use of alumina films in manufacture of IC industry. The other uses of alumina films include spark plug insulators, micro-electric substrates and insulating heat sinks. Silicon dioxide has the properties which helps to use them as the insulator material in IC technology as it has high thermal stability and making it useful for device integration and has high dielectric strength and thermal expansion coefficient is very low and in this paper we have taken these three materials and these materials have been implemented in our model and advertence the outcomes that which material is working efficiently and preciously.

3D Integration technology is probably the best methodologies among others which suits CMOS applications with in various layers of devices are stacked with high thickness interconnects between the layers. In 3D IC structure electrical and thermal models are introduced for the interface between Through-Silicon-Via’s (TSV’s). TSV’s can be used to enable the 3-D platform; however this TSV’s will represent additional concerns. TSV-to-substrate and TSV-to-TSV noise coupling is assessed inside the 3-D IC’s. A vertical TSV’s design is proposed to reduce the area per TSV, capacitive coupling and effective inductance as compared with the other classical design patterns. TSV’s and substrate noise coupling is one of the major drawbacks in 3D IC. Some of the researchers have tried to overcome the problems in TSV based 3D IC due to noise coupling using different models, materials and different circuit techniques. This paper shows the complete road map for the noise coupling reduction in future IC integration.

3D IC Integration shows the most emerging technology for future integration nodes which is now a most important trend for the semiconductor industries. Through-silicon-via (TSV) based integration is the prime technique to facilitate 3D IC integration without compromising the Moore’s law. It is likely to usher the IC industries a paradigm shift from planar integration as it provides major benefits like improvement of system performance, power and enables heterogeneous integration. In this paper, we report Germanium/poly-germanium as an substitute material for Silicon/poly-silicon due to its superior carrier mobility. Mobility of electrons and holes in c-Silicon is 1500 cm2/V-s and 450 cm2/V-s respectively, where as in c-Germanium, the respective values are 3900 cm2/V-s and 1900 cm2/V-s. Therefore, considering these carrier mobility values we can envisage that poly germanium will be one of the ideal candidate towards realizing a high speed TSV interconnect when compared with poly-silicon. Nevertheless, even though copper is used widely to fill TSVs, it is also bereft of proper thermal expansion match with Silicon/dielectric (SiO2). The coefficient of thermal expansion (CTE) of Cu (∼17.5x 10-6 /°C) is many times more than of silicon (∼2.5x 10-6/°C). Hence, there will be heavy mismatch between Cu filled TSV and Silicon/SiO2, and then it creates stress and strain between the interfaces. The CTE of germanium (5.8x 10–6/°C) is very close to Silicon, thus there CTE mismatch is very less, this fact is also an added advantage for Germanium to challenge copper as TSV material.

Now a day’s Artificial intelligence and deep learning techniques recommended critical E-commerce applications. The human computing, computer aided designs cannot understand the alternative offline and online products. Therefore, customers are critical to find out the products such as groceries, fashion and health. However, it is a major task to overcome this limitation for human perceptions. In this research work an advanced FCNN deep learning model is proposed with global thresholding technique. For this work selecting the digital images and online images for pre-processing and classification. At primary stage segmentation is applied then classification is performed through FCNN, at final calculating the performance measures such as accuracy 98.7%, sensitivity 98.7% and throughput 99.23% has been achieved and outcomes are challenging the present technology.

Electrocardiogram (ECG) is widely used technique in study of heart beat irregularities such as cardiac arrhythmias, sinus rhythms and heart failure. It is a significant and popular technique to classify and detect the cardiac infraction. ECG signal analyses the electric activity of heart and outputs it in the form of waveforms which help in detection of heart irregularities. The main goal of this research work is to classify the arrhythmia with more accurate results in less computational time. The research is carried in machine learning technique- SVM classifier using Discrete Wavelet Transform (DWT). In this methodology, ECG samples of three different classes-Normal Sinus Rhythm, Congestive Heart Failure and Cardiac Arrhythmia were collected from MIT-BIH and BIDMC databanks. The collected signals were prepared into training set and testing set with a ratio of 70:30 percent respectively. Total 190 features were extracted from the prepared data using Discrete Wavelet Transform. DWT was chosen as it has the ability to vary the window size depending on the frequency. The extracted features were given to SVM classifier, which is best for classification purpose. The results were evaluated using the testing set and the final results were plotted using a confusion matrix. The performance accuracy of the model is 95.92 percent.

Alzheimer's is a dynamic ailment that decimates the mind's memory and its general functioning. Unfortunately till now, no single test can diagnosis this disease. Cerebrum checks alone can't be considered as a key factor to decide if the individual is experiencing it or not. As of now, the physician is in a conclusion that an individual is suffering from Alzheimer's on premise of the reports of the relations in regards to the social proclivity and checking the past clinical record. Artificial intelligence along with Machine Learning calculations perhaps in a situation to adjust this model. Big processing, in light of the fact that the data is taken through various sources with complex and creating circumstances that make certain to develop later on. Along these lines, in that, we'll take consequences of what extent level of patients get the illness as positive data and negative data. The proposed arrangement shows a big processing model, from the data mining perspective. Utilizing classifiers, this paper presents the work by preparing Alzheimer's rate and qualities are appearing as a disarray framework using different machine learning algorithms. The earlier research proved that the detection of Alzheimer’s disease using Support Vector Machine classifier and obtained very less accuracy. In view of this there is need of increasing the accuracy. So, this paper presenting different algorithms to classify the data to improve the efficiency in detecting the mentioned disease and observed that the Support Vector Machine with linear kernel model gives better accuracy than other models.

Traditional, constant breadth Adder Plantlet “AP” structure proposed from the full-breadth AP setup utilizing straight or post-reduction technique. In straight or post reduction, a single lower request bit of every adder yield of full-breadth adder Plantlet is post-shortened. Then the circumstance of straight-reduction, lower arrange bits of conclusive phase adder yield remain shortened. Both these techniques don’t give a proficient structure. In this concise, a new plan is displayed to acquire fixed-breadth AP prearrangement utilizing shortened information. An inclination estimation process dependent on probabilistic procedure is introduced to make up for the reduction mistake. The proposed constant-breadth AP structure for input-vector sizes 8 and 16 area-postpone item putting something aside for word-length sizes “8, 12, 16” individually. What’s more, figures the yield nearly with same precision post-shortened constant-breadth AP, the most elevated exactness in the middle of the current constant-breadth AP. To speed up the adder plantlet operation we will use “Modified Full adder” (mux based full adder) for high speed operation.

Through-silicon-via (TSV) technology has emerged as the key technology to enable 3D IC integration. 3D integration is seen as the leading candidate, which can help in sustaining Moore’s Law to future IC technology nodes. It clearly set a benchmark to the IC industry due to its tremendous benefits in performance, data bandwidth, and supports heterogeneous integration. In 3D IC structure TSV’s are used to carry the overall electrical signal between the layers. The major drawback in the TSV based 3D integration is noise coupling between aggressive (electrical signal carrying TSV; ETSV) and victim TSV’s (ground). It can be reduced by creating very good isolation between TSV’s and Si substrate by using different liner materials around TSV’s. In this work, we have addressed various TSV models to reduce noise coupling between the TSV’s. The models we have studied are single layer model and stacked layers of liner model. Apart from this we have studied how the core materials in the TSV reduces noise coupling between aggressive and victim TSV’s. Also, we have shown the high frequency study for proposed models. Finally, our work shows single layer model with Crystalline-Germanium is comparably better noise coupling reduction even at higher frequency.

The MLF-CNN includes a proposition production phase and also a diagnosis stage. In the first stage, they develop an MLF area proposal system and also pop the question to utilize a summation fusion strategy for integration of the two convolution layers. Intelligent video-surveillance is currently an active research industry in pc sight as well as artificial intelligence techniques. It delivers helpful resources for monitoring operators and forensic video private detectives. Individual re-identification (PReID) is one with these tools. Several approaches have been proposed to raise the functionality of PReID. One of the systems, a lot of scientists, made use of deep semantic networks (DNNs) as a result of their far better efficiency and fast completion at exam opportunity. Our objective is to offer potential researchers the job being done on PReID today.

Compared to OFDM, FBMC is the modulation technique that has enhanced spectral characteristics. To support higher data rates over the time and frequency selective channels FBMC plays a crucial role compared to OFDM. Simulation results shows that over flat fading channel, there is no significant changes between OFDM and FBMC, where as in frequency selective and time selective channels. FBMC shows superior performance improvement over Conventional OFDM system.

Ab-initio calculation use to study the band gap, electronic structure and optical properties of orthorhombic structure of pure Sr2SiO4 and doped with Eu2+ and Dy3+ phosphors. Our calculation is based on density functional theory (DFT) her used GGA + U approaches with SIESTA code. In order to explore the details electronic properties find their band structure, projected density of state, density of state of these systems are performed. Studies the optical properties are shown in dielectric function, refractive index, reflectivity and absorption coefficient are also performed in ambient conditions energy between 0 and 20 eV.

Skin cancer is considered as one of the most dangerous types of cancers and there is a drastic increase in the rate of deaths due to lack of knowledge on the symptoms and their prevention. Thus, early detection at premature stage is necessary so that one can prevent the spreading of cancer. Skin cancer is further divided into various types out of which the most hazardous ones are Melanoma, Basal cell carcinoma and Squamous cell carcinoma. This project is about detection and classification of various types of skin cancer using machine learning and image processing tools. In the pre-processing stage, dermoscopic images are considered as input. Dull razor method is used to remove all the unwanted hair particles on the skin lesion, then Gaussian filter is used for image smoothing. For noise filtering and to preserve the edges of the lesion, Median filter is used. Since color is an important feature in analyzing the type of cancer, color-based k-means clustering is performed in segmentation phase. The statistical and texture feature extraction is implemented using Asymmetry, Border, Color, Diameter, (ABCD) and Gray Level Co-occurrence Matrix (GLCM). The experimental analysis is conduted on ISIC 2019 Challenge dataset consisting of 8 different types of dermoscopic images. For classification purpose, Multi-class Support Vector Machine (MSVM) was implemented and the accuracy obtained is about 96.25.

Industrialization and urbanization in India have caused heavy environmental pollution. Most of the surface water had been polluted due to the environmental influence. Providing better quality drinking water to public is also a challenge due to pollution in the ground water and contamination even during distribution. Thus, it is very necessary to have adequate methods and equipment for water protection and drinking water quality measurement is an important aspect for the purpose. The protection of public health is an imperative and the potential of millions of severe effects from water contamination is not unrealistic. There is a need of In-situ monitoring, instant collection and calibration of data rather than manual collection of samples and testing. Wireless Sensor Network (WSN) has paved its significance into various applications. Although manual monitoring of water quality has been done, it requires a lot of labor, time and equipment. So, there is a need to develop a robust and reliable smart system where a real time monitoring of parameters of water quality for different water distribution tanks is done all the while. In this paper, Water Quality Monitoring (WQM) in a predefined Wireless Sensor zone using Zigbee Technology is implemented. Water Quality can be accessed in practical systems through the sensors which send the water quality data to the base station. Now a days, Renewable energy power generation is playing a main role in which Solar power is widely used. Maximum Power Point Tracking (MPPT) Controller improves the efficiency of Solar Power System. In this paper, a flexible, reliable Wireless Senor Network (WSN) based method of monitoring the quality of water with maximum power point tracking controlled solar PV system is developed.

Infectious diseases like typhoides, dengues, etc. are caused by water and environmental changes. It is important to provide medications for such infection patients. Conventional approaches for the correct diagnosis of patients are not acceptable. To make an appropriate diagnosis it is important to accurately identify patient status. Although advancement in reliable diagnostics of technology remains an essential mission. This thesis suggested a classification approach for certain infectious diseases. 173 Typhoid fever patients and 119 Dengue Fever patients were collected clinically. Crafted a hybrid method using voting set technique that was constructed as base classification in machine learning techniques. The diagnostic results can be classified according to the classificator's majority of votes. Data set tests were carried out and the results were contrasted with traditional computer teaching methods, including the Group Baive Bayesian, K-Nearest Neighbor and the Decision Tree. Analysis indicates that, in the case of typhoid and dengue-fever infected patients, the suggested hybrid solution has a 97.2% precision. Focusing on infectious diseases, several traditional approaches were analysed and the proposed hybrid methodology focused on an ensemble voting strategy that showed the highest efficiency in classifying infectious diseases.

An advanced System-on-Chip (SoC) architecture is designed to identify different valuable minerals embedded in the earth’s surface in this paper. Materials such as gold, diamond, petrol, and coal generate high revenues for developing and established nations. Therefore, to identify different materials, an efficient electronic device is designed in which scanning becomes an important parameter. This investigation is performed by an SoC-based ultrasonic module designed using Xilinx Vivado and verified on the ZYNQ-7000 FPGAboard. This architecture consists of a code generation unit, modulator, delays, emitter, demodulator, correlator, detector, and receiver. These modules are developed using Verilog HDL and deliver output on high-level processing screen. The experimental results outperform the conventional methodology with respect to reduced power consumption and area, this model is compared with existing methods to identify the operational performance. This SoC based ultrasonic scanning method improves the power consumption by 73.24% and area 72.24%.

In comparison to the OFDM (Orthogonal Frequency Division), Filter Bank multicarrying function (FBMC) has superior spectral properties, which complicates operational multiple input and mixed outputs. By extending a symbol in time, we can totally remove the imaginary interference. This gives the ease of applying all MIMO strategies known in OFDM to FBMC. The approach itself is of little complexity since it is based on hadamard matrices. While FBMC spreading allows complicated orthogonality to be restored in one block of communication, interference may be seen from the neighbouring blocks. By inserting a time security slot, the signal-to - interference ratio can also be improved. Furthermore, we examine the effect on the propagation process of a time channel. The estimation of the potential for Bit Error in double selective channels has been improved. In doubly selective channels, that is, time selectivity and frequency selectivity, we study the degeneration of output between OFDM and FBMC. In order to do this, we derive closed-form Bit Error Probability (BEP) terminology for the arbitrary linear modulation methods dependent on one-tap equalizers, in which our general BEP expressions include OFDM and FBMC.

Leukemia – a type of blood cancer which was caused by abnormal growth of White Blood Cells (WBC) in bone marrow. Accurate identification of effected patients and diagnosing within time is most important otherwise it causes to death. Machine learning algorithms are most popular for accurate identification of patient status. Presenting 152 patients data which was clinically collected. In this paper, proposing Bagging with Random split function for classification of leukemia patients. Also compared the classifier performance with various ensemble techniques – Multiclass classifier, Logit Boost, Stacking and Random Committee classifiers. Experiments are conducted and proven that bagging with Random split function shows 95% accuracy on classification of leukemia disease dataset.

Gait patterns and coordination are altered in diabetic cardiomyopathy patients with Parkinson's disease. There is little information with possible explanations. Fall identification and simulation in all age groups, including the use of Fuzzy logic and sensor data for evaluating gait error. The current proposal emphasizes about fall prediction and estimation of diabetic cardiomyopathy disorders for aged, adults and infants. The inputs for the fluctuating inference system are the patient's height, gyroscope, age and weight. RMS error, Estimation and identification are the output variables where inputs are from the MIMO-used sensor data. Input and output variables and rules are supplemented with the membership functions. The extracted features are equivalent to ordinary random mean square error values. The application of a fuzzy Mamdani technique uses a triangular-trapezoidal logic to obtain a random mean square error, classification and recognition requirements for the elderly, adults and children. IoT monitors the collected outputs and store in cloud. The accuracy of a study through triangular trapezoidal approaches of 20 patients with Parkinson's diabetes cardiopathy in the fuzzy logic is 95%. A wide range of fields such as digital control, data detection, decision interpretation, expert systems and computer vision have been employed successfully.

This work addresses the robot controlled by hand gesture and voice control. In the technology era, the space between the physical and the digital world is brought closer by the introduction of gesture concept. For all the dangerous tasks we prefer technology rather than people. Even though these robots are being controlled manually in the early stages, these can now be controlled via voice and gestures. This technology of gesture and voice recognition can be defined by the interaction between the computer and the body language of human beings. This constructs the communication link between technology and mankind. The target of this work is to upgrade the complete security to the robot and to simplify the controlling mechanism.

Agricultural productivity plays a major role in an Indian economy; therefore the disease detection in the field of agriculture is important. Farmers struggle a lot for proper crop production due to multiple diseases affecting the plant so there is a need to detect the disease at initial stage. One major disease in the crop is leaf spot. The purpose of the proposed system is to identify the leaf spot using image processing techniques. In this research the disease detection is done in four stages, image acquisition, image segmentation, feature extraction and classification. For image segmentation is done with K-means clustering method and features are computed from disease affected cluster. Features such as Contrast, Correlation, Energy, Homogeneity, Mean, Standard Deviation and Variance are extracted. The extracted features from disease cluster are given as classifier inputs to classify the disease. The classifier used in this paper is neural network (NN) classifier. It is observed that the accuracies for bacterial leaf spot and target spot of cotton leaf diseases as 90% and 80% respectively. For tomato leaf diseases- septoria leaf spot and leaf mold as 100%.

Planning and performance of any wireless channel in different environment is analysed by the path loss prediction models. In this paper the path-loss between the Base-Station (BS) and Mobile-Station (MS) for the Received-Signal-Strength (RSS) in different wireless environments is estimated. Non-isotropic antennas are used with different transmitter and receiver gains. A mathematical expression is proposed to compute path loss with antenna gains of 0.5 and 1 by varying the distance up to 1km. Then the path loss is optimized by introducing the path loss component that varies with environment. The path loss component is varied from 2 to 6. The shadowing effect is modelled by log-distance method. In this paper Gaussian random variable is considered with a mean zero and standard deviation is taken as 3dB. Simulation results show the path loss component increases by increasing the gain of antenna. The path loss is also calculated by IEEE 802.16d model and Hata-model. It is observed that as the gain of antenna decreases the path-loss increases and vice-versa. Path-loss also increases by distance and shadowing effects.

Many heart diseases can be identified and cured at very early stage by studying the changes in the features of electrocardiogram (ECG) signal. In this paper, an algorithm to detect Atrial Fibrillation (AF) in the ECG signal is developed. For correct detection of AF, pre-processing and feature extraction of ECG signal shall be performed before it detects AF. After considering the ECG signal from the database, in the preprocessing stage de-noising of the ECG signal is carried out in order to obtain a clean ECG signal. After pre-processing, before feature extraction R peak detection is carried out for the signal. Since R peak is having the highest amplitude, and therefore it is detected in the first round and subsequently location of other peaks of the ECG signals are performed. After completing preprocessing and feature extraction using Discrete Wavelet Transform (DWT) applied based on inverted T wave logic and ST-segment elevation. Our classification algorithm was demonstrated to successfully acquire, analyze and interpret ECGs for the presence of AF indicating its potential to support m-Health diagnosis, monitoring, and management of therapy in AF patients.

Wireless sensor networks have plenty of small sensor nodes. These sensor nodes direct the sensed data to the Base Station (BS). The transmission of data to the BS utilizes more energy. Many clustering algorithms are available for improving energy efficiency in homogenous and heterogeneous networks. This paper investigates the protocol which minimizes the energy consumed with respect to network lifetime and stability period. In this paper it is considered the Distributed Energy Efficient Clustering (DEEC) and Threshold Distributed Energy Efficient Clustering (TDEEC) in different heterogeneous network scenarios. The performance of TDEEC and DEEC is compared by varying the parameters-advanced nodes, super nodes, fraction of advance and super nodes. The network life and stability period is approximated for 100 nodes in 10000 rounds. It is observed that numbers of nodes alive are more in TDEEC than DEEC after completion of 10000 rounds. The packets transmitted to the BS in TDEEC increases from 2lakhs to 7 lakhs for 1700 to 7000 rounds but in DEEC packets transmitted to the BS are maintained constant 2 lakhs after 7000 rounds. It is observed the TDEEC is the best protocol when compared to DEEC based on stability period and network life-time.