Micro-Electronics and Telecommunication Engineering

Inhaltsverzeichnis

1. Frontmatter

2. A Review on MIMO Dielectric Resonator Antenna for 5G Application

   Rajeshwari R. Malekar, Hema Raut, Laxmikant Shevada, Sumit Kumar

   Abstract

   Multiple input multiple output (MIMO) is the next-generation technology for the 5G mobile terminals. MIMO uses multiple antennas and integrates multiple antennas within constrained space which is a crucial issue. This requires high isolation between antennas. MIMO technology will increase system capacity, link reliability, and the data rate required for 5G communication. This paper presents a recent MIMO dielectric resonator antenna (DRA) proposed by authors for future 5G applications. It also compares various techniques used to improve isolation between antennas and gain for MIMO DRA while maintaining a compact size. In this paper, we have compared different 5G antennas available in the literature based on parameters like isolation, gain, radiation efficiency, and envelope correlation coefficient (ECC).

3. Comparative Study of CMOS and FinFET-Based SRAM Cell Using SVL Technique

   Deepika Sharma, Shilpi Birla

   Abstract

   The data stability issues in SRAM cells become more prominent with the decreasing of feature sizes in CMOS technology. The focus of memory manufacturers always remains on reducing power consumption and stability improvements of the SRAM cells. In this work, the critical parameters of FinFET and CMOS transistors based on SRAM cells are compared. This paper focuses on the simulation of 10T SRAM cell topologies design using sleep transistor, drowsy cache, and self-controllable voltage technique (SVL) techniques and their comparison. Among all these techniques, SVL shows the best results by consuming 4768 nw power for FinFET technology and 15.8383 n W power for CMOS technology. Evaluated results show 40%, 26%, and 10% improvement in HSNM, RSNM, and WSNM, respectively, and 80.93% reduction in leakage power. FinFET design SRAM cells result in higher RSNM, HSNM, and WSNM. For simulation, the HSPICE tool is used at 32 nm PTM CMOS and FinFET transistor with a supply voltage of 900 mV and 25 °C temperature.

4. Capacity Evaluation and Analysis of Long Range for IoT

   Asmita Singh, Abhishek Tyagi, Sindhu Hak Gupta

   Abstract

   Internet of Things(IoT) thought is growing in the past few years, and the range of IoT devices is increasing apace. With the advancement of wireless networks, the evolution of cutting-edge data analytics and the decline in the outlay of connected devices, there is a rise in cloud platform adoption, and the market is anticipated to evolve at a positive pace. LoRaWAN is the progressive evolved technology that is anticipated as it holds up a large sector of the billions of approximated IoT. This paper focuses on the network QoS by analyzing the signal quality and channel capacity on the different configuration settings of LoRa. The simulated result demonstrates the utmost combination to achieve maximum channel capacity, SNR, and minimum bit error rate irrespective of the spreading factor, code rate, and bandwidth. Result demonstrates that there are approx. 27.21% more nodes can be active at SF-7 concerning SF-12 and concludes that capacity deteriorates as the SF increases which further concludes that link quality is 27.1% averagely superior in SF-7 region that the SF-12.

5. VLSI Implementation of PN Sequence Generator Using Two Different Multiplication Algorithms

   Vaibhav Chaudhari, Surbhi Rathore, Aakash Gadge, V. S. Kanchana Bhaaskaran

   Abstract

   Wireless communication system plays an essential role in data transmission system. In wireless systems like satellite network, mobile communication, or any data transmitted network, a specified band is allotted. Such network has its own specified allotted spectrum for secure communication. Safe data transmission and reception are important and difficult tasks. Such data transmission needs some secure techniques to transmit data securely, and one such technique is hiding the transmission data or hiding the message signals. To provide security to message signal, random noise is added through pseudo-noise (PN) sequence generator. When PN sequence is added to a message signal, it resembles like a noise making it hard to decode and increases the signal bandwidth to utilize the whole spectrum. In this paper, the approach to generate PN sequence using Vedic multiplier has been proposed. In a Vedic multiplier, multiplication is done by using Urdhva Tiryagbhyam sutra, and basic multiplication method is used to design conventional multiplier. The PN sequence generator is implemented using Verilog HDL. The comparative analysis of Vedic multiplier over the conventional multiplier is implemented using Altera FPGA. Vedic multiplication gives an effective result when compared to conventional multipliers.

6. High-Performance Disease Prediction and Recommendation Generation Healthcare System Using I3 Algorithm

   P. J. Sathish Kumar, V. Auxilia Osvin Nancy, N. Sathish, K. Kajendran, N. Pugazhendi, S. Balaji

   Abstract

   The increase in number of disease challenges medical practitioner in making right decisions. As most diseases have the same set of symptoms, the medical practitioner struggles to take decision on recognizing disease as well as right treatment methods. A number of approaches are available for the disease identification and providing treatment, but finding the right approach is what matters. To solve this issue, an inter-/intra-disease impact, the disease-based prediction and recommendation generation method, is presented. The method first reads the input data set and produces a series of clusters with the samples obtained. In the second level, the method estimates inter-disease impact measure and intra-disease impact measure on various disease classes for every data point of the data collection. Using these two measures, the method computes I2-Disease weight for each data point in assigning a label to the data points. For the classification, the method estimates symptomatic disease weight based on inter-/intra-symptom correlation assessment. Based on the selected disease class, a set of treatment samples is populated and ranked according to their curing rate.

7. Performance Analysis of ISOWC Link Considering Different Modulation Schemes

   Naina Dahiya, Anuranjana, Sanmukh Kaur

   Abstract

   The development of optical communications systems from long fibers to compact wireless networks has led to the possibility of linking two satellites using a wireless optical link. The optical communication systems have been upgraded to use of wireless systems. The two satellites can be connected using optical wireless link. The optical wireless communication allows the exchange of information between two satellites at a higher speed. To act as a source of light, laser can be used and as an optical link, and a high-frequency light signal can be used. In this paper, we are analyzing and optimizing the different aspects of an optical communication link by selecting different modulation scheme. The IS-OWC link has been analyzed using PSK, DPSK, and OQPSK. The bit error rate (BER) and Q-factor have been used as the main determinant to analyze the performance of optical link between two satellites.

8. Different Diagnostic Aids and the Improved Scope of Establishing Early Breast Cancer Diagnosis

   K. Venkata Ratna Prabha, D. Vaishali, Pallikonda Sarah Suhasini, K. Subhashini, P. Ramesh

   Abstract

   A good prognosis of cancer therapies mainly depends on how early the condition is being diagnosed and hence the initiation of appropriate treatment measures. In view of availability and affordability of a wide array of diagnostic approaches and methods, an attempt is made to provide a brief insight into each technique that leads to a noninvasive mode of determining the cancerous condition at an early stage. Many of these modern methods utilize radioisotopes to determine the extent of the condition in three-dimensional anatomical manners unlike the invasive histological examination of biopsies. A sound knowledge of these modern methods and understanding the importance of early diagnosis and treatment response are much needed for the better prognosis and well-being of the condition. This paper presents the detailed review of types of cancer, its diagnosis, treatments and also the recent methods of diagnosis using deep learning techniques.

9. Deforestation Mapping Using MODIS Tree Cover Mask and Sentinel-1 Images

   Prachi Kaushik, Suraiya Jabin

   Abstract

   Forests are an essential resource that needs to be conserved. Excessive cutting of trees for urbanization and growth has led to rapid deforestation in parts of Haryana and its neighboring areas. In this study, tree cover mapping is done over a period of five years (2015–2019) using Sentinel-1 ground range detected band-C images. To distinguish between the land cover classes, a rich set of features play an important role. Based on the second-order statistics, gray level co-occurrence (GLCM) features are extracted from the image to study the uniformity between the pixels. A binary classification of the study area into tree and non-tree area is carried out by supervised random forest algorithm. According to the analysis, the net rate of reduction of the tree cover in parts Haryana and its neighboring areas, i.e., parts of New Delhi, is calculated as 3.1% in successive years.

10. Assessing the Suitability of DMG-HK Trapezoidal FinFET for High Temperature Applications

    Priyanshi Goyal, Harsupreet Kaur

    Abstract

    In this paper, we have developed a temperature-dependent analytical model to study the effectiveness of DMG-HK Trapezoidal FinFET for high temperature operation. It is demonstrated that the proposed device with incorporation of dual material gate and high K layer leads to less degradation in device performance at high temperature as compared to conventional devices. The improvement is seen in terms of reduced threshold voltage sensitivity and DIBL with change in temperature along with suppressed hot carrier effects and improved subthreshold slope.

11. Optimization of the CNN Model for Hand Sign Language Recognition Using Adam Optimization Technique

    Simrann Arora, Akash Gupta, Rachna Jain, Anand Nayyar

    Abstract

    To communicate with someone, hand signs prove to be more effective than just words to emphasize and structure the conversation and give more clarity. Some machines, like a driving car or a robot, are also operated by hand gestures as they are more convenient to interpret, but devising the recognition of hand gesture system is an extremely arcane commission since the model should be clever enough to recognize the hand gestures in distinct positions and orientation. In this research, the convolution neural network (CNN) model of deep learning (DL) is utilized for training the hand sign language image dataset. In addition to this, the Adam optimization technique, which is known to leverage the adaptive learning technique for figuring out the learning rate for every parameter, is utilized here for determining the optimized values of hyperparameters. The dataset is taken from GitHub and is contributed by the Turkey Ankara Ayrancı Anadolu High School students. The dataset consists of 218 hand sign sample images of each of the ten digits ranging from 0–9. After determining the optimized values of various hyperparameters, the proposed framework is then validated upon the validation dataset. The training and validation loss over the optimized number of epochs comes out to be 0.021 and 0.064, respectively. Conclusively, the investigational outcomes depict that the anticipated optimized CNN model displays an increased accuracy of 98%.

12. Hybrid Spectrum Access Strategy for Throughput Enhancement of Cognitive Radio Network

    Indu Bala, Kiran Ahuja, Anand Nayyar

    Abstract

    Recently, cognitive radio technology has gained worldwide attention due to its potential to overcome spectrum scarcity. The technology allows unlicensed users to use licensed spectrum in a manner such that minimum or no interference may be experienced by licensed users. To do so, the conventional scheme divides the time frame into two slots i.e. sensing slot and data transmission slot. In this scenario, the achievable throughput of unlicensed user fundamentally depends on the accuracy of sensing results since the sensing error in terms of miss detection and false alarm results into collision and spectrum underutilization, respectively. To overcome this bottleneck, an improved frame structure is proposed in this paper with two sensing slots and one data transmission slot. Based on the sensing results of the current and previous frame, the proposed scheme allows unlicensed users to switch between underlay and interweave mode of communication to enhance secondary user’s throughput. It is shown that proposed scheme has a potential to achieve 50% more throughput as compare to the conventional schemes, when channel is occupied by primary user. The simulation results are presented to illustrate the effectiveness of the proposed scheme. Moreover, the performance of proposed scheme is also compared with the conventional schemes to validate the results.

13. Speech Emotion Recognition Through Extraction of Various Emotional Features Using Machine and Deep Learning Classifiers

    Kunal Jain, Anand Nayyar, Lakshay Aggarwal, Rachna Jain

    Abstract

    Identification of emotions along with the semantics from the speech signals is still one of the most challenging tasks because of the arising difficulty in the extraction and selection of the appropriate emotion features. The speech emotion recognition (SER) system has become one of the active research topics in recent years. Many researchers proposed various models to enhance the machine responses during the man–machine interactions by identifying the emotional state of the customer. This research work aims to propose a multi-class classification model to extract and classify the emotional state of the speaker into eight classes, namely happy, sad, surprised, disgust, neutral, calm, angry, and fearful. In this research work, two machine learning classifiers, namely k-nearest neighbors and random forest, are used along with the multi-layer perceptron classifier of deep learning for determining the emotional features from speech signals. The dataset for this research is obtained from the RAVDESS. Along with the audio speeches of 24 actors, the audio song dataset is also utilized for the better training of the proposed model. The performance of all three algorithms is compared and evaluated by using the confusion matrix and accuracy plots. The empirical results show that the KNN classification model of ML has performed better than other proposed algorithms with increased accuracy of 76.25%.

14. Fusion-Based Biometric Identification Using Palmprint Images

    K. Vidhya, T. R. Ganesh Babu

    Abstract

    Higher identification accuracy is obtained in multibiometrics than single biometrics. The proposed work improves accuracy in personal identification for achieving high standard security in real-world applications. Among numerous biometric technologies, palmprint recognition drives more attention due to its virtuous performance. It is easy to implement, and better results can be obtained by combining the left palmprint images with that of right. The proposed method executes multibiometrics by systematically combining the palmprint images of left hand and right hand. Fusion is performed using scores generated from palmprint images for performing fusion. The proposed algorithm considers the nature of left palmprint image and right palmprint image to deed the resemblance of palmprints of both hands of same subject. Perfect identification performance is obtained which gives better results while comparing with the previous identification methods.

15. PHY Layer Design for MIH in GNU Radio

    Akshada Bhosale, Ganesh Rahate, Nilkanth Chopade

    Abstract

    Handover is an essential process in wireless networks where mobile node changes its access points during the call. Due to heterogeneity in the networks and coexistence of various wireless technologies like wireless fidelity (WiFi), long-term evolution (LTE), wireless interoperability for microwave access (WiMAX), etc., the vertical handover is essential for mobile unit. Vertical handover (VHO) happens to be amongst heterogeneous technologies. Media-independent handover (MIH) is an IEEE 802.21 framework that defines the horizontal and vertical handover process amongst IEEE and non-IEEE standards. MIH suggests an intermediate layer in the technology stack just above physical and media access control (PHY-MAC) layer and greatly relies on PHY-MAC for events and triggers for handover. The handover delays and subsequent packet loss decide quality of service in heterogeneous networks. The various triggers received from PHY-MAC layer rely on hardware/software design of underlying technology. The PHY layer of technologies like WiFi, WiMAX, and LTE relies on orthogonal frequency division multiplexing (OFDM). OFDM is an advanced version of multicarrier modulation system with high spectral efficiency, resistant to inter-symbol interference, high data rate, and less complex in design. In this paper, we propose design of OFDM transreceiver in GNU radio from our future perspective of MIH implementation in software defined radio (SDR) platform like Universal Software Radio Peripheral (USRP).Our design is based on various signal processing blocks and can successfully transfer the text in GNU radio platform.

16. Machine Learning Technique for Target-Based Sentiment Analysis

    Jyoti Srivastava, Neha Katiyar

    Abstract

    The study covered with machine learning strategies utilized in the analysis of sentiments at both the sentence and viewpoint levels. It likewise tended to the advantages and inconveniences of the different condition of-the-craftsmanship procedures. This paper perceived that the arrangements viability contrasted by the technique. As far as estimation system, there is an absence of institutionalization. Accordingly, it is beyond the realm of imagination to expect to reach determinations from the present condition of-the-workmanship approaches where strategy gives the best result to every area. In any case, a move from the customary word-based way to deal with semantic-based idea driven methodology is obviously apparent in the measurement level sentiment analysis. Machine learning approaches alongside semantic idea driven methodology can create another age of calculations that can react to language and significant level settings. This gap will likewise leave an enormous region for the scientists to find out experimental and emotional views of a human

17. Performance Improvement in Target Detection Using Various Techniques in Complex Matched Filter in Radar Communication

    Ajay Suri, Soham Gautam, Ridhi Aggarwal

    Abstract

    The term radar was coined in 1940 by the United States Navy and is derived from three terms, radio detection and ranging. Radar transmits electromagnetic (EM) waves of radio frequency (RF). Radar is also helpful in classifying targets as known or unknown; a known target may be monitored to check the progress of the respective target. An unknown target may be a theft unit; it may or may not be with the intentions of causing harm. Radar also faces interferences while its operation, namely clutter or some intentional/unintentional jammers (Han C et al, Multimed Tools Appl 1–16, 2018 [Han C, Guangyu G, Yu Z (2018) Real-time small traffic sign detection with revised faster-RCNN. Multimed Tools Appl 1–16]). Moreover, radar also faces obstruction by its own thermal noise. Nowadays, radar is advanced enough to implement the two-dimensional and three-dimensional mapping. Radar is also widely used for collision avoidance and earth resource monitoring (Hu et al, IEEE Sens J 18:3152–3162, 2018 [Hu et al. in IEEE Sensors J 18:3152–3162, 2018]).

18. Unhindered Safety Monitoring System for Underground Workers

    G. Shanmugaraj, B. V. Santhosh Krishna, S. SriSahithya, M. Sandhya, T. H. Monikca

    Abstract

    Though the earth is heading toward automation, there are still many works which could be performed only by mankind. Such jobs always tend to be dangerous and life demanding. Even today, jobs like sewage cleaning and mining fall under this category. In such cases, real-time health monitoring systems for these workers will be helpful. In this paper, the device presented will monitor the heart beat rate, the body temperature, the methane concentration and also the atmospheric oxygen concentration and provides alert to the worker and the exterior unit. When parameters deviate from the safe limit range, it will alert the workers to stay safe and detect toxic gases before any harm.