2016 IEEE Annual India Conference (INDICON)最新文献

{"title":"Design of high speed CRC algorithm for ethernet on FPGA using reduced lookup table algorithm","authors":"Bajarangbali, P. Anand","doi":"10.1109/INDICON.2016.7839009","DOIUrl":"https://doi.org/10.1109/INDICON.2016.7839009","url":null,"abstract":"This paper describes the design and development of modified CRC algorithm for the hardware implementation on FPGA to meet the speed constraint for Ethernet, using the reduced lookup table algorithm. This algorithm can be applied for any length of data, by processing it in a block of 16 bytes at a time. The last block may have less than 16 bytes. To process an input block of 16 bytes, the algorithm first forms an optimized table of pre-calculated CRC. Corresponding to the input data, lookup from this table is done and the results from the table lookup are combined by XOR operations to form the final CRC of the input data. The Ethernet data whose CRC needs to be calculated is processed in blocks of 128 bits at clock frequency of 312.5 MHz to achieve a throughput of 40Gbps. The entire design is functionally verified using ModelSim SE Plus 6.3g. Applications in Internet of Things and Machine-to-Machine require real time big data platforms and Artificial Intelligence platforms, where there is high demand for lower latency and high speed network infrastructure. To create such a reliable network infrastructure at high speeds, a hardware accelerated CRC error detection needs to be used.","PeriodicalId":283953,"journal":{"name":"2016 IEEE Annual India Conference (INDICON)","volume":"533 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114498378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Applicability of big data analytics to massive MIMO systems","authors":"Pallaviram Sure, C. Babu, C. Bhuma","doi":"10.1109/INDICON.2016.7839093","DOIUrl":"https://doi.org/10.1109/INDICON.2016.7839093","url":null,"abstract":"The evolving 5G standards promise green communications with enhanced data services and significant link reliability. Massive multi input multi output (MIMO) techniques are the driving force behind green communications, since they provide better energy efficiency with reduced transmit power. The massive data generated from such mobile communication systems, is a rich data source of great value. Procuring useful analytics from this precious resource, a big data aware 5G mobile communication system can be developed. A particular choice of big analytics brings in the concept of large random matrix models and single ring law. In this paper, first, big data analytics is performed in the context of a mobile user communicating to, either a massive MIMO or a massive MIMO orthogonal frequency division multiplexing (OFDM) system. Constructive insights such as transmitted (source) signal correlation analysis (attributed to certain network events), channel correlation analysis (attributed to user mobility) have been extracted. Ring law also has its roots in signal detection, which suggests that few other signal detection algorithms may be suitable candidates for signal/channel correlation analysis. Therefore, second, a proposed extension of an information theoretic criterion (ITC) based signal detection algorithm, for correlation analysis, is compared with ring law. Using massive MIMO and MIMO-OFDM system simulations, the said correlation analyses have confirmed the prevalence of ring law. Third, it is deduced that integrating big data analytics with massive MIMO system improves spectral efficiency.","PeriodicalId":283953,"journal":{"name":"2016 IEEE Annual India Conference (INDICON)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116970117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A simplified space vector PWM for cascaded H-Bridge inverter including over modulation operation","authors":"B. Sirisha, P. Satish Kumar","doi":"10.1109/INDICON.2016.7839038","DOIUrl":"https://doi.org/10.1109/INDICON.2016.7839038","url":null,"abstract":"This paper presents a simplified space vector pulse width modulation method for multilevel inverters. In this method, the desired switching states of the reference voltage vector and switching time calculations are done online through generalized simple expressions without any predetermined data in the memory lookup table. The proposed method is also extended for over modulation region by incorporating the concept of reference voltage vector and ON-time modification. This new control method is attractive in terms of reduction in computational complexity, minimization of voltage and current THD and the extension of range for over modulation operation; the simulation has been carried out for cascaded H-Bridge five level inverter including over modulation range using MATLAB/Simulink. The hardware implementation of the proposed method has been done using XILINX SPARTAN-3A FPGA Processor. The simulation waveforms validate the harmonic analysis of voltage and current at different modulation index.","PeriodicalId":283953,"journal":{"name":"2016 IEEE Annual India Conference (INDICON)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123723765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Semi-supervised clustering using seeded-kMeans in the feature space of ELM","authors":"R. Roul, S. Sahay","doi":"10.1109/INDICON.2016.7838892","DOIUrl":"https://doi.org/10.1109/INDICON.2016.7838892","url":null,"abstract":"Extreme learning machine (ELM) is based on single layer feed forward neural networks (SLFNs) and has become a rapidly developing learning technology today. Recently developed Multilayer form of ELM called ML-ELM which is based on the architecture of deep learning, become more popular compared to other traditional classifiers because of its important qualities such as multiple non-linear transformation of input data, higher level abstraction of data, learning different form of input data, capable of managing huge volume of data etc. In addition to the above, another good quality which ML-ELM possesses is its ability to map the input feature vector non-linearly to an extended dimensional feature space for giving better performance. This paper proposes an approach where unsupervised and semi-supervised clustering using kMeans and seeded-kMeans have been done in ML-ELM feature space. The empirical results of the proposed approach on two benchmark datasets outperform the results of clustering done in TF-IDF vector space. Also, it is observed that in ML-ELM feature space, the results of seeded-kMeans are better compared to the traditional kMeans.","PeriodicalId":283953,"journal":{"name":"2016 IEEE Annual India Conference (INDICON)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123728582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and development of novel control strategy for trajectory tracking of mobile robot: Featured with tracking error minimization","authors":"R. Anushree, B. K. S. Prasad","doi":"10.1109/INDICON.2016.7839162","DOIUrl":"https://doi.org/10.1109/INDICON.2016.7839162","url":null,"abstract":"In this paper, trajectory control of the mobile robot is implemented to minimise the trajectory tracking error. It was found that there is a challenging task to orient the robot for any trajectory path to be traced. A novel control strategy for two wheel drive robot is proposed to trace the path of a given trajectory. The control algorithm developed for differential drive robot to track trajectory is composed of Lyapunov controller and least mean square algorithm as an observer. The proposed control law for the kinematic model of a differential drive robot resulted in minimized trajectory tracking error. Also, the linear and non-linear trajectories can be tracked using the proposed control strategy.","PeriodicalId":283953,"journal":{"name":"2016 IEEE Annual India Conference (INDICON)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121968826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An investigation on the stiffness variation in a synergistically configured SMA actuator","authors":"D. Nalini, D. Ruth, K. Dhanalakshmi","doi":"10.1109/INDICON.2016.7839029","DOIUrl":"https://doi.org/10.1109/INDICON.2016.7839029","url":null,"abstract":"This paper presents the study of stiffness variation in a linear/translational actuator which is a synergistic configuration of a passive compression spring with shape memory alloy (SMA) wire(s). The synergistic SMA based actuator uses an aiding force to create repetitive motion, to be capable of producing increased displacement. The range of stiffness of the actuator relies on two controlling factors i.e., the number of SMA wires and the energy storing capacity of the spring. Increase in the number of SMA wires offers simultaneous improvement of force and stiffness. The above is analyzed theoretically and verified through experiments. The tracking performance of the actuator is obtained through simulation with stiffness feedback for PID control action, using the data acquired from real time experiments.","PeriodicalId":283953,"journal":{"name":"2016 IEEE Annual India Conference (INDICON)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116838340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improve timing margins on multi-rank DDR3 RDIMM using read-on die termination sequencing","authors":"A. Lingambudi, S. Vijay, W. Becker, Preetham Raghavendra, Saravanan Sethuraman, Sivarama K Pullelli","doi":"10.1109/INDICON.2016.7838946","DOIUrl":"https://doi.org/10.1109/INDICON.2016.7838946","url":null,"abstract":"Modern computer systems have large amounts of DRAM running at fast cycle times. JEDEC standards for DDR3 DRAMs set the bounds of operation, but there is significant opportunity for maximizing the operating performance and reliability by optimizing the electrical parameters and the register settings across the many DIMMs in a system. Specifically, it is essential for the system designers to maximize the setup and hold timing margins for robust system operation. In this paper the hold timing of the data bus read operation is investigated. The methodology is presented and applied to setting the On-Die Termination (ODT) start/stop delay settings for optimal operation. The settings are verified by hardware characterization that confirms the updated delay settings improve the timing margin by performing a timing schmoo and observation of the waveforms with a logic analyzer and oscilloscope.","PeriodicalId":283953,"journal":{"name":"2016 IEEE Annual India Conference (INDICON)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117126514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational biomechanics in craniofacial fractures","authors":"E. Shobha, H. Raghuveer, S. Nagesh, K. Vinay, R. Dilip Kumar, N. Prashanth, Vinod Rangan","doi":"10.1109/INDICON.2016.7838912","DOIUrl":"https://doi.org/10.1109/INDICON.2016.7838912","url":null,"abstract":"The objective of the current research is focused on numerical simulation of cranio facial fracture under impact loading. Biomechanics of fracture, especially of a complex part of a human body such as skull, is one of the emerging areas of applications of computational bio-mechanics to understand the behavior of the skull during a traumatic injury, such as head impact during accidents. FEA (Finite Element Analysis) a numerical simulation methodology conventionally used in structural analysis has gained significant attention in biomechanics. Fracture biomechanics plays a key role in not only identifying weak areas of the skull but also designing and developing better techniques in treatment of fractures to restore form, function and aesthetics of the facial skeleton. Virtual simulation in medical field has opened new possibilities to predict the behaviour of the components of human skeleton when subjected to external load (trauma). The future in maxillofacial surgery is evidence based practice and FEA helps obtaining relevant data in different clinical scenarios. In particular, in this study a 3D finite element model of the skull is created starting with a CT scan data. All complexities of the skull geometry, including bones and muscles forming the skeletal structure is considered for creating the numerical model. This numerical model is then subjected to frontal, lateral. vertical, occlusal and angulated impact load. Impact analysis is done and weak areas susceptible for fracture and hence failures are identified. Further implants of various designs and materials used in craniofacial fracture fixations are placed in different fracture situations in the virtual model and subjected to different impact load conditions. This will enable the study of fracture and stability of the fracture of the skull under cranio-facial fracture conditions. The results from the analysis then can be used to come up with optimum locations of implant for different type of impact situations. This is expected to complement the existing treatment methodologies used by surgeons and amount of trauma and pain on the patient can be reduced. Further, appropriate knowledge of fracture biomechanics can be used to create safety measures in automobile designing hence preventing and reducing severity of facial injuries. Designing guards to sport helmets can reduce the intensity of facial injuries in sport accidents.","PeriodicalId":283953,"journal":{"name":"2016 IEEE Annual India Conference (INDICON)","volume":" 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120832599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"VLSI-design and FPGA-implementation of GMSK-demodulator architecture using CORDIC engine for low-power application","authors":"L. Kumar, Deepak Mittal, R. Shrestha","doi":"10.1109/INDICON.2016.7838954","DOIUrl":"https://doi.org/10.1109/INDICON.2016.7838954","url":null,"abstract":"This paper proposes low-power design of the Gaussian-Minimum Shift-Keying (GMSK) demodulator using baseband quadrature signals. High-level architecture of this demodulator incorporates CO-ordinate Rotation-DIgital Computer (CORDIC) engine to accept the in-phase and quadrature components from received GMSK signal to generate phase angle and magnitude of the GMSK signal vector at half the sampling frequency thereby reducing the power consumption. Additionally, the design of differentiator and synchronizer for the suggested GMSK demodulator has been carried out. The proposed demodulator is implemented in field-programmable gate-array (FPGA) and post-route simulated for functional verification. Thereafter, BER performance analysis of this design has been carried out in Additive White Gaussian Noise (AWGN) channel environment. Finally, the suggested architecture is synthesized and post-layout simulated using 90 nm CMOS technology node. It occupies a core area of 0.12 mm2 with 17770 gates and consumes 4.42 mW at 167 MHz of clock frequency.","PeriodicalId":283953,"journal":{"name":"2016 IEEE Annual India Conference (INDICON)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124638209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated severity scoring for diabetic retinopathy using fundus photography","authors":"P. Kumar, R. Deepak, P. Sasi, R. R. Kumar, A. Sathar, V. Sahasranamam","doi":"10.1109/INDICON.2016.7838960","DOIUrl":"https://doi.org/10.1109/INDICON.2016.7838960","url":null,"abstract":"Diabetic Retinopathy (DR) is a micro-vascular complication of diabetes and is one of the major causes for blindness. DR is asymptomatic in early stages and when symptoms surface irreversible damage would have already occurred to the retina hence screening is of at most importance. Here, we propose an automated method for severity scoring of DR which can be utilized for automated filtering and prioritizing patients for ophthalmic review. The proposed method assesses the image quality, detects red lesions and white lesions. The output of the optic disc detection was used to demarcate the retinal image into four regions. Lesion specific features extracted from demarcated regions were used to create a multi-class SVM classifier which in turn was used to arrive at a DR severity score. The proposed method was validated on 829 retinal images and achieved an accuracy of 72.4%. Grouping all the positive grades into single class resulted in a sensitivity of 89.9% and specificity of 77.8%.","PeriodicalId":283953,"journal":{"name":"2016 IEEE Annual India Conference (INDICON)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129716018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}