Kongu Engineering College

About: Kongu Engineering College is a based out in . It is known for research contribution in the topics: Cluster analysis & Control theory. The organization has 2001 authors who have published 1978 publications receiving 16923 citations.

Application of Voter Insertion Algorithm for Fault Management using Triple Modular Redundancy (TMR) Technique

Abstract: In recent days, the reliability and the accuracy of the system plays an important role in every industry starting from medical, mechanical, electrical, to everything. It is hard to achieve hundred percent accuracy and also to reduce all the possible errors happening. A technique called redundancy is used to increase the reliability in fields which uses combinational logic circuits for its working operation. The triple modular redundancy mechanism is famous for fault reduction and is also referred to as the fault tolerance mechanism. The construction of proper voter algorithm plays a vital role in TMR implementation. In fault-tolerant computing area, the most priority is given to the hardware fault tolerance systems. In this article, the model of triple modular redundancy is discussed and the improvements for voter circuit algorithms are proposed. The various aspects of Triple Modular Redundancy technique such as reliability improvement, usage in critical operations and field improvement are also analyzed in this paper. In addition to this, the comparison of TMR with various redundancy techniques such as Hamming code (SEC – DED), Standby sparing, NMR with spares, etc. is presented for the proper selection of redundancy mechanism for the particular application depending upon the user requirement.

Frictional study of alumina, 316 stainless steel, phosphor bronze versus carbon as mechanical seals under dry sliding conformal contact:

Abstract: This paper presents an investigative study on the frictional characteristics of alumina, 316 stainless steel, and phosphor bronze materials against resin-impregnated carbon in the form of mechanica...

Studies on adsorptive removal of Direct Green 6 using a non-conventional activated carbon and polypyrrole composite

Abstract: Application of activated carbon (CSAC) and polypyrrole composite (PPC) developed from the precursor, fruit of the gardening plant material Cordia sebestena has been investigated for the removal of Direct Green 6 (DG6) from aqueous solution. Batch adsorption experiments were carried out using CSAC and PPC and a comparative study was also made. Effect of parameters, namely initial dye concentration, contact time, pH and temperature was studied. The kinetic studies were analysed with pseudo-first-order and pseudo-second-order models. Experimental isotherm data were also analysed with Langmuir and Freundlich adsorption isotherm models. Thermodynamic parameters were also measured. Experimental results showed that percentage removal of DG6 was higher with PPC than the percentage removal obtained by CSAC. Hence, it is proposed that PPC is superior and efficient adsorbent for the removal of DG6 from aqueous solutions.

Dynamic Resource Allocation for SDN and Edge Computing based 5G Network

Abstract: The exponential increase in network traffic leads to considerable stress in 5G communication. The ultra-high reliability and low latency communication in 5G provides an insight on application of artificial intelligence with big volume of data. To meet the constraints of reduced delay, edge computing can be deployed. Edge computing is a promising solution for reduction in latency of computation-intensive tasks. And also, for reliable transmission, software defined network controller can be employed in the centralized infrastructure. So, this proposed work focuses on the forecasting of traffic flow in the network and dynamic allocation of the resource by the centralized infrastructure. The traffic prediction is modeled with Long Short-Term Memory neural network based on long-term time series traffic flow in the network. The effectiveness of the proposed model is analyzed with the real-time captured data which exhibit improved prediction accuracy.

Pitch-Based Voice Activity Detection for Feedback Cancellation and Noise Reduction in Hearing Aids

Abstract: In hearing aid (HA) systems, amplification of speech signals is done to compensate the hearing loss of patients. Background noise and feedback signals may also get amplified which degrade the intelligibility and quality of speech. To achieve high de-noise efficiency, signal processing unit in HA system has voice activity detector (VAD). The conventional VAD detects voice based on zero crossing rate or energy of input signals. However, these methods cannot perform well at low SNR or non-stationary noise environments. Since pitch is a special characteristic of speech and is basically independent of noise intensity, VAD based on pitch can have high accuracy even when the noise spectrum is changing drastically. In this paper, pitch-based VAD is presented and its accuracy is checked against zero crossing rate-based VAD (ZCR-VAD). For noise reduction, an improved multi-band spectral over-subtraction algorithm is employed along with the high accurate pitch-based VAD. For feedback cancellation, the performance of adaptive algorithms like NLMS, RLS and affine projection (AP) algorithms with pitch-based VAD is compared and it is observed that AP is suitable for feedback cancellation. The proposed noise reduction and feedback cancellation algorithm with pitch-based VAD method is tested with NOIZEUS speech database and with real-time noisy speech signals. The simulation results show that the accuracy of pitch-VAD is about 23% higher than that of ZCR-VAD. The SNR for the proposed noise reduction and feedback cancellation with pitch-VAD method is improved by 10 dB than conventional spectral subtraction and adaptive algorithms. Mean opinion score (MOS) obtained for the proposed method is 4.3 out of 5.