Voice Recognition in Noisy Environment Using Array of Microphone

The performance of voice recognition reduces significantly in noisy environments, where the voice signals are distorted severely by addition of noise signal and reverberations. In such environments we can use array of microphone and use beamforming techniques to reduce the effect of noise signals. Presently, microphone-array-based voice recognition is done in two independent stages: first beamforming by array processing and then sending it for recognition. To reduce the effect of noise that is to reduce the distortion in voice waveform array processing algorithm is designed to enhance the signal before feature extraction and recognition. In Beamforming technique an array of sensors, in our case sensors are microphones, is used so that maximum reception can be achieved in a desired specified direction that is in the presence of noise, by the use of estimation of direction algorithm while signals from undesired direction are rejected though they are of same frequency. This is done by using delay and sum method in which the outputs from an array of microphones are delayed by some time so when they are added together, a particular part of the sound field is amplified over other undesired or interfering sources. Then the focussed voice wave is sent to voice recognition algorithm. Correlation algorithm is used for the voice recognition. The algorithm is based on the fact correlation graph between same signal is symmetric and value of correlation is maximum. The system development for this voice recognizer will be done using MATLAB for this project. Using MATLAB a GUI is created which has different function buttons to perform different tasks

Energy aware and privacy preserving protocols for ad hoc networks with applications to disaster management

Disasters can have a serious impact on the functioning of communities and societies. Disaster management aims at providing efficient utilization of resources during pre-disaster (e.g. preparedness and prevention) and post-disaster (e.g. recovery and relief) scenarios to reduce the impact of disasters. Wireless sensors have been extensively used for early detection and prevention of disasters. However, the sensor\u27s operating environment may not always be congenial to these applications. Attackers can observe the traffic flow in the network to determine the location of the sensors and exploit it. For example, in intrusion detection systems, the information can be used to identify coverage gaps and avoid detection. Data source location privacy preservation protocols were designed in this work to address this problem. Using wireless sensors for disaster preparedness, recovery and relief operations can have high deployment costs. Making use of wireless devices (e.g. smartphones and tablets) widely available among people in the affected region is a more practical approach. Disaster preparedness involves dissemination of information among the people to make them aware of the risks they will face in the event of a disaster and how to actively prepare for them. The content is downloaded by the people on their smartphones and tablets for ubiquitous access. As these devices are primarily constrained by their available energy, this work introduces an energy-aware peer-to-peer file sharing protocol for efficient distribution of the content and maximizing the lifetime of the devices. Finally, the ability of the wireless devices to build an ad hoc network for capturing and collecting data for disaster relief and recovery operations was investigated. Specifically, novel energy-adaptive mechanisms were designed for autonomous creation of the ad hoc network, distribution of data capturing task among the devices, and collection of data with minimum delay --Abstract, page iii

A Wideband Injection-Locking Scheme and Quadrature Phase Generation in 65-nm CMOS

A novel technique for wideband injection locking in an LC oscillator is proposed. Phased-lock-loop and injection-locking elements are combined symbiotically to achieve wide locking range while retaining the simplicity of the latter. This method does not require a phase frequency detector or a loop filter to achieve phase lock. A mathematical analysis of the system is presented and the expression for new locking range is derived. A locking range of 13.4-17.2 GHz and an average jitter tracking bandwidth of up to 400 MHz were measured in a high- Q LC oscillator. This architecture is used to generate quadrature phases from a single clock without any frequency division. It also provides high-frequency jitter filtering while retaining the low-frequency correlated jitter essential for forwarded clock receivers

Generalized Gradient Flows with Provable Fixed-Time Convergence and Fast Evasion of Non-Degenerate Saddle Points

Gradient-based first-order convex optimization algorithms find widespread applicability in a variety of domains, including machine learning tasks. Motivated by the recent advances in fixed-time stability theory of continuous-time dynamical systems, we introduce a generalized framework for designing accelerated optimization algorithms with strongest convergence guarantees that further extend to a subclass of non-convex functions. In particular, we introduce the GenFlow algorithm and its momentum variant that provably converge to the optimal solution of objective functions satisfying the Polyak-{\L}ojasiewicz (PL) inequality in a fixed time. Moreover, for functions that admit non-degenerate saddle-points, we show that for the proposed GenFlow algorithm, the time required to evade these saddle-points is uniformly bounded for all initial conditions. Finally, for strongly convex-strongly concave minimax problems whose optimal solution is a saddle point, a similar scheme is shown to arrive at the optimal solution again in a fixed time. The superior convergence properties of our algorithm are validated experimentally on a variety of benchmark datasets.Comment: Accepted to Transactions on Automatic Control (TAC

Design of an optimal multi-layer neural network for eigenfaces based face recognition

Face recognition is one of the most popular problems in the field of image analysis. In this paper, we discuss the design of an optimal multi-layer neural network for the task of face recognition. There are many issues while designing the neural network like number of nodes in input layer, output layer and hidden layer(s), setting the values of learning rate and momentum, updating of weights. Lastly, the criteria for evaluating the performance of the neural network and stopping the learning are to be decided. We discuss all these design issues in the light of the eigenfaces based face recognition. We report the effects of variations of these parameters on number of training cycles required to get optimal results. We also list the optimized values for these parameters. In our experiments, we use two face databases namely ORL and UMIST. These databases are used to construct the eigenfaces. The original faces are reconstructed using the top eigenfaces. The factors used in the reconstruction of the faces are used as the inputs to the neural network

Lossless gray image compression using logic minimization

A novel approach for the lossless compression of gray images is presented. A prediction process is performed followed by the mapping of prediction residuals. The prediction residuals are then split into bit–planes. Two-dimensional (2D) differencing operation is applied to bit-planes prior to segmentation and classification. Performing an Exclusive-OR logic operation between neighboring pixels in the bit planes creates the difference image. The difference image can be coded more efficiently than the original image whenever the average run length of black pixels in the original image is greater than two. The 2d difference bit-plane is divided in to windows or block of size 16*16 pixels. The segmented 2d difference image is partitioned in to non-overlapping rectangular regions of all white and mixed 16*16 blocks. Each partitioned block is transformed in to Boolean switching function in cubical form, treating the pixel values as a output of the function. Minimizing these switching functions using Quine- McCluskey minimization algorithm performs compression

GASTRIC ANTIULCER AND ULCER HEALING EFFECTS OF PUNICA GRANATUM L. PEEL EXTRACT IN RATS: ROLE OF OFFENSIVE AND DEFENSIVE MUCOSAL FACTORS AND OXIDATIVE STRESS

Objective: The present work incorporates the study of gastric antiulcer and ulcer healing effects of dried Punica granatum (PG) peel 50% ethanol extract (PGE) in rats.Methods: PGE (100 mg/kg) was administered orally once daily to rats either before or after induction of gastric ulcers (GU) for 7 d. Antiulcer effects of PGE were seen against acute GU, induced by pylorus ligation (PL), cold restraint stress (CRS), aspirin and ethanol while, ulcer healing in acetic acid (AA)-induced chronic GU in rats. Ulcer index (UI), gastric juice volume, acid-pepsin and mucin secretions and gastric mucosal glycoproteins, free radicals (LPO and NO) and antioxidants (SOD and GSH) were estimated.Results: PGE showed a decrease in UI in all GU models (45.6 to 79.7%, P<0.05 to P<0.001) indicating both protective and healing effects. PGE showed little or no effects on volume, acid-pepsin concentration and output but increased mucin secretion (55.1%, P<0.05) and mucosal glycoproteins (35.7%, P<0.05) in PL rats. CRS rats showed an increase in LPO and NO (48.4 to 58.3%, P<0.01) and SOD (21.8%, P<0.01) but decrease in GSH and CAT (33.1 to 44.8%, P<0.01 to P<0.001) compared with unstressed rats. PGE-treated CRS rats showed a decrease in LPO and NO (44.1 to 61.2, P<0.01 to P<0.001) and SOD (13.2%, P<0.01) and increase in GSH and CAT (43.8 to 48.7%, P<0.01 to P<0.001) compared with CRS rats.Conclusion: PGE seemed to have ulcer cytoprotective effects due to enhanced mucosal resistance and reduction in oxidative mucosal damage possibly via high antioxidant activity

Identification of prognostic and susceptibility markers in chronic myeloid leukemia using next generation sequencing

Background: Incidence of Chronic Myeloid Leukemia (CML) is continuously increasing and expected to reach 100,000 patients every year by 2030. Though the discovery of Imatinib Mesylate (IM) has brought a paradigm shift in CML treatment, 20% patients show resistance to this tyrosine kinase inhibiter (TKI). Therefore, it is important to identify markers, which can predict the occurrence and prognosis of CML. Clinical Exome Sequencing, panel of more than 4800 genes, was performed in CML patients to identify prognostic and susceptibility markers in CML.Methods: Enrolled CML patients (n=18) were segregated as IM responders (n=10) and IM failures (n=8) as per European Leukemia Net (ELN), 2013 guidelines. Healthy controls (n=5) were also enrolled. DNA from blood of subjects was subjected to Next Generation Sequencing. Rare mutations present in one patient group and absent in another group were considered as prognostic markers, whereas mutations present in more than 50% patients were considered as susceptibility markers.Result: Mutations in genes associated with cancer related functions were found in different patient groups. Four variants: rs116201358, rs4014596, rs52897880 and rs2274329 in C8A, UNC93B1, APOH and CA6 genes, respectively, were present in IM responders; whereas rs4945 in MFGE8 was present in IM failures. Mutations in HLA-DRB1 (rs17878951), HLA-DRB5 (rs137863146), RPHN2 (rs193179333), CYP2F1 (rs116958555), KCNJ12 (rs76684759) and FUT3 (rs151218854) were present as susceptibility markers.Conclusion: The potential genetic markers discovered in this study can help in predicting response to IM as frontline therapy. Susceptibility markers may also be used as panel for individuals prone to have CML.Keywords: Chronic Myeloid Leukemia, Genetic Markers, Next Generation Sequencing (NGS