Energy and area efficient techniques for data converters

Data converters are ubiquitous building blocks of a signal chain. The rapid increase in communication and connectivity devices presents new avenues for pushing the state of the art analog to digital converters. Techniques for improving resolution, bandwidth, linearity and bit-error rate, while reducing the power, energy and area is the motivation for this research. This research focuses on achieving this goal by enabling circuit techniques, architecture techniques and calibration methods. The following techniques are proposed for enabling power, area and energy efficient analog to digital converter techniques. 1. A capacitor switching scheme for successive approximation ADC is introduced to enable 93.4% energy reduction and 75 % reduction in capacitor area as compared to a conventional SAR ADCs. 2. Asynchronous correlated level shifting technique for improving current source linearity and power supply rejection ratio of zero crossing based circuits is proposed. This technique enables asynchronous ADC architectures for energy efficient system. 3. Unified gain enhancement model is proposed to catalogue gain enhancement techniques. Class-A+ and Replicated Parallel Gain Enhancement (RPGe) amplifiers are introduced as parallel gain enhancement techniques for switched capacitor circuits. A prototype pipelined ADC using RPGE amplifier achieves 74.9 dB SNDR, 90.8 dB SFDR, 87 dB THD at 20 MS/s. Built in 1P4M 0.18 μm technology and operating at 1.3 V supply, the ADC consumes 5.9 mW. The ADC occupies 3.06 sq. mm and has a figure of merit of 65 fJ /conversion step. Extracted simulation results of the prototype pipeline ADC using dynamic RPGE amplifier achieve 74 dB SNDR, 90 dB SFDR, and 85 dB THD at 30 MS /s in a 0.18 μm process. The ADC consumes 6.6 mW from a 1.3 V supply and achieves a figure of merit of 40 fJ/C-S. 4. A low-gain amplifier based V-T converter is utilized along with a TDC to replace the function of flash ADC and the DAC references in a pipeline ADC. The simulated/ extracted performance of the chip is 12bit, 100 MHz in 65nm process while consuming approximately 8-9 mA from 1 V supply. 5. A measurement technique for detecting and correcting bit-error rate in ADCs is proposed. This multi-path ADC technique squares the bit-error rate of the ADC without consuming additional analog power. The area increase is negligible compared to the conventional modular redundancy techniques. This technique can be applied to digitally detect and correct single event transients for ADCs. A three-path ADC can restore the ADC performance independent of the input frequency and number of errors in a single path. 6. LMS algorithm is used to estimate the VCO non-linearity by using the VCO as a Nyquist ADC and utilizing a slow but accurate ADC. The simulated ADC performance improves from 5 bits to 7.8 bits by using a second order fit to the VCO non-linearity

A study on surgeon performing bedside ultrasonogram in acute appendicitis with histopathologigal correlation

INTRODUCTION: Diagnosing appendicitis is primarily a clinical evaluation. This would lead to increased negativity on histopathological examination. Diagnosing appendicitis may require adjuvant studies such as computed tomography or ultrasound. Combining clinical evaluation with surgeon performed ultrasonogram may increase diagnostic accuracy, reduce time delay, reduces complications and decrease radiation & costs. METHODS: A prospective study was conducted with a diagnosis of acute appendicitis. A surgeon performed a clinical examination and ultrasonogram to make the diagnosis. Final diagnosis was confirmed by histopathological examination (Gold standard). Results were grouped and tabulated. The Sensitivity, Specificity, Predictive value & Accuracy of surgeon performing ultrasound were analysed. As ultrasonogram was performed by Radiologist, we compared Surgeon performed ultrasonogram with radiologist in cohort of patients. Analysis was performed by kappa value and fisher exact test. RESULTS: One hundred and twelve patients were evaluated. Eighty six patients had appendicitis (76.8%). The negative appendectomy rate by clinical examination was 23.2%. The accuracy of surgeon was 92% & yielded sensitivity & specificity as 94% & 81.4%. Radiologist performed ultrasonogram on 35 patients yielded an accuracy of 85.7%. Surgeon performed ultrasonogram on those 35 patients yielded an accuracy of 82.8%. The argument between surgeon and radiologist was good (kappa value- 0.778) implying the surgeon is effective and reliable as radiologists. CONCLUSION: Accuracy of surgeon performing ultrasonogram was similar with of radiologist performed. Further, when surgeon performs both clinical examination and ultrasonogram a high level of accuracy can be achieved. Based on our study with these high degree of accuracy, surgeon performed bedside ultrasonogram can be used as a primary diagnostic tool in initial evaluation of patient along with clinical examination in cases of acute appendicitis

RLIS: resource limited improved security beyond fifth generation networks using deep learning algorithms.

This study explores the feasibility of allocating finite resources beyond fifth generation networks for extended reality applications through the implementation of enhanced security measures via offloading analysis (RLIS). The quantification of resources is facilitated through the utilization of parameters, namely energy, capacity, and power, which are equipped with proximity constraints. These constraints are then integrated with activation functions in both multilayer perceptron and long short term memory models. Furthermore, the system model has been developed using vision-based computing, which involves managing data queues in terms of waiting periods to minimize congestion for data transmission with limited resources. The major significance of the proposed method is to utilize allocated spectrums for future generation networks by allocating necessary resources and therefore high usage of resources by all users can be avoided. In addition the advantage of the proposed method is secure the networks that operate beyond 5G where more number of users will try to share the allocated resources that needs to be provided with high security conditions

A PIPELINED APPROACH FOR FPGA IMPLEMENTATION OF BI MODAL BIOMETRIC PATTERN RECOGNITION

ABSTRACT A Biometric system is essentially a pattern recognition system that makes use of biometric traits to recognize individuals. Systems which are built upon multiple sources of information for establishing identity which are known as multimodal biometric systems can overcome some of the limitations like noisy captured data, intra class variations etc… In this paper a Bi modal biometric system of iris and palm print based on Wavelet Packet Transform (WPT), gabor filters and a neural classifier implemented in FPGA is described. Iris is the unique observable visible feature present in the detailed texture of each eye. Palmprint is referred to the textural data like principal lines wrinkles and ridges present in the palm. The visible texture of a person's iris and palm print is encoded into a compact sequence of 2-D wavelet packet coefficients constituting a biometric signature or a feature vector code. In this paper, a novel multi-resolution approach based on WPT for recognition of iris and palmprint is proposed. With an adaptive threshold, WPT sub image coefficients are quantized into 1, 0 or -1 as biometric signature resulting in the size of biometric signature as 960 bits. The combined pattern vector of palm print features and iris features are formed using fusion at feature level and applied to the pattern classifier. The Learning Vector Quantization neural network is used as pattern classifier and a recognition rate of 97.22% is obtained. A part of the neural network is implemented for input data of 16 dimensions and 12 input classes and 8 output classes, using virtex-4 xc4vlx15 device. This system can complete recognition in 3.25 microseconds thus enabling it being suitable for real time pattern recognition tasks

An archetypal determination of mobile cloud computing for emergency applications using decision tree algorithm.

Numerous users are experiencing unsafe communications due to the growth of big network mediums, where no node communication is detected in emergency scenarios. Many people find it difficult to communicate in emergency situations as a result of such communications. In this paper, a mobile cloud computing procedure is implemented in the suggested technique in order to prevent such circumstances, and to make the data transmission process more effective. An analytical framework that addresses five significant minimization and maximization objective functions is used to develop the projected model. Additionally, all mobile cloud computing nodes are designed with strong security, ensuring that all the resources are allocated appropriately. In order to isolate all the active functions, the analytical framework is coupled with a machine learning method known as Decision Tree. The suggested approach benefits society because all cloud nodes can extend their assistance in times of need at an affordable operating and maintenance cost. The efficacy of the proposed approach is tested in five scenarios, and the results of each scenario show that it is significantly more effective than current case studies on an average of 86%

Substantial Phase Exploration for Intuiting Covid using form Expedient with Variance Sensor

This article focuses on implementing wireless sensors for monitoring exact distance between two individuals and to check whether everybody have sanitized their hands for stopping the spread of Corona Virus Disease (COVID). The idea behind this method is executed by implementing an objective function which focuses on maximizing distance, energy of nodes and minimizing the cost of implementation. Also, the proposed model is integrated with a variance detector which is denoted as Controlled Incongruity Algorithm (CIA). This variance detector is will sense the value and it will report to an online monitoring system named Things speak and for visualizing the sensed values it will be simulated using MATLAB. Even loss which is produced by sensors is found to be low when CIA is implemented. To validate the efficiency of proposed method it has been compared with prevailing methods and results prove that the better performance is obtained and the proposed method is improved by 76.8% than other outcomes observed from existing literatures

Factors influencing wind turbine avoidance behaviour of a migrating soaring bird

Wind energy production has expanded as an alternative to carbon emitting fossil fuels, but is causing impacts on wildlife that need to be addressed. Soaring birds show concerning rates of collision with turbine rotor blades and losses of critical habitat. However, how these birds interact with wind turbines is poorly understood. We analyzed high-frequency GPS tracking data of 126 black kites (Milvus migrans) moving near wind turbines to identify behavioural mechanisms of turbine avoidance and their interaction with environmental variables. Birds flying within 1000 m from turbines and below the height of rotor blades were less likely to be oriented towards turbines than expected by chance, this pattern being more striking at distances less than 750 m. Within the range of 750 m, birds showed stronger avoidance when pushed by the wind in the direction of the turbines. Birds flying above the turbines did not change flight directions with turbine proximity. Sex and age of birds, uplift conditions and turbine height, showed no effect on flight directions although these factors have been pointed as important drivers of turbine collision by soaring birds. Our findings suggest that migrating black kites recognize the presence of wind turbines and behave in a way to avoid then. This may explain why this species presents lower collision rates with wind turbines than other soaring birds. Future studies should clarify if turbine avoidance behaviour is common to other soaring birds, particularly those that are facing high fatality rates due to collision

Comparative study of Immediate Implant with Alloplastic Bone Graft and Immediate Implant with Combination of Platelet Rich Fibrin Enhanced Alloplastic Bone Graft: A Randomized Control Trial

Replacement of the missing teeth is an important factor in the dental field. It promotes functions and esthetics to the oral cavity. Replacement includes Removable partial denture, Fixed partial denture and Complete denture. In this situation, Dental implants play a pivotal role in the field of oral and maxillofacial surgery. Dental implants have been classified as immediate, delayed immediate and conventional implants. It exhibits a long term process and good follow up. The majority of the population wants to shorten the treatment period. As per the patient’s concern, immediate implant techniques were established. Herewith a comparative study on immediate implant with bone graft alone and bone graft along with Platelet rich fibrin. We met with some errors and good results which have been discussed in this study

Self-Supervised Representation Learning for Content Based Image Retrieval

Automotive technologies and fully autonomous driving have seen a tremendous growth in recent times and have benefitted from extensive deep learning research. State-of-the-art deep learning methods are largely supervised and require labelled data for training. However, the annotation process for image data is time-consuming and costly in terms of human efforts. It is of interest to find informative samples for labelling by Content Based Image Retrieval (CBIR). Generally, a CBIR method takes a query image as input and returns a set of images that are semantically similar to the query image. The image retrieval is achieved by transforming images to feature representations in a latent space, where it is possible to reason about image similarity in terms of image content. In this thesis, a self-supervised method is developed to learn feature representations of road scenes images. The self-supervised method learns feature representations for images by adapting intermediate convolutional features from an existing deep Convolutional Neural Network (CNN). A contrastive approach based on Noise Contrastive Estimation (NCE) is used to train the feature learning model. For complex images like road scenes where mutiple image aspects can occur simultaneously, it is important to embed all the salient image aspects in the feature representation. To achieve this, the output feature representation is obtained as an ensemble of feature embeddings which are learned by focusing on different image aspects. An attention mechanism is incorporated to encourage each ensemble member to focus on different image aspects. For comparison, a self-supervised model without attention is considered and a simple dimensionality reduction approach using SVD is treated as the baseline. The methods are evaluated on nine different evaluation datasets using CBIR performance metrics. The datasets correspond to different image aspects and concern the images at different spatial levels - global, semi-global and local. The feature representations learned by self-supervised methods are shown to perform better than the SVD approach. Taking into account that no labelled data is required for training, learning representations for road scenes images using self-supervised methods appear to be a promising direction. Usage of multiple query images to emphasize a query intention is investigated and a clear improvement in CBIR performance is observed. It is inconclusive whether the addition of an attentive mechanism impacts CBIR performance. The attention method shows some positive signs based on qualitative analysis and also performs better than other methods for one of the evaluation datasets containing a local aspect. This method for learning feature representations is promising but requires further research involving more diverse and complex image aspects

Self-Supervised Representation Learning for Content Based Image Retrieval

Automotive technologies and fully autonomous driving have seen a tremendous growth in recent times and have benefitted from extensive deep learning research. State-of-the-art deep learning methods are largely supervised and require labelled data for training. However, the annotation process for image data is time-consuming and costly in terms of human efforts. It is of interest to find informative samples for labelling by Content Based Image Retrieval (CBIR). Generally, a CBIR method takes a query image as input and returns a set of images that are semantically similar to the query image. The image retrieval is achieved by transforming images to feature representations in a latent space, where it is possible to reason about image similarity in terms of image content. In this thesis, a self-supervised method is developed to learn feature representations of road scenes images. The self-supervised method learns feature representations for images by adapting intermediate convolutional features from an existing deep Convolutional Neural Network (CNN). A contrastive approach based on Noise Contrastive Estimation (NCE) is used to train the feature learning model. For complex images like road scenes where mutiple image aspects can occur simultaneously, it is important to embed all the salient image aspects in the feature representation. To achieve this, the output feature representation is obtained as an ensemble of feature embeddings which are learned by focusing on different image aspects. An attention mechanism is incorporated to encourage each ensemble member to focus on different image aspects. For comparison, a self-supervised model without attention is considered and a simple dimensionality reduction approach using SVD is treated as the baseline. The methods are evaluated on nine different evaluation datasets using CBIR performance metrics. The datasets correspond to different image aspects and concern the images at different spatial levels - global, semi-global and local. The feature representations learned by self-supervised methods are shown to perform better than the SVD approach. Taking into account that no labelled data is required for training, learning representations for road scenes images using self-supervised methods appear to be a promising direction. Usage of multiple query images to emphasize a query intention is investigated and a clear improvement in CBIR performance is observed. It is inconclusive whether the addition of an attentive mechanism impacts CBIR performance. The attention method shows some positive signs based on qualitative analysis and also performs better than other methods for one of the evaluation datasets containing a local aspect. This method for learning feature representations is promising but requires further research involving more diverse and complex image aspects