Development of Digital Signal Processing Platform for Digital Hearing Aid

ABSTRACT: There has been a tremendous growth for the past few years in the field of VLSI and real time signal processing. Number of signal processing algorithms have been developed which allow the user to process real time signals such as speech to accomplish the signal with desired quality. Development of CMOS technology provides wide selection of low power FPGAs to be used in Digital system design. This paper introduces an efficient method to design a Digital Programmable Hearing Aid system working in association with Analog interface module. Real time signal processing algorithms such as Feedback and echo cancellation, Adaptive filtering , Dynamic range compression, Digital filter for decimation along with FFT and IFFT algorithms have been discussed using a 32 bit RISC processor core and hardware digital signal processing block, both residing in a Xilinx FPGA. Hardware configuration and software implementation are discussed in detail. Xilinx Spartan3, FPGA is used in system design. Spartan3 board has inbuilt ADC inside the chip so there is no need to use external CODEC system in design. Finally the challenges and the future work for additional improvement are discussed

InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis

In this paper, we present InstructABSA, Aspect Based Sentiment Analysis (ABSA) using the instruction learning paradigm for all ABSA subtasks: Aspect Term Extraction (ATE), Aspect Term Sentiment Classification (ATSC), and Joint Task modeling. Our method introduces positive, negative, and neutral examples to each training sample, and instruction tunes the model (Tk-Instruct) for each ABSA subtask, yielding significant performance improvements. Experimental results on the Sem Eval 2014, 15, and 16 datasets demonstrate that InstructABSA outperforms the previous state-of-the-art (SOTA) approaches on all three ABSA subtasks (ATE, ATSC, and Joint Task) by a significant margin, outperforming 7x larger models. In particular, InstructABSA surpasses the SOTA on the Rest14 ATE subtask by 7.31% points, Rest15 ATSC subtask by and on the Lapt14 Joint Task by 8.63% points. Our results also suggest a strong generalization ability to new domains across all three subtasksComment: 4 pages, 2 figures, 5 tables, 5 appendix page

Management of sobbing tot in a pediatric dental office: A review

The most common way by which child expresses fear and anxiety is by crying at dental office. Proper knowledge and understanding are required to deliver effective dental treatment to a child by the application of various behavioral management techniques. Treating a crying child is one of the most demanding and tiring situations encountered in dentistry. Behavior management in a crying child is a continuum interaction with the child, directed toward communication and education in an endeavor, to allay anxiety and fear and to promote understanding of good oral health and the process by which it is achieved. This article was aimed to review the various reasons for stimulation of cry of the child in the dental office and behavior techniques employed by the dentist for proper management of the child

A blockchain and deep neural networks-based secure framework for enhanced crop protection

The problem faced by one farmer can also be the problem of some other farmer in other regions. Providing information to farmers and connecting them has always been a challenge. Crowdsourcing and community building are considered as useful solutions to these challenges. However, privacy concerns and inactivity of users can make these models inefficient. To tackle these challenges, we present a cost-efficient and blockchain-based secure framework for building a community of farmers and crowdsourcing the data generated by them to help the farmers’ community. Apart from ensuring privacy and security of data, a revenue model is also incorporated to provide incentives to farmers. These incentives would act as a motivating factor for the farmers to willingly participate in the process. Through integration of a deep neural network-based model to our proposed framework, prediction of any abnormalities present within the crops and their predicted possible solutions would be much more coherent. The simulation results demonstrate that the prediction of plant pathology model is highly accurate