A Survey on Application Specific Processor Architectures for Digital Hearing Aids

Gerlach, Lukas; Payá-Vayá, Guillermo; Blume, Holger

doi:10.1007/s11265-021-01648-0

Cited by 5 publications

(5 citation statements)

References 66 publications

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“…Frequency range of operation [3] Low frequency inductive Less than 200 kHz implants (pacemakers, ICD etc.) [4] [5] Implant communication 9 -315kHz [6] Bioelectrical impedance meter 50 kHz, 250 kHz [7] Electrical Impedance Myography (EIM) 50 kHz [8] [9] [10] Electrical Impedance Tomography (EIT) 50 kHz to 250 kHz [11] CMOS wearable non-invasive 100 Hz to 1 MHz impedance meter [12] Hearing Aid 32 khz to 8.00 Mhz [13] Magnetic Particle Imaging 1 kHz to 100 kHz (MPI) systems [14] [15] Low data-rate Body 10 kHz to 10 MHz Couple communication (BCC) [16] Home Health Hub 200 kHz to 1.0 MHz to the cloud. Some of these devices, e.g.…”

Section: Reference Medical Applicationmentioning

confidence: 99%

Single-Rail Adiabatic Logic for Energy-Efficient and CPA-Resistant Cryptographic Circuit in Low-Frequency Medical Devices

Degada

Thapliyal

2022

IEEE Open J. Nanotechnol.

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Designing energy-efficient and secure cryptographic circuits in low-frequency medical devices are challenging due to low-energy requirements. Also, the conventional CMOS logic-based cryptographic circuits solutions in medical devices can be vulnerable to side-channel attacks (e.g. correlation power analysis (CPA)). In this article, we explored single-rail Clocked CMOS Adiabatic Logic (CCAL) to design an energy-efficient and secure cryptographic circuit for low-frequency medical devices. The performance of the CCAL logic-based circuits was checked with a power clock generator (2N2P-PCG) integrated into the design for the frequency range of 50 kHz to 250 kHz. The CCAL logic gates show an average of approximately 48% energy-saving and more than 95% improvement in security metrics performance compared to its CMOS logic gate counterparts. Further, the CCAL based circuits are also compared for energy-saving performance against dual-rail adiabatic logic, 2-EE-SPFAL, and 2-SPGAL. The adiabatic CCAL gates save on an average of 55% energy saving compared to 2-EE-SPFAL and 2-SPGAL over the frequency range of 50 kHz to 250 kHz. To check the efficacy of CCAL to design a larger cryptographic circuit, we implemented a case-study design of a Substitution-box (S-box) of popular lightweight PRESENT-80 encryption. The case-study implementation (2N2P-PCG integrated into the design) using CCAL shows more than 95% energy saving compared to CMOS for the frequency 50 kHz to 125 kHz and around 60% energy saving at frequency 250 kHz. At 250 kHz, compared to the dual-rail adiabatic designs of S-box based on 2-EE-SPFAL and 2-SPGAL, the CCAL based S-box shows 32.67% and 11.21% of energy savings, respectively. Additionally, the CCAL logic gate structure requires a lesser number of transistors compared to dual-rail adiabatic logic. The case-study implementation using CCAL saves 45.74% and 34.88% transistor counts compared to 2-EE-SPFAL and 2-SPGAL. The article also presents the effect of varying tank capacitance in 2N2P-PCG over energy efficiency and security performance. The CCAL based case-study was also subjected against CPA. The CCAL-based S-box case study successfully protects the revelation of the encryption key against the CPA attack, However, the key was revealed in CMOS-based case-study implementation.

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Section: Reference Medical Applicationmentioning

confidence: 99%

Single-Rail Adiabatic Logic for Energy-Efficient and CPA-Resistant Cryptographic Circuit in Low-Frequency Medical Devices

Degada

Thapliyal

2022

IEEE Open J. Nanotechnol.

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“…The audio data extracts include MFCC, support vector machines (SVM) various neural models are some of the frequently used classifiers [11]. Recent research for hearing aids introduces a new algorithm such as feedback cancellation (FBC), constriction of dynamic range (WDRC) and noise cancellation or reduction (NR) [12]. The hearing aids' flexibility and programmability are crucial because the algorithms for the HA system should be suitable for various ear impairments [13].…”

Section: Introductionmentioning

confidence: 99%

Aquila Based Adaptive Filtering for Hearing Aid with Optimized Performance

2023

IJIES

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In recent days, one of the most serious health problems in humans is hearing loss. For helping the hearing impairment, the Hearing Aid (HA) system is used, but it may lack in various aspects such as delay, matching error, power consumption, low complexity and unwanted noise. Many techniques were introduced to overcome these issues, but they still have problems. Therefore, this research uses a novel Aquila-based Adaptive Filter (AbAF) for improving the HA system with the optimized parameters of filter. Here, the incorporation of the Aquila optimizer along with the adaptive filter is considered as the key novelty of this present work. The main motive of this proposed model is to filter the noisy features more possible range for improve the HA performance. Hence, the Aquila agent removes the unwanted noise by updating the adaptive processor's filter coefficient and optimizing the filter parameters such as matching error, delay, complexity, and bandwidth and power consumption. Moreover, the suggested model is designed and executed in the MATLAB platform. Finally, the performance parameters were estimated and compared with the past studies to validate the performance improvement. The presented model scored the reduced Matching error as 0.3% and reduced power consumption as 10%, which quite lower than the compared models.

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“…Highperformance digital hearing aids require a classification algorithm to achieve audio classification capability. Therefore, an appropriate audio signal processing algorithm and parameter configuration can be adjusted according to the surrounding environment to improve product performance and user experience [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Audio Classification Algorithm for Hearing Aids Based on Robust Band Entropy Information

Jin

Fan

2022

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Audio classification algorithms for hearing aids require excellent classification accuracy. To achieve effective performance, we first present a novel supervised method, involving a spectral entropy-based magnitude feature with a random forest classifier (SEM-RF). A novel-feature SEM based on the similarity and stability of band signals is introduced to improve the classification accuracy of each audio environment. The random forest (RF) model is applied to perform the classification process. Subsequently, to resolve the problem of decreasing classification accuracy of the SEM-RF algorithm in mixed speech environments, an improved algorithm, ImSEM-RF, is proposed. The SEM features and corresponding phase features are fused on multiple time resolutions to form a robust multi-time resolution magnitude and phase (multi-MP) feature, which improves the stability of the feature with which the speech signal interferes. The RF model is improved using the linear discriminant analysis (LDA) method to form a linear discriminant analysis-random forest (LDA-RF) joint classification model, which performs model acceleration. Through experiments on hearing aid research data sets for acoustic environment recognition, the effectiveness of the SEM-RF algorithm was confirmed on a background audio signal dataset. The classification accuracy increased by approximately 7% compared with the background noise classification algorithm using an RF tree classifier. The validity of the ImSEM-RF algorithm in speech-interference environments was confirmed using the speech in the background audio signal dataset. Compared with the SEM-RF algorithm, the classification accuracy was improved by approximately 2%. The LDA-RF reduced the program’s running time by >80% with multi-MP features compared with RF.

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A Survey on Application Specific Processor Architectures for Digital Hearing Aids

Cited by 5 publications

References 66 publications

Single-Rail Adiabatic Logic for Energy-Efficient and CPA-Resistant Cryptographic Circuit in Low-Frequency Medical Devices

Single-Rail Adiabatic Logic for Energy-Efficient and CPA-Resistant Cryptographic Circuit in Low-Frequency Medical Devices

Aquila Based Adaptive Filtering for Hearing Aid with Optimized Performance

Audio Classification Algorithm for Hearing Aids Based on Robust Band Entropy Information

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