2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)最新文献_第8页

Character Reassignment for Hardware Trojan Detection 字符重分配硬件木马检测

2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS) Pub Date : 2021-08-09 DOI: 10.1109/MWSCAS47672.2021.9531813

Noah Waller, Hunter Nauman, Derek Taylor, Rafael Del Carmen, J. Di

引用次数: 0

CMOS Power-Amplifier Design Perspectives for 6G Wireless Communications 6G无线通信CMOS功率放大器设计展望

2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS) Pub Date : 2021-08-09 DOI: 10.1109/MWSCAS47672.2021.9531862

Zisong Wang, Huan Wang, P. Heydari

引用次数: 5

A Modified Echo State Network for Time Independent Image Classification 一种用于时间无关图像分类的改进回声状态网络

2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS) Pub Date : 2021-08-09 DOI: 10.1109/MWSCAS47672.2021.9531776

S. Gardner, M. Haider, L. Moradi, V. Vantsevich

{"title":"A Modified Echo State Network for Time Independent Image Classification","authors":"S. Gardner, M. Haider, L. Moradi, V. Vantsevich","doi":"10.1109/MWSCAS47672.2021.9531776","DOIUrl":"https://doi.org/10.1109/MWSCAS47672.2021.9531776","url":null,"abstract":"Image classification is typically performed with highly trained feed-forward machine learning algorithms like deep neural networks and support vector machines. The image can be treated as a time-series input when applied to the network multiple times, opening the way for recurrent neural networks to perform tasks like image classification, semantic segmentation and auto-encoding. With this approach, ultra-fast training, network optimization, and short-term memory effects allows for dynamic, low-volume datasets to be quickly learned without heavy image pre-processing or feature extraction; the main limitation being that input images need labeled output images for training, as is also true of most standard approaches. In this work, the MNIST handwritten digit dataset is used as a benchmark to evaluate metrics of a modified Echo State Network for static image classification. The image array is passed through a noise filter multiple times as the Echo State Network converges to a classification. This highly dynamic approach easily adapts to sequential image (video) tasks like object tracking and is effective with small datasets. Classification rates reach 95.3% with sample size of 10000 handwritten digits and training time of approximately 5 minutes. Progression of this research enables discrete image and time-series classification under a single algorithm, with low computing power and memory requirements.","PeriodicalId":6792,"journal":{"name":"2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)","volume":"15 1","pages":"255-258"},"PeriodicalIF":0.0,"publicationDate":"2021-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80745132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Energy Efficient Comparator-Less Current-Mode TFET-CMOS Co-Integrated Scalable Flash ADC 高能效无比较器电流模式TFET-CMOS协集成可扩展闪存ADC

2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS) Pub Date : 2021-08-09 DOI: 10.1109/MWSCAS47672.2021.9531911

N. Gupta, H. Shrimali, A. Makosiej, A. Vladimirescu, A. Amara

引用次数: 1

Surrogate Model based Co-Optimization of Deep Neural Network Hardware Accelerators 基于代理模型的深度神经网络硬件加速器协同优化

2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS) Pub Date : 2021-08-09 DOI: 10.1109/MWSCAS47672.2021.9531708

Hendrik Wöhrle, M. D. L. Alvarez, Fabian Schlenke, A. Walsemann, M. Karagounis, F. Kirchner

{"title":"Surrogate Model based Co-Optimization of Deep Neural Network Hardware Accelerators","authors":"Hendrik Wöhrle, M. D. L. Alvarez, Fabian Schlenke, A. Walsemann, M. Karagounis, F. Kirchner","doi":"10.1109/MWSCAS47672.2021.9531708","DOIUrl":"https://doi.org/10.1109/MWSCAS47672.2021.9531708","url":null,"abstract":"In this paper, we present an ASIC based on 22FDX/FDSOI technology for the detection of atrial fibrillation in human electrocardiograms using neural networks. The ASIC consists of a RISC-V core for supporting software components and an application-specific machine learning IP core (ML-IP), which is used to implement the computationally intensive inference. The ASIC was designed for maximum energy efficiency. A special feature of the ML-IP is its modular, generic and scalable design of the ML-IP which allows to specify the quantization of each computational operation, the degree of parallelization and the architecture of the neural network. This in turn allows the use of ML-based optimization techniques to perform co-optimization for hardware design and architecture of the neural network (NNs). Here, a multi-objective optimization of the overall system is performed with respect to computational efficiency at a given classification accuracy and speed by using a multi-objective optimization, which is carried out using a probabilistic surrogate model. This model tries to find the optimal neural network architecture with a minimum number of training, simulation and evaluation steps.","PeriodicalId":6792,"journal":{"name":"2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)","volume":"28 1","pages":"40-45"},"PeriodicalIF":0.0,"publicationDate":"2021-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78525140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Machine Learning Based Smart Contact-less pH Sensing and Classification 基于机器学习的智能非接触式pH值传感与分类

2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS) Pub Date : 2021-08-09 DOI: 10.1109/MWSCAS47672.2021.9531918

M. Saberi, S. Gardner, M. Haider

{"title":"A Machine Learning Based Smart Contact-less pH Sensing and Classification","authors":"M. Saberi, S. Gardner, M. Haider","doi":"10.1109/MWSCAS47672.2021.9531918","DOIUrl":"https://doi.org/10.1109/MWSCAS47672.2021.9531918","url":null,"abstract":"With the ever increasing world population, there is a critical need for healthy food resources. Fish are the most environmentally-friendly animal protein to produce, efficiently converting feed into meat while generating a fraction of the greenhouse gasses of livestock production. Therefore, fish farming is one of the most important fields for a sustainable future. Since there is no way for fishes in fish farming pools to migrate into healthier water, a key factor in this industry is to maintain the water quality in standard conditions. Out of different key measurements used to quantify water quality, pH is among the essentials. In this study a portable, cheap, non contact, reusable, and machine learning-based pH sensing system is introduced. This helps farmers to quantify the pH quality of their pools without spending significant amounts of money on measurement equipment. This work introduces a sensitive, non-invasive and reflection-based optical sensor along with an Autoencoder-ESN framework for pH sensing. Using the Autoencoder guarantees at least 5 percent better classification in comparison with simple Echo State Networks. Long lifetimes of the sensor along with high sensitivity of the machine learning algorithm makes this system valuable for local farmers.","PeriodicalId":6792,"journal":{"name":"2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)","volume":"12 1","pages":"1049-1052"},"PeriodicalIF":0.0,"publicationDate":"2021-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78804837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Long Short-Term Memory with Spin-Based Binary and Non-Binary Neurons 基于自旋的二进制和非二进制神经元的长短期记忆

2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS) Pub Date : 2021-08-09 DOI: 10.1109/MWSCAS47672.2021.9531773

Shadi Sheikhfaal, Meghana Reddy Vangala, Adekunle A. Adepegba, R. Demara

引用次数: 2

PySyn: A Rapid Synthesis for Mixed-Signal Machine Learning Classification PySyn:混合信号机器学习分类的快速综合

2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS) Pub Date : 2021-08-09 DOI: 10.1109/MWSCAS47672.2021.9531745

Farid Kenarangi, Inna Partin-Vaisband

引用次数: 0

Facial Recognition System Using DWT, DCT, and Multilayer Sigmoid Neural Network Classifier 使用DWT, DCT和多层s型神经网络分类器的人脸识别系统

2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS) Pub Date : 2021-08-09 DOI: 10.1109/MWSCAS47672.2021.9531718

Genevieve Sapijaszko, W. Mikhael

{"title":"Facial Recognition System Using DWT, DCT, and Multilayer Sigmoid Neural Network Classifier","authors":"Genevieve Sapijaszko, W. Mikhael","doi":"10.1109/MWSCAS47672.2021.9531718","DOIUrl":"https://doi.org/10.1109/MWSCAS47672.2021.9531718","url":null,"abstract":"Facial recognition systems have seen widespread use in numerous applications, including identity verification for phone security, missing person identification, and forensic investigations. The purpose of this study is to improve both the speed and accuracy of a facial recognition system, thus enhancing its suitability for real-world applications. The proposed system reduces overall computational complexity by using simple algorithms and transforms such as grayscaling, a two-dimensional discrete wavelet transform, and a two-dimensional discrete cosine transform. The classification algorithm increases accuracy by using a straight-forward multilayer sigmoid neural network, which better correlates the input and output data than existing methods. The recognition system is tested with four freely accessible datasets: the ORL, YALE, FERET-c, and FEI. A test set based on the combination of all datasets is also utilized to evaluate the system performance. Results show that the system still maintains high recognition rates despite reducing complexity compared to popular existing methods.","PeriodicalId":6792,"journal":{"name":"2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)","volume":"19 1","pages":"533-536"},"PeriodicalIF":0.0,"publicationDate":"2021-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73153920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design of a 5-Bit Current Steering DAC for Driving High Forward Voltage LEDs 驱动高正向电压led的5位电流转向DAC的设计

2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS) Pub Date : 2021-08-09 DOI: 10.1109/MWSCAS47672.2021.9531744

Seyedfakhreddin Nabavi, A. Pourzadi, S. Bhadra

{"title":"Design of a 5-Bit Current Steering DAC for Driving High Forward Voltage LEDs","authors":"Seyedfakhreddin Nabavi, A. Pourzadi, S. Bhadra","doi":"10.1109/MWSCAS47672.2021.9531744","DOIUrl":"https://doi.org/10.1109/MWSCAS47672.2021.9531744","url":null,"abstract":"Over past decades, light-emitting diodes (LEDs) have been identified as an ordinary part of many industrial and biomedical applications and many attempts done to enlarge their versatility. This paper proposes a 5-bit current steering DAC with the capability of driving two LEDs in a commercial OSRAM photoplethysmography (PPG) sensor which have different forward voltages. The DAC operates based on the thermometer-code conversion and is designed for 65 nm TSMC technology. Combined with a LED driver circuit it is able to convert a 5-bit digital input to an LED current signal. Results indicate that the implemented DAC can reach up to 50 M samples per second (MS/s) and changing its input by 1 LSB leads to 940 µA variation in the LED current. It is shown that the DAC system can independently drive two LEDs with the forward voltages of 1.8 V and 2.8 V at different time instants. According to the binary input signal of the DAC, the amplitude of the driving current signal, which identifies the brightness of LEDs, can be varied between 3.29 mA and 32.45 mA at a maximum frequency of 50 KS/s.","PeriodicalId":6792,"journal":{"name":"2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)","volume":"25 1","pages":"1045-1048"},"PeriodicalIF":0.0,"publicationDate":"2021-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74259344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0