Department of Electrical and Electronics Engineering

Permanent URI for this collectionhttp://localhost:4000/handle/123456789/1925

Browse

Has files: NoSubject: search.filters.subject.Feature extraction

Search Results

Now showing 1 - 9 of 9
  • No Thumbnail Available
    Item
    Semg signal acquisition: of hand movements for feature extraction and classification
    (IEEE, 2025-04) Yenuganti, Sujan
    This paper explores the classification of surface electromyography (sEMG) signals for hand movement recognition using time and frequency domain features used for feature extraction. Three hand movements were recorded from four healthy subjects for signal analysis achieving an SNR range of 14.81dB to 23.14dB. Two machine learning classifiers, medium neural network (MNN) and cubic support vector machine (CSVM), were evaluated to determine their effectiveness performance. MNN achieved the highest accuracy of 93.8%, demonstrating robust feature separation, while CSVM provided a simpler but slightly less precise result at 92.5%. The findings underscore the potential of MNN in classification of hand movements and aids on the development of advanced prosthetic control systems.
  • No Thumbnail Available
    Item
    A study of machine learning algorithms for hand gesture classification of sEMG signals Available to Purchase
    (Emerald, 2025-04) Yenuganti, Sujan
    This paper presents a cost-effective signal acquisition circuitry (SAC) for capturing surface electromyography (sEMG) data to classify different hand movements using advanced machine learning algorithms. The SAC, comprising an instrumentation amplifier, a Sallen–Key band-pass filter and a noninverting amplifier, is designed and tested on a portable printed circuit board. The purpose of this paper is to perform feature extraction and data segmentation for effective analysis and processing of the recorded sEMG signals.
  • No Thumbnail Available
    Item
    AgriSegNet: Deep Aerial Semantic Segmentation Framework for IoT-Assisted Precision Agriculture
    (IEEE, 2021-04) Chamola, Vinay
    Aerial inspection of agricultural regions can provide crucial information to safeguard from numerous obstacles to efficient farming. Farmland anomalies such as standing water, weed clusters, hamper the farming practices, which causes improper use of farm area and disrupts agricultural planning. Monitoring of farmland and crops through Internet-of-Things (IoT)-enabled smart systems has potential to increase the efficiency of modern farming techniques. Unmanned Aerial Vehicle (UAV)-based remote sensing is a powerful technique to acquire farmland images on a large scale. Visual data analytics for automatic pattern recognition from the collected data is useful for developing Artificial intelligence (AI)-assisted farming models, which holds great promise in improving the farming outputs by capturing the crop patterns, farmland anomalies and providing predictive solutions to the inherent challenges faced by farmers. In this work, we propose a deep learning framework AgriSegNet for automatic detection of farmland anomalies using multiscale attention semantic segmentation of UAV acquired images. The proposed model is useful for monitoring of farmland and crops to increase the efficiency of precision farming techniques.
  • No Thumbnail Available
    Item
    Real time human face location and recognition system using single training image per person
    (IEEE, 2011) Ajmera, Pawan K.
    This paper presents an automatic real time face location and recognition system. The proposed approach detects the face using the combination of hue, saturation and intensity (H SI) and luminance, red chrominance and blue chrominance (Y CrCb) color Space models. The left most, right most and top most pixels of face are detected using threshold values of parameters. One of the eyes is located using the blue chrominance. The second eye, center of the eyes, and the bottom most part of face is detected using geometrical similarity. The face is cropped using these defined boundaries to extract facial region only. The facial features of cropped image are extracted using the combination of Radon and wavelet transform. The technique computes Radon projections in different orientations and captures the directional features of face images. Further the wavelet transform applied on Radon space provides multiresolution features of the facial images. For classification, the nearest neighbor classifier has been used. The performance and robustness of the proposed system is tested on a face database of 785 images of 157 subjects acquired in conditions similar to those encountered in real world applications. The system achieves a recognition rate of 97.8 % and an equal error rate (EER) of about 2.4% for 157 subjects.
  • No Thumbnail Available
    Item
    A multi-modal and multi-algorithmic biometric system combining iris and face
    (IEEE, 2015) Ajmera, Pawan K.
    In this paper, we have developed an algorithm which combines features from human iris and face for person verification. Iris recognition is one of the most accurate biometric modalities having verification results close to 98%. On the other hand, face is one of the most widely used biometric features because of its ease of capture. We have adapted score level fusion strategy for our system. However, in addition to this, we are using two different features for face: Gabor filters based and Local Binary Patterns (LBP) based. The iris features are extracted using Daugman's Gabor filters based approach. Using this information, we have developed a multi-modal (combining iris and face), multi-algorithmic (using two different algorithms for feature extraction from face) biometric system. With this system, we achieved more than 85% improvement in the verification performance in terms of Equal Error Rate as compared to the uni-biometrics based system.
  • No Thumbnail Available
    Item
    Speech Recognition of Isolated Words in Noisy Conditions Using Radon Transform and Discrete Cosine Transform Based Features Derived from Speech Spectrogram
    (CIIT, 2012) Ajmera, Pawan K.
    This paper presents a new feature extraction technique for speech recognition using Radon Transform (RT) and Discrete Cosine Transform (DCT). A spectrogram is a time varying spectrum(forming an image) that shows how the spectral density of a signal varies with time. In the proposed scheme speech specific features have been extracted by applying image processing technique to the patterns available in the spectrogram. Radon transform has been used to derive the effective acoustic features from speech spectrogram. The proposed technique computes radon projections for nine orientations and captures the acoustic characteristics of the speech spectrogram. DCT applied on Radon projections yields low dimensional feature vectors. The technique is computationally efficient, speaker-independent, robust to session variations and insensitive to additive noise. Radon projections for seven orientations capture the acoustic characteristics of the spectrogram. The performance of the proposed algorithm has been evaluated in presence of additive white Gaussian noise from (30dB to -5dB SNR) on Texas Instruments-46(TI-46) speech database. The performance of the proposed technique in noisy environment is much better than existing popular algorithms
  • No Thumbnail Available
    Item
    Text-independent speaker identification using Radon and discrete cosine transforms based features from speech spectrogram
    (Elsevier, 2011-11) Ajmera, Pawan K.
    This paper presents a new feature extraction technique for speaker recognition using Radon transform (RT) and discrete cosine transform (DCT). The spectrogram is compact, efficient in representation and carries information about acoustic features in the form of pattern. In the proposed method, speaker specific features have been extracted by applying image processing techniques to the pattern available in the spectrogram. Radon transform has been used to derive the effective acoustic features from the speech spectrogram. Radon transform adds up the pixel values in the given image along a straight line in a particular direction and at a specific displacement. The proposed technique computes Radon projections for seven orientations and captures the acoustic characteristics of the spectrogram. DCT applied on Radon projections yields low dimensional feature vector. The technique is computationally efficient, text-independent, robust to session variations and insensitive to additive noise. The performance of the proposed algorithm has been evaluated using the Texas Instruments and Massachusetts Institute of Technology (TIMIT) and our own created Shri Guru Gobind Singhji (SGGS) databases. The recognition rate of the proposed algorithm on TIMIT database (consisting of 630 speakers) is 96.69% and for SGGS database (consisting of 151 speakers) is 98.41%. These results highlight the superiority of the proposed method over some of the existing algorithms.
  • No Thumbnail Available
    Item
    Palm-print recognition based on quality estimation and feature dimension
    (Inder Science, 2022-04) Ajmera, Pawan K.
    Human identification exploitation biometric traits are more and more in style in recent years. Among the widely used biometric traits, palm-print is a vital one because of its acquisition convenience and comparatively high recognition results. The paper proposes a palm-print recognition system based on quality estimation and feature dimensions. Initially, a quality assessment is applied on the extracted region of interest (ROI) images. Gabor filter is employed to extract the palm-print features having various scales and orientations. The kernel-based dimensionality reduction is applied in the full space that reduces the high-dimensional Gabor features. The experiments are conducted on the PolyU, IIT-Delhi and CASIA palm-print databases. The best recognition performance in terms of an equal error rate (EER) of 0.051% and recognition rate (RR) of 98.34% was achieved on PolyU database. Experimental results prove the effectiveness of the proposed approach.
  • No Thumbnail Available
    Item
    Finger Knuckleprint Based Personal Authentication Using Siamese Network
    (IEEE, 2019) Gupta, Karunesh Kumar; Tiwari, Kamlesh
    Online security is a major concern today and incidents of forged identity cards and hacked passwords are common throughout the world. Therefore, there is a need for robust personal authentication mechanisms using biometrics for various access control systems. Popular biometric traits such as fingerprint have problems in rural areas, due to wearing down of fingerprint pattern from hard manual labor. This is also a problem for people who work with calcium oxide, because it is known to dissolve the upper layers of the skin due to its basicity. This paper proposes a finger-knuckle-print (FKP) based human authentication system that is immune to the above problems because the finger dorsal region is not exposed to labor surfaces. The paper uses pre-processed knuckle ROI images to train a Siamese convolutional neural network model. The proposed algorithm has been validated using open-source PolyU finger-knuckle-print database from 165 individuals, and has achieved 99.24% CRR, 0.78% EER that is better than the state-of-the-art.