nils-herrmann/clean_scirepeval huggingface dataset:
Multi-feature sparse-based defect detection and classification in semiconductor units

Automated inspection systems have been used extensively for high-speed defect detection, gaging and quality control. In the semiconductor manufacturing industry, assembly and testing processes are getting more complex resulting in a greater tendency of defects to impact the production process. Currently available defect detection and classification systems are customized and hard-wired to the detection of particular classes of defects and cannot deal with new unknown classes. Shortage of defective units, similarities within different classes of defects, wide variations within the same defect class, and data imbalance are the basic challenges for this problem. This paper presents a novel stacking-based multi-feature, sparse-based defect detection and classification method that is robust to data imbalance and low number of training samples. Experimental results on real-world data from Intel show that the proposed approach results in a high classification accuracy as compared to existing methods.
A Locally Weighted Fixation Density-Based Metric for Assessing the Quality of Visual Saliency Predictions

With the increased focus on visual attention (VA) in the last decade, a large number of computational visual saliency methods have been developed over the past few years. These models are traditionally evaluated by using performance evaluation metrics that quantify the match between predicted saliency and fixation data obtained from eye-tracking experiments on human observers. Though a considerable number of such metrics have been proposed in the literature, there are notable problems in them. In this paper, we discuss shortcomings in the existing metrics through illustrative examples and propose a new metric that uses local weights based on fixation density, which overcomes these flaws. To compare the performance of our proposed metric at assessing the quality of saliency prediction with other existing metrics, we construct a ground-truth subjective database in which saliency maps obtained from 17 different VA models are evaluated by 16 human observers on a five-point categorical scale in terms of their visual resemblance with corresponding ground-truth fixation density maps obtained from eye-tracking data. The metrics are evaluated by correlating metric scores with the human subjective ratings. The correlation results show that the proposed evaluation metric outperforms all other popular existing metrics. In addition, the constructed database and corresponding subjective ratings provide an insight into which of the existing metrics and future metrics are better at estimating the quality of saliency prediction and can be used as a benchmark.
Error-resilient video coding and application to telemedicine

Digital imagery and video are a vital part of medical consultation and diagnosis. A telemedicine program linking international hospitals improves patient care, promotes more rapid diagnosis, and may hasten improved survival rates for children with cancer. However, in many developing countries and in rural and remote areas, reliable wired communications lines are not available, in which case telemedicine needs to be conducted over wireless transmission media. Although capable of providing the user with considerable mobility, the inherent noise-laden properties and reduced bandwidth requirements of wireless communication pose challenging design problems when compressed digital video is transmitted. This paper presents a low bit-rate error-resilient channel-optimized coder for the transmission of video over the binary symmetric channel. The proposed coder uses a channel-optimized trellis-coded quantization (COTCQ) stage that is designed to optimize the video coding based on the channel characteristics. The resilience to channel errors is obtained by optimizing the coder performance only at the level of the source encoder with no explicit channel coding for error protection. The objective of this effort is to design, test, and implement on real-world wireless systems, error-resilient, low bit rate, video coding systems for the reliable transmission of medical imagery and video over noisy wireless channels. Simulation results show that our coder can produce a visually pleasing rendition of the original signal even at high channel error rates.
Understanding how image quality affects deep neural networks

Image quality is an important practical challenge that is often overlooked in the design of machine vision systems. Commonly, machine vision systems are trained and tested on high quality image datasets, yet in practical applications the input images can not be assumed to be of high quality. Recently, deep neural networks have obtained state-of-the-art performance on many machine vision tasks. In this paper we provide an evaluation of 4 state-of-the-art deep neural network models for image classification under quality distortions. We consider five types of quality distortions: blur, noise, contrast, JPEG, and JPEG2000 compression. We show that the existing networks are susceptible to these quality distortions, particularly to blur and noise. These results enable future work in developing deep neural networks that are more invariant to quality distortions.
Canonic signed digit FIR filter design

A new algorithm is presented for the design of fixed point FIR digital filters that can be realized using a minimal number of adders while satisfying both the desired hardware implementation constraints and the frequency domain design constraints. The proposed algorithm makes use of a two-stage search method with a new soft-switching optimization criterion, minimizing the Chebyshev error norm in a discrete canonic signed digit (CSD) space. The proposed design method is implemented as a Matlab-based software package CSDFIR that allows the design of both Chebyshev optimal floating point and fixed point CSD FIR filters.
Integrating Visual Programming, Instrumentation, and Embedded DSP Technology into Freshman Introduction to Engineering Design

Arizona State University's electrical engineering department is redesigning its Freshman introduction to engineering design course with the objective of increasing students' retention and the students' interest in engineering careers. National instruments (NI) products, including the LabVIEWtrade embedded DSP module, which supports embedded real-time solutions, the SPEEDY-33 DSP board, and the NI ELVIS system, are used in the redesigned course in order to expose the students to exciting hands-on learning while fostering creativity. Moreover, students will be able to design, implement, test and compare the same applications in both the digital domain, using the NI SPEEDY-33 board and the LabVIEWtrade embedded DSP module, and the analog domain, using NI ELVIS and LabVIEWtrade. The developed applications will introduce students to fundamental concepts in signal & image processing, circuits, communications, biomedical systems, and robotics
Automated Void Detection in Solder Balls in the Presence of Vias and Other Artifacts

Voids are one of the major defects in solder balls and their detection and assessment can help in reducing unit and board yield issues caused by excessive or very large voids. Voids are difficult to detect using manual inspection alone. 2-D X-ray machines are often used to make voids visible to an operator for manual inspection. Automated methods do not give good accuracy in void detection and measurement because of a number of challenges present in 2-D X-ray images. Some of these challenges include vias, plated-through holes, reflections from the the plating or vias, inconsistent lighting, background traces, noise, void-like artifacts, and parallax effects. None of the existing methods that has been researched or utilized in equipment could accurately and repeatably detect voids in the presence of these challenges. This paper proposes a robust automatic void detection algorithm that detects voids accurately and repeatably in the presence of the aforementioned challenges. The proposed method operates on the 2-D X-ray images by first segregating each individual solder ball, including balls that are overshadowed by components, in preparation for treating each ball independently for void detection. Feature parameters are extracted through different classification steps to classify each artifact detected inside the solder ball as a candidate or phantom void. Several classification steps are used to tackle the challenges exhibited in the 2-D X-ray images. The proposed method is able to detect different-sized voids inside the solder balls under different brightness conditions and voids that are partially obscured by vias. Results show that the proposed method achieves a correlation squared of 86% when compared with manually measured and averaged data from experienced operators from both 2-D and 3-D X-ray tools. The proposed algorithm is fully automated and benefits the manufacturing process by reducing operator inspection time and removing the manual measurement variability from the results, thus providing a cost-effective solution to improve output product quality.
A reduced-reference perceptual quality metric for texture synthesis

This paper presents a reduced-reference quality metric that quantifies the perceptual quality of the synthesized textures. The metric is based on the change in perceived regularity between the original and the synthesized textures. The perceived regularity is quantified through a modified texture regularity metric based on visual attention. It is shown through subjective testing that the proposed metric has a strong correlation with the Mean Opinion Score for the fidelity of synthesized textures and outperforms the state-of-the-art full-reference quality metrics.
Block-Adaptive Wyner-Ziv Coding for Transform-Domain Distributed Video Coding

This paper presents a transform-domain distributed video compression (DVC) system with block-adaptive Wyner-Ziv coding. In the proposed system, the source symbols are reformatted into several blocks. The parity bits are requested only for the blocks with decoding errors. More accurate placement of the parity bits results in an improved system performance that is much closer to the performance of H.263 interframe coding as compared to existing DCT-based DVC schemes. Coding results and comparison with existing DVC schemes and with H.263 are presented to illustrate the performance of the proposed system.
An end to end, real time, CDPD wireless video coding and transmission system

We present an end to end adaptive video coding and transmission system that is designed for the Cellular Digital Packet Data (CDPD) wireless networks. The proposed system consists of (1) an embedded wavelet bit-plane coding algorithm, which is capable of adapting the compression ratio to the available bandwidth; (2) an efficient and error resilient fixed-length packetization scheme based on randomization in the wavelet domain; and (3) a feedback rate control algorithm, which is capable of estimating the available network bandwidth based on the acknowledgement information received by the server. Experimental results show that our system achieves good perceptual quality while maintaining high bandwidth utilization.
AQT-DVC: Adaptive Quantization for transform-domain distributed video coding

This paper presents a rate-distortion based Adaptive QuanTization (AQT) scheme for transform-domain distributed video coding (DVC). In the proposed DVC system, the Wyner-Ziv frame is divided into partitions and is adaptively quantized in the transform domain based on estimated local rate-distortion (R-D) characteristics. The RD characteristics are estimated at the decoder using a correlation model between the original source information and the side information. Rate-distortion performance results and comparisons with existing DVC schemes and with H.264 are presented.
Generative Sensing: Transforming Unreliable Sensor Data for Reliable Recognition

This paper introduces a deep learning enabled generative sensing framework which integrates low-end sensors with computational intelligence to attain a high recognition accuracy on par with that attained with high-end sensors. The proposed generative sensing framework aims at transforming low-end, low-quality sensor data into higher quality sensor data in terms of achieved classification accuracy. The low-end data can be transformed into higher quality data of the same modality or into data of another modality. Different from existing methods for image generation, the proposed framework is based on discriminative models and targets to maximize the recognition accuracy rather than a similarity measure. This is achieved through the introduction of selective feature regeneration in a deep neural network (DNN). The proposed generative sensing will essentially transform low-quality sensor data into high-quality information for robust perception. Results are presented to illustrate the performance of the proposed framework.
A no-reference perceptual image sharpness metric based on a cumulative probability of blur detection

In this paper, a no-reference objective sharpness metric based on a cumulative probability of blur detection is proposed. The metric is evaluated by taking into account the Human Visual System (HVS) response to blur distortions. The perceptual significance of the metric is validated through subjective experiments. It is shown that the proposed metric results in a very good correlation with subjective scores especially for images with varying foreground and background perceived blur qualities. This is accomplished with a significantly lower computational complexity as compared to existing methods that take into account the visual attention information.
Selective FEC for error-resilient image coding and transmission using similarity check functions

This paper introduces the concept of similarity check functions that measure the degree of corruption in transmitted multimedia data packets. Rather than directly discarding or directly retransmitting erroneous packets, the degree of corruption contributed by a particular packet is measured by a similarity check function at the receiver, which does not require explicit knowledge of the source data. Accordingly, if the packet is found to be badly corrupted based on a specified similarity criteria, it can be considered as a lost packet and recovered using forward error correcting codes or retransmission. An example of a similarity check function design and a selective forward error correction (S-FEC) scheme that allocates channel protection bits only to those packets that are in most need of correction, is presented to illustrate the proposed concept.
Adaptive texture synthesis based on perceived texture regularity

This paper presents the influence of perceived texture regularity on the visual quality of textures that are synthesized through parametric and non-parametric approaches. It is shown through subjective testing that textures with different degrees of perceived regularity exhibit different degrees of vulnerability to texture synthesis artifacts. The paper also proposes a method for adaptively selecting the appropriate texture synthesis algorithm based on an objective no-reference texture regularity metric. It is shown through subjective testing that the proposed adaptive texture synthesis performs better than the individual methods.
Visual quality assessment of reconstructed background images

A clean background image is of great importance in multiple applications such as video surveillance, object tracking and context-based video encoding, but acquiring a clean background image in public areas is seldom possible. Many algorithms have been developed to initialize the background from videos and images. This paper presents a database consisting of 13 different scenes that can be used for benchmarking the performance of background initialization algorithms. We also conducted a subjective study on the perceptual quality of background images that are reconstructed using existing background initialization algorithms. The obtained subjective scores are used to evaluate existing image quality metrics and their capability in predicting the perceived quality of reconstructed background images.
Introduction to Engineering: A Starter's Guide with Hands-On Digital Multimedia and Robotics Explorations

This lecture provides a hands-on glimpse of the field of electrical and computer engineering. The broad range of hands-on applications utilize LabVIEW and the NI-SPEEDY-33 hardware to explore concepts such as basic computer input and output, basic robotic principals, and introductory signal processing and communication concepts such as signal generation, modulation, music, speech, and audio and image/video processing. These principals and technologies are introduced in a very practical way and are fundamental to many of the electronic and computerized devices we use today. Some examples include audio level meter and audio effects, music synthesizer, real-time autonomous robot, image and video analysis, and DTMF modulation found in touch-tone telephone systems. Table of Contents: Getting Familiar with LabVIEW and SPEEDY-33 / Applications using LEDs and Switches using the SPEEDY-33 / Noise Removal / Music Equalizer / Telephone / Digital Audio Effects: Echo and Reverb / Music Composer / ntroduction to Robotics / AM Radio / Modem / Digital Image Processing Fundamentals / Applications using USB Camera / Appendix: VIs at a Glance
JPEG2000 encoding with perceptual distortion control

In this paper, a new encoding approach is proposed to control the JPEG2000 encoding in order to reach a desired perceptual quality. The new method is based on a vision model that incorporates various masking effects of human visual perception and a perceptual distortion metric that takes spatial and spectral summation of individual quantization errors into account. Compared with the conventional rate-based distortion minimization JPEG2000 encoding, the new method provides a way to generate consistent quality images at a lower bit rate.
Wavelet-based image coder with channel-optimized trellis-coded quantization

This paper presents a wavelet-based image coder optimized for transmission over binary symmetric channels (BSC). The proposed coder uses a channel-optimized trellis-coded quantization (COTCQ) stage that is designed to optimize the image coding based on the channel characteristics. This optimization is performed only at the level of the source encoder, and does not include any channel coding for error protection. Consequently, the proposed channel-optimized image coder is especially suitable for wireless transmission due to its reduced complexity. Furthermore, the improvement over TCQ-based image coders is significant. Examples are presented to illustrate the performance of the proposed COTCQ-based image coder.
An embedded scaling-based arbitrary shape region-of-interest coding method for JPEG2000

The shape information of objects is perceptually very significant and can aid in the recognition of objects. Depending upon the bit budget, Internet browsing applications can take advantage of an arbitrary-shape region-of-interest (ROI) coding method by sending only the shape information first, and then progressively transmitting the ROI texture, followed by the less important non-ROI texture information. Unfortunately, the existing ROI coding methods, including the JPEG2000-based methods, do not support separate coding and decoding of the shape information. In particular, the maxshift and the scaling-based methods of JPEG2000 are limited in that the former does not allow the coding of any non-ROI bitplanes prior to the coding of the ROI bitplanes, and the latter supports only rectangular and circular ROIs. The paper presents a novel ROI-based coding method that extends the scaling-based method of JPEG2000, and allows the coding of arbitrary-shape ROIs, as well as the separate coding and transmission of the ROI shape information prior to the texture information. Coding results are presented to illustrate the performance of the proposed ROI-based coding method.
Efficient design of families of FIR filters by transformation

This paper introduces an efficient technique for designing a parameterized family of one- and multi-dimensional filters by regarding it as a single multi-dimensional filter. The new design technique is very fast since it reduces the design of a large set of filters to the design of a complex or real one-dimensional filter which is then mapped to produce the desired set of filters. It has the additional advantage that the one- and multi-dimensional filters in the set can be designed and realized efficiently without having to explicitly compute the coefficients of the separate filters. Among the types of filters which can be designed with this technique are seismic migration filters and linear-phase filters.
Selective Error Detection for Error-Resilient JPEG2000 Coding

This paper presents a selective error detection (SED) scheme that is based on a similarity check function designed for the JPEG2000 compression algorithm. The presented scheme takes advantage of the error-resilience properties of JPEG2000 to significantly reduce the number of corrupted data packets that need to be retransmitted in a typical ARQ scheme. The degree of corruption induced by a particular codeblock is measured by a similarity check function at the receiver, without using any explicit knowledge regarding the original source data. Accordingly, if the data is found to be badly corrupted based on a specified similarity criteria, it is considered unusable and retransmitted. On the other hand, if the corrupted data does not significantly affect the quality of the decoded image, it is simply processed by the decoder without retransmission. Simulation results comparing the proposed selective retransmission scheme to full retransmission of corrupted packets are provided to illustrate the performance of the proposed method
Quantifying the intra and inter subband correlations in the zerotree-based wavelet image coders

The basis of compression is redundancy removal. In digital image compression, discrete wavelet transform (DWT) is applied to remove the inter-pixel redundancies. Although the DWT is very powerful at removing the linear redundancy, there are still various correlations left in the DWT coefficients. These correlations can be modeled as within-subband clustering(Intra) and cross-subband similarity(Inter). The success of recent wavelet image coders can be mainly attributed to the innovative strategies for data organization and representation that exploit these Inter and Intra correlations one way or the other. In this paper, we try to quantify the performance loss if these correlations are removed. Experiments are performed on two best zero-tree coders: set partitioning in hierarchical trees (SPIHT) and set partitioned embedded block coder (SPECK). After recapitulate the data organization adopted in SPIHT and SPECK in a more general tree formation framework, a block or coefficient based pseudo-randomization is applied to remove the Inter, Intra correlation. Our experimental results indicate that the performance loss due to the removal of Intra correlation is much bigger than the performance loss due to the removal of Inter correlation. Therefore, the excellent PSNR performance of the well-known SPIHT algorithm should be much credited for the data structure that exploit the Intra correlation although only the Inter correlation is widely mentioned in the previous literature.