Question: DPICO: A high-speed deep packet inspection engine utilizing compact finite automata.

This paper unveils an optimized string searching algorithm designed to locate the initial occurrence of a character string, "pattern," within a larger string, "text." The distinguishing feature of this algorithm lies in its approach to matching characters; it begins from the end of the "pattern" string, enabling it to traverse the "text" string in significant leaps by leveraging the information gleaned from this reverse matching process. Consequently, this algorithm exhibits a unique characteristic where, in the majority of scenarios, it does not need to inspect all of the first "i" characters of "text." The average number of characters that are actually examined decreases as the length of "pattern" increases. For instance, when searching for a random English pattern of five characters, the algorithm typically inspects only "i"/4 characters of "text" prior to identifying a match at position "i." Moreover, the algorithm has been engineered to ensure that, on average, fewer than "i" + "pattern length" machine instructions are executed. These findings are substantiated through empirical evidence and a comprehensive theoretical analysis of the algorithm's average performance. It is also worth noting that the worst-case scenario for the algorithm's behavior remains linear in terms of "i" + "pattern length," provided there is sufficient array space available for tables that are linear in "pattern length" plus the size of the alphabet. This efficient algorithm stands as a significant advancement in the field of string searching, offering a notable improvement in speed and computational resource utilization over conventional methods.

Convolutional Neural Networks (CNNs) are currently adopted to solve an ever greater number of problems, ranging from speech recognition to image classi cation and segmentation. The large amount of processing required by CNNs calls for dedicated and tailored hardware support methods. Moreover, CNN workloads have a streaming nature, well suited to recon gurable hardware architectures such as FPGAs. The amount and diversity of research on the subject of CNN FPGA acceleration within the last 3 years demonstrates the tremendous industrial and academic interest. This paper presents a state-of-the-art of CNN inference accelerators over FPGAs. The computational workloads, their parallelism and the involved memory accesses are analyzed. At the level of neurons, optimizations of the convolutional and fully connected layers are explained and the performances of the di erent methods compared. At the network level, approximate computing and datapath optimization methods are covered and state-of-the-art approaches compared. The methods and tools investigated in this survey represent the recent trends in FPGA CNN inference accelerators and will fuel the future advances on e cient hardware deep learning.

As applications such as Apple Siri, Google Now, Microsoft Cortana, and Amazon Echo continue to gain traction, web-service companies are adopting large deep neural networks (DNN) for machine learning challenges such as image processing, speech recognition, natural language processing, among others. A number of open questions arise as to the design of a server platform specialized for DNN and how modern warehouse scale computers (WSCs) should be outfitted to provide DNN as a service for these applications.
 In this paper, we present DjiNN, an open infrastructure for DNN as a service in WSCs, and Tonic Suite, a suite of 7 end-to-end applications that span image, speech, and language processing. We use DjiNN to design a high throughput DNN system based on massive GPU server designs and provide insights as to the varying characteristics across applications. After studying the throughput, bandwidth, and power properties of DjiNN and Tonic Suite, we investigate several design points for future WSC architectures. We investigate the total cost of ownership implications of having a WSC with a disaggregated GPU pool versus a WSC composed of homogeneous integrated GPU servers. We improve DNN throughput by over 120x for all but one application (40x for Facial Recognition) on an NVIDIA K40 GPU. On a GPU server composed of 8 NVIDIA K40s, we achieve near-linear scaling (around 1000x throughput improvement) for 3 of the 7 applications. Through our analysis, we also find that GPU-enabled WSCs improve total cost of ownership over CPU-only designs by 4-20x, depending on the composition of the workload

Large grain data flow (LGDF) programming is natural and convenient for describing digital signal processing (DSP) systems, but its runtime overhead is costly in real time or cost-sensitive applications. In some situations, designers are not willing to squander computing resources for the sake of programmer convenience. This is particularly true when the target machine is a programmable DSP chip. However, the runtime overhead inherent in most LGDF implementations is not required for most signal processing systems because such systems are mostly synchronous (in the DSP sense). Synchronous data flow (SDF) differs from traditional data flow in that the amount of data produced and consumed by a data flow node is specified a priori for each input and output. This is equivalent to specifying the relative sample rates in signal processing system. This means that the scheduling of SDF nodes need not be done at runtime, but can be done at compile time (statically), so the runtime overhead evaporates. The sample rates can all be different, which is not true of most current data-driven digital signal processing programming methodologies. Synchronous data flow is closely related to computation graphs, a special case of Petri nets. This self-contained paper develops the theory necessary to statically schedule SDF programs on single or multiple processors. A class of static (compile time) scheduling algorithms is proven valid, and specific algorithms are given for scheduling SDF systems onto single or multiple processors.

We propose a new 64-bit blockcipher Piccolo supporting 80 and 128-bit keys. Adopting several novel design and implementation techniques, Piccolo achieves both high security and notably compact implementation in hardware. We show that Piccolo offers a sufficient security level against known analyses including recent related-key differential attacks and meet-in-the-middle attacks. In our smallest implementation, the hardware requirements for the 80 and the 128-bit key mode are only 683 and 758 gate equivalents, respectively. Moreover, Piccolo requires only 60 additional gate equivalents to support the decryption function due to its involution structure. Furthermore, its efficiency on the energy consumption which is evaluated by energy per bit is also remarkable. Thus, Piccolo is one of the competitive ultra-lightweight blockciphers which are suitable for extremely constrained environments such as RFID tags and sensor nodes.

Dynamic information flow tracking (DIFT) is an effective security countermeasure for both low-level memory corruptions and high-level semantic attacks. However, many software approaches suffer large performance degradation, and hardware approaches have high logic and storage overhead. We propose a flexible and light-weight hardware/software co-design approach to perform DIFT based on secure page allocation. Instead of associating every data with a taint tag, we aggregate data according to their taints, i.e., putting data with different attributes in separate memory pages. Our approach is a compiler-aided process with architecture support. The implementation and analysis show that the memory overhead is little, and our approach can protect critical information, including return address, indirect jump address, and system call IDs, from being overwritten by malicious users.

This paper presents a hardware design of context-based adaptive binary arithmetic coding (CABAC) for the emerging High efficiency video coding (HEVC) standard. While aiming at higher compression efficiency, the CABAC in HEVC also invests a lot of effort in the pursuit of parallelism and reducing hardware cost. Simulation results show that our design processes 1.18 bins per cycle on average. It can work at 357 MHz with 48.940K gates targeting 0.13 μm CMOS process. This processing rate can support real-time encoding for all sequences under common test conditions of HEVC standard conforming to the main profile level 6.1 of main tier or main profile level 5.1 of high tier.

Time sensitive applications rely on deterministic low latency, highly reactive, and trustable networks, as opposed to today's best effort internet. We review existing solutions and propose in this paper Deterministic Dynamic Network as a solution for end-to-end time-sensitive optical networking.

The power consumed by a circuit varies according to the activity of its individual transistors and other components. As a result, measurements of the power used by actual computers or microchips contain information about the operations being performed and the data being processed. Cryptographic designs have traditionally assumed that secrets are manipulated in environments that expose no information beyond the specified inputs and outputs. This paper examines how information leaked through power consumption and other side channels can be analyzed to extract secret keys from a wide range of devices. The attacks are practical, non-invasive, and highly effective—even against complex and noisy systems where cryptographic computations account for only a small fraction of the overall power consumption. We also introduce approaches for preventing DPA attacks and for building cryptosystems that remain secure even when implemented in hardware that leaks.

A 352 288 pixel CMOS image sensor chip with perpixel single-slope ADC and dynamic memory in a standard digital 0.18m CMOS process is described. The chip performs “snapshot” image acquisition, parallel 8-bit A/D conversion, and digital readout at continuous rate of 10 000 frames/s or 1 Gpixels/s with power consumption of 50 mW. Each pixel consists of a photogate circuit, a three-stage comparator, and an 8-bit 3T dynamic memory comprising a total of 37 transistors in 9.4 9.4 m with a fill factor of 15%. The photogate quantum efficiency is 13.6%, and the sensor conversion gain is 13.1 V/e . At 1000 frames/s, measured integral nonlinearity is 0.22% over a 1-V range, rms temporal noise with digital CDS is 0.15%, and rms FPN with digital CDS is 0.027%. When operated at low frame rates, on-chip power management circuits permit complete powerdown between each frame conversion and readout. The digitized pixel data is read out over a 64-bit (8-pixel) wide bus operating at 167 MHz, i.e., over 1.33 GB/s. The chip is suitable for general high-speed imaging applications as well as for the implementation of several still and standard video rate applications that benefit from high-speed capture, such as dynamic range enhancement, motion estimation and compensation, and image stabilization.

Content Delivery Networks (CDNs) are increasingly deployed for their efficient content delivery and are often integrated with Software Defined Networks (SDNs) to achieve centrality and programmability of the network. However, these networks are also an attractive target for network attackers whose main goal is to exhaust network resources. One attack approach is to over-flood the OpenFlow switch tables containing routing information. Due to the increasing number of different flooding attacks such as DDoS, it becomes difficult to distinguish these attacks from normal traffic when evaluated with traditional attack detection methods. This paper proposes an architectural method that classifies and defends all possible forms of DDoS attack and legitimate Flash Crowd traffic using a segregated dimension functioning cognitive process based in a controller module. Our results illustrate that the proposed model yields significantly enhanced performance with minimal false positives and false negatives when classified with optimal Support Vector Machine and Logistic Regression algorithms. The traffic classifications initiate deployment of security rules to the OpenFlow switches, preventing new forms of flooding attacks. To the best of our knowledge, this is the first work conducted on SDN-driven CDNi used to detect and defend against all possible DDoS attacks through traffic segregated dimension functioning coupled with cognitive classification.


Question: DPICO: a rapid deep packet inspection engine utilizing compact finite automata.

In this paper, we introduce a novel coding scheme designed to effectively handle errors resulting from deletions, insertions, and reversals in binary sequences. Unlike traditional error-correcting codes that primarily focus on correcting bit flips, our approach is tailored to address the specific challenges posed by these more complex types of errors. The proposed coding scheme employs a unique encoding strategy that allows for the detection and correction of deletions, insertions, and reversals within binary data. This is achieved through the integration of redundancy into the binary sequence in a manner that enables the receiver to recover the original data even when such errors occur. The scheme is particularly useful in communication systems where data integrity is critical and where errors can significantly impact the reliability of the transmitted information. By addressing deletions, insertions, and reversals, our coding method enhances the robustness of data transmission in environments prone to these types of disturbances. This advancement represents a significant improvement over conventional error-correcting codes, which often struggle to handle these more nuanced types of data corruption.

**Question**: A Comparative Study of the Impact of Different Sampling Techniques on the Performance of Data Mining Algorithms

**Documents**: A comparative study of different machine learning techniques for the prediction of protein-protein interactions

**Similar Documents**: This research presents a comprehensive analysis of the effectiveness of various machine learning techniques in the prediction of protein-protein interactions (PPIs). Given the critical role of PPIs in biological processes, accurately predicting these interactions is crucial for advancing our understanding of cellular mechanisms and disease states. The study evaluates a range of machine learning algorithms, including support vector machines, random forests, and neural networks, among others. Each algorithm is applied to predict PPIs using a dataset of known interactions, and their performances are compared based on several metrics, such as accuracy, precision, recall, and F1-score. The comparative analysis reveals the strengths and weaknesses of each technique, offering insights into the most suitable algorithms for PPI prediction tasks. This work not only contributes to the field of computational biology but also highlights the importance of selecting the right machine learning approach for specific biological problems. By identifying the most effective algorithms for PPI prediction, the study aims to facilitate more accurate and efficient computational methods for understanding and modeling biological interactions.

As data becomes dynamic, large, and distributed, there is increasing demand for what have become known as distributed stream algorithms. Since continuously collecting the data to a central server and processing it there is infeasible, a common approach is to define local conditions at the distributed nodes, such that—as long as they are maintained—some desirable global condition holds.
 Previous methods derived local conditions focusing on communication efficiency. While proving very useful for reducing the communication volume, these local conditions often suffer from heavy computational burden at the nodes. The computational complexity of the local conditions affects both the runtime and the energy consumption. These are especially critical for resource-limited devices like smartphones and sensor nodes. Such devices are becoming more ubiquitous due to the recent trend toward smart cities and the Internet of Things. To accommodate for high data rates and limited resources of these devices, it is crucial that the local conditions be quickly and efficiently evaluated.
 Here we propose a novel approach, designated CB (for Convex/Concave Bounds). CB defines local conditions using suitably chosen convex and concave functions. Lightweight and simple, these local conditions can be rapidly checked on the fly. CB’s superiority over the state-of-the-art is demonstrated in its reduced runtime and power consumption, by up to six orders of magnitude in some cases. As an added bonus, CB also reduced communication overhead in all the tested application scenarios.

In the horizontal semiconductor business model where the designer's intellectual property (IP) is transparent to foundry and to other entities on the production chain, integrated circuits (ICs) overbuilding and IP piracy are prevalent problems. Active metering is a suite of methods enabling the designers to control their chips postfabrication. We provide a comprehensive description of the first known active hardware metering method and introduce new formal security proofs. The active metering method uniquely and automatically locks each IC upon manufacturing, such that the IP rights owner is the only entity that can provide the specific key to unlock or otherwise control each chip. The IC control mechanism exploits: 1) the functional description of the design, and 2) unique and unclonable IC identifiers. The locks are embedded by modifying the structure of the hardware computation model, in the form of a finite state machine (FSM). We show that for each IC hiding the locking states within the modified FSM structure can be constructed as an instance of a general output multipoint function that can be provably efficiently obfuscated. The hidden locks within the FSM may also be used for remote enabling and disabling of chips by the IP rights owner during the IC's normal operation. An automatic synthesis method for low overhead hardware implementation is devised. Attacks and countermeasures are addressed. Experimental evaluations demonstrate the low overhead of the method. Proof-of-concept implementation on the H.264 MPEG decoder automatically synthesized on a Xilinix Virtex-5 field-programmable gate array (FPGA) further shows the practicality, security, and the low overhead of the new method.

A network covert channel is a mechanism that can be used to leak information across a network in violation of a security policy and in a manner that can be difficult to detect. In this paper, we describe our implementation of a covert network timing channel, discuss the subtle issues that arose in its design, and present performance data for the channel. We then use our implementation as the basis for our experiments in its detection. We show that the regularity of a timing channel can be used to differentiate it from other traffic and present two methods of doing so and measures of their efficiency. We also investigate mechanisms that attackers might use to disrupt the regularity of the timing channel, and demonstrate methods of detection that are effective against them.

Cryptographic methods are widely used within networking and digital rights management. Numerous algorithms exist, e.g. spanning VPNs or distributing sensitive data over a shared network infrastructure. While these algorithms can be run with moderate performance on general purpose processors, such processors do not meet typical embedded systems requirements (e.g. area, cost and power consumption). Instead, specialized cores dedicated to one or a combination of algorithms are typically used. These cores provide very high bandwidth data transmission and meet the needs of embedded systems. However, with such cores changing the algorithm is not possible without replacing the hardware. This paper describes a fully programmable processor architecture which has been tailored for the needs of a spectrum of cryptographic algorithms and has been explicitly designed to run at high clock rates while maintaining a significantly better performance/area/power tradeoff than general purpose processors. Both the architecture and instruction set have been developed to achieve a bits-per-clock rate of greater than one, even with complex algorithms. This performance will be demonstrated with standard cryptographic algorithms (AES and DES) and a widely used hash algorithm (MD5).

Integrated circuits (ICs) are now designed and fabricated in a globalized multi-vendor environment making them vulnerable to malicious design changes, the insertion of hardware trojans/malware and intellectual property (IP) theft. Algorithmic reverse engineering of digital circuits can mitigate these concerns by enabling analysts to detect malicious hardware, verify the integrity of ICs and detect IP violations.
 In this paper, we present a set of algorithms for the reverse engineering of digital circuits starting from an unstructured netlist and resulting in a high-level netlist with components such as register files, counters, adders and subtracters. Our techniques require no manual intervention and experiments show that they determine the functionality of more than 51% and up to 93% of the gates in each of the practical test circuits that we examine.

Software-defined networking separates the control and forwarding planes of a network to make it more programmable and application- aware. As one of the initial implementations of SDN, OpenFlow abstracts a forwarding device as a flow table and realizes flow processing by applying the "match-and-act" principle. However, the protocol-dependent nature of OpenFlow still limits the programmability of the forwarding plane. Hence, in this article, we discuss how to leverage protocol-oblivious forwarding (POF) to further enhance the network programmability such that the forwarding plane becomes protocol-independent and can be dynamically reprogrammed to support new protocol stacks seamlessly. We first review the development of OpenFlow and explain the motivations for introducing POF. Then we explain the working principle of POF, discuss our efforts on realizing the POF development ecosystem, and show our implementation of POF-based source routing as a novel use case. Finally, we elaborate on the first WAN-based POF network testbed that includes POF switches located in two cities in China.

Network Function Virtualization (NFV) and Software-Defined Networking (SDN) are deeply changing the networking field by introducing software at any level, aiming at decoupling the logic from the hardware. Together, they bring several benefits, mostly in terms of scalability and flexibility. Up to now, SDN has been used to support NFV from the routing and the architectural point of view. In this paper we present Kathará, a framework based on containers, that allows network operators to deploy Virtual Network Functions (VNFs) through the adoption of emerging data-plane programmable capabilities, such as P4-compliant switches. It also supports the coexistence of SDN and traditional routing protocols in order to set up arbitrarily complex networks. As a side effect, thanks to Kathará, we demonstrate that implementing NFV by means of specific-purpose equipment is feasible and it provides a gain in performance while preserving the benefits of NFV. We measure the resource consumption of Kathará and we show that it performs better than frameworks that implement virtual networks using virtual machines by several orders of magnitude.

The network on-chip (NoC) is a critical resource shared by various on-chip components. An efficient NoC arbitration policy is crucial in providing global fairness and improving system performance. In this preliminary work, we demonstrate an idea of utilizing deep reinforcement learning to guide the design of more efficient NoC arbitration policies. We relate arbitration to a self-learning decision making process. Results show that the deep reinforcement learning approach can effectively reduce packet latency and has potential for identifying interesting features that could be utilized in more practical hardware designs.

Large grain data flow (LGDF) programming is natural and convenient for describing digital signal processing (DSP) systems, but its runtime overhead is costly in real time or cost-sensitive applications. In some situations, designers are not willing to squander computing resources for the sake of programmer convenience. This is particularly true when the target machine is a programmable DSP chip. However, the runtime overhead inherent in most LGDF implementations is not required for most signal processing systems because such systems are mostly synchronous (in the DSP sense). Synchronous data flow (SDF) differs from traditional data flow in that the amount of data produced and consumed by a data flow node is specified a priori for each input and output. This is equivalent to specifying the relative sample rates in signal processing system. This means that the scheduling of SDF nodes need not be done at runtime, but can be done at compile time (statically), so the runtime overhead evaporates. The sample rates can all be different, which is not true of most current data-driven digital signal processing programming methodologies. Synchronous data flow is closely related to computation graphs, a special case of Petri nets. This self-contained paper develops the theory necessary to statically schedule SDF programs on single or multiple processors. A class of static (compile time) scheduling algorithms is proven valid, and specific algorithms are given for scheduling SDF systems onto single or multiple processors.

In the last few years, many different attacks against computing platform targeting hardware or low level firmware have been published. Such attacks are generally quite hard to detect and to defend against as they target components that are out of the scope of the operating system and may not have been taken into account in the security policy enforced on the platform. In this paper, we study the case of remote attacks against network adapters. In our case study, we assume that the target adapter is running a flawed firmware that an attacker may subvert remotely by sending packets on the network to the adapter. We study possible detection techniques and their efficiency. We show that, depending on the architecture of the adapter and the interface provided by the NIC to the host operating system, building an efficient detection framework is possible. We explain the choices we made when designing such a framework that we called NAVIS and give details on our proof of concept implementation.


Question: Can we surpass Hadamard multiplexing? A data-driven strategy for designing and analyzing computational imaging systems.

This paper explores the issue of single image object motion deblurring from a static camera perspective, addressing the inherent ill-posedness of deblurring moving objects due to the degradation of high spatial frequencies in captured blurred images. A coded exposure camera technique is introduced, which modulates the light integration pattern through a binary-coded shutter opening and closing sequence within the exposure time. The choice of code is critical, as it aims to ensure the invertibility of the resulting point spread function (PSF) for optimal deconvolution outcomes. However, the paper emphasizes the importance of PSF estimation alongside PSF invertibility for the success of deconvolution algorithms. It is observed that PSF estimation is facilitated by a smooth motion blur, which can be compromised by codes optimized solely for PSF invertibility, leading to non-smooth blur patterns. The study proposes a balanced approach, demonstrating that both PSF invertibility and estimation can be satisfied, albeit with a minor increase in deconvolution noise. Guidelines are provided for selecting codes with enhanced PSF estimation capabilities, and two search criteria are outlined for identifying the optimal code given a specific length. Comparative theoretical analysis is presented to assess the performance of the proposed code against those optimized only for PSF invertibility. Moreover, the paper illustrates a straightforward method for implementing coded exposure on a consumer-grade machine vision camera without requiring additional hardware. The efficacy of the proposed codes for motion deblurring is substantiated through real-world experimental results.

Binomial filters are simple and efficient structures based on the binomial coefficients for implementing Gaussian filtering. They do not require multipliers and can therefore be implemented efficiently in programmable hardware. There are many possible variations of the basic binomial filter structure, and they provide a wide range of space-time trade-offs; a number of these designs have been captured in a parametrised form and their features are compared. This technique can be used for multi-dimensional filtering, provided that the filter is separable. The numerical performance of binomial filters, and their implementation using field-programmable devices for an image processing application, are also discussed.

The present phase of Machine Learning is characterized by supervised learning algorithms relying on large sets of labeled examples (n→∞). The next phase is likely to focus on algorithms capable of learning from very few labeled examples (n → 1), like humans seem able to do. We propose an approach to this problem and describe the underlying theory, based on the unsupervised, automatic learning of a “good” representation for supervised learning, characterized by small sample complexity (n). We consider the case of visual object recognition though the theory applies to other domains. The starting point is the conjecture, proved in specific cases, that image representations which are invariant to translations, scaling and other transformations can considerably reduce the sample complexity of learning. We prove that an invariant and unique (discriminative) signature can be computed for each image patch, I, in terms of empirical distributions of the dot-products between I and a set of templates stored during unsupervised learning. A module performing filtering and pooling, like the simple and complex cells described by Hubel and Wiesel, can compute such estimates. Hierarchical architectures consisting of this basic Hubel-Wiesel moduli inherit its properties of invariance, stability, and discriminability while capturing the compositional organization of the visual world in terms of wholes and parts. The theory extends existing deep learning convolutional architectures for image and speech recognition. It also suggests that the main computational goal of the ventral stream of visual cortex is to provide a hierarchical representation of new objects/images which is invariant to transformations, stable, and discriminative for recognition—and that this representation may be continuously learned in an unsupervised way during development and visual experience. This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF 1231216. http://cbmm.mit.edu CBMM Paper March, 2014 1–24 ar X iv :1 31 1. 41 58 v5 [ cs .C V ] 1 1 M ar 2 01 4 Unsupervised learning of invariant representations with low sample complexity: the magic of sensory cortex or a new framework for machine learning? Fabio Anselmi ∗ †, Joel Z. Leibo ∗ , Lorenzo Rosasco ∗ † , Jim Mutch ∗ , Andrea Tacchetti ∗ † , and Tomaso Poggio ∗ † ∗Center for Brains, Minds and Machines, Massachusetts Institute of Technology, Cambridge, MA 02139, and †Istituto Italiano di Tecnologia, Genova, 16163 The present phase of Machine Learning is characterized by supervised learning algorithms relying on large sets of labeled examples (n→∞). The next phase is likely to focus on algorithms capable of learning from very few labeled examples (n → 1), like humans seem able to do. We propose an approach to this problem and describe the underlying theory, based on the unsupervised, automatic learning of a “good” representation for supervised learning, characterized by small sample complexity (n). We consider the case of visual object recognition though the theory applies to other domains. The starting point is the conjecture, proved in specific cases, that image representations which are invariant to translations, scaling and other transformations can considerably reduce the sample complexity of learning. We prove that an invariant and unique (discriminative) signature can be computed for each image patch, I, in terms of empirical distributions of the dot-products between I and a set of templates stored during unsupervised learning. A module performing filtering and pooling, like the simple and complex cells described by Hubel and Wiesel, can compute such estimates. Hierarchical architectures consisting of this basic Hubel-Wiesel moduli inherit its properties of invariance, stability, and discriminability while capturing the compositional organization of the visual world in terms of wholes and parts. The theory extends existing deep learning convolutional architectures for image and speech recognition. It also suggests that the main computational goal of the ventral stream of visual cortex is to provide a hierarchical representation of new objects/images which is invariant to transformations, stable, and discriminative for recognition—and that this representation may be continuously learned in an unsupervised way during development and visual experience.1 Invariance | Hierarchy | Convolutional networks | Visual cortex It is known that Hubel and Wiesel’s original proposal [1] for visual area V1—of a module consisting of complex cells (C-units) combining the outputs of sets of simple cells (S-units) with identical orientation preferences but differing retinal positions—can be used to construct translationinvariant detectors. This is the insight underlying many networks for visual recognition, including HMAX [2] and convolutional neural nets [3, 4]. We show here how the original idea can be expanded into a comprehensive theory of visual recognition relevant for computer vision and possibly for visual cortex. The first step in the theory is the conjecture that a representation of images and image patches, with a feature vector that is invariant to a broad range of transformations—such as translation, scale, expression of a face, pose of a body, and viewpoint—makes it possible to recognize objects from only a few labeled examples, as humans do. The second step is proving that hierarchical architectures of Hubel-Wiesel (‘HW’) modules (indicated by ∧ in Fig. 1) can provide such invariant representations while maintaining discriminative information about the original image. Each ∧ -module provides a feature vector, which we call a signature, for the part of the visual field that is inside its “receptive field”; the signature is invariant to (R) affine transformations within the receptive field. The hierarchical architecture, since it computes a set of signatures for l=4

While deep learning methods have achieved state-of-theart performance in many challenging inverse problems like image inpainting and super-resolution, they invariably involve problem-specific training of the networks. Under this approach, each inverse problem requires its own dedicated network. In scenarios where we need to solve a wide variety of problems, e.g., on a mobile camera, it is inefficient and expensive to use these problem-specific networks. On the other hand, traditional methods using analytic signal priors can be used to solve any linear inverse problem; this often comes with a performance that is worse than learning-based methods. In this work, we provide a middle ground between the two kinds of methods — we propose a general framework to train a single deep neural network that solves arbitrary linear inverse problems. We achieve this by training a network that acts as a quasi-projection operator for the set of natural images and show that any linear inverse problem involving natural images can be solved using iterative methods. We empirically show that the proposed framework demonstrates superior performance over traditional methods using wavelet sparsity prior while achieving performance comparable to specially-trained networks on tasks including compressive sensing and pixel-wise inpainting.

A variety of methods for modeling scattered data are discussed, with an emphasis on two types of data: volumetric and spherical. To demonstrate the performance of the various methods, results from an empirical study using trivariate scattered data are presented. The author's objective is to provide information to aid in selecting a type of method or to form the basis for customizing a method for a particular application.<<ETX>>

Both the original version of David Lowe's innuential and classic algorithm for tracking known objects and a reformulation of it implemented by Ishii et al. rely on (diierent) approximated imaging models. Removing their simplifying assumptions yields a fully pro-jective solution with signiicantly improved accuracy and convergence, and arguably better computation{ time properties.

Single image blind deconvolution aims to estimate the unknown blur from a single observed blurred image and recover the original sharp image. Such task is severely ill-posed and typical approaches involve some heuristic or other steps without clear mathematical explanation to arrive at an acceptable solution. We show that a straightforward maximum a posteriory estimation combined with very sparse priors and an efficient numerical method can produce results, which compete with much more complicated state-of-the-art methods.

We propose an algorithm for the real time realistic simulation of multiple anisotropic scattering of light in a volume. Contrary to previous real-time methods we account for all kinds of light paths through the medium and preserve their anisotropic behavior.
 Our approach consists of estimating the energy transport from the illuminated cloud surface to the rendered cloud pixel for each separate order of multiple scattering. We represent the distribution of light paths reaching a given viewed cloud pixel with the mean and standard deviation of their entry points on the lit surface, which we call the collector area. At rendering time for each pixel we determine the collector area on the lit cloud surface for different sets of scattering orders, then we infer the associated light transport. The fast computation of the collector area and light transport is possible thanks to a preliminary analysis of multiple scattering in plane-parallel slabs and does not require slicing or marching through the volume.
 Rendering is done efficiently in a shader on the GPU, relying on a cloud surface mesh augmented with a Hypertexture to enrich the shape and silhouette. We demonstrate our model with the interactive rendering of detailed animated cumulus and cloudy sky at 2--10 frames per second.

The purpose of computer-aided detection or diagnosis (CAD) technology has so far been to serve as a second reader. If, however, all relevant lesions in an image can be detected by CAD algorithms, use of CAD for automatic reading or prescreening may become feasible. This work addresses the question how to fuse information from multiple CAD algorithms, operating on multiple images that comprise an exam, to determine a likelihood that the exam is normal and would not require further inspection by human operators. We focus on retinal image screening for diabetic retinopathy, a common complication of diabetes. Current CAD systems are not designed to automatically evaluate complete exams consisting of multiple images for which several detection algorithm output sets are available. Information fusion will potentially play a crucial role in enabling the application of CAD technology to the automatic screening problem. Several different fusion methods are proposed and their effect on the performance of a complete comprehensive automatic diabetic retinopathy screening system is evaluated. Experiments show that the choice of fusion method can have a large impact on system performance. The complete system was evaluated on a set of 15 000 exams (60 000 images). The best performing fusion method obtained an area under the receiver operator characteristic curve of 0.881. This indicates that automated prescreening could be applied in diabetic retinopathy screening programs.

For centuries, the conventional approach to lens design has been to grind the surfaces of a uniform material in such a manner as to sculpt the paths that rays of light follow as they transit through the interfaces. Refractive lenses formed by this procedure of bending the surfaces can be of extremely high quality, but are nevertheless limited by geometrical and wave aberrations that are inherent to the manner in which light refracts at the interface between two materials. Conceptually, a more natural--but usually less convenient--approach to lens design would be to vary the refractive index throughout an entire volume of space. In this manner, far greater control can be achieved over the ray trajectories. Here, we demonstrate how powerful emerging techniques in the field of transformation optics can be used to harness the flexibility of gradient index materials for imaging applications. In particular we design and experimentally demonstrate a lens that is broadband (more than a full decade bandwidth), has a field-of-view approaching 180 degrees and zero f-number. Measurements on a metamaterial implementation of the lens illustrate the practicality of transformation optics to achieve a new class of optical devices.

In this paper we propose a parallel high performance FFT algorithm based on a multi-dimensional formulation. We use this to solve a commonly encountered FFT based kernel on a distributed memory parallel machine, the IBM scalable parallel system, SP1. The kernel requires a forward FFT computation of an input sequence, multiplication of the transformed data by a coefficient array, and finally an inverse FFT computation of the resultant data. We show that the multidimensional formulation helps in reducing the communication costs and also improves the single node performance by effectively utilizing the memory system of the node. We implemented this kernel on the IBM SP1 and observed a performance of 1.25 GFLOPS on a 64-node machine.


Question: Is it possible to outperform Hadamard multiplexing? A data-driven approach to design and analyze computational imaging systems.

This paper delves into the connection between natural image statistics and neural representation, exploring the hypothesis that sensory neurons are finely tuned by evolutionary and developmental mechanisms to optimally process the statistical characteristics of their environmental inputs. The foundational concept of coding efficiency, initially proposed by Attneave (1954) and later by Barlow (1961), posits that information theory serves as a bridge linking environmental statistics to neural responses. With the advancement of statistical modeling techniques and the availability of powerful computational resources, scientists have been able to construct and empirically validate more complex statistical models for visual images. This has facilitated the experimental examination of the efficient coding hypothesis, both for individual neurons and for neuron populations. The investigation encompasses not only theoretical models but also practical applications, such as the analysis of neural responses to naturalistic stimuli and the development of computational models that mimic neural processing. Through these studies, researchers aim to elucidate the mechanisms by which neural systems efficiently encode and process visual information, contributing to a deeper understanding of visual perception and neural coding.

This paper considers a multiple-input-multiple-output (MIMO) system with low-resolution analog-to-digital converters (ADCs). In this system, we propose a novel detection framework that performs data symbol detection without explicitly knowing channel state information at a receiver. The underlying idea of the proposed framework is to exploit supervised learning. Specifically, during channel training, the proposed approach sends a sequence of data symbols as pilots so that the receiver learns a nonlinear function that is determined by both a channel matrix and a quantization function of the ADCs. During data transmission, the receiver uses the learned nonlinear function to detect which data symbols were transmitted. In this context, we propose two blind detection methods to determine the nonlinear function from the training-data set. We also provide an analytical expression for the symbol-vector-error probability of the MIMO systems with one-bit ADCs when employing the proposed framework. Simulations demonstrate the performance improvement of the proposed framework compared to existing detection techniques.

Multiple resolution analysis of images is a current trend in computer vision. In most cases, only spatial resolution has been considered. However, image resolution has an additional aspect: gray level, or color, resolution. Color resolution has traditionally been considered in the area of computer graphics. By defining a suitable measure for the comparison of images, changes in resolution can be treated with the same tools as changes in color resolution. A gray tone image, for example, can be compared to a halftone image having only two colors (black and white), but of higher spatial resolution. An important application can be in pyramids, one of the most commonly used multiple (spatial) resolution schemes, where this approach provides a tool to change the color resolution as well. Increasing color resolution while reducing spatial resolution to retain more image details and prevent aliasing is an example of the possibility to find optimal combinations of resolution reduction, spatial and color, to best fit an application.

Surveying a suite of algorithms that offer a solution to managing large document archives.

In this paper, we propose a framework for both gradient descent image and object alignment in the Fourier domain. Our method centers upon the classical Lucas & Kanade (LK) algorithm where we represent the source and template/model in the complex 2D Fourier domain rather than in the spatial 2D domain. We refer to our approach as the Fourier LK (FLK) algorithm. The FLK formulation is advantageous when one preprocesses the source image and template/model with a bank of filters (e.g., oriented edges, Gabor, etc.) as 1) it can handle substantial illumination variations, 2) the inefficient preprocessing filter bank step can be subsumed within the FLK algorithm as a sparse diagonal weighting matrix, 3) unlike traditional LK, the computational cost is invariant to the number of filters and as a result is far more efficient, and 4) this approach can be extended to the Inverse Compositional (IC) form of the LK algorithm where nearly all steps (including Fourier transform and filter bank preprocessing) can be precomputed, leading to an extremely efficient and robust approach to gradient descent image matching. Further, these computational savings translate to nonrigid object alignment tasks that are considered extensions of the LK algorithm, such as those found in Active Appearance Models (AAMs).

We generalize the alternating minimization algorithm recently proposed in [32] to efficiently solve a general, edge-preserving, variational model for recovering multichannel images degraded by withinand cross-channel blurs, as well as additive Gaussian noise. This general model allows the use of localized weights and higher-order derivatives in regularization, and includes a multichannel extension of total variation (MTV) regularization as a special case. In the MTV case, we show that the model can be derived from an extended half-quadratic transform of Geman and Yang [14]. For color images with three channels and when applied to the MTV model (either locally weighted or not), the per-iteration computational complexity of this algorithm is dominated by nine fast Fourier transforms. We establish strong convergence results for the algorithm including finite convergence for some variables and fast q-linear convergence for the others. Numerical results on various types of blurs are presented to demonstrate the performance of our algorithm compared to that of the MATLAB deblurring functions. We also present experimental results on regularization models using weighted MTV and higher-order derivatives to demonstrate improvements in image quality provided by these models over the plain MTV model.

We propose a novel algorithm for uncalibrated photometric stereo. While most of previous methods rely on various assumptions on scene properties, we exploit constraints in lighting configurations. We first derive an ambiguous reconstruction by requiring lights to lie on a view centered cone. This reconstruction is upgraded to Euclidean by constraints derived from lights of equal intensity and multiple view geometry. Compared to previous methods, our algorithm deals with more general data and achieves high accuracy. Another advantage of our method is that we can model weak perspective effects of lighting, while previous methods often assume orthographical illumination. We use both synthetic and real data to evaluate our algorithm. We further build a hardware prototype to demonstrate our approach.

Spotlight synthetic aperture radar images can be formed from the complex phase history data using two main techniques: 1) polar-to-Cartesian interpolation followed by twodimensional inverse Fourier transform (2DFFT) , and 2) convolution backprojection (CBP) . CBP has been widely used to reconstruct medical images in computer aided tomography, and only recently has been applied to form synthetic aperture radar imagery. It is alleged that CBP yields higher quality images because 1) all the Fourier data are used and 2) the polar formatted data is used directly to form a 2D Cartesian image and therefore 2D interpolation is not required. This report compares the quality of images formed by CBP and several modified versions of the 2DFFT method. We show from an image quality point of view that CBP is equivalent to first windowing the phase history data and then interpolating to an exscribed rectangle. From a mathematical perspective, we should expect this conclusion since the same Fourier data are used to form the SAR image. We next address the issue of parallel implementation of each algorithm. We dispute previous claims that CBP is more readily parallelizable than the 2DFFT method. Our conclusions are supported by comparing execution times between massively parallel implementations of both algorithms, showing that both experience similar decreases in computation time, but that CBP takes significantly longer to form an image. This page intentionally left blank

The optimal storage-computation tradeoff is characterized for a MapReduce-like distributed computing system with straggling nodes, where only a part of the nodes can be utilized to compute the desired output functions. The result holds for arbitrary output functions and thus generalizes previous results that restricted to linear functions. Specifically, in this work, we propose a new information-theoretical converse and a new matching coded computing scheme, that we call coded computing for straggling systems (CCS).

Large-area displays made up of several projectors show significant variation in color. Here, we identify different projector parameters that cause the color variation and study their effects on the luminance and chrominance characteristics of the display. This work leads to the realization that luminance varies significantly within and across projectors, while chrominance variation is relatively small, especially across projectors of same model. To address this situation, we present a method to achieve luminance matching across all pixels of a multiprojector display that results in photometrically uniform displays. We use a camera as a measurement device for this purpose. Our method comprises a one-time calibration step that generates a per channel per projector luminance attenuation map (LAM), which is then used to correct any image projected on the display at interactive rates on commodity graphics hardware. To the best of our knowledge, this is the first effort to match luminance across all the pixels of a multiprojector display.

We introduce the concept of a Pixel Stream Editor. This forms the basis for an interactive synthesizer for designing highly realistic Computer Generated Imagery. The designer works in an interactive Very High Level programming environment which provides a very fast concept/implement/view iteration cycle.Naturalistic visual complexity is built up by composition of non-linear functions, as opposed to the more conventional texture mapping or growth model algorithms. Powerful primitives are included for creating controlled stochastic effects. We introduce the concept of "solid texture" to the field of CGI.We have used this system to create very convincing representations of clouds, fire, water, stars, marble, wood, rock, soap films and crystal. The algorithms created with this paradigm are generally extremely fast, highly realistic, and asynchronously parallelizable at the pixel level.


Question: Can we surpass Hadamard multiplexing? A data-driven approach to design and analyze computational imaging systems.

The challenge of blue screen matting, a specific instance of the broader matting problem, involves the extraction of a foreground image from a background, typically within a film frame context, to enable the substitution of a different background. This technique, often applied when the background is of a uniform color—historically blue, hence the term blue screen matting—has also been adapted for other constant colors, such as yellow or green. Despite the mathematical proof suggesting that the general problem of constant color matting is unsolvable under typical conditions, the technique remains widely utilized in the film and video industries. This apparent contradiction is resolved by identifying and applying constraints to the problem that either lead to feasible solutions or significantly narrow down the solution space. We illustrate that an algorithmic resolution can be achieved by filming the foreground object against two distinct constant backgrounds, regardless of their specific colors, as long as they differ across the entire frame. This method effectively expands the solution space, offering a practical workaround to the theoretical limitations of constant color matting.

**Question**: A Direct Search Method to solve Economic Dispatch Problem with Valve-Point Effect

**Documents**: A hybrid of genetic algorithm and particle swarm optimization for recurrent network design

**Similar Documents**: This paper introduces an innovative hybrid evolutionary learning algorithm, the Hybrid Genetic Algorithm-Particle Swarm Optimization (HGAPSO), designed for optimizing recurrent neural/fuzzy network architectures. Unlike conventional methods, HGAPSO integrates the strengths of genetic algorithms (GA) and particle swarm optimization (PSO) to enhance the design process of these complex networks. The algorithm employs a unique elite strategy, where the top-performing individuals from the population are enhanced through PSO, simulating natural maturation. These enhanced elites then form half of the new generation, while the other half is created via crossover and mutation operations on the enhanced elites. HGAPSO's effectiveness is demonstrated in two distinct applications: the design of a fully connected recurrent neural network for a temporal sequence production problem, and the design of a Takagi-Sugeno-Kang-type recurrent fuzzy network for dynamic plant control. Comparative analysis with traditional GA and PSO techniques underscores HGAPSO's superior performance in optimizing recurrent network designs.

We develop channel estimation agorithms for millimeter wave (mmWave) multiple input multiple output (MIMO) systems with one-bit analog-to-digital converters (ADCs). Since the mmWave MIMO channel is sparse due to the propagation characteristics, the estimation problem is formulated as a one-bit compressed sensing problem. We propose a modified EM algorithm that exploits sparsity and has better performance than the conventional EM algorithm. We also present a second solution using the generalized approximate message passing (GAMP) algorithm to solve this optimization problem. The simulation results show that GAMP can reduce mean squared error in the important low and medium SNR regions.

Underwater imaging is important for scientific research and technology, as well as for popular activities. We present a computer vision approach which easily removes degradation effects in underwater vision. We analyze the physical effects of visibility degradation. We show that the main degradation effects can be associated with partial polarization of light. We therefore present an algorithm which inverts the image formation process, to recover a good visibility image of the object. The algorithm is based on a couple of images taken through a polarizer at different orientations. As a by product, a distance map of the scene is derived as well. We successfully used our approach when experimenting in the sea using a system we built. We obtained great improvement of scene contrast and color correction, and nearly doubled the underwater visibility range.

To describe the log-likelihood computation in our model, let us consider a two scale pyramid for the moment. Given a (vectorized) j × j image I , denote by l = d(I) the coarsened image, and h = I − u(d(I)) to be the high pass. In this section, to simplify the computations, we use a slightly different u operator than the one used to generate the images displayed in Figure 3 of the paper. Namely, here we take d(I) to be the mean over each disjoint block of 2× 2 pixels, and take u to be the operator that removes the mean from each 2× 2 block. Since u has rank 3d/4, in this section, we write h in an orthonormal basis of the range of u, then the (linear) mapping from I to (l, h) is unitary. We now build a probability density p on Rd2 by p(I) = q0(l, h)q1(l) = q0(d(I), h(I))q1(d(I)); in a moment we will carefully define the functions qi. For now, suppose that qi ≥ 0, ∫ q1(l) dl = 1, and for each fixed l, ∫ q0(l, h) dh = 1. Then we can check that p has unit integral: ∫

Recent years have seen increasing attention and significant progress in many-light rendering, a class of methods for the efficient computation of global illumination. The many-light formulation offers a unified mathematical framework for the problem reducing the full lighting transport simulation to the calculation of the direct illumination from many virtual light sources. These methods are unrivaled in their scalability: they are able to produce artifact-free images in a fraction of a second but also converge to the full solution over time. In this state-of-the-art report, we have three goals: give an easy-to-follow, introductory tutorial of many-light theory; provide a comprehensive, unified survey of the topic with a comparison of the main algorithms; and present a vision to motivate and guide future research. We will cover both the fundamental concepts as well as improvements, extensions, and applications of many-light rendering.

Surfaces covered by ultrathin plasmonic structures--so-called metasurfaces--have recently been shown to be capable of completely controlling the phase of light, representing a new paradigm for the design of innovative optical elements such as ultrathin flat lenses, directional couplers for surface plasmon polaritons and wave plate vortex beam generation. Among the various types of metasurfaces, geometric metasurfaces, which consist of an array of plasmonic nanorods with spatially varying orientations, have shown superior phase control due to the geometric nature of their phase profile. Metasurfaces have recently been used to make computer-generated holograms, but the hologram efficiency remained too low at visible wavelengths for practical purposes. Here, we report the design and realization of a geometric metasurface hologram reaching diffraction efficiencies of 80% at 825 nm and a broad bandwidth between 630 nm and 1,050 nm. The 16-level-phase computer-generated hologram demonstrated here combines the advantages of a geometric metasurface for the superior control of the phase profile and of reflectarrays for achieving high polarization conversion efficiency. Specifically, the design of the hologram integrates a ground metal plane with a geometric metasurface that enhances the conversion efficiency between the two circular polarization states, leading to high diffraction efficiency without complicating the fabrication process. Because of these advantages, our strategy could be viable for various practical holographic applications.

Higher-order tensors and their decompositions are abundantly present in domains such as signal processing (e.g., higher-order statistics [1] and sensor array processing [2]), scientific computing (e.g., discretized multivariate functions [3]?[6]), and quantum information theory (e.g., representation of quantum many-body states [7]). In many applications, the possibly huge tensors can be approximated well by compact multilinear models or decompositions. Tensor decompositions are more versatile tools than the linear models resulting from traditional matrix approaches. Compared to matrices, tensors have at least one extra dimension. The number of elements in a tensor increases exponentially with the number of dimensions, and so do the computational and memory requirements. The exponential dependency (and the problems that are caused by it) is called the curse of dimensionality. The curse limits the order of the tensors that can be handled. Even for a modest order, tensor problems are often large scale. Large tensors can be handled, and the curse can be alleviated or even removed by using a decomposition that represents the tensor instead of using the tensor itself. However, most decomposition algorithms require full tensors, which renders these algorithms infeasible for many data sets. If a tensor can be represented by a decomposition, this hypothesized structure can be exploited by using compressed sensing (CS) methods working on incomplete tensors, i.e., tensors with only a few known elements.

In translucent objects, light paths are affected by multiple scattering, which is polluting any observation. Confocal imaging reduces the influence of such global illumination effects by carefully focusing illumination and viewing rays from a large aperture to a specific location within the object volume. The selected light paths still contain some global scattering contributions, though. Descattering based on high frequency illumination serves the same purpose. It removes the global component from observed light paths. We demonstrate that confocal imaging and descattering are orthogonal and propose a novel descattering protocol that analyzes the light transport in a neighborhood of light transport paths. In combination with confocal imaging, our descattering method achieves optical sectioning in translucent media with higher contrast and better resolution.

This paper introduces a new algorithm for enhancing image resolution from an image sequence. The approach we propose herein uses the integrating resampler for warping. The method is a direct computation, which is fundamentally different from the iterative back-projection approaches proposed in previous work. This paper shows that image-warping techniques may have a strong impact on the quality of image resolution enhancement. By coupling the degradation model of the imaging system directly into the integrating resampler, we can better approximate the warping characteristics of real sensors, which also significantly improve the quality of super-resolution images. Examples of super-resolutions are given for gray-scale images. Evaluations are made visuallyby comparing the resulting images and those using bi-linear resampling and back-projection and quantitativelyusing OCR as a fundamental measure. The paper shows that even when the images are qualitatively similar, quantitative differences appear in machine processing. q 2000 Elsevier Science B.V. All rights reserved. Keywords: Super-resolution; Imaging-consistent restoration/reconstruction; Integrating resampling; Integrating resampler; Quantitative evaluatio n; OCR; Bilinear resampling; Image reconstruction; Image restoration; Image warping 1. Background and introduction The basic premise of super-resolution is to combine images (and in some work scene/data models) to produce an image that has higher “resolution” than the original image. It is not sufficient to just resample the image—size is not the same as resolution. Increasing resolution could be viewed as either increasing the signal-to-noise ratio while keeping the size fixed and/or approximating the image at a larger size with reasonable approximations for frequencies higher than those representable at the original size. In either case, it requires additional information and assumptions. There are, of course, some fundamental limits on what can be gained; lens and photo-sites cause blur, and the discrete sampling causes frequencies above the Nyquist rate to alias. While in general the aliasing is theoretically impossible to separate, the problem occurs mostly along sharp “intensity edges”, so multiple images with slightly different imaging positions can be used to reduce its impact. There are two different groups of research that use the term super-resolution—one is based on multiple images, which will be considered within this paper; the other uses “scene/object” models. Early research on image-based super-resolution dates back to the work by Huang and Tsai [8]. They solved the problem in the frequency domain, but they disregarded sensor blurring. Furthermore, they assumed only inter-frame translations. Gross [7] solved the problem by first merging the low resolution images by interpolation and then obtaining the high-resolution image by deblurring the merged image. Again, only inter-frame translations were considered. Peleg and Keren [11,14] solved the problem by first estimating an initial guess of the high-resolution image, then simulating the imaging process and minimizing the difference between the observed and simulated low-resolution images. Irani and Peleg [9,10] used a back-projection method similar to that used in tomography to minimize the same difference. Bascle et al. [1] used the same back-projection method to minimize the difference between the predicted and actual images except that a simple motion blur model was included. All previous work ignores the impact of image warping techniques. In this paper, we show that warping techniques may have a strong impact on the quality of image resolution enhancement. Image warping has been around almost as long as image processing, allowing users to reshape the image geometry. Image warping requires the underlying image to be “resampled” at non-integer locations; hence, it requires image reconstruction. When the goal of warping is to produce output for human viewing, only moderately accurate image intensities are needed. In these cases, techniques Image and Vision Computing 18 (2000) 761–771 0262-8856/00/$ see front matter q 2000 Elsevier Science B.V. All rights reserved. PII: S0262-8856(99)00044-X www.elsevier.com/locate/imavis * Corresponding author. Tel.: 11-610-7584061. E-mail address:tboult@eecs.lehigh.edu (T.E. Boult). using bi-linear interpolation have been found sufficient. However, as a step for applications such as super-resolution, the precision of the warped intensity values is often important. For these problems, bi-linear image reconstruction may not be sufficient. This paper shows how the integrating resampler can be used to enhance image resolution. This builds upon the ideas of imaging-consistent restoration/reconstruction algorithms proposed in Ref. [2] and the integrating resampler proposed in Ref. [5]. A final contribution of this paper is a quantitative comparison and analysis of our techniques. In this paper, we use a character recognition rate based on a commercial OCR program. We chose this because it is easy to make it quantitative, and we consider this to be a good measure for other applications such as license-plate reading and other pattern recognition problems. In what follows in this paper, the image formation process and the relationship between restoration, reconstruction, and super-resolution is briefly reviewed in Section 2. The integrating resampler—an efficient method for warping using imaging-consistent reconstruction/restoration algorithms—is given in Section 3. This algorithm is well suited for today’s pipelined micro-processors. In addition, the integrating resampler can allow for modifications of the intensity values to better approximate the warping characteristics of real sensors. The super-resolution algorithm where we compare the integrating resampler with bi-linear resampling is presented in Section 4. Quantitative measurement of super-resolution imaging using OCR is shown in Section 5. 2. Image formation and image restoration In this section, we briefly review the image formation process as well as the sensor model proposed in Ref. [2]. Image formation is generally described as a sequence of filtering operations. See Fig. 1. Let f x; y be the intensity distribution of a scene at the front aperture of a lens. That distribution is acted upon by h1x; y; the blurring component of the lens, yielding f1x; y: A geometric distortion functionh2x; y is then applied to produce imagef2u; v: At this point, f2 strikes the image sensor where it undergoes additional blurring by a point spread functionh3u; v to generate image f3u; v: This blurring reflects the limitation of the sensor to accurately resolve each point without the influence of neighboring points. We choose to use a simple model wherein this blurring takes place within one pixel because for CCD and CID cameras, the physical boundaries between photo-sites generally allow only insignificant charge transfer between pixels. Imagef3 undergoes spatial sampling as it hits the discrete photo-sites in a CCD or CID camera. The combination of h3 with sampling is known as areas sampling. It reflects the finite size of the sampling area. If h3 is taken to be an impulse, then we have point sampling. This ideal, often assumed for theoretical considerations, is generally not true in practice. In either case, intensities in the sampled image Is are now defined only for integer values of u and v. The digital imageI u; v is obtained via an analog-todigital converter that quantizes the samples of Is. This completes the image formation process, leaving I u; v as the starting point for subsequent processing, including image reconstruction and restoration. Reconstruction and restoration start with I (and models for one or morehis), and seek to solve for one or more of the fjs. Recoveringf is known as image restoration and is of considerable interest in image processing. Note that recovering a functional form off2 is an intra-pixel restoration. Reconstruction limits itself to the problem of deriving the continuous functionf3. Restoration attempts to estimate the original input functionf2 (or f1). The goal of super-resolution is to recover a discrete approximation, with resolution higher than Is, to either f1 (plain super-resolution) orf (super-resolution with deblurring). Because super-resolution increases the signal-to-noise ratio, it significantly aids in the ill-condition problem of deblurring. 3. Imaging-consistency and the integrating resampler An algorithm is called imaging-consistent if it is the exact solution for some input function, which, according to the imaging model, would have generated the measured input. For image reconstruction, we achieve this by first restoring the image to yieldf2, then blurring it again by the pixel’s PSF. Details can be found in Ref. [2]. Only one-dimensional image models are presented herein; higher dimensions are treated separately. M.-C. Chiang, T.E. Boult / Image and Vision Computing 18 (2000) 761–771 762 Fig. 1. The image formation process and the relationship between restoration, reconstruction, and super-resolution. The first imaging-consistent method to consider is based on a piecewise quadratic model for the image. If we assume a Rect PSF filter (1 inside and 0 outside the pixel), the imaging consistent algorithm is easy to derive. To ensure that the function is continuous and that the method is local, we define the value of the reconstruction at the pixel boundarieski andki11 to be equal toEi andEi11: Any method of approximation could be used to compute Ei, though our examples will only include linear interpolation and cubic convolution. See [3, Section 2.3] for more details. Given the valuesEi at the pixel edges, an additional constraint, that the integral across the pixel must equal Vi, results in exactly three constraints: qi21=2  Ei ; qi1=2  Ei11; Z1=2 2 1=2 qix dx Vi : 1 From Eq. (1), one can det

This paper surveys current technology and research in the area of digital color imaging. In order to establish the background and lay down terminology, fundamental concepts of color perception and measurement are first presented using vector-space notation and terminology. Present-day color recording and reproduction systems are reviewed along with the common mathematical models used for representing these devices. Algorithms for processing color images for display and communication are surveyed, and a forecast of research trends is attempted. An extensive bibliography is provided.

Binomial filters are simple and efficient structures based on the binomial coefficients for implementing Gaussian filtering. They do not require multipliers and can therefore be implemented efficiently in programmable hardware. There are many possible variations of the basic binomial filter structure, and they provide a wide range of space-time trade-offs; a number of these designs have been captured in a parametrised form and their features are compared. This technique can be used for multi-dimensional filtering, provided that the filter is separable. The numerical performance of binomial filters, and their implementation using field-programmable devices for an image processing application, are also discussed.


Question: Is it possible to outperform Hadamard multiplexing? A data-driven approach for designing and evaluating computational imaging systems.

This paper delves into the significance of image statistics in the perception of surface qualities, particularly focusing on how these statistics influence our judgment of material properties. Traditionally, studies on surface appearance have predominantly centered on textureless, matte surfaces; however, the attention has recently shifted towards real-world surfaces that exhibit gloss and intricate mesostructure. The appearance of such surfaces is the outcome of a sophisticated interplay between illumination, reflectance, and surface geometry, making it challenging to isolate and analyze these components from an image. If there were straightforward image statistics that could effectively indicate surface properties, their use would be highly beneficial. Our research reveals that the skewness of the luminance histogram and the skewness of sub-band filter outputs are notably correlated with surface gloss, while showing an inverse correlation with surface albedo (diffuse reflectance). There is substantial evidence to suggest that human observers utilize skewness, or a comparable measure of histogram asymmetry, in their assessments of surface qualities. When an image of a surface exhibits positively skewed statistics, it tends to be perceived as darker and glossier compared to a similar surface with less skewness, regardless of whether the skewness is intrinsic to the original image or introduced through digital manipulation. Moreover, we uncover a visual after-effect based on skewness: adapting to patterns with skewed statistics can alter the perceived lightness and glossiness of surfaces that are subsequently observed. We propose the existence of neural mechanisms that are sensitive to skewed statistics, and their outputs can be employed in the estimation of surface properties.

**Question**: A Direct Search Method to solve Economic Dispatch Problem with Valve-Point Effect

**Documents**: An evolutionary algorithm for optimal power flow problem considering valve-point effect
This paper proposes a novel evolutionary algorithm for solving the optimal power flow (OPF) problem considering valve-point effect. The valve-point effect, which is a nonlinear and non-convex characteristic of thermal power plants, has a significant impact on the economic dispatch problem. The proposed algorithm is based on the differential evolution (DE) algorithm, which is a population-based optimization technique. To handle the valve-point effect, the algorithm incorporates a special mutation strategy and a local search procedure. The mutation strategy is designed to generate new solutions that can escape from local optima, while the local search procedure is used to refine the solutions obtained by the mutation strategy. The proposed algorithm is tested on the IEEE 30-bus system and compared with other optimization algorithms, such as the particle swarm optimization (PSO) algorithm and the genetic algorithm (GA). The results show that

Convolutional Neural Networks (CNNs) are typically trained in the RGB color space. However, in medical imaging, we believe that pixel stain quantities offer a fundamental view of the interaction between tissues and stain chemicals. Since the optical density (OD) colorspace allows to compute pixel stain quantities from pixel RGB intensities using the Beer-Lambert’s law, we propose a stain deconvolutional layer, hereby named as SD-Layer, affixed at the front of CNN that performs two functions: (1) it transforms the input RGB microscopic images to Optical Density (OD) space and (2) this layer deconvolves OD image with the stain basis learned through backpropagation and provides tissue-specific stain absorption quantities as input to the following CNN layers. With the introduction of only nine additional learnable parameters in the proposed SD-Layer, we obtain a considerably improved performance on two standard CNN architectures: AlexNet and T-CNN. Using the T-CNN architecture prefixed with the proposed SD-Layer, we obtain 5-fold crossvalidation accuracy of 93.2% in the problem of differentiating malignant immature White Blood Cells (WBCs) from normal immature WBCs for cancer detection.

medical fields has led to the automated processing ofpictorial data. Here, a device called the cytocomputer searches for genetic mutations. A computer revolution has occurred not only in technical fields but also in medicine, where vast amounts of information must be processed quickly and accurately. Nowhere is the need for image processing techniques more apparent than in clinical diagnosis or mass screening applications where data take the form of digital images. New high-resolution scanning techniques such as computed tomography, nuclear magnetic resonance, po-sitron emission tomography, and digital radiography produce images containing immense amounts of relevant information for medical analysis. But as these scanning techniques become more vital to clinical diagnosis, the work for specialists who must visually examine the resultant images increases. In many cases, quantitative data in the form of measurements and counts are needed to supplement nonimage patient data, and the manual extraction of these data is a time-consuming and costly step in an otherwise automated process. Furthermore, subtle variants of shade and shape can be the earliest clues to a diagnosis, placing the additional burden of complete thoroughness on the examining specialist. For the last five years, the University of Michigan and the Environmental Research Institute of Michigan have conducted a unique series of studies that involve the processing of biomedical imagery on a highly parallel computer specifically designed for image processing. System designers have incorporated the requirements of extracting a verifiable answer from an image in a reasonable time into an integrated approach to hardware and software design. The system includes a parallel pipelined image processor, called a cytocomputer, and a high-level language specifically created for image processing, C-3PL, the cytocomputer parallel picture processing language. These studies have involved a great many people from both the medical and engineering communities and have highlighted the interdisciplinary aspects of biomedical image processing. The methods have been tested in anatomy, developmental biology, nuclear medicine, car-diology, and transplant rejection. The general consensus is that quantification by automated image analysis not only increases diagnostic accuracy but also provides significant data not obtainable from qualitative analysis alone. One study in particular, on which descriptions in this article are based, involves a joint effort by the University of Michigan's human genetics and electrical and computer engineering departments and is supported by a grant from the National Cancer Institute. Basically, automated image analysis is being applied via sophisticated biochemical and computer techniques to derive …

Previous work has demonstrated that the image variation of many objects (human faces in particular) under variable lighting can be effectively modeled by low-dimensional linear spaces, even when there are multiple light sources and shadowing. Basis images spanning this space are usually obtained in one of three ways: a large set of images of the object under different lighting conditions is acquired, and principal component analysis (PCA) is used to estimate a subspace. Alternatively, synthetic images are rendered from a 3D model (perhaps reconstructed from images) under point sources and, again, PCA is used to estimate a subspace. Finally, images rendered from a 3D model under diffuse lighting based on spherical harmonics are directly used as basis images. In this paper, we show how to arrange physical lighting so that the acquired images of each object can be directly used as the basis vectors of a low-dimensional linear space and that this subspace is close to those acquired by the other methods. More specifically, there exist configurations of k point light source directions, with k typically ranging from 5 to 9, such that, by taking k images of an object under these single sources, the resulting subspace is an effective representation for recognition under a wide range of lighting conditions. Since the subspace is generated directly from real images, potentially complex and/or brittle intermediate steps such as 3D reconstruction can be completely avoided; nor is it necessary to acquire large numbers of training images or to physically construct complex diffuse (harmonic) light fields. We validate the use of subspaces constructed in this fashion within the context of face recognition.

Implementing E cient, Portable Computations for Machine Learning

Image resolution can be improved when the relative displacements in image sequences are known accurately, and some knowledge of the imaging process is available. The proposed approach is similar to back-projection used in tomography. Examples of improved image resolution are given for gray-level and color images, when the unknown image displacements are computed from the image sequence.

Compressive Sensing has become one of the standard methods of face recognition within the literature. We show, however, that the sparsity assumption which underpins much of this work is not supported by the data. This lack of sparsity in the data means that compressive sensing approach cannot be guaranteed to recover the exact signal, and therefore that sparse approximations may not deliver the robustness or performance desired. In this vein we show that a simple £2 approach to the face recognition problem is not only significantly more accurate than the state-of-the-art approach, it is also more robust, and much faster. These results are demonstrated on the publicly available YaleB and AR face datasets but have implications for the application of Compressive Sensing more broadly.

A novel microstrip directional filter for multiplexing applications is presented. This device uses composite right-left-handed transmission lines and resonators to achieve dual-band frequency response. In addition, by cascading two or more stages using dual frequency immitance inverters, multi-pole configurations can be obtained. Simulation and experimental results are presented with good agreement.

Non-blind deconvolution is a key component in image deblurring systems. Previous deconvolution methods assume a linear blur model where the blurred image is generated by a linear convolution of the latent image and the blur kernel. This assumption often does not hold in practice due to various types of outliers in the imaging process. Without proper outlier handling, previous methods may generate results with severe ringing artifacts even when the kernel is estimated accurately. In this paper we analyze a few common types of outliers that cause previous methods to fail, such as pixel saturation and non-Gaussian noise. We propose a novel blur model that explicitly takes these outliers into account, and build a robust non-blind deconvolution method upon it, which can effectively reduce the visual artifacts caused by outliers. The effectiveness of our method is demonstrated by experimental results on both synthetic and real-world examples.

Nonparametric Bayesian Models for Joint Analysis of Imagery and Text

We propose a holographic image restoration method using an autoencoder, which is an artificial neural network. Because holographic reconstructed images are often contaminated by direct light, conjugate light, and speckle noise, the discrimination of reconstructed images may be difficult. In this paper, we demonstrate the restoration of reconstructed images from holograms that record page data in holographic memory and quick response codes by using the proposed method.