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Table Of Integrals Series And Products

Due to object detection’s close relationship with video analysis and image understanding, it has attracted much research attention in recent years. Traditional object detection methods are built on handcrafted features and shallow trainable architectures. Their performance easily stagnates by constructing complex ensembles that combine multiple low-level image features with high-level context from object detectors and scene classifiers. With the rapid development in deep learning, more powerful tools, which are able to learn semantic, high-level, deeper features, are introduced to address the problems existing in traditional architectures. These models behave differently in network architecture, training strategy, and optimization function. In this paper, we provide a review of deep learning-based object detection frameworks. Our review begins with a brief introduction on the history of deep learning and its representative tool, namely, the convolutional neural network. Then, we focus on typical generic object detection architectures along with some modifications and useful tricks to improve detection performance further. As distinct specific detection tasks exhibit different characteristics, we also briefly survey several specific tasks, including salient object detection, face detection, and pedestrian detection. Experimental analyses are also provided to compare various methods and draw some meaningful conclusions. Finally, several promising directions and tasks are provided to serve as guidelines for future work in both object detection and relevant neural network-based learning systems.

Object Detection With Deep Learning: A Review

The Theory of Matrices. By F R. Gantmacher. Two volumes, pp. 374 and 276. 1959. (Translated from the Russian by K. A. Hirsch; Chelsea Publishing Company, New York)

Sphere Packings, Lattices and Groups

Frequencies from 100 GHz to 3 THz are promising bands for the next generation of wireless communication systems because of the wide swaths of unused and unexplored spectrum. These frequencies also offer the potential for revolutionary applications that will be made possible by new thinking, and advances in devices, circuits, software, signal processing, and systems. This paper describes many of the technical challenges and opportunities for wireless communication and sensing applications above 100 GHz, and presents a number of promising discoveries, novel approaches, and recent results that will aid in the development and implementation of the sixth generation (6G) of wireless networks, and beyond. This paper shows recent regulatory and standard body rulings that are anticipating wireless products and services above 100 GHz and illustrates the viability of wireless cognition, hyper-accurate position location, sensing, and imaging. This paper also presents approaches and results that show how long distance mobile communications will be supported to above 800 GHz since the antenna gains are able to overcome air-induced attenuation, and present methods that reduce the computational complexity and simplify the signal processing used in adaptive antenna arrays, by exploiting the Special Theory of Relativity to create a cone of silence in over-sampled antenna arrays that improve performance for digital phased array antennas. Also, new results that give insights into power efficient beam steering algorithms, and new propagation and partition loss models above 100 GHz are given, and promising imaging, array processing, and position location results are presented. The implementation of spatial consistency at THz frequencies, an important component of channel modeling that considers minute changes and correlations over space, is also discussed. This paper offers the first in-depth look at the vast applications of THz wireless products and applications and provides approaches for how to reduce power and increase performance across several problem domains, giving early evidence that THz techniques are compelling and available for future wireless communications.

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Wireless Communications and Applications Above 100 GHz: Opportunities and Challenges for 6G and Beyond

An approach to watermarking digital images using non-regular wavelets is advanced. Non-regular transforms spread the energy in the transform domain. The proposed method leads at the same time to increased image quality and increased robustness with respect to lossy compression. The approach provides robust watermarking by suitably creating watermarked messages that have energy compaction and frequency spreading. Our experimental results show that the application of non-regular wavelets, instead of regular ones, can furnish a superior robust watermarking scheme. The generated watermarked data is more immune against non-intentional JPEG and JPEG2000 attacks.

Robust Image Watermarking Using Non-Regular Wavelets

The discrete cosine transform (DCT) is a widely-used and important signal processing tool employed in a plethora of applications. Typical fast algorithms for nearly-exact computation of DCT require floating point arithmetic, are multiplier intensive, and accumulate round-off errors. Recently proposed fast algorithm arithmetic cosine transform (ACT) calculates the DCT exactly using only additions and integer constant multiplications, with very low area complexity, for null mean input sequences. The ACT can also be computed non-exactly for any input sequence, with low area complexity and low power consumption, utilizing the novel architecture described. However, as a trade-off, the ACT algorithm requires 10 non-uniformly sampled data points to calculate the eight-point DCT. This requirement can easily be satisfied for applications dealing with spatial signals such as image sensors and biomedical sensor arrays, by placing sensor elements in a non-uniform grid. In this work, a hardware architecture for the computation of the null mean ACT is proposed, followed by a novel architectures that extend the ACT for non-null mean signals. All circuits are physically implemented and tested using the Xilinx XC6VLX240T FPGA device and synthesized for 45 nm TSMC standard-cell library for performance assessment.

VLSI Computational Architectures for the Arithmetic Cosine Transform

A new multiplication-free transform derived from DHT is in- troduced: the RHT. Investigations on the properties of the RHT led us to the concept of weak-inversion. Using new constructs, we show that RHT is not involutional like the DHT, but exhibits quasi-involutional property, a new definition derived from the periodicity of matrices. Thus instead of using the actual inverse transform, the RHT is viewed as an involutional transform, allowing the use of direct (multiplication-free) to evaluate the inverse. A fast algorithm to compute RHT is presented. This algorithm show embedded properties. We also extended RHT to the two-dimensional case. This permitted us to perform a preliminary analysis on the effects of RHT on images. Despite of some SNR loss, RHT can be very interesting for applications involving image monitoring associated to decision making, such as military applications or medical imaging.

Rounded Hartley Transform: A Quasi-involution

Synthetic Aperture Radar (SAR) imagery plays a central role as a source of unique data for Geographic Information Systems These data sets provide complementary information to that provided by optical and infra-red sensors as, for instance, Landsat TM, CBERS-2, IKONOS and SPOT, to name a few SAR sensors capture information about the target roughness and its dielectric properties, and their imaging capabilities are able to penetrate clouds, fog, rain and even some types of land cover as, for instance, forest canopies A major issue related to the use of SAR images is their statistical behavior It is well known that classical Gaussian and additive models do not hold for such data The multiplicative model (MM) has been extensively tested with success, and it is able to explain phenomenological aspects of the image formation One of the most important distributions related to the MM is the G° law The G° distribution, as all other laws related to the MM, greatly departs from the Gaussian model This paper assesses the SAR image discrimination capabilities of selected parametric methods based on divergences measures, when compared to the nonparametric Kolmogorov-Smirnov testing methodology The importance of the Triangular and Arithmetic-Geometric distances is quantified with respect to the Kullback-Leibler parametric and Kolmogorov-Smirnov non-parametric classical distances by means of Monte Carlo simulation

Measuring Synthetic Aperture Radar target differences with stochastic distances

Image and video compression plays a major role in multimedia transmission. Specifically the discrete cosine transform (DCT) is the key tool employed in a vast variety of compression standards such as H.265/HEVC due to its remarkable energy compaction properties. Rapid growth in digital imaging applications, such as multimedia and automatic surveillance that operates with limited bandwidths has led to extensive development of video processing systems. The main objective of this paper is to discuss some DCT approximations equipped with fast algorithms which require minimum addition operations and zero multipliers or bit-shifting operations leading to significant reductions in chip area and power consumption compared to conventional DCT algorithms.
We provide complete design details for several k × k, k = 8, 16 blocked 2-D algorithms for DCT computation
with video evaluation using HEVC software encoder. Custom digital architectures are proposed, simulated and implemented on Xilinx FPGAs and verified in conjunction with software models.

Renato J. Cintra

Papers

Robust Image Watermarking Using Non-Regular Wavelets

VLSI Computational Architectures for the Arithmetic Cosine Transform

Rounded Hartley Transform: A Quasi-involution

Measuring Synthetic Aperture Radar target differences with stochastic distances

Low-complexity multiplierless DCT approximations for low-power HEVC digital IP cores