Amount Of Bits Research Articles

Effect of bit-size reduced half-precision floating-point format on image pixel characterization for AI applications

Image processing is an essential first step towards fully utilizing robotics, deep learning, and machine learning techniques. Using techniques like image enhancement, restoration, and segmentation are able to extract pertinent information from images and use it for task execution and decision-making. However, the hardware implementation of these algorithms demands more delay, area, and power. This work proposes a new mantissa bit-size reduced half-precision floating-point format for processing and characterizing image pixels for machine learning algorithms. In the realm of imaging, lowering the mantissa bit-size in floating-point can conserve area and power when utilized for internal calculations. Together with the area-power reduction, there is also a progressive reduction in image quality. For any image application, it becomes necessary to monitor the area and power trade-offs related to the amount of bits used to process the raw data. This work assists in selecting the bit-size for internal computations based on the accuracy requirements of the application by reporting the area, and power for various bit size reductions. The updated pixel values after applying the mantissa bit-size reduction are displayed in this study, along with a theoretical explanation of its inaccuracy. Since multipliers and adders are needed for the majority of mathematical equations in machine learning image algorithms, they are developed later in this work to process the image. The processed image is based on various adjusted pixel values, and the experimental findings demonstrate 75.2% to 21.3% area optimization, and 66.43% to 20.4% power optimization. However, determining the PSNR and MSE values of the processed image allows for quality validation.

Hardware optimization for effective switching power reduction during data compression in GOLOMB rice coding.

Loss-less data compression becomes the need of the hour for effective data compression and computation in VLSI test vector generation and testing in addition to hardware AI/ML computations. Golomb code is one of the effective technique for lossless data compression and it becomes valid only when the divisor can be expressed as power of two. This work aims to increase compression ratio by further encoding the unary part of the Golomb Rice (GR) code so as to decrease the amount of bits used, it mainly focuses on optimizing the hardware for encoding side. The algorithm was developed and coded in Verilog and simulated using Modelsim. This code was then synthesised in Cadence Encounter RTL Synthesiser. The modifications carried out show around 6% to 19% reduction in bits used for a linearly distributed data set. Worst-case delays have been reduced by 3% to 8%. Area reduction varies from 22% to 36% for different methods. Simulation for Power consumption shows nearly 7% reduction in switching power. This ideally suggest the usage of Golomb Rice coding technique for test vector compression and data computation for multiple data types, which should ideally have a geometrical distribution.

Attacking (EC)DSA scheme with ephemeral keys sharing specific bits

In this paper, we present a deterministic attack on (EC)DSA signature scheme, providing that several signatures are known such that the corresponding ephemeral keys share a certain amount of bits without knowing their value. By eliminating the shared blocks of bits between the ephemeral keys, we get a lattice of dimension equal to the number of signatures having a vector containing the private key. We compute an upper bound for the distance of this vector from a target vector, and next, using Kannan's enumeration algorithm, we determine it and hence the secret key. The attack can be made highly efficient by appropriately selecting the number of shared bits and the number of signatures.

Thermodynamic costs of temperature stabilization in logically irreversible computation

Abstract In recent years, great efforts are devoted to reducing the work cost of the bit operation, but it is still unclear whether these efforts are sufficient for resolving the temperature stabilization problem in computation. By combining information thermodynamics and a generalized constitutive model which can describe Fourier heat conduction as well as non-Fourier heat transport with nonlocal effects, we here unveil two types of the thermodynamic costs in the temperature stabilization problem. Each type imposes an upper bound on the amount of bits operated per unit time per unit volume, which will eventually limit the speed of the bit operation. The first type arises from the first and second laws of thermodynamics, which is independent of the boundary condition and can be circumvented in Fourier heat conduction. The other type is traceable to the third law of thermodynamics, which will vary with the boundary condition and is ineluctable in Fourier heat conduction. These thermodynamic costs show that reducing the work cost of the bit operation is insufficient for resolving the temperature stabilization problem in computation unless the work cost vanishes.

Speed limits to information erasure considering synchronization between heat transport and work cost

With the rapid development of the information technology (IT), heat transport and work cost in the information processing system gain significant scientific interest, but their synchronization problem has not been investigated hitherto. By the virtue of the finite-time Landauer principle, we here study the scenarios wherein one-dimensional Fourier heat transport synchronizes with the work cost of non-quasistatic information erasure. It is demonstrated that in such scenarios, the steady-state temperature distribution and harmonic temperature wave will respectively impose certain speed limits to information erasure, which give upper bounds on the amount of bits erased per unit time. The underlying physics of these speed limits are respectively the local well-definedness of the absolute temperature and the unsteadiness of the harmonic temperature wave. In engineering, the speed limit imposed by the steady-state temperature distribution can be understood as a performance limitation concomitant with the temperature stabilization, which quantitatively reveals the cost of the temperature stabilization.

Comparative Analysis of Multimedia Compression Algorithms

Data compression reduces the amount of bits needed to encode information, conserving expensive resources like disc space and transmission bandwidth. By using data compression, an information block's real size is kept as little as possible. The compression process consists of two steps: encoding, which uses fewer bits to compress a message, and decoding, which uses the compressed representation to roughly resemble or replicate the original message. The two main types of data compression are lossless compression and lossy approaches. The Huffman, Lempel-Ziv, and Run Length encoding techniques were all examined in this research, along with three other commonly used compression techniques. According to the research, Run Length Encoding was the best method for reducing the size of text, web pages, images, and sound.

Designing and simulation a 15-bit successive approximation register analog-to-digital converter

Analog-to-digital converters (ADC) are widely employed to monitor long-term signal characteristics in wireless sensor networks and healthcare electronic devices. It is critical in these applications to use an energy-efficient ADC to extend battery life. This paper presents a 15-bit successive-approximation register (SAR) ADC for using in biomedical processing systems. The segmentation degrees (the amount of bits in each divided capacitive sub-array) are optimized to minimize switching power and area based on linearity and matching requirements. The proposed SAR ADC is simulated by using Simulink of Matlab. The simulated results show that the ADC achieves 14.78-bit of effective numbers of bits (ENoB), 111.5 dB of the spurious-free dynamic range (SFDR) with 90.74 dB of signal-to-noise ratio (SNR) at a sampling rate of 10MHz.

The use of binary digit mapping on ASCII characters to create a high-capacity, undetectable text steganography.

Plain text is commonly utilized for online news and social media transmission due to its lightweight and cross-platform nature. Unfortunately, it may easily be exploited by attackers (For instance, in cases when the language is altered for specific goals, illegal access to the material or its abuse is possible.) To solve this problem, we embed secret texts (ST) within seemingly innocuous cover texts (CT) so that we can track CT for modifications. Based on the findings of this study, a method is provided for ST embedding in CT in which binary ST digits are converted to CT binary digits through Characters in ASCII (containing whitespace, punctuation, and special characters). Before commencing the embedding procedure, Using a One-Time Pad (OTP), the ST text was translated into ciphertext, and the value of each character was changed into a binary integer with a length of seven bits. As opposed to the ST text, which required additional time to encode, the CT text could be converted into a binary integer using just the first seven bits. During the embedding process, It was decided to swap the first bit of the ST character for the first bit of the CT character, which both had the same amount of bits (putting the first bit of the ST character, for example, on the same line as the first bit of the CT character). Any piece of ST that was recorded on any bit of CT served as a stage key. Extracting ST from CT requires the stage key. The embedded information served effectively as text steganography or watermark, and the CT's appearance was unaffected by the embedding procedure. In other words, the 1 character of ST might be concealed by using 7 CT characters. Also, a Jaro-Winkler Distance of 1 meant that the stage texts made couldn't be told apart from CT by looking at them.

Dynamic Channel Selection and Transmission Scheduling for Cognitive Radio Networks

Cognitive radio networks (CRNs) are expected to be promising techniques for improving the spectrum efficiency of wireless network utility in the squeezed sub-6-GHz frequency bands. Nevertheless, frequency allocation and transmission scheduling for secondary users (SUs) in CRNs suffer from no prior knowledge of other SUs’ network behaviors or the distribution of the amount of data generated at each SU. As a countermeasure, this article develops a protocol for the joint channel selection and transmission scheduling such that SUs with heterogeneous data transmission demands could be served with limited spectrum resources. Then, we formulate the dynamic optimization of the protocol as mutually embedded Markov decision processes (MDPs). To address the intractable MDPs, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning-based channel selection and transmission scheduling based on reinforcement learning with basis function approximation are, respectively, proposed. It is shown that compared with various baselines, the proposed channel selection algorithm enables each SU to select the best frequency-domain channel that does not interfere with other SUs. In particular, the proposed transmission scheduling algorithm outperforms algorithms based on off-the-shelf approaches, such as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning and Lyapunov optimization, in terms of both energy efficiency and long-term accumulative amount of bits at each SU.

Optimized active contor segmentation model for medical image compression

Nowadays, medical imaging systems tend to have greatest impact on disease identification, diagnosis, and surgical preparation. To save hardware space and transmission bandwidth, it is important to reduce data redundancy in the image. Compressing images is very important for storage and transmitting purposes as it decreases the amount of bits required while retaining the critical information content encapsulated in the image document. This makes the system more peculiar in this field. The proposed paradigm image segmentation is the first step attained by the Optimized Active Contour Model (OACM). Using a new Modified marriage in honey bees optimization model (MMBO), ACM's weighting factor and maximum iteration are fine-tuned. Thereby, the collected inputimage is segmented into N-ROI and ROI. The ROI marked field will indeed be encoded using ISPIHT-based lossy compression model, whereas the non-ROI area is encoded using DCT based lossy compression model. In terms of BSC, the outcomes from both ISPIHT algorithm and DCTmodel are merged and the compressed image is itsoutput. Then, the compressed image will then be subjected to image decompression. This will include bit-stream segregation, which will be processed separately for the ROI and non-ROI regions using ISPIHT decoder and DCT-based decomposition. This process results in the original image. A comparative evaluation is undergone between the proposed and the existing techniques under PSNR, SSIM, and CR. Accordingly, the PSNR of the Proposed model is (∼)0.22, which is 26 %, 50 %, 60 %, and 55 % superior to the conventional methods like the MFO, LA, MBO, and JCF-LA.

Common Message Acknowledgments: Massive ARQ Protocols for Wireless Access

Massive random access plays a central role in supporting the Internet of Things (IoT), where a subset of a large population of users simultaneously transmit small packets to a central base station. While there has been much research on the design of protocols for massive access in the uplink, the problem of providing message acknowledgments back to the users has been somewhat neglected. Reliable communication needs to rely on two-way communication for acknowledgement and retransmission. Nevertheless, because of the many possible subsets of active users, providing acknowledgments requires a significant amount of bits. Motivated by this, we define the problem of massive ARQ (Automatic Retransmission reQuest) protocol and introduce efficient methods for joint encoding of multiple acknowledgements in the downlink. The key idea towards reducing the number of bits used for massive acknowledgments is to allow for a small fraction of false positive acknowledgments. We analyze the implications of this approach and the impact of acknowledgment errors in scenarios with massive random access. Finally, we show that these savings can lead to a significant increase in the reliability when retransmissions are allowed since it allows the acknowledgment message to be transmitted more reliably using a much lower rate.

Reprogramming 3D TLC Flash Memory based Solid State Drives

NAND flash memory-based SSDs have been widely adopted. The scaling of SSD has evolved from plannar (2D) to 3D stacking. For reliability and other reasons, the technology node in 3D NAND SSD is larger than in 2D, but data density can be increased via increasing bit-per-cell. In this work, we develop a novel reprogramming scheme for TLCs in 3D NAND SSD, such that a cell can be programmed and reprogrammed several times before it is erased. Such reprogramming can improve the endurance of a cell and the speed of programming, and increase the amount of bits written in a cell per program/erase cycle, i.e., effective capacity. Our work is the first to perform a real 3D NAND SSD test to validate the feasibility of the reprogram operation. From the collected data, we derive the restrictions of performing reprogramming due to reliability challenges. Furthermore, a reprogrammable SSD (ReSSD) is designed to structure reprogram operations. ReSSD is evaluated in a case study in RAID 5 system (RSS-RAID). Experimental results show that RSS-RAID can improve the endurance by 35.7%, boost write performance by 15.9%, and increase effective capacity by 7.71%, with negligible overhead compared with conventional 3D SSD-based RAID 5 system.

New method for route efficient energy calculations with mobile-sink for wireless sensor networks

&lt;p&gt;Despite proposing a number of algorithms and protocols, especially those related to routing, for the purpose of reducing energy consumption in wireless sensor networks, which is one of the most important issues facing this type of network. In this research paper, energy consumption and cost are calculated taking into account energy consumption and the amount of data transferred to a thousand nodes through specific paths towards the mobile sink. The proposed model simulated by sending various amounts of data with specific path to know the energy consumption of each track and the network life time with 250, 500, and 1000 bits. Cost calculated using various weight for each track of these paths and the coefficient of movement time and path loss factor and others related to the transmission and receiving circuits. And finally, the results compared with a previous method it showed the efficiency of our method used and calculating 1000 nodes with various amount of bits to show the experimental results. Deep learning used to remember each and every path of each position or nearby to avoid calculation cost later.&lt;/p&gt;

An intelligent compression system for wireless capsule endoscopy images

In this paper, we propose an intelligent compression system that addresses the problems of energy limitations of wireless video capsule endoscopy. The principle is to include a classification feedback loop, based on deep learning, to determine the importance of the images being transmitted. This classification is used with a simple prediction-based compression algorithm to allow an intelligent management of the limited energy of the capsule. For this, the capsule starts by transmitting a subsampled version of each image with a small rate. The images will be decoded and classified, automatically, to detect any possible lesions. Following the classification result, the images considered as important, for diagnosis, will be enhanced with additional content, whereas the less important ones will be recorded with low quality. In this way, large amounts of bits will be saved, without affecting the diagnosis. The saved energy can be used to extend the life of the capsule or to increase the resolution and frame rate of some WCE images. The results of classification show an accuracy of more than 99%, which allowed us to code losslessly almost all the important images of our test sequences. Our results also show that many additional images can be transmitted and their number depend on the used subsampling and the number of important images.

Low Power and High Speed Reversible R2B FFT using Reversible Gate for 5G Technology

Investigation accomplish in the communication, it has been realized that multi-carrier technique are more captivating for the enlargement of 5G system. The Tx and Rx end, both are used to transform for signal change one to another domain. This paper, radix-2 butterfly (R2B) FFT is implementing using programmable reversible gate (RG). The fundamental issue in utilizing irreversible gate is the loss of information because of the issue of heat dissipation, since the measure of amount of energy is relative to the quantity of bits erased. To beat this issue, RG are utilized, as they don't cause heat dissipation, and in this way there will be no loss of information. R2B FFT is whole process adder and sub-tractor. Reversible gate are used to RH (A/S) and RF (A/S) and implemented R2B FFT. All design is simulate Xilinx 14.2i and calculate reversible parameter.

An Enhanced Satellite Image Compression Using Hybrid (DWT, DCT and SVD) Algorithm

Storing images consumes a lot of storage space due to the large number of bits used to represent them. These bits are comprised of pixels that make up the image. These heavy images are also very difficult to be transmitted over channels due to their great size. Compression involves the reduction of the amount of bits used in representing an image and consequently reducing the size of that image without losing any detail from the image. There are so many image compression techniques used to achieve this feat, but they have drawbacks such as lack of a model that can compress a satellite image, lack of adaptive reversible techniques for compression and inability to compress complex images such as satellite images. This work, proposed an hybrid Discrete Wavelet Transform, Discrete Cosine Transform and Singular Value Decomposition (DCT-DWT-SVD)-based techniques for satellite image compression. The algorithms were combined to breakdown the images into blocks/matrices and assign certain values to them depending on the concentration of colour bits around the region. The areas with higher bits are reduced and compression is achieved. A hybrid methodology of Agile and Waterfall model was used in this approach. The model was implemented using MATLAB and satellite images gotten from a public repository. The Compression ratio was 0.9990 and 0.9941 for the two images compressed which shows high and efficient compression. The Mean Square Error (MSE) was 2.51 which is low. This study will be beneficial to remote sensor companies, Graphic designers and the research community.

Zigzag-Decodable Reconstruction Codes With Asymptotically Optimal Repair for All Nodes

Zigzag-decodable codes have been proposed for distributed storage systems to achieve fast decoding of uncoded data packets through the iterative decoding of data bits from coded packets. To maintain high data availability, it is critical to minimize the repair bandwidth by downloading the least amount of bits for repairing any lost packet. In this work, we propose zigzag-decodable reconstruction (ZDR) codes which achieve asymptotically minimum repair bandwidth for repairing a single node, while preserving the high computational efficiency due to zigzag decoding. We present two explicit constructions of ZDR codes such that any node of ZDR codes can be repaired with asymptotically minimum repair bandwidth. The first construction is based on the well-designed encoding matrix and a generic transformation, while the second construction is designed by recursively employing the proposed generic transformation for any existing zigzag-decodable code. Moreover, we show that the proposed two classes of ZDR codes can be decoded by the zigzag decoding algorithm and have less computational complexity than the existing codes with asymptotically or exactly minimum repair bandwidth.

Forward-looking content aware encoding for next generation UHD, HDR, WCG, and HFR

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">As we envision a world of life-like TV, enhanced in all possible degrees of freedom, immersive UHD-2/8K high dynamic range (HDR) wide color gamut (WCG) high frame rate (HFR) are knocking at our doors. Although technology is improving at a constant pace, processing and transmitting such an amazing amount of information remains an incredible challenge. The British Broadcasting Corp. (BBC) transmitted an ultrahigh-definition (UHD) HDR live feed of the Fédération Internationale de Football Association (FIFA) 2018 World Cup at 36 Mb/s, however, the ATEME TITAN Live encoder does it already at 22 Mb/s and lower. Even though the future International Telecommunication Union – Telecommunication (ITU-T) International Organization for Standardization/International Electrotechnical Commission’s (ISO/IEC) versatile video coding (VVC) is promising a 50% coding efficiency gain compared to high-efficiency video coding (HEVC), compression will not be enough, thus leading to strategies like UHD forum Phase B guidelines recommending content-aware encoding. This paper shows how the future challenges of video compression can be met by leveraging both expertise in optimized compression and the latest advances in artificial intelligence, thus spending exactly the right amount of bits on each piece of video information. A practical implementation of content-aware encoding adapted to upcoming video formats is described, and results and analysis are provided</i> .

Exploiting Random Chemistry for Digital Security

Randomness is such an important resource of our digital information era that new random number generators are invented continuously. Typically, these are electronic devices. Recently, researchers of the Universities of Glasgow and Edinburgh devised a novel method using stochastic chemical reactions.

Optimal entanglement-assisted almost-random access codes

Entanglement-assisted random access codes (EARACs) compress a large amount of bits, such that any chosen one of them can be recovered with a large probability of success. They have been used for information theoretic treatments of semi-device-independent randomness certification and other communication complexity problems. For high-dimensional problems, the access request distribution can be used to obtain better performance, which until now could only be achieved through numerical methods. This paper makes an analytical account of access request distribution for arbitrary input sizes. We introduce entanglement-assisted almost-random access codes (EAARACs), which maximize the average probability of access under an arbitrary distribution of requests, thus refining EARACs and generalizing the methods used in the study of low-dimensional communication complexity problems. We analytically find the optimal probability of success for EAARACs under arbitrary request distributions and provide an illustrative numerical example.