Smaller Bit Research Articles

REHAS: Robust and Efficient Hyperelliptic Curve‐Based Authentication Scheme for Internet of Drones

ABSTRACTInternet of Drones (IoD) is one of the most beneficial and has many versatile applications like Surveillance and Security, Delivery and Logistics, Environmental Monitoring, Agriculture, and so forth. The IoD network is crucial for collecting sensitive data like geo‐coordinates, vehicle traffic data, and property details while surveying the various deployment locations in smart cities. The communication between users and drones can be compromised over insecure wireless channels by multiple attacks such as Man‐in‐the‐middle‐attack, Denial of Service, and so forth. Many schemes have already been propounded in the field of IoD. Still, many of them cannot address the resource constraints problem of drones, and existing protocols have higher computation and communication costs. Therefore, this paper has proposed a robust and efficient Hyper‐Elliptic Curve‐based authentication scheme (REHAS), which provides a session key for secure communication. Artificial Identities are generated using a hash function and random numbers. Fuzzy Extractor is used for user biometric authentication, which makes the smart device secure when lost. HECC is used with a smaller bit size of 80 bits rather than ECC of 160 bits. The security of the REHAS has been ensured using Scyther simulation. Furthermore, the resilience, safety, and robustness of REHAS are ensured by Informal security analysis. Lastly, a comparative study of the REHAS has been performed with other related Authentication and key agreement (AKA) protocols regarding communication cost, Computation cost, and security features, demonstrating that REHAS incurred less computation cost (6.7171 ms), communication overhead (1696 bits), and energy consumption (22.5 mJ) than other existing AKA schemes.

Characterizing Hardware Utilization on Edge Devices when Inferring Compressed Deep Learning Models

Implementing edge AI involves running AI algorithms near the sensors. Deep Learning (DL) Model has successfully tackled image classification tasks with remarkable performance. However, their requirements for huge computing resources hinder the implementation of edge devices. Compressing the model is an essential task to allow the implementation of the DL model on edge devices. Post-training quantization (PTQ) is a compression technique that reduces the bit representation of the model weight parameters. This study looks at the impact of memory allocation on the latency of compressed DL models on Raspberry Pi 4 Model B (RPi4B) and NVIDIA Jetson Nano (J. Nano). This research aims to understand hardware utilization in central processing units (CPU), graphics processing units (GPU),and memory. This study focused on the quantitative method, which controls memory allocation and measures warm-up time, latency, CPU, and GPU utilization. Speed comparison among inference of DL models on RPi4B and J. Nano. This paper observes the correlation between hardware utilization versus the various DL inference latencies. According to our experiment, we concluded that smaller memory allocation led to high latency on both RPi4B and J. Nano. CPU utilization on RPi4B. CPU utilization in RPi4B increases along with the memory allocation; however, the opposite is shown on J. Nano since the GPU carries out the main computation on the device. Regarding computation, thesmaller DL Size and smaller bit representation lead to faster inference (low latency), while bigger bit representation on the same DL model leads to higher latency.

High-Quality Image Compression Algorithm Design Based on Unsupervised Learning.

Increasingly massive image data is restricted by conditions such as information transmission and reconstruction, and it is increasingly difficult to meet the requirements of speed and integrity in the information age. To solve the urgent problems faced by massive image data in information transmission, this paper proposes a high-quality image compression algorithm based on unsupervised learning. Among them, a content-weighted autoencoder network is proposed to achieve image compression coding on the basis of a smaller bit rate to solve the entropy rate optimization problem. Binary quantizers are used for coding quantization, and importance maps are used to achieve better bit allocation. The compression rate is further controlled and optimized. A multi-scale discriminator suitable for the generative adversarial network image compression framework is designed to solve the problem that the generated compressed image is prone to blurring and distortion. Finally, through training with different weights, the distortion of each scale is minimized, so that the image compression can achieve a higher quality compression and reconstruction effect. The experimental results show that the algorithm model can save the details of the image and greatly compress the memory of the image. Its advantage is that it can expand and compress a large number of images quickly and efficiently and realize the efficient processing of image compression.

5G Channel Estimation Based on Whale Optimization Algorithm

This paper presents a novel approach, based on the whale optimization algorithm (WOA), for channel estimation in wireless communication systems. The proposed method provides a means to accurately estimate the wireless channel, while not requiring the statistical characteristics of the channel. This method uses the WOA to search for the best channel statistical characteristics toward the ultimate goal of having the smallest bit error rate (BER). The proposed approach is aimed at enhancing the efficiency of pilot-based OFDM systems under frequency-selective fading channels used in the performance testing of 5G New Radio gNodeB. In terms of BER and mean square error (MSE), the performance of the proposed WOA-based channel estimation algorithm is evaluated and compared with the conventional least square (LS) and minimum mean square error (MMSE) algorithms. The simulation results demonstrate that the proposed algorithm provides highly competitive performance over the MMSE algorithm and significantly outperforms the LS algorithm in a variety of system configurations. Since the requirement on prior channel statistics information makes the MMSE algorithm impractical or extremely complex, the proposed WOA-based channel estimation algorithm should be a suitable and promising candidate for dealing with channel estimation problems. The simulation framework is implemented in MATLAB and available upon request.

DESAIN SIMULASI UNJUK KERJA BARKER CODE PADA KANAL FREQUENCY SELECTIVE FADING

The signal that arrives at the receiving end is the sum of the various transmissions passed by the signal (multipath) which is influenced by the channel so that there is signal interference. Many innovations have been made so that the received signal can have high security, one of which is spread spectrum with a dual access scheme to increase the ability of many spectrum users in a limited number without disturbing one another. This simulation was tested with the aim of spreading the spectrum with different code lengths for the same bit length of information, and observed from the change in the BER value obtained from the MATLAB 2018a simulation results to find out how the barker code performance on the frequency selective fading channel on a single user. The analysis of this research is obtained from the simulation results in the form of a graph that shows the BER value compared to Eb/No. The BER value shown by the barker code performance on the flat fading channel, which is compared based on the existing flat theory, shows the graph trend and the BER value generated from the simulation is close to the theoretical flat BER value. The performance of the barker code on the frequency selective fading channel is carried out by varying the number of multipath components 7, 5, and 3. The simulation results show that multipath 3 is the best with the smallest bit error value compared to multipath 7 and 5. Barker code performance is strongly influenced by the number of multipath components, the more the number of multipath components, the more performance of the barker code bit error, and vice versa.

Bézier Coefficients Matrix for ElGamal Elliptic Curve Cryptosystem

It is well-known that cryptography is a branch of secrecy in science and mathematics, which usually preserves the confidentiality and authenticity of the information, where its growth is parallel with the rapid evolution of the internet and communication. As one of the prominent public key cryptosystems, the Elliptic Curve Cryptosystem (ECC) offers efficiency and complex mathematical operations with a smaller bit compared to other types of public key schemes. Throughout the evolution of cryptography, ElGamal Elliptic Curve Cryptosystem (ElGamal ECC) revolved from ElGamal public key scheme for user efficiency and privacy. In this study, an improved method will be introduced using ElGamal ECC as the foundation with the incorporation of the Bézier curve coefficient matrix, where the ElGamal ECC value is considered as the control point of the Bézier curve during the encryption and decryption processes. The proposed method is designed to develop a robust ciphertext system algorithm for better efficiency and to increase the level of protection in ElGamal ECC. In this paper, the performance of the proposed method is compared with the normal ElGamal ECC. The results of this study show that the proposed method offers no significant difference in terms of the implementation time during the encryption and decryption process. However, it does offer extra layers of protection when operated with complex mathematical operations.

Performance Impact of Signal Reflections in a Single–Fiber Bidirectional System

Single–fiber bidirectional transmission systems using the same wavelength for both directions suffer from reflection induced interference. Experimental results demonstrate the dependence of system performance and of the threshold of the forward error correction (FEC) on signal waveform variations induced by fiber effects. Larger signal distortions lead to slightly larger impact of the interference, but reduce the shift of the FEC threshold to smaller bit error ratios.

Improving Signal to Noise Ratios in Ion Mobility Spectrometry and Structures for Lossless Ion Manipulations (SLIM) using a High Dynamic Range Analog-to-Digital Converter.

Signal digitization is a commonly overlooked part of ion mobility-mass spectrometry (IMS-MS) workflows, yet it greatly affects signal-to-noise ratio and MS resolution measurements. Here, we report on the integration of a 2 GS/s, 14-bit ADC with structures for lossless ion manipulations (SLIM-IMS-MS) and compare the performance to a commonly used 8-bit ADC. The 14-bit ADC provided a reduction in the digitized noise by a factor of ∼6, owing largely to the use of smaller bit sizes. The low baseline allowed threshold voltage levels to be set very close to the MCP baseline voltage, allowing for as much signal to be acquired as possible without overloading or excessive digitization of MCP baseline noise. Analyses of Agilent tuning mixture ions and a mixture of heavy labeled phosphopeptides showed that the 14-bit ADC provided a ∼1.5-2× signal-to-noise (S/N) increase for high intensity ions, such as the Agilent tuning mixture ions and the 2+ and 3+ charge states of many phosphopeptide constituents. However, signal enhancements were as much as 10-fold for low intensity ions, and the 14-bit ADC enabled discernible signal intensities otherwise lost using an 8-bit digitizer. Additionally, the 14-bit ADC required ∼14-fold fewer mass spectra to be averaged to produce a mass spectrum with a similar S/N as the 8-bit ADC, demonstrating ∼10× higher measurement throughput. The high resolution, low baseline, and fast speed of the new 14-bit ADC enables high performance digitization of MS, IMS-MS, and SLIM-IMS-MS spectra and provides a much better picture of analyte profiles in complex mixtures.

Flexible 5G New Radio LDPC Encoder Optimized for High Hardware Usage Efficiency

Quasi-cyclic low-density parity-check (QC–LDPC) codes are introduced as a physical channel coding solution for data channels in 5G new radio (5G NR). Depending on the use case scenario, this standard proposes the usage of a wide variety of codes, which imposes the need for high encoder flexibility. LDPC codes from 5G NR have a convenient structure and can be efficiently encoded using forward substitution and without computationally intensive multiplications with dense matrices. However, the state-of-the-art solutions for encoder hardware implementation can be inefficient since many hardware processing units stay idle during the encoding process. This paper proposes a novel partially parallel architecture that can provide high hardware usage efficiency (HUE) while achieving encoder flexibility and support for all 5G NR codes. The proposed architecture includes a flexible circular shifting network, which is capable of shifting a single large bit vector or multiple smaller bit vectors depending on the code. The encoder architecture was built around the shifter in a way that multiple parity check matrix elements can be processed in parallel for short codes, thus providing almost the same level of parallelism as for long codes. The processing schedule was optimized for minimal encoding time using the genetic algorithm. The optimized encoder provided high throughputs, low latency, and up-to-date the best HUE.

Compositional Quantification of Invariance Feedback Entropy for Networks of Uncertain Control Systems

In the context of uncertain control systems, the notion of invariance feedback entropy (IFE) quantifies the state information required by any controller to render a subset $Q$ of the state space invariant. IFE equivalently also quantifies the smallest bit rate, from the coder to the controller in the feedback loop, above which $Q$ can be made invariant over a digital noiseless channel. In this letter, we consider discrete-time uncertain control systems described by difference inclusions and establish three results for IFE. First, we show that the IFE of a discrete-time uncertain control system $\Sigma $ and a nonempty set $Q$ is upper bounded by the largest possible IFE of $\Sigma $ and any member of any finite partition of $Q$ . Second, we consider two uncertain control systems, $\Sigma _{1}$ and $\Sigma _{2}$ , which are identical except for the transition function, such that the behavior of $\Sigma _{1}$ is included within that of $\Sigma _{2}$ . For a given nonempty subset of the state space, we show that the IFE of $\Sigma _{2}$ is larger or equal to the IFE of $\Sigma _{1}$ . Third, we establish an upper bound for the IFE of a network of uncertain control subsystems in terms of the IFEs of smaller subsystems. Further, via an example, we show that the upper bound is tight for some systems. Finally, to illustrate the effectiveness of the results, we compute an upper bound and a lower bound of the IFE of a network of uncertain, linear, discrete-time subsystems describing the evolution of temperature of 100 rooms in a circular building.

Gradient-Guided Residual Learning for Inverse Halftoning and Image Expanding

Inverse halftoning and image expanding refer to problems to restore the pixel values of images from compressed images of smaller bit depth. Since these two problems are ill-posed, there are few perfect solutions. Recently, deep convolutional neural networks (DCNN) have shown their powerful ability in inverse halftoning and image expanding. However, the restored images still suffer from visual artifacts or fine details loss due to the improper design of network structure. To this end, this paper proposes a residual learning model for inverse halftoning and image expanding. The whole model consists of two progressive stages. The first stage is a gradient-guided DCNN, which coarsely recovers the main content of the image with the guidance of the predicted gradients. The second stage is a residual network, which learns the residual maps to fine-tune the coarse images, leading better local detail representation. Extensive experiments, including visual quality and numerical evaluation, are performed on the COCO data set. Results show that our method achieves the best performance when compared to the state-of-art methods.

Channel Capacity Estimation in MIMO-OFDM Wireless Systems

The errors in the wireless channels are identified and rectified by using different techniques. The Viterbi Decoder is very commonly implemented technique used for detection and correction of errors. The Multiple Input Multiple Output–Orthogonal Frequency Division Multiplexing (MIMO-OFDM) technology is used in 4G wireless communication systems. While combining MIMO with OFDM systems, it will perform by increased in reliability, smaller Bit Error Rate (BER). In this paper, a low Bit Error Rate was achieved with the use of good channel coding technique and modulation scheme.

Analysis of Modified Least Significant Bit Polynomial Function Algorithm For Securing Digital Image

Nowadays, there are many types of information, among which is the digital image. Some information must be secured, so it has to be made confidential. One of the methods to secure data is by steganography, wherein information is hidden inside another media called cover. Least Significant Bit (LSB) is the smallest bit in the binary array from the value of a byte. The polynomial function is the total of the power product in one or more variables with coefficients. The digital image can be used as a cover in steganography. The pixel selection for containing the message may be determined by the polynomial function so that the polynomial function can be used as the embedding and extraction key. The larger the ratio between the dimensions of the cover image to the image message, the better values of PSNR and MSE obtained. It can be concluded that the combination of the LSB method and polynomial function may serve as a modification to the LSB method in steganography.

More on phase-change materials for data storage

The piece by Ashley Smart on page 16 of the January 2018 issue of Physics Today highlights the ongoing effort to develop rapidly switchable phase-change materials for computer memory and storage. Our work at IBM Research in the early 1980s helped jump-start that effort: It demonstrated for the first time fast crystallization and thermally stable data storage in a reversible phase-change material and clarified the underlying physics.11. M. Chen, K. A. Rubin, R. W. Barton, Appl. Phys. Lett. 49, 502 (1986). https://doi.org/10.1063/1.97617The key to short crystallization times was picking materials that do not require phase separation and associated diffusion to crystallize. The material must also have a glass transition temperature high enough to guarantee stability of the amorphous phase, into which the data are recorded. And it must be put into a properly designed thermal structure so that heat pulses of different duration can alternatively crystallize and amorphize it.We demonstrated high-speed crystallization with non-phase-separating compounds such as germanium telluride and antimony telluride. We were aware of materials—pure aluminum, for instance—that crystallize so fast that they could not be thermally quenched to an amorphous phase. In conventional thin-film structures, GeTe suffered the same problem. Building it into a higher-cooling-rate structure, however, increased the quench rate and made amorphization possible. We also understood that amorphous GeTe, with a structure similar to a close-packed liquid, would often be kinetically disposed to form a metastable face-centered cubic crystalline phase. That extended the range of workable compositions beyond those suggested by an (equilibrium) phase diagram.We shared our understanding with researchers at Matsushita Electric Industrial (now Panasonic), who then expanded the set of fast-crystallizing materials to include the GeSbTe-type materials. The smaller bit geometries associated with modern nonvolatile memories allow for higher quench rates and thus enable faster-crystallizing materials to be considered.The latest research from China, as highlighted by Smart, is notable in introducing “rational design,” such as the doping of SbTe with scandium to seed heterogeneous nucleation. It raises an interesting question: How would the picture change if Sc were deposited in a discontinuous layer rather than dispersed throughout the SbTe film?ReferenceSection:ChooseTop of pageReference <<CITING ARTICLES1. M. Chen, K. A. Rubin, R. W. Barton, Appl. Phys. Lett. 49, 502 (1986). https://doi.org/10.1063/1.97617, Google ScholarCrossref, ISI© 2018 American Institute of Physics.

Growth mechanism and magnetic properties of Co nanowire arrays by AC electrodeposition

Co nanowire arrays with diameters of 25 nm, 50 nm and 75 nm were prepared in pores of anodic aluminum oxide (AAO) templates via an AC electrodeposition technique. Filling rate of Co nanowires in the pores of AAO template was studied at various voltages. Studies showed that Co nanowire can be full of the pores of AAO template at high voltage, which suggested a two dimensional (2D) titled plane growth mode for Co nanowire growing up by AC electrodeposition technique. The growth process of Co nanowires was illustrated in detail. Theoretical analysis indicated that thermodynamic factor was responsible for the filling rate of Co nanowires in the pores of AAO template. The magnetic behaviors of Co nanowire arrays with varying diameters were investigated at room temperature. The results have confirmed that coercivity (Hc), remanent magnetization (Mr) and squareness (SQ), measured along wire axis, decrease with increasing of Co nanowires diameter. Moreover, the SQ of Co nanowire arrays with diameter of 25 nm was much higher than those of others in this direction. It can be attributed to both the magnetostatic coupling among the wires and the arrangement of Co grains in the AAO pores. This work will be beneficial for a magnetic media with perpendicular anisotropy to allow a smaller bit size and increase the recording density.

Electric-Field Inputs for Molecular Quantum-Dot Cellular Automata Circuits

Quantum-dot cellular automata (QCA) is a low-power, non-von-Neumann, general-purpose paradigm for classical computing using transistor-free logic. An elementary QCA device called a "cell" is made from a system of coupled quantum dots with a few mobile charges. The cell's charge configuration encodes a bit, and quantum charge tunneling within a cell enables device switching. Arrays of cells networked locally via the electrostatic field form QCA circuits, which mix logic, memory and interconnect. A molecular QCA implementation promises ultra-high device densities, high switching speeds, and room-temperature operation. We propose a novel approach to the technical challenge of transducing bits from larger conventional devices to nanoscale QCA molecules. This signal transduction begins with lithographically-formed electrodes placed on the device plane. A voltage applied across these electrodes establishes an in-plane electric field, which selects a bit packet on a large QCA input circuit. A typical QCA binary wire may be used to transmit a smaller bit packet of a size more suitable for processing from this input to other QCA circuitry. In contrast to previous concepts for bit inputs to molecular QCA, this approach requires neither special QCA cells with fixed states nor nanoelectrodes which establish fields with single-electron specificity. A brief overview of the QCA paradigm is given. Proof-of-principle simulation results are shown, demonstrating the input concept in circuits made from two-dot QCA cells. Importantly, this concept for bit inputs to molecular QCA may enable solutions to or provide insights into other challenges to the realization of molecular QCA, such as the demonstration of molecular device switching, the read-out of molecular QCA states, and the layout of molecular QCA circuits.

Robustness of critical bit rates for practical stabilization of networked control systems

In this paper we address the question of robustness of critical bit rates for the stabilization of networked control systems over digital communication channels. For a deterministic nonlinear system, the smallest bit rate above which practical stabilization (in the sense of set-invariance) can be achieved is measured by the invariance entropy. Under the assumptions of chain controllability and uniform hyperbolicity on the set of interest, we prove that the invariance entropy varies continuously with respect to system parameters. Hence, in this case the critical bit rate is robust with respect to small perturbations.

Analysis and Implementation of High Performance Reconfigurable Finite Impulse Response Filter Using Distributed Arithmetic

The finite impulse response (FIR) digital filters are commonly used in many digital signal processing systems. For higher order filters the implementation of reconfigurable random access memory based FIR filter becomes costly and the speed is reduced. This research paper presents an efficient distributed arithmetic (DA) based approach for reduced area and low power implementation of the FIR filters whose filter coefficients are dynamic in nature. Due to the complexity in implementation of higher order filters, a portion of bit serial based computation in the look up table (LUT) is used. However the shared LUTs are used instead of RAM based LUTs, Shared LUTs involves the concept of LUT partitioning where the coefficients are split into vectors of smaller bit lengths which reduce the depth of the LUT. The possible partial DA results for these vectors are stored in register banks that are shared between multiple DA units. Since the register banks are shared, the area is effectively reduced. Hence an application specific integrated circuits implementation is used. When compared to the existing 256 tap FIR filter, the area has been reduced by 62.16% for vector bit length (M), M = 2 and by 75.84% for M = 4 and the power consumption has been reduced by 42.67% for M = 2 and by 64.02% for M = 4.

ОРТОГОНАЛЬНЫЕ ФАЗОКОДИРОВАННЫЕ СИГНАЛЫ С ДОПОЛНИТЕЛЬНОЙ ОТНОСИТЕЛЬНОЙ ФАЗОВОЙ МАНИПУЛЯЦИЕЙ

The paper examines the version of a equal-energy orthogonal phase-coded signals with additional differential phase modulation of codewords. Adding differential phase modulation of codewords to the phase-coded signal made it possible to reduce the probability of bit error while maintaining the possibility of incoherent detection. A single differential quadrature phase modulation of codewords provides the lowest probability of bit error among the signals under consideration, but in the absence of a frequency offset. At small frequency offsets, the single differential binary phase modulation of codewords has the smallest bit error probability. However, two-fold differential binary phase modulation with a slight loss relative to a single differential modulation in the AWGN channel has the lowest sensitivity to the frequency offset. Additional differential phase modulation it possible to reduces the number of orthogonal signals while preserving the probability of a bit error, or reduces the probability of a bit error while maintaining the occupied bandwidth.

LETAM: A low energy truncation-based approximate multiplier

In this paper, we propose an energy efficient approximate multiplier design obtained by truncating the input operands. In the structure, the n-bit multiplication operation is transformed to a smaller bit length multiplication plus some add and shift operations. The simple calculation core makes the multiplier a scalable yet low power structure. Also, we suggest an output quality-tunable multiplier providing ability to change the output quality during the multiplication operation. The characteristics of the proposed multiplier are compared with those of the exact and some other approximate multipliers in a 45 nm technology. The comparison reveals an average improvement of 89.2% (74.9%) in terms of the energy (area) compared to that of the exact multiplier. The utility of the proposed multiplier in a JPEG encoder application is also investigated. The results reveal that the Peak Signal to Noise Ratio (PSNR) reduction is at most 0.15 dB compared to that of the exact one.