Low Bit Research Articles

Proof-of-principle demonstration of free space semi-quantum key distribution based on the single-state protocol

Semi-quantum key distribution (SQKD) allows a quantum user and a classical user to share a string of secret keys, providing support for application scenarios that cannot withstand the high cost of quantum resources. In this paper, we propose what we believe to be the first proof-of-principle experimental demonstration of free space SQKD based on the single-state protocol, which is equipped with polarization encoding scheme employing the method of selective modulation. During the half-hour test time for each operation, the overall experiment obtained the original key rate of 107.2 kbps at the repetition frequency of 100 MHz, and the average bit error rate was determined to be 1.65% for CTRL operation and 0.64% for SIFT operation. The experimental results indicate that our system exhibits commendable performance and stability at a low bit error rate that represents a significant initial stride toward the future practical deployment of SQKD systems within free-space channels.

Low-Bit-Depth Detection for Phase Retrieval with Higher Efficiency in Holographic Data Storage

In the past, comprehensive information was imperative for image processing, prompting a preference for high-depth cameras. However, in our research, we discovered that the abundance of image details may impede phase retrieval. Consequently, this paper presents an iterative phase retrieval method based on a low bit depth. Through simulations and experiments, this approach has proven effective in evidently enhancing phase retrieval outcomes. Furthermore, the concept of low bit depth holds promise for broader application across diverse domains within the field of image retrieval.

Deep underwater image compression for enhanced machine vision applications

Underwater image compression is fundamental in underwater visual applications. The storage resources of autonomous underwater vehicles (AUVs) and underwater cameras are limited. By employing effective image compression methods, it is possible to optimize the resource utilization of these devices, thereby extending the operational time underwater. Current image compression methods neglect the unique characteristics of the underwater environment, thus failing to support downstream underwater visual tasks efficiently. We propose a novel underwater image compression framework that integrates frequency priors and feature decomposition fusion in response to these challenges. Our framework incorporates a task-driven feature decomposition fusion module (FDFM). This module enables the network to understand and preserve machine-friendly information during the compression process, prioritizing task relevance over human visual perception. Additionally, we propose a frequency-guided underwater image correction module (UICM) to address noise issues and accurately identify redundant information, enhancing the overall compression process. Our framework effectively preserves machine-friendly features at a low bit rate. Extensive experiments across various downstream visual tasks, including object detection, semantic segmentation, and saliency detection, consistently demonstrated significant improvements achieved by our approach.

A Power-Efficient Envelope-Detector-Less Amplitude-Shift-Keying Forward Telemetry for Wirelessly Powered Biomedical Devices.

This paper proposes an envelope-detector-less (EDL) amplitude-shift-keying (ASK) forward telemetry (FT) demodulator for wireless power/data transfer (WPDT) systems. The EDL ASK FT demodulator can substitute bulky and power-hungry components, which are an envelope detector and an analog comparator in the conventional ASK FT demodulator, with a digital controller, reducing both power dissipation and chip area. The proposed demodulator shares the gate control signals of pass transistors, which are used in an ac-dc regulator for wireless power reception, to maintain a constant load voltage while efficiently demodulating the forward telemetry data. Also, a proposed digital cleaner in the EDL demodulator refines this control signal into a wide pulse without suffering from resonant frequency noise, while a synchronizer can align its frequency with the data rate and resonant frequency. The 0.25-μm CMOS prototype chip of the proposed power-path-less EDL ASK FT demodulator, equipped with the ac-dc regulator, demonstrates a significant 38.2% reduction in power dissipation compared to the conventional ASK FT demodulator. Moreover, the EDL ASK FT demodulator occupies only 0.023-mm2 silicon area and achieves a low bit error rate (BER) less than 10-4 while maintaining a regulated voltage of 4.5 V on the load.

Interference-enhanced micro-vision-based single-shot imaging of five degrees-of-freedom error motions for ultra-precision rotary axes

The measurement of five degrees-of-freedom (5-DOF) error motions, including radial, axial, and tilt motions, is crucial for ultra-precision rotary axes, which are key components of ultra-precision machine tools and instrumentation. In this study, we propose an interference-enhanced micro-vision technique to concurrently derive the 5-DOF error motions from a single-shot two-dimensional image, which was captured by a standard industrial camera equipped with an interference objective lens. By consolidating the essential features into a single optical path, the interference-enhanced micro-vision technique ingeniously merges machine micro-vision and modified white-light interference to detect in-plane and out-of-plane motions. Numerical simulations demonstrated, the basic principle for deriving the 5-DOF error motions, and the magnification of objective lens had inconsistent effects on the measurement accuracy for the radial and tilt motions, i.e. higher magnification led to higher radial accuracy but lower tilt accuracy. As practical application, the error motion detection capability was demonstrated by simultaneously measuring the 5-DOF synchronous and asynchronous error motions for a typical air bearing spindle at rotation speeds of 8.33, 108.33, and 308.33 rpm. The synchronous errors were nearly identical at various spindle speeds. However, because of system dynamics, increased vibrations were observed to be superimposed on the basic tilt error motions as the spindle speeds increased, which were verified by the vibration marks imprinted on the turned surfaces. For the 5-DOF motion measurements, the least-square fitting using large-volume edge and greyscale data of the captured image enabled super-high resolutions, despite using a camera with a relatively large pixel size and low bit depth. These results demonstrate that the proposed interference-enhanced micro-vision technique is a simple and effective tool for measuring spatial error motions in ultra-precision rotary axes.

Enhanced Ultra-Wideband Communication System with OFDM Modulation: Performance Analysis in LOS and NLOS Conditions

Ultra-wideband (UWB) communication systems, employing Orthogonal Frequency Division Multiplexing (OFDM) modulation, are renowned for their high data rates, low power consumption, and robust performance in multipath environments. However, these systems face significant challenges in varying propagation conditions, specifically under Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) scenarios. In LOS conditions, the signal travels directly from the transmitter to the receiver without obstructions, typically resulting in optimal performance characterized by high transmission efficiency, strong Signal-to-Noise Ratio (SNR), low Bit Error Rate (BER), and maximum throughput. Conversely, NLOS conditions involve obstructions and reflections that cause multipath propagation and signal degradation. This leads to reduced transmission efficiency, lower SNR, higher BER, and diminished throughput. This study examines the performance of an UWB communication system using OFDM modulation in both LOS and NLOS conditions. The performance metrics evaluated include transmission efficiency, SNR,BER, and throughput. In LOS conditions, the system demonstrates high transmission efficiency, superior SNR, minimal BER, and maximum throughput due to the unobstructed signal path. In contrast, NLOS conditions result in decreased transmission efficiency, lower SNR, increased BER, and reduced throughput due to signal attenuation and multipath effects. These findings highlight the significant impact of environmental conditions on UWB-OFDM system performance and underscore the importance of optimizing such systems for reliable communication in varied deployment scenarios.

Spread spectrum for covert communication in ultra-violet communication system

In this paper, we propose a covert communication scheme based on a spread spectrum for ultra-violet communication. We first characterize the system model of the communication system, where Warden is adopted to monitor the legitimated user transmission based on the binary hypothesis test and Kolmogorov-Smirnov test. To avoid periodic transmission of the pilot sequence which potentially degrades covertness, we propose a blind synchronization approach, such that the pilot sequence does not need to be transmitted. It is observed from system-level simulation that the spread spectrum-based approach can achieve both low detection bit error rate and covertness under weak light intensity. Moreover, we experimentally evaluate the detection performance and covertness of the proposed approach.

Video Coding for Machines: Compact Visual Representation Compression for Intelligent Collaborative Analytics.

As an emerging research practice leveraging recent advanced AI techniques, e.g. deep models based prediction and generation, Video Coding for Machines (VCM) is committed to bridging to an extent separate research tracks of video/image compression and feature compression, and attempts to optimize compactness and efficiency jointly from a unified perspective of high accuracy machine vision and full fidelity human vision. With the rapid advances of deep feature representation and visual data compression in mind, in this paper, we summarize VCM methodology and philosophy based on existing academia and industrial efforts. The development of VCM follows a general rate-distortion optimization, and the categorization of key modules or techniques is established including feature-assisted coding, scalable coding, intermediate feature compression/optimization, and machine vision targeted codec, from broader perspectives of vision tasks, analytics resources, etc. From previous works, it is demonstrated that, although existing works attempt to reveal the nature of scalable representation in bits when dealing with machine and human vision tasks, there remains a rare study in the generality of low bit rate representation, and accordingly how to support a variety of visual analytic tasks. Therefore, we investigate a novel visual information compression for the analytics taxonomy problem to strengthen the capability of compact visual representations extracted from multiple tasks for visual analytics. A new perspective of task relationships versus compression is revisited. By keeping in mind the transferability among different machine vision tasks (e.g. high-level semantic and mid-level geometry-related), we aim to support multiple tasks jointly at low bit rates. In particular, to narrow the dimensionality gap between neural network generated features extracted from pixels and a variety of machine vision features/labels (e.g. scene class, segmentation labels), a codebook hyperprior is designed to compress the neural network-generated features. As demonstrated in our experiments, this new hyperprior model is expected to improve feature compression efficiency by estimating the signal entropy more accurately, which enables further investigation of the granularity of abstracting compact features among different tasks.

Advancing Fab-Compatible Color-Selective Organic Photodiodes: Tailored Molecular Design and Nanointerlayers.

High-performance organic photodiodes (OPDs) and OPD-based image sensors are primarily realized using solution processes based on various additives and coating methods. However, vacuum-processed OPDs, which are more compatible with large-scale production, have received little attention, thereby hindering their integration into advanced systems. This study introduces innovations in the material and device structures to prepare superior vacuum-processed OPDs for commercial applications. A series of vacuum-processable, low-cost p-type semiconductors is developed by introducing an electron-rich cyclopentadithiophene core containing various electron-accepting moieties to fine-tune the energy levels without any significant structural or molecular weight changes. An additional nanointerlayer strategy is used to control the crystalline orientation of the upper-deposited photoactive layer, compensating for device performance reduction in inverted, top-illuminated OPDs. These approaches yielded an external quantum efficiency of 70% and a specific detectivity of 2.0 × 1012 Jones in the inverted structures, which are vital for commercial applications. These OPDs enabled visible-light communications with extremely low bit error rates and successful X-ray image capture.

A Low-Bit-Rate Image Semantic Communication System Based on Semantic Graph

In the progress of research in the field of semantic communication, most efforts have been focused on optimizing the signal-to-noise ratio (SNR), while the design and optimization of the bit rate required for transmission have been relatively neglected. To address this issue, this study introduces an innovative low-bit-rate image semantic communication system model, which aims to reconstruct images through the exchange of semantic information rather than traditional symbol transmission. This model employs an image feature extraction and optimization reconstruction framework, achieving visually satisfactory and semantically consistent reconstruction performance at extremely low bit rates (below 0.03 bits per pixel (bpp)). Unlike previous methods that used pixel accuracy as the standard for distortion measurement, this research introduces multiple perceptual metrics to train and evaluate the proposed image semantic encoding model, aligning more closely with the fundamental purpose of semantic communication. Experimental results demonstrate that, compared to WebP, JPEG, and deep learning-based image codecs, our model not only provides a more visually pleasing reconstruction effect but also significantly reduces the required bit rate while maintaining semantic consistency.

Average capacity and bit error rate of vortex gamma beams propagating in non-Kolmogorov atmospheric turbulence

In recent years, free-space optical communication based on various vortex beams has gained significant attention due to its high channel capacity and low bit error rate (BER). To investigate a novel type of vortex beam (termed as gamma beam) and its application in free-space optical communication (FSO), a comprehensive analysis of its transmission performance in weak-to-strong non-Kolmogorov turbulence has been conducted for the first time. Based on the extended Rytov method, the propagation behaviors of the gamma beam via weak-to-strong non-Kolmogorov turbulent atmosphere is explored, revealing that gamma beams may outperform LG beams and HyGG beams in certain short links. Numerical calculations are performed to analyze the effects of transmission distance, rms beam radius, receiver aperture, and other parameters on the average capacity and BER. Our results are potentially significant for free-space optical communication based on orbital angular momentum.

Generative Refinement for Low Bitrate Image Coding Using Vector Quantized Residual

Generative Refinement for Low Bitrate Image Coding Using Vector Quantized Residual

Reconstruction flow recurrent network for compressed video quality enhancement

We present a reconstruction flow for the task of compressed video quality enhancement (VQE). Compressed videos often suffer from various coding artifacts, such as blocking and blurring, especially under low bit-rate. VQE aims to suppress these artifacts to improve the visual quality. Frame similarity can be utilized to enhance low-quality frames given their neighboring high-quality frames, for which motion estimation becomes important. Previous approaches often calculate optical flow for the motion compensation. On the other hand, video coding contains a rich set of block motion vectors, forming a coding flow, which may or may not correspond to the scene motion, but to places that deliver the minimum compression error. In contrast, such a valuable coding flow has always been ignored in VQE previously. In this work, we combine these two motion sources into a new flow, namely reconstruction flow, for the purpose of high-quality VQE. Specifically, we estimate optical flows from RGB frames and extract coding flows from coding streams, which are then merged by a fusion module to generate reconstruction flow. Besides, our network is built upon a recurrent network to utilize global temporal information. The deep features are warped according to the reconstruction flow and fed into the subsequent reconstruction module with spatial-variant kernel attention. Our method is evaluated on the leading MFQE2.0 dataset, which demonstrates superior performances when compared to the existing state-of-the-art methods.

Data security using low bit encoding algorithm and rsa algorithm

Data security using low bit encoding algorithm and rsa algorithm

High-Throughput Polar Code Decoders with Information Bottleneck Quantization.

In digital baseband processing, the forward error correction (FEC) unit belongs to the most demanding components in terms of computational complexity and power consumption. Hence, efficient implementation of FEC decoders is crucial for next-generation mobile broadband standards and an ongoing research topic. Quantization has a significant impact on the decoder area, power consumption and throughput. Thus, lower bit widths are preferred for efficient implementations but degrade the error correction capability. To address this issue, a non-uniform quantization based on the Information Bottleneck (IB) method is proposed that enables a low bit width while maintaining the essential information. Many investigations on the use of the IB method for Low-density parity-check code) LDPC decoders exist and have shown its advantages from an implementation perspective. However, for polar code decoder implementations, there exists only one publication that is not based on the state-of-the-art Fast Simplified Successive-Cancellation (Fast-SSC) decoding algorithm, and only synthesis implementation results without energy estimation are shown. In contrast, our paper presents several optimized Fast-SSC polar code decoder implementations using IB-based quantization with placement and routing results using advanced 12 nm FinFET technology. Gains of up to 16% in area and 13% in energy efficiency are achieved with IB-based quantization at a Frame Error Rate (FER) of 10-7 and a polar code of N=1024,R=0.5 compared to state-of-the-art decoders.

Hierarchical Training of Deep Neural Networks Using Early Exiting.

Deep neural networks (DNNs) provide state-of-the-art accuracy for vision tasks, but they require significant resources for training. Thus, they are trained on cloud servers far from the edge devices that acquire the data. This issue increases communication cost, runtime, and privacy concerns. In this study, a novel hierarchical training method for DNNs is proposed that uses early exits in a divided architecture between edge and cloud workers to reduce the communication cost, training runtime, and privacy concerns. The method proposes a brand-new use case for early exits to separate the backward pass of neural networks between the edge and the cloud during the training phase. We address the issues of most available methods that, due to the sequential nature of the training phase, cannot train the levels of hierarchy simultaneously or they do it with the cost of compromising privacy. In contrast, our method can use both edge and cloud workers simultaneously, does not share the raw input data with the cloud, and does not require communication during the backward pass. Several simulations and on-device experiments for different neural network architectures demonstrate the effectiveness of this method. It is shown that the proposed method reduces the training runtime for VGG-16 and ResNet-18 architectures by 29% and 61% in CIFAR-10 classification and by 25% and 81% in Tiny ImageNet classification, respectively, when the communication with the cloud is done over a low bit rate channel. This gain in the runtime is achieved, while the accuracy drop is negligible. This method is advantageous for online learning of high-accuracy DNNs on sensor-holding low-resource devices such as mobile phones or robots as a part of an edge-cloud system, making them more flexible in facing new tasks and classes of data.

On the importance of precision in cortical bone drilling: Integrating experimental validation and computational modeling

BackgroundCortical bone drilling is integral to orthopedic and dental surgeries, yet challenges such as thermal necrosis persist. Previous finite element (FE) models may overlook critical parameters, impacting accuracy. This study aims to integrate experimental and computational approaches to predict essential parameters—initial temperature, point angle, and spindle speed—enhancing precision in cortical bone drilling. MethodsBovine cortical samples were utilized to systematically investigate the impact of four independent parameters on maximum temperature (MT) and maximum thrust force (MTF). Parameters included drill bit initial temperature (IT), diameter, point angle, and spindle speed (225–2700 rpm, feed rate 0.5–3 mm/s). Experimental procedures involved an orthopedic handpiece with titanium drill bits. DEFORM-3D V6.02 facilitated FE simulation, with the validated model developed for the second stage of the drilling process. ResultsThe validated model highlighted the significant impact of drill bit IT on MT, predicting a 26.14 % decrease in final bone temperature as IT decreased from 25 to 5 °C. Increasing the point angle from 70 to 120° resulted in a 13.1 % MT increase and a 26.9 % decrease in MTF. Spindle speed variations exhibited a 48.3 % temperature increase and an 82.8 % MTF decrease. ConclusionsIntegrating experimental validation and computational modeling offers a comprehensive approach to predict drilling parameters. Precision in cortical bone drilling can be optimized by selecting specific parameters, including lower drill bit IT, smaller point angles, and controlled spindle speeds. This optimization reduces the risk of bone necrosis and thermal damage, thereby enhancing surgical outcomes.

Multi-User Detection Based on Improved Cheetah Optimization Algorithm

Targeting the issues of slow speed and inadequate precision of optimal solution calculation for multi-user detection in complex noise environments, this paper proposes a multi-user detection algorithm based on a Hybrid Cheetah Optimizer (HCO). The algorithm first optimizes the control parameters and individual update mechanism of the Cheetah Optimizer (CO) algorithm using a nonlinear strategy to improve the uniformity and discretization of the individual search range, and then dynamically introduces a differential evolutionary algorithm into the improved selection mechanism of the CO algorithm, which is utilized to fine-tune the solution space and maintain the local diversity during the fast search process. Simulation results demonstrate that this detection algorithm not only realizes fast convergence with a very low bit error rate (BER) at eight iterations but also has obvious advantages in terms of noise immunity, resistance to far and near effects, communication capacity, etc., which greatly improves the speed and accuracy of optimal position solving for multi-user detection and can achieve the purpose of accurate solving in complex environments.

Enhanced voice over 5G transmission with unequal error protection and generative adversarial networks

SummaryVoice services have progressed significantly since the launch of mobile phones and have evolved from analog technology to digital ones. Since the launch of 5G, voice over 5G (Vo5G) will slowly take over legacy mobile technologies to provide better voice services. Nevertheless, interruptions and noise will continue to affect speech communication over wireless networks, ensuing in a reduced Quality of Service (QoS). Three enhanced transmission schemes for Vo5G are proposed. The first proposal benefits from the difference in weight of the bits of internet Low Bitrate Code (iLBC) coded packets to offer them diverse degrees of protection i.e. Unequal Error Protection (UEP). UEP enhances the reception quality by allowing the more significant bits to be decoded with greater reliability during 5G transmission. The second and third schemes substitute the usual Quadrature Amplitude Modulation (QAM) constellation with a Statistical QAM (SQAM) one. The second scheme arranges the significant bits on the SQAM constellation and the remaining bits onto the conventional QAM arrangement. For the third scheme, all the bits are arranged on the SQAM constellation. Generative Adversarial Networks (GAN) were incorporated into the third scheme as the third scheme provided better gains over the first and second schemes. GAN was used to synthesize and enhance parts of the speech file that were lost during transmission over the 5G channel resulting in a gain in Segmented Signal to Noise Ratio (SSNR). The addition of GAN boosted the gain obtained by the third scheme by 1.45 dB with 16‐QAM at a code rate of 1/3.

Analysis of unmanned aerial vehicle communication performance with optic sensor integration

This paper presents a novel algorithmic framework to enhance unmanned aerial vehicle (UAV) communication performance through the adaptive integration of optical sensor data. The framework features a dynamic sensor integration algorithm (DSIA) that optimizes sensor bitrates based on real-time bandwidth availability and mission-driven priorities. An adaptive compression scheme works alongside the DSIA to maintain high data fidelity and resolution at lower bitrates. Together, these algorithms provide an efficient and scalable solution for managing high-volume UAV sensor data under varying conditions. The efficiency gains are demonstrated through simulations highlighting the framework’s ability to reduce packet loss and latency while maximizing image quality for a given communication bandwidth constraint. The scalable architecture ensures robustness across diverse UAV platforms, sensor types, and operational scenarios. Simulation results validate up to a 75 % reduction in latency and a 45 % improvement in image resolution compared to conventional fixed bitrate approaches. By optimizing sensor data relevancy and transmission fidelity, this framework enables more effective UAV communication and enhances mission performance.