High Throughput Efficiency Research Articles

Direct observation of the evolution behavior of micro to nanoscale precipitates in austenitic heat-resistant steel via electron channeling contrast imaging

Precipitation directly determines the high-temperature properties of the austenitic heat-resistant steels. The precipitations of MX, Z-phase, M23C6 and σ-phase, ranging from micrometers to nanometers, are commonly characterized using the scanning electron microscopy (SEM) and transmission electron microscopy (TEM). However, details of their evolution behavior are still unclear due to the limited SEM resolution using the traditional sampling method and the limited spatial resolution of TEM. In this work, field emission SEM-based electron channel contrast imaging (ECCI) techniques with flexible sampling routes were introduced to observe the precipitation evolution behavior of HR3C steel after aging at 700 °C for 8095 h. We showed that the coarse primary-MX and Z-phase in the as-received steel are relatively stable during aging. The coarse M23C6 and tiny secondary Z-phase dispersions were rapidly formed along the grain/twin boundaries and within dislocation arrays inside the grain interior, respectively. We further found that the M23C6 at grain boundaries would change from continuous to semi-continuous due to the formation of σ-phases, while in twin boundaries, it would become continuous over aging. Moreover, we showed that the σ-phases were in-situ transformed from M23C6 at the grain boundaries via its dissolution, facilitating the nucleation of tiny Z-phase inside the σ-phase grains, and this phenomenon has not been reported so far. We have demonstrated that ECCI with an electrolytic-polishing-based sampling route is effective in revealing multi-scale precipitation with high-throughput efficiency, and it allows the direct observation of the complex behavior of precipitates down to the nanoscale using a bulk sample. This method can be used as an efficient way for the quantitative microstructure study.

A Smart Water Bottle and Companion App (HidrateSpark 3) to Improve Bladder-Filling Compliance in Patients With Prostate Cancer Receiving Radiotherapy: Nonrandomized Trial of Feasibility and Acceptability.

Patients with prostate cancer undergoing radiation therapy (RT) need comfortably full bladders to reduce toxicities during treatment. Poor compliance is common with standard of care written or verbal instructions, leading to wasted patient value (PV) and clinic resources via poor throughput efficiency (TE). Herein, we assessed the feasibility and acceptability of a smartphone-based behavioral intervention (SBI) to improve bladder-filling compliance and methods for quantifying PV and TE. In total, 36 patients with prostate cancer were enrolled in a single-institution, closed-access, nonrandomized feasibility trial. The SBI consists of a fully automated smart water bottle and smartphone app. Both pieces alert the patient to empty his bladder and drink a personalized volume goal, based on simulation bladder volume, 1.25 hours before his scheduled RT. Patients were trained to adjust their volume goal and notification times to achieve comfortably full bladders. The primary end point was met if qualitative (QLC) and quantitative compliance (QNC) were >80%. For QLC, patients were asked if they prepared their bladders before daily RT. QNC was met if bladder volumes on daily cone-beam tomography were >75% of the simulation's volume. The Service User Technology Acceptability Questionnaire (SUTAQ) was given in person pre- and post-SBI. Additional acceptability and engagement end points were met if >3 out of 5 across 4 domains on the SUTAQ and >80% (15/18) of patients used the device >50% of the time, respectively. Finally, the impact of SBI on PV and TE was measured by time spent in a clinic and on the linear accelerator (linac), respectively, and contrasted with matched controls. QLC was 100% in 375 out of 398 (94.2%) total treatments, while QNC was 88.9% in 341 out of 398 (85.7%) total treatments. Of a total score of 5, patients scored 4.33 on privacy concerns, 4 on belief in benefits, 4.56 on satisfaction, and 4.24 on usability via SUTAQ. Further, 83% (15/18) of patients used the SBI on >50% of treatments. Patients in the intervention arm spent less time in a clinic (53.24, SEM 1.71 minutes) compared to the control (75.01, SEM 2.26 minutes) group (P<.001). Similarly, the intervention arm spent less time on the linac (10.67, SEM 0.40 minutes) compared to the control (14.19, SEM 0.32 minutes) group (P<.001). This digital intervention trial showed high rates of bladder-filling compliance and engagement. High patient value and TE were feasibly quantified by shortened clinic times and linac usage, respectively. Future studies are needed to evaluate clinical outcomes, patient experience, and cost-benefit. ClinicalTrials.gov NCT04946214; https://www.clinicaltrials.gov/study/NCT04946214.

A D2D user pairing algorithm based on motion prediction and power control

AbstractUser pairing plays an important role in device‐to‐device (D2D) relay communication, contributing significantly to maintaining low energy consumption, high throughput, and overall energy efficiency in the communication system. To achieve these purposes, an attention‐based long short‐term memory motion prediction model (AT‐LSTM) and propose a joint power control algorithm. Leveraging these techniques, we also propose a D2D user pairing algorithm, distance–power–SINR pairing algorithm (DPSPA), which comprehensively considers factors such as D2D communication distances, transmit power, and signal‐to‐interference‐plus‐noise ratio. Initially, the AT‐LSTM model is utilized to predict the location of users. Subsequently, the distance between the user terminal device and each communication point and the base station, filtering cache points, and non‐cache points within the D2D communication radius are calculated. Then, based on the distance, required transmission power, and signal‐to‐interference‐plus‐noise ratio of each point, the evaluation index (the best matching product) is obtained. Finally, the point with the maximum best matching product is selected for D2D direct communication mode, D2D relay communication mode, or cellular communication mode. Simulation results demonstrate that DPSPA effectively reduces system energy consumption, enhances system throughput, and improves overall energy efficiency.

Energy-efficient synthetic antiferromagnetic skyrmion-based artificial neuronal device

Magnetic skyrmions offer unique characteristics such as nanoscale size, particle-like behavior, topological stability, and low depinning current density. These properties make them promising candidates for next-generation spintronics-based memory and neuromorphic computing. However, one of their distinctive features is their tendency to deviate from the direction of the applied driving force that may lead to the skyrmion annihilation at the edge of nanotrack during skyrmion motion, known as the skyrmion Hall effect (SkHE). To overcome this problem, synthetic antiferromagnetic (SAF) skyrmions that having bilayer coupling effect allows them to follow a straight path by nullifying SkHE making them alternative for ferromagnetic (FM) counterpart. This study proposes an integrate-and-fire (IF) artificial neuron model based on SAF skyrmions with asymmetric wedge-shaped nanotrack having self-sustainability of skyrmion numbers at the device window. The model leverages inter-skyrmion repulsion to replicate the IF mechanism of biological neuron. The device threshold, determined by the maximum number of pinned skyrmions at the device window, can be adjusted by tuning the current density applied to the nanotrack. Neuronal spikes occur when initial skyrmion reaches the detection unit after surpassing the device window by the accumulation of repulsive force that result in reduction of the device’s contriving current results to design of high energy efficient for neuromorphic computing. Furthermore, work implements a binarized neuronal network accelerator using proposed IF neuron and SAF-SOT-MRAM based synaptic devices for national institute of standards and technology database image classification. The presented approach achieves significantly higher energy efficiency compared to existing technologies like SRAM and STT-MRAM, with improvements of 2.31x and 1.36x, respectively. The presented accelerator achieves 1.42x and 1.07x higher throughput efficiency per Watt as compared to conventional SRAM and STT-MRAM based designs.

A GSO‐based multi‐objective technique for performance optimization of blockchain‐based industrial Internet of things

SummaryThe latest developments in the industrial Internet of things (IIoT) have opened up a collection of possibilities for many industries. To solve the massive IIoT data security and efficiency problems, a potential approach is considered to satisfy the main needs of IIoT, such as high throughput, high security, and high efficiency, which is named blockchain. The blockchain mechanism is considered a significant approach to boosting data protection and performance. In the quest to amplify the capabilities of blockchain‐based IIoT, a pivotal role is accorded to the Glowworm Swarm Optimization (GSO) algorithm. Inspired by the collaborative brilliance of glowworms in nature, the GSO algorithm offers a unique approach to harmonizing these conflicting aims. This paper proposes a new approach to improve the performance optimization of blockchain‐based IIoT using the GSO algorithm due to the blockchain's contradictory objectives. The proposed blockchain‐based IIoT system using the GSO algorithm addresses scalability challenges typically associated with blockchain technology by efficiently managing interactions among nodes and dynamically adapting to network demands. The GSO algorithm optimizes the allocation of resources and decision‐making, reducing inefficiencies and bottlenecks. The method demonstrates considerable performance improvements through extensive simulations compared to traditional algorithms, offering a more scalable and efficient solution for industrial applications in the context of the IIoT. The extensive simulation and computational study have shown that the proposed method using GSO considerably improves the objective function and blockchain‐based IIoT systems' performance compared to traditional algorithms. It provides more efficient and secure systems for industries and corporations.

High-Throughput Bioprinting of Spheroids for Scalable Tissue Fabrication.

Tissue biofabrication that replicates an organ-specific architecture and function requires physiologically-relevant cell densities. Bioprinting using spheroids has the potential to create constructs with native cell densities, but its application is limited due to the lack of practical, scalable techniques. This study presents HITS-Bio (High-throughput Integrated Tissue Fabrication System for Bioprinting), a novel multiarray spheroid bioprinting technology enabling scalable tissue fabrication by rapidly positioning a number of spheroids simultaneously using a digitally-controlled nozzle array (DCNA) platform. HITS-Bio achieves an unprecedented speed, an order of magnitude faster compared to existing techniques while maintaining high cell viability (>90%). The platform's ability to pattern multiple spheroids simultaneously enhances fabrication rates proportionally to the size of DCNA used. The utility of HITS-Bio was exemplified in multiple applications, including intraoperative bioprinting with microRNA transfected spheroids for calvarial bone regeneration (∼30 mm 3 ) in a rat model achieving a near-complete defect closure (∼91% in 3 weeks and ∼96% in 6 weeks). Additionally, the successful fabrication of scalable cartilage constructs (1 cm 3 ) containing ∼600 chondrogenic spheroids highlights its high-throughput efficiency (under 40 min per construct) and potential for repairing volumetric tissue defects.

FPGA-enhanced system-on-chip for finger vein-based biometric system using novel DL model

In an era dominated by technology, the imperative for robust personal authentication in electronic information systems becomes increasingly evident. A secure and dependable solution to address this need is biometric authentication. Due to their intrinsic features of being universal, unique, and fraud-resistant, finger vein-based recognition systems have gained importance. Veins provide an efficient barrier against misleading methods since they are buried under the skin and undetectable to human sight. While many researchers focus on advanced technology for finger-vein-based authentication systems, existing research has often overlooked significant challenges, such as short datasets, high computational complexity, and a lack of efficient and lightweight feature descriptors. This paper proposes a unique method for automated Finger Vein Recognition (FVR) based on a fusion model known as “CNN-ViT” for FVR. Transfer learning-based Convolutional Neural Network (CNN) models, such as Inception-V3 and ResNet-50, compute the correlation of adjacent pixels to process texture-based features. Furthermore, shape-based features are processed using the vision transformer (ViT) model to determine the relationship between distant pixels. The combination of these three models enables the learning of textural features based on forms, contributing to more effective finger vein identification. In addition to our databases, we utilize two benchmark databases, FV-USM and SDUMLA-HMT, to validate our experiments. Our proposed approach achieves outstanding accuracy values of 99.95 %, 98.9 %, and 97.78 % on both the benchmark and our datasets. When compared to previous methods, the proposed Deep Learning (DL) model outperforms state-of-the-art models, demonstrating higher recognition rates and accuracy. To prototype the proposed FVR system, a Zynq XCZU4EV UltraScale + Multiprocessor System-On-Chip (MPSoC) was employed. The proposed model exhibits high throughput and competitive power efficiency, making it an excellent choice for scenarios where computing performance is critical, albeit utilizing more power and resources. This was established through a comprehensive examination of FPGA resource utilization and performance metrics.

Adaptive coalition with Kullback‐Leibler divergence for cooperative spectrum sensing (KLDCSS) in cognitive radio networks

SummaryEffective, reliable, and rapid spectrum sensing in concordance with maximum throughput efficiency is necessary at the cognitive radio network to maximize the network's utilization. An adaptive coalition using Kullback–Leibler divergence (KLD)‐based cooperative spectrum sensing scheme (KLDCSS) with a nominal sensing time to optimize the spectral efficiency is presented in this paper. In a coalition‐based spectrum sensing scheme, cooperating secondary users (SUs) are allocated to different coalitions to sense different Primary User (PU) channels. The same cognitive users allocated to sense the primary user channels are allotted to detect in different sensing phases. In the proposed sensing scheme, the cooperating SUs are adaptively allocated to different coalitions to maximize the throughput efficiency. Collaborating cognitive users with a standard signal‐to‐noise ratio and distinct energy levels that are characterized by the localized probabilities of detection and false alarm is considered. An adaptive coalition and user allocation algorithm are proposed to optimize the average opportunistic throughput and diminish the sensing overhead. The expression for throughput efficiency in terms of average throughput and sensing accuracy is derived. In this scheme, the SU computes the log‐likelihood ratio (LLR) from the acquired sample and forwards the same to the fusion center. The fusion center then accumulates the LLR values computed by the SU to estimate the likelihood of spectrum availability. Simulation results highlighting the competitive edge of the proposed scheme, with higher throughput efficiency over various numbers of SUs, coalitions, and signal‐to‐noise ratios, are presented.

BlockFog: A Blockchain-based Framework for Intrusion Defense in IOT Fog Computing

In the rapidly evolving domain of the Internet of Things (IoT) and fog computing, maintaining security, scalability, and efficient operation poses significant challenges. Addressing these issues, this study introduces "BlockFog," a novel blockchain-based framework designed to bolster intrusion defense in IoT fog computing environments. The core objective of BlockFog is to counteract the vulnerabilities inherent in decentralized IoT ecosystems by leveraging blockchain technology for enhanced security and transparency. The framework's innovative design integrates crucial components such as Device Onboarding &amp; Identity Management, Data Integrity &amp; Logging, Smart Contract-Driven Intrusion Detection, Automated Blockchain Responses, Secure Peer-to-Peer Communication, and a Lightweight Consensus Mechanism. These elements work collectively to ensure the security and functionality of IoT devices within the fog computing paradigm. BlockFog stands out for its meticulous approach to handling high transaction volumes with off-chain computations and layer-2 solutions, ensuring data integrity and facilitating seamless audit processes. The framework's resilience is further demonstrated through its robust response to evolving cyber threats, incorporating Over-the-Air (OTA) updates and advanced data protection mechanisms like zero-knowledge proofs. A comparative analysis highlights BlockFog's superior performance against existing models. The results reveal BlockFog's lower latency rates in normal, high traffic, and attack scenarios, its higher throughput efficiency, and its more effective resource utilization in terms of CPU, memory, and bandwidth usage. Moreover, BlockFog exhibits an enhanced ability to detect and respond to malicious activities, including DDoS attacks, with significantly higher accuracy than its counterparts. These findings underscore BlockFog's potential in redefining security and operational paradigms in IoT fog computing, making it a robust, agile, and transparent framework suitable for the current digital landscape.

ORBIT for E. coli: kilobase-scale oligonucleotide recombineering at high throughput and high efficiency.

Microbiology and synthetic biology depend on reverse genetic approaches to manipulate bacterial genomes; however, existing methods require molecular biology to generate genomic homology, suffer from low efficiency, and are not easily scaled to high throughput. To overcome these limitations, we developed a system for creating kilobase-scale genomic modifications that uses DNA oligonucleotides to direct the integration of a non-replicating plasmid. This method, Oligonucleotide Recombineering followed by Bxb-1 Integrase Targeting (ORBIT) was pioneered in Mycobacteria, and here we adapt and expand it for Escherichiacoli. Our redesigned plasmid toolkit for oligonucleotide recombineering achieved significantly higher efficiency than λ Red double-stranded DNA recombineering and enabled precise, stable knockouts (≤134 kb) and integrations (≤11 kb) of various sizes. Additionally, we constructed multi-mutants in a single transformation, using orthogonal attachment sites. At high throughput, we used pools of targeting oligonucleotides to knock out nearly all known transcription factor and small RNA genes, yielding accurate, genome-wide, single mutant libraries. By counting genomic barcodes, we also show ORBIT libraries can scale to thousands of unique members (>30k). This work demonstrates that ORBIT for E. coli is a flexible reverse genetic system that facilitates rapid construction of complex strains and readily scales to create sophisticated mutant libraries.

CMOS plus stochastic nanomagnets enabling heterogeneous computers for probabilistic inference and learning

Extending Moore’s law by augmenting complementary-metal-oxide semiconductor (CMOS) transistors with emerging nanotechnologies (X) has become increasingly important. One important class of problems involve sampling-based Monte Carlo algorithms used in probabilistic machine learning, optimization, and quantum simulation. Here, we combine stochastic magnetic tunnel junction (sMTJ)-based probabilistic bits (p-bits) with Field Programmable Gate Arrays (FPGA) to create an energy-efficient CMOS + X (X = sMTJ) prototype. This setup shows how asynchronously driven CMOS circuits controlled by sMTJs can perform probabilistic inference and learning by leveraging the algorithmic update-order-invariance of Gibbs sampling. We show how the stochasticity of sMTJs can augment low-quality random number generators (RNG). Detailed transistor-level comparisons reveal that sMTJ-based p-bits can replace up to 10,000 CMOS transistors while dissipating two orders of magnitude less energy. Integrated versions of our approach can advance probabilistic computing involving deep Boltzmann machines and other energy-based learning algorithms with extremely high throughput and energy efficiency.

Identification of antihypertensive peptides from lupine using a machine learning approach

Bioactive products with antihypertensive biological activity, isolated from natural sources, have been the subject of growing interest in recent years. This is due to their widespread use in medicine for the treatment and prevention of various diseases, as well as dietary supplements for athletes or their inclusion in diets for overweight people. One such source is Lupine. Lupine beans are delicious and useful. They can be used in food as a nutritional source of vegetable proteins. They are also rich in polyphenols, carotenoids, and phytosterols. The approaches to screen antihypertensive peptides, based on information technologies and more concretely on machine learning, doubtlessly have higher throughput and rapid speed than the in vivo and in vitro procedures. Therefore, the scientific literature abounds with articles offering various artificial intelligence algorithms for predicting food-derived antihypertensive peptides. In this study, an Adaptive Boosting (AdaBoost) algorithm was developed for these purposes. The results showed that the AdaBoost model as a novel auxiliary tool is feasible to screen for antihypertensive peptides derived from food, with high throughput and high efficiency.

Advances in microfluidic-based DNA methylation analysis

DNA methylation has been extensively investigated in recent years, not least because of its known relationship with various diseases. Progress in analytical methods can greatly increase the relevance of DNA methylation studies to both clinical medicine and scientific research. Microfluidic chips are excellent carriers for molecular analysis, and their use can provide improvements from multiple aspects. On-chip molecular analysis has received extensive attention owing to its advantages of portability, high throughput, low cost, and high efficiency. In recent years, the use of novel microfluidic chips for DNA methylation analysis has been widely reported and has shown obvious superiority to conventional methods. In this review, we first focus on DNA methylation and its applications. Then, we discuss advanced microfluidic-based methods for DNA methylation analysis and describe the great progress that has been made in recent years. Finally, we summarize the advantages that microfluidic technology brings to DNA methylation analysis and describe several challenges and perspectives for on-chip DNA methylation analysis. This review should help researchers improve their understanding and make progress in developing microfluidic-based methods for DNA methylation analysis.

Florets for Chiplets: Data Flow-aware High-Performance and Energy-efficient Network-on-Interposer for CNN Inference Tasks

Recent advances in 2.5D chiplet platforms provide a new avenue for compact scale-out implementations of emerging compute- and data-intensive applications including machine learning. Network-on-Interposer (NoI) enables integration of multiple chiplets on a 2.5D system. While these manycore platforms can deliver high computational throughput and energy efficiency by running multiple specialized tasks concurrently, conventional NoI architectures have a limited computational throughput due to their inherent multi-hop topologies. In this paper, we propose Floret, a novel NoI architecture based on space-filling curves (SFCs). The Floret architecture leverages suitable task mapping, exploits the data flow pattern, and optimizes the inter-chiplet data exchange to extract high performance for multiple types of convolutional neural network (CNN) inference tasks running concurrently. We demonstrate that the Floret architecture reduces the latency and energy up to 58% and 64%, respectively, compared to state-of-the-art NoI architectures while executing datacenter-scale workloads involving multiple CNN tasks simultaneously. Floret achieves high performance and significant energy savings with much lower fabrication cost by exploiting the data-flow awareness of the CNN inference tasks.

A Review of Hybrid VLC/RF Networks: Features, Applications, and Future Directions.

The expectation for communication systems beyond 5G/6G is to provide high reliability, high throughput, low latency, and high energy efficiency services. The integration between systems based on radio frequency (RF) and visible light communication (VLC) promises the design of hybrid systems capable of addressing and largely satisfying these requirements. Hybrid network design enables complementary cooperation without interference between the two technologies, thereby increasing the overall system data rate, improving load balancing, and reducing non-coverage areas. VLC/RF hybrid networks can offer reliable and efficient communication solutions for Internet of Things (IoT) applications such as smart lighting, location-based services, home automation, smart healthcare, and industrial IoT. Therefore, hybrid VLC/RF networks are key technologies for next-generation communication systems. In this paper, a comprehensive state-of-the-art study of hybrid VLC/RF networks is carried out, divided into four areas. First, indoor scenarios are studied considering lighting requirements, hybrid channel models, load balancing, resource allocation, and hybrid network topologies. Second, the characteristics and implementation of these hybrid networks in outdoor scenarios with adverse conditions are analyzed. Third, we address the main applications of hybrid VLC/RF networks in technological, economic, and socio-environmental domains. Finally, we outline the main challenges and future research lines of hybrid VLC/RF networks.

Why Is White Noise Not Enough? Using Radio Front-End Models While Designing 6G PHY

Each subsequent generation of wireless standards imposes stricter spectral and energy efficiency demands. So far, layered wireless transceiver architectures have been used, allowing for instance to separate channel decoding algorithms from the front-end design. Such an approach may need to be reconsidered in the upcoming 6G era. Especially hardware-originated distortions have to be taken into account while designing other layer algorithms, as high throughput and energy efficiency requirements will push these devices to their limits, revealing their non-linear characteristics. In such a context, this paper will shed some light on the new degrees of freedom enjoyed while cross-layer designing as well as controlling multicarrier and multiantenna transceivers in 6G systems.

Hashing for secure optical information compression in a heterogeneous convolutional neural network

In recent years, heterogeneous machine learning accelerators have become of significant interest to science, engineering, and industry. At the same time, the looming post-quantum encryption era instigates the demand for increased data security. From a hardware processing point of view, electronic computing hardware is challenged by electronic capacitive interconnect delay and associated energy consumption. In heterogeneous systems, such as electronic–photonic accelerators, parasitic domain crossings limit throughput and speed. With analog optical accelerators exhibiting a strong potential for high throughput (up to petaoperations per second) and operation efficiency, their ability to perform machine learning classification tasks on encrypted data has not been broadly recognized. This work is a significant step in that direction. Here, we present an optical hashing and compression scheme that is inspired by SWIFFT, a post-quantum hashing family of algorithms. High degree optical hardware-to-algorithm homomorphism allows one to optimally harvest the potential of free-space data processing: innate parallelism, low latency tensor by-element multiplication, and zero-energy Fourier transformation operations. The algorithm can provide several orders of magnitude increase in processing speed as compared to optical machine learning accelerators with non-compressed input. This is achieved by replacing slow, high-resolution CMOS cameras with ultra-fast and signal-triggered CMOS detector arrays. Additionally, information acquired in this way will require much lower transmission throughput, less in silico processing power, storage, and will be pre-hashed, facilitating optical information security. This concept has the potential to allow heterogeneous convolutional Fourier classifiers to approach the performance of their fully electronic counterparts and enables data classification on hashed data.

Optimized performance analysis of nonrecursive low-density parity-check-coded Gaussian minimum shift keying in 5G-application

We present the low-density parity-check-coded nonrecursive Gaussian minimum shift keying-based hybrid modulation scheme for free-space optical (FSO) communications under optimal signal-to-noise ratio, limited power, and spectrum resources to support 5G and beyond applications with high-throughput efficiency. Our work investigates and approximates the error rate of the proposed scheme across a negative exponential stochastic FSO channel model with misalignment-induced fading. Using the Meijer-G function over the combined channel probability density function, we develop a unique closed-form formulation of outage probability. The potential benefits of utilizing the normalized beamwidth to achieve the desired error rate ( ≈ 10 − 12 ) is discussed. An improvement of 88.64% in the case of narrow beamwidth is perceived which collaborates with the proposed analytical expressions. The precision of our study is supported by numerical data from Monte Carlo simulation.

Energy and MCS Optimization in HARQ Protocol for Ultrareliable Regime With Maximized Throughput

In this article, we propose a cross-layer approach to maximize the throughput efficiency of the hybrid automatic repeat request (HARQ) protocol while guaranteeing ultrareliable communication (URC). The study combines adaptive modulation and coding and power control, at the physical layer, with automatic repeat request (ARQ)/HARQ at the data-link layer. The proposed optimization approach, based on genetic algorithms, allows a judicious allocation of the available energy budget and a suitable modulation and coding scheme (MCS) choice while keeping the same energy budget. For a given signal-to-noise ratio (SNR), a vector of energy and an MCS are modeled as one individual. The individuals with the highest throughput efficiency are selected to be used in generating the next population through crossover of the survivals and mutation. The individual with the highest throughput efficiency in the last population presents the solution offering the optimized parameters used in the packet transmission. We compare our solution to an existing one that exploits Lagrange multipliers. The results show that the proposed genetic algorithm based solution ensures URC with packet erasure probability less than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$10^{-5}$</tex-math></inline-formula> from SNR values of 7 and 13 dB in static and fading channels, respectively.

RAPID: Approximate Pipelined Soft Multipliers and Dividers for High Throughput and Energy Efficiency

The rapid updates in error-resilient applications along with their quest for high throughput have motivated designing fast approximate functional units for Field-Programmable Gate Arrays (FPGAs). Studies that proposed imprecise functional techniques are posed with three shortcomings: first, most inexact multipliers and dividers are specialized for Application-Specific Integrated Circuit (ASIC) platforms. Second, state-of-the-art (SoA) approximate units are substituted, mostly in a single kernel of a multi-kernel application. Moreover, the end-to-end assessment is adopted on the Quality of Results (QoR), but not on the overall gained performance. Finally, existing imprecise components are not designed to support a pipelined approach, which could boost the operating frequency/throughput of, e.g., division-included applications. In this paper, we propose RAPID, the first pipelined approximate multiplier and divider architecture, customized for FPGAs. The proposed units efficiently utilize 6-input Look-up Tables (6-LUTs) and fast carry chains to implement Mitchell's approximate algorithms. Our novel error-refinement scheme not only has negligible overhead over the baseline Mitchell's approach but also boosts its accuracy to 99.4% for arbitrary size of multiplication and division. Experimental results demonstrate the efficiency of the proposed pipelined and non-pipelined RAPID multipliers and dividers over accurate counterparts. Moreover, the end-to-end evaluations of RAPID, deployed in three multi-kernel applications in the domains of bio-signal processing, image processing, and moving object tracking for Unmanned Air Vehicles (UAV) indicate up to 45% improvements in area, latency, and Area-Delay-Product (ADP), respectively, over accurate kernels, with negligible loss in QoR.