Hardware Constraints Research Articles

Timing-Aware Qubit Mapping and Gate Scheduling Adapted to Neutral Atom Quantum Computing

As a less developed but potential quantum technology, neutral atoms (NA) can provide advantages including higher qubit connectivity, longer-range interactions, and much more native multi-control gates than superconductivity. Long-range interactions, however, prevent parallelism of interacting qubit pairs with their surrounding restriction zones. Therefore, the quantum program cannot be run directly on a neutral atom quantum computer (NAQC) unless compiled. The recent compiling study of NAQC applies simple layer-by-layer scheduling and does not consider the difference in gate duration. To address the above issues, we focus on the qubit mapping and quantum gate scheduling () problem of quantum circuits to meet hardware constraints of superconductivity and even neutral atoms. Our goal is to shorten the execution time to mitigate decoherence noise. We propose a block-game-like abstraction mechanism which is an abstract model of the problem and aware of the duration difference of gates and several other NA characteristics. Based on the abstraction, we propose a heuristic greedy algorithm (HGA) to solve the problem efficiently. To further speed up the execution of the circuit, we embed HGA into a Monte Carlo tree search (MCTS) framework to solve the problem, which consumes more compiling time but achieves a better result. Comparing our MCTS algorithm with the only recent algorithm for NAQC, the average speedup ratio is 1.75× for several quantum circuits collected from RevLib, and 1.18× for circuits from Qiskit Lib.

Farm Friendly Chat Bot

This study presents the development of a chat room and a chatbot designed to facilitate discussions on prevalent farming issues among peers and experts. Its primary aim is to provide timely support to farmers in making informed decisions about their farming practices. To create a structured framework for these conversations, a standardized set of questions was formulated through consultations and surveys involving farmers, experts, and other stakeholders. The questions were analyzed to extract 'intents,' representing the specific information or assistance users might seek, and 'examples,' which are concrete instances users provide to express their particular intent. Additionally, 'entities' were identified to represent distinct objects or concepts related to these intents. The model was trained using the Artificial Intelligence Markup Language (AIML) to predict the intent based on the provided examples. This training process enhances the chatbot's ability to understand and respond to user queries effectively. Furthermore, the chatbot was deployed on a cloud platform, reducing the computational resources required on the client end. This approach ensures accessibility and usability for a broader user base without significant hardware constraints.

Generative Adversarial Networks (GANs) for Audio-Visual Speech Recognition in Artificial Intelligence IoT

This paper proposes a novel multimodal generative adversarial network AVSR (multimodal AVSR GAN) architecture, to improve both the energy efficiency and the AVSR classification accuracy of artificial intelligence Internet of things (IoT) applications. The audio-visual speech recognition (AVSR) modality is a classical multimodal modality, which is commonly used in IoT and embedded systems. Examples of suitable IoT applications include in-cabin speech recognition systems for driving systems, AVSR in augmented reality environments, and interactive applications such as virtual aquariums. The application of multimodal sensor data for IoT applications requires efficient information processing, to meet the hardware constraints of IoT devices. The proposed multimodal AVSR GAN architecture is composed of a discriminator and a generator, each of which is a two-stream network, corresponding to the audio stream information and the visual stream information, respectively. To validate this approach, we used augmented data from well-known datasets (LRS2-Lip Reading Sentences 2 and LRS3) in the training process, and testing was performed using the original data. The research and experimental results showed that the proposed multimodal AVSR GAN architecture improved the AVSR classification accuracy. Furthermore, in this study, we discuss the domain of GANs and provide a concise summary of the proposed GANs.

Angle-Based SLAM on 5G mmWave Systems: Design, Implementation, and Measurement

Simultaneous localization and mapping (SLAM) is a key technology that provides user equipment (UE) tracking and environment mapping services, enabling the deep integration of sensing and communication. The millimeter-wave (mmWave) communication, with its larger bandwidths and antenna arrays, inherently facilitates more accurate delay and angle measurements than sub-6 GHz communication, thereby providing opportunities for SLAM. However, none of the existing works have realized the SLAM function under the 5G New Radio (NR) standard due to specification and hardware constraints. In this study, we investigate how 5G mmWave communication systems can achieve situational awareness without changing the transceiver architecture and 5G NR standard. We implement 28 GHz mmWave transceivers that deploy OFDM-based 5G NR waveform with 160 MHz channel bandwidth, and we realize beam management following the 5G NR. Furthermore, we develop an efficient successive cancellation-based angle extraction approach to obtain angles of arrival and departure from the reference signal received power measurements. On the basis of angle measurements, we propose an angle-only SLAM algorithm to track UE and map features in the radio environment. Thorough experiments and ray tracing-based computer simulations verify that the proposed angle-based SLAM can achieve sub-meter level localization and mapping accuracy with a single base station and without the requirement of strict time synchronization. Our experiments also reveal many propagation properties critical to the success of SLAM in 5G mmWave communication systems.

AliasNet: Alias artefact suppression network for accelerated phase-encode MRI

Sparse reconstruction is an important aspect of MRI, helping to reduce acquisition time and improve spatial-temporal resolution. Popular methods are based mostly on compressed sensing (CS), which relies on the random sampling of k-space to produce incoherent (noise-like) artefacts. Due to hardware constraints, 1D Cartesian phase-encode under-sampling schemes are popular for 2D CS-MRI. However, 1D under-sampling limits 2D incoherence between measurements, yielding structured aliasing artefacts (ghosts) that may be difficult to remove assuming a 2D sparsity model. Reconstruction algorithms typically deploy direction-insensitive 2D regularisation for these direction-associated artefacts. Recognising that phase-encode artefacts can be separated into contiguous 1D signals, we develop two decoupling techniques that enable explicit 1D regularisation and leverage the excellent 1D incoherence characteristics. We also derive a combined 1D + 2D reconstruction technique that takes advantage of spatial relationships within the image. Experiments conducted on retrospectively under-sampled brain and knee data demonstrate that combination of the proposed 1D AliasNet modules with existing 2D deep learned (DL) recovery techniques leads to an improvement in image quality. We also find AliasNet enables a superior scaling of performance compared to increasing the size of the original 2D network layers. AliasNet therefore improves the regularisation of aliasing artefacts arising from phase-encode under-sampling, by tailoring the network architecture to account for their expected appearance. The proposed 1D + 2D approach is compatible with any existing 2D DL recovery technique deployed for this application.

Modularized and scalable compilation for double quantum dot quantum computing

Any quantum program on a realistic quantum device must be compiled into an executable form while taking into account the underlying hardware constraints. Stringent restrictions on architecture and control imposed by physical platforms make this very challenging. In this paper, based on the quantum variational algorithm, we propose a novel scheme to train an Ansatz circuit and realize high-fidelity compilation of a set of universal quantum gates for singlet-triplet qubits in semiconductor double quantum dots, a fairly heavily constrained system. Furthermore, we propose a scalable architecture for a modular implementation of quantum programs in this constrained systems and validate its performance with two representative demonstrations, the Grover’s algorithm for the database searching (static compilation) and a variant of variational quantum eigensolver for the Max-Cut optimization (dynamic compilation). Our methods are potentially applicable to a wide range of physical devices. This work constitutes an important stepping-stone for exploiting the potential for advanced and complicated quantum algorithms on near-term devices.

Interaction graph-based characterization of quantum benchmarks for improving quantum circuit mapping techniques

To execute quantum circuits on a quantum processor, they must be modified to meet the physical constraints of the quantum device. This process, called quantum circuit mapping, results in a gate/circuit depth overhead that depends on both the circuit properties and the hardware constraints, being the limited qubit connectivity a crucial restriction. In this paper, we propose to extend the characterization of quantum circuits by including qubit interaction graph properties using graph theory-based metrics in addition to previously used circuit-describing parameters. This approach allows for an in-depth analysis and clustering of quantum circuits and a comparison of performance when run on different quantum processors, aiding in developing better mapping techniques. Our study reveals a correlation between interaction graph-based parameters and mapping performance metrics for various existing configurations of quantum devices. We also provide a comprehensive collection of quantum circuits and algorithms for benchmarking future compilation techniques and quantum devices.

Room-aware portable Auditory Augmented Reality: Real-time spatial audio generation with geometric data analysis

This study proposes an innovative and practical approach to spatial acoustic rendering for Auditory Augmented Reality (AAR) in small room usage. AAR aims to enhance user experiences by overlaying spatially appropriate sounds in the real environment, creating the illusion that virtual sounds coexist in the physical world. However, current technological constraints in hardware and AAR rendering present challenges in creating a truly immersive sensation of “being there.” To address these challenges, we present a dynamic processing framework for a typical mobile AAR setting. This framework combines a game-audio engine with 3-D visual computation both specialized for real-time processing, providing a plausible spatial impression with lightweight computation. The process involves capturing room information by the Light Detection And Ranging (LiDAR) sensor mounted on the device, followed by estimating its dominant acoustic materials. The generated data will be used to compute dynamic early reflections and late reverberation. Early reflections are calculated up to the fourth reflection using the image source method, considering the acoustic materials. Late reverberation is generated using the Feedback Delay Network (FDN), with room frequency characteristics and spatial directivity varying relative to the listener's position. All components will be represented in fifth-order Ambisonics and binauralized with head-tracking. Notably, this dynamic rendering method could be light enough to handle all processing on a single mobile device.

METER: A Mobile Vision Transformer Architecture for Monocular Depth Estimation

Depth estimation is a fundamental knowledge for autonomous systems that need to assess their own state and perceive the surrounding environment. Deep learning algorithms for depth estimation have gained significant interest in recent years, owing to the potential benefits of this methodology in overcoming the limitations of active depth sensing systems. Moreover, due to the low cost and size of monocular cameras, researchers have focused their attention on monocular depth estimation (MDE), which consists in estimating a dense depth map from a single RGB video frame. State of the art MDE models typically rely on vision transformers (ViT) architectures that are highly deep and complex, making them unsuitable for fast inference on devices with hardware constraints. Purposely, in this paper, we address the problem of exploiting ViT in MDE on embedded devices. Those systems are usually characterized by limited memory capabilities and low-power CPU/GPU. We propose METER, a novel lightweight vision transformer architecture capable of achieving state of the art estimations and low latency inference performances on the considered embedded hardwares: NVIDIA Jetson TX1 and NVIDIA Jetson Nano. We provide a solution consisting of three alternative configurations of METER, a novel loss function to balance pixel estimation and reconstruction of image details, and a new data augmentation strategy to improve the overall final predictions. The proposed method outperforms previous lightweight works over the two benchmark datasets: the indoor NYU Depth v2 and the outdoor KITTI.

GAIA-Universe: Everything is Super-Netify.

Pre-training on large-scale datasets has played an increasingly significant role in computer vision and natural language processing recently. However, as there exist numerous application scenarios that have distinctive demands such as certain latency constraints and specialized data distributions, it is prohibitively expensive to take advantage of large-scale pre-training for per-task requirements. we focus on two fundamental perception tasks (object detection and semantic segmentation) and present a complete and flexible system named GAIA-Universe(GAIA), which could automatically and efficiently give birth to customized solutions according to heterogeneous downstream needs through data union and super-net training. GAIA is capable of providing powerful pre-trained weights and searching models that conform to downstream demands such as hardware constraints, computation constraints, specified data domains, and telling relevant data for practitioners who have very few datapoints on their tasks. With GAIA, we achieve promising results on COCO, Objects365, Open Images, BDD100 k, and UODB which is a collection of datasets including KITTI, VOC, WiderFace, DOTA, Clipart, Comic, and more. Taking COCO as an example, GAIA is able to efficiently produce models covering a wide range of latency from 16 ms to 53 ms, and yields AP from 38.2 to 46.5 without whistles and bells. GAIA is released at https://github.com/GAIA-vision.

Siamese Networks for Speaker Identification on Resource-Constrained Platforms

This paper investigates the implementation of a lightweight Siamese neural network for enhancing speaker identification accuracy and inference speed in embedded systems. Integrating speaker identification into embedded systems can improve portability and versatility. Siamese neural networks achieve speaker identification by comparing input voice samples to reference voices in a database, effectively extracting features and classifying speakers accurately. Considering the trade-off between accuracy and complexity, as well as hardware constraints in embedded systems, various neural networks could be applied to speaker identification. This paper compares the incorporation of CNN architectures targeted for embedded systems, MCUNet, SqueezeNet and MobileNetv2, to implement Siamese neural networks on a Raspberry Pi. Our experiments demonstrate that MCUNet achieves 85% accuracy with a 0.23-second inference time. In comparison, the larger MobileNetv2 attains 84.5% accuracy with a 0.32-second inference time. Additionally, contrastive loss was superior to binary cross-entropy loss in the Siamese neural network. The system using contrastive loss had almost 68% lower loss scores, resulting in a more stable performance and more accurate predictions. In conclusion, this paper establishes that an appropriate lightweight Siamese neural network, combined with contrastive loss, can significantly improve speaker identification accuracy, and enable efficient deployment on resource-constrained platforms.

3D Brain Tumour Segmentation Using UNet with Quantitative Analysis of the Tumour Features

Brain tumours are abnormal cells that grow within the brain and may cause severe harm to an individual. The diagnosis of tumours often requires a manual review of Magnetic Resonance (MR) images by a skilled radiologist, which is a laborious task. The study aims to use a deep learning-based approach to segment the brain tumour and quantitatively evaluate the performance of the model by comparing the statistical distribution of five textural and four non-textural feature differences between the ground truth and the prediction. Textural features were extracted from a Grey Level Co-occurrence Matrix (GLCM) and averaged over four different directions. A UNet model was constructed with MONAI using BraTS 2020 dataset. Due to hardware constraints, the model was trained over a short epoch range of 100 but managed to achieve a mean Dice similarity coefficient (DSC) of 0.86. The quantitative analysis showed that the ground truth and the prediction had low differences in most features, except for percentage variation in signal intensity, energy, correlation and Hausdorff distance, which could be attributed to false positive voxels that are far from the ground truth. The textural properties of homogeneity, entropy, and correlation had an average absolute percentage difference of less than 5% from the ground truth.

What is a File on a Phone? Personal Information Management Practices Amongst WhatsApp Users

The file, as a means of sharing structured information across time and place, has spanned centuries of ICTs. Despite much CSCW research on file management practices in organizational desktop computing settings, little research has examined how files are managed and shared using mobile devices. Our research asks, how do mobile phones and mobile applications impact the personal information management (PIM) practices involving files for young adults? In this interview and task observation study, we examined the file management practices of mobile phone users (N=25), both Android and iOS owners who use the superapp WhatsApp. Superapps like WeChat and WhatsApp are increasingly characterized as technology of life for the majority world, particularly among mobile-first users. We find that many grammars of action related to managing digital files in personal computing and email contexts persist, but participants' sharing practices are influenced by media ideologies of friends, family, colleagues; and that their file management practices are shaped by the affordances of WhatsApp, as well as mobile operating systems and device hardware constraints. These results will be useful to researchers examining collective PIM practices; mobile communication practices between users and businesses via messaging apps; and theories of personal information practices related to the sharing and storage of digital files among mobile-first cohorts who increasingly share and access information via platforms and mobile applications.

Multi-Aperture Coherent Digital Combining Based on Complex-Valued MIMO 2N × 2 Adaptive Equalizer for FSO Communication

Collecting light using multiple small apertures offers a lot of advantages over doing so using a single large aperture, including the effective mitigation of scintillation under strong turbulence, low implementation cost, and ease of scalability. However, the tricky of a multi-aperture system is to combine the multiple branches signals with static skew mismatch and with time-varying characteristics of gain, phase and state of polarization, especially for coherent digital combining of polarization multiplexing (PM) signals. In this paper, a complex-valued multiple-input multiple-output (MIMO) 2N×2 adaptive equalizer is designed to combine multi-aperture signals, and the tap coefficient of each branch is calculated based on the constant modulus algorithm (CMA). This technique relaxes hardware constraints on the time alignment of multi-aperture branch since the skew can be compensated electronically thanks to the proposed blind digital combining algorithm. In addition, laser linewidth within evaluation range smaller than 10 MHz has no effect on the performance of 2N×2 adaptive equalizer. Moreover, the feasibility of the proposed adaptive equalizer for moderate and strong turbulence suppression is verified by a 10-Gbps data rate PM-QPSK modulation offline two- and four-aperture simulated turbulence experiment.

Using Extended Reality in Flight Simulators: A Literature Review.

Virtual reality has long been utilized in the games industry and is emergent for pilot training in the military and commercial airline sectors. Its usefulness as a mechanism of skills transfer to the real world has not been well researched or considered. This paper follows the PRISMA methodology to present a systematic quantitative literature review (SQLR) on the use of extended reality in flight simulators. It also encompasses recent studies of teaching and learning in immersive, virtual environments in non-aviation disciplines. The review identified 39 papers spanning all areas of the virtuality continuum across academic, commercial, and military aviation sectors, as well as engineering and medicine. The SQLR found that extended reality in flight simulators is being introduced in the commercial and military aviation sectors. However, within academia, hardware constraints have hindered the ability to provide positive empirical evidence of simulator effectiveness. While virtual reality may not replace traditional flight simulators in the near future, the technology is available to supplement classroom training activities and some aspects of simulator procedure training with promising cognitive learning outcomes.

Two-Timescale-Based Beam Training for RIS-Aided Millimeter-Wave Multi-User MISO Systems

Reconfigurable intelligent surfaces (RISs), as a promising technology for 6 G communications, have received considerable attention. Herein, we investigate the downlink precoding in the RIS-aided millimeter-wave (mmWave) multi-user multiple-input single-output (MU-MISO) system, where the access point (AP) uses hybrid digital and analog precoders. Due to the constraints of practical hardware and the difficulty of acquiring channel state information, we consider codebook-based passive beamforming and analog beamforming, and then formulate the downlink precoding problem as a mixed-integer nonlinear programming (MINLP) problem. To solve this NP-hard MINLP problem with low channel estimation overheads, we investigate beam training for the codebook-based passive beamforming and analog beamforming, and the MINLP problem is then reduced to a traditional MU-MISO precoding problem. To further reduce the beam training overheads, we propose a two-timescale-based beam training (TT-BT) algorithm. We prove that the TT-BT algorithm achieves the maximum codebook-based passive and analog beamforming gain. To avoid the limitations imposed the use of the TT property, we design the analog precoder by aligning the analog beamforming to RISs. Moreover, we characterize the rate loss due to codebook-based quantization. Compared with the empirical BT (EBT) algorithm, the proposed TT-BT algorithm achieves better performance with significantly lower training and feedback overheads.

An Efficient Impulsive Adaptive Dynamic Programming Algorithm for Stochastic Systems.

In this study, a novel general impulsive transition matrix is defined, which can reveal the transition dynamics and probability distribution evolution patterns for all system states between two impulsive "events," instead of two regular time indexes. Based on this general matrix, the policy iteration-based impulsive adaptive dynamic programming (IADP) algorithm along with its variant, which is a more efficient IADP (EIADP) algorithm, are developed in order to solve the optimal impulsive control problems of discrete stochastic systems. Through analyzing the monotonicity, stability, and convergency properties of the obtained iterative value functions and control laws, it is proved that the IADP and EIADP algorithms both converge to the optimal impulsive performance index function. By dividing the whole impulsive policy into smaller pieces, the proposed EIADP algorithm updates the iterative policies in a "piece-by-piece" manner according to the actual hardware constraints. This feature of the EIADP method enables these ADP-based algorithms to be fully optimized to run on all "sizes" of computing devices including the ones with low memory spaces. A simulation experiment is conducted to validate the effectiveness of the present methods.

Security and privacy concerns in assisted living environments

Assisted living environments use interconnected devices to assist people with limitations in performing daily activities. In these environments, human activity recognition is critical for detecting abnormal situations, such as falls or health problems, and providing appropriate assistance to inhabitants. Despite their advantages, assisted living environments raise security and privacy concerns due to the collection and storage of sensitive data about their inhabitants. This paper addresses security and privacy concerns related to intelligent environments designed to assist individuals with limitations. It discusses the weaknesses of IoT devices and domotic technologies and presents research conducted by authors to mitigate these issues. This study shows that, despite hardware constraints, it is possible to design a relatively secure assisted living environment that prevents hacker attacks and data leaks. Additionally, it is essential to comprehend which information should be shared with external entities, such as health care services, and when to share it to ensure the inhabitants’ well-being. Our main goal has been to gather knowledge to improve the privacy and data protection of technology-rich assisted living environments implemented to assist people with limitations and their family members in performing their daily tasks.

A prince for the sleeping beauty - NFT for soliton signal processing

The soliton, the eigen-solution of nonlinear Schrodinger equation (NLSE), has been investigated in many branches of physics. The first prediction of a soliton in optical fiber could be dated back to 1973. Since then, numerous theoretical and experimental works on solitons have been carried out, including soliton communication and soliton generation in fiber lasers. However, due to the constraints of hardware and digital signal processing methods, research on solitons have been stalled for decades. Recently, the nonlinear Fourier transform (NFT) has attracted significant attention in the field of optical fiber communications and soliton-based signal processing, which provides a powerful mathematical framework for analyzing and understanding the behavior of solitons in optical fibers, enabling the development of novel signal processing techniques. The NFT technique has brought the soliton back to life. It has been widely recognized that in nonlinear frequency domain, the nonlinear crosstalk resulting from the Kerr effect is practically negligible, and fiber nonlinearity damage caused by the Kerr effect can be accounted for as a linear transform factor. Moreover, NFT has been demonstrated for the analysis of laser radiation, highlighting its potential in characterizing ultrafast pulses in the nonlinear frequency domain. In this submission, we review the advancements of NFT concerning both in soliton communication and characterization, focusing on practical implementation aspects. With many practical implementation aspects still being open, our mini review is aimed to assist researchers in evaluating the potential, identifying challenges, and envisioning future directions in NFT-based signal processing technologies. And wake up the sleeping beauty – the soliton!

Practical hardware for evolvable robots.

The evolutionary robotics field offers the possibility of autonomously generating robots that are adapted to desired tasks by iteratively optimising across successive generations of robots with varying configurations until a high-performing candidate is found. The prohibitive time and cost of actually building this many robots means that most evolutionary robotics work is conducted in simulation, but to apply evolved robots to real-world problems, they must be implemented in hardware, which brings new challenges. This paper explores in detail the design of an example system for realising diverse evolved robot bodies, and specifically how this interacts with the evolutionary process. We discover that every aspect of the hardware implementation introduces constraints that change the evolutionary space, and exploring this interplay between hardware constraints and evolution is the key contribution of this paper. In simulation, any robot that can be defined by a suitable genetic representation can be implemented and evaluated, but in hardware, real-world limitations like manufacturing/assembly constraints and electrical power delivery mean that many of these robots cannot be built, or will malfunction in operation. This presents the novel challenge of how to constrain an evolutionary process within the space of evolvable phenotypes to only those regions that are practically feasible: the viable phenotype space. Methods of phenotype filtering and repair were introduced to address this, and found to degrade the diversity of the robot population and impede traversal of the exploration space. Furthermore, the degrees of freedom permitted by the hardware constraints were found to be poorly matched to the types of morphological variation that would be the most useful in the target environment. Consequently, the ability of the evolutionary process to generate robots with effective adaptations was greatly reduced. The conclusions from this are twofold. 1) Designing a hardware platform for evolving robots requires different thinking, in which all design decisions should be made with reference to their impact on the viable phenotype space. 2) It is insufficient to just evolve robots in simulation without detailed consideration of how they will be implemented in hardware, because the hardware constraints have a profound impact on the evolutionary space.