Robust H-infinity Controller Design Using Frequency-Domain Data

A new robust controller design method is developed for linear time-invariant single-input single-output systems presented by their frequency response data. The performance specifications are in terms of the upper bounds on the infinity norm of weighted closed-loop frequency responses. The designed controller is robust in terms of frequency-domain disk and polytopic uncertainty as well as multimodel uncertainty. The necessary and sufficient conditions for the existence of such controllers are presented by a set of convex constraints. The practical issues to compute fixed-order rational H-infinity controllers by convex optimization techniques are discussed. The experimental results on an electromechanical system illustrate the effectiveness of the proposed method

Task-Oriented Over-the-Air Computation for Multi-Device Edge AI

Departing from the classic paradigm of data-centric designs, the 6G networks for supporting edge AI features task-oriented techniques that focus on effective and efficient execution of AI task. Targeting end-to-end system performance, such techniques are sophisticated as they aim to seamlessly integrate sensing (data acquisition), communication (data transmission), and computation (data processing). Aligned with the paradigm shift, a task-oriented over-the-air computation (AirComp) scheme is proposed in this paper for multi-device split-inference system. In the considered system, local feature vectors, which are extracted from the real-time noisy sensory data on devices, are aggregated over-the-air by exploiting the waveform superposition in a multiuser channel. Then the aggregated features as received at a server are fed into an inference model with the result used for decision making or control of actuators. To design inference-oriented AirComp, the transmit precoders at edge devices and receive beamforming at edge server are jointly optimized to rein in the aggregation error and maximize the inference accuracy. The problem is made tractable by measuring the inference accuracy using a surrogate metric called discriminant gain, which measures the discernibility of two object classes in the application of object/event classification. It is discovered that the conventional AirComp beamforming design for minimizing the mean square error in generic AirComp with respect to the noiseless case may not lead to the optimal classification accuracy. The reason is due to the overlooking of the fact that feature dimensions have different sensitivity towards aggregation errors and are thus of different importance levels for classification. This issue is addressed in this work via a new task-oriented AirComp scheme designed by directly maximizing the derived discriminant gain

Integrated Sensing-Communication-Computation for Over-the-Air Edge AI Inference

Edge-device co-inference refers to deploying well-trained artificial intelligent (AI) models at the network edge under the cooperation of devices and edge servers for providing ambient intelligent services. For enhancing the utilization of limited network resources in edge-device co-inference tasks from a systematic view, we propose a task-oriented scheme of integrated sensing, computation and communication (ISCC) in this work. In this system, all devices sense a target from the same wide view to obtain homogeneous noise-corrupted sensory data, from which the local feature vectors are extracted. All local feature vectors are aggregated at the server using over-the-air computation (AirComp) in a broadband channel with the orthogonal-frequency-division-multiplexing technique for suppressing the sensing and channel noise. The aggregated denoised global feature vector is further input to a server-side AI model for completing the downstream inference task. A novel task-oriented design criterion, called maximum minimum pair-wise discriminant gain, is adopted for classification tasks. It extends the distance of the closest class pair in the feature space, leading to a balanced and enhanced inference accuracy. Under this criterion, a problem of joint sensing power assignment, transmit precoding and receive beamforming is formulated. The challenge lies in three aspects: the coupling between sensing and AirComp, the joint optimization of all feature dimensions' AirComp aggregation over a broadband channel, and the complicated form of the maximum minimum pair-wise discriminant gain. To solve this problem, a task-oriented ISCC scheme with AirComp is proposed. Experiments based on a human motion recognition task are conducted to verify the advantages of the proposed scheme over the existing scheme and a baseline.Comment: This work was accepted by IEEE Transactions on Wireless Communications on Aug. 12, 202

Graphene Oxide/BiOCl Nanocomposite Films as Efficient Visible Light Photocatalysts

A novel graphene oxide/BiOCl (GO/BiOCl) nanocomposite film was prepared via a spread coating method. In visible-light photocatalytically degrading Rhodamine B (RhB) experiments, 2 wt% GO/BiOCl could degrade 99% of RhB within 1.5 h and the rate constant was 12.2 times higher than that of pure BiOCl. The degradation efficiency still kept at 80% even after 4 recycles, evidencing the relatively good recyclability. The enhancement was attributed to the improvement of visible light adsorption and charge separation. Holes and superoxide radicals·O2- played a major role as reactive species. The values of conduction band and valence band for GO and BiOCl were calculated and a new photocatalytic mechanism of GO/BiOCl nanocomposite was proposed

Integrating Sensing, Communication, and Power Transfer: From Theory to Practice

To support the development of internet-of-things applications, an enormous population of low-power devices are expected to be incorporated in wireless networks performing sensing and communication tasks. As a key technology for improving the data collection efficiency, integrated sensing and communication (ISAC) enables simultaneous data transmission and radar sensing by reusing the same radio signals. In addition to information carriers, wireless signals can also serve as energy delivers, which enables simultaneous wireless information and power transfer (SWIPT). To improve the energy and spectrum efficiency, the advantages of ISAC and SWIPT are expected to be exploited, leading to the emerging technology of integrating sensing, communication, and power transfer (ISCPT). In this article, a timely overview of ISCPT is provided with the description of the fundamentals, the characterization of the theoretical boundary, the discussion on the key technologies, and the demonstration of the implementation platform.Comment: This paper has been submitted to IEEE for possible publicatio

Hybrid GRU-CNN Bilinear Parameters Initialization for Quantum Approximate Optimization Algorithm

The Quantum Approximate Optimization Algorithm (QAOA), a pivotal paradigm in the realm of variational quantum algorithms (VQAs), offers promising computational advantages for tackling combinatorial optimization problems. Well-defined initial circuit parameters, responsible for preparing a parameterized quantum state encoding the solution, play a key role in optimizing QAOA. However, classical optimization techniques encounter challenges in discerning optimal parameters that align with the optimal solution. In this work, we propose a hybrid optimization approach that integrates Gated Recurrent Units (GRU), Convolutional Neural Networks (CNN), and a bilinear strategy as an innovative alternative to conventional optimizers for predicting optimal parameters of QAOA circuits. GRU serves to stochastically initialize favorable parameters for depth-1 circuits, while CNN predicts initial parameters for depth-2 circuits based on the optimized parameters of depth-1 circuits. To assess the efficacy of our approach, we conducted a comparative analysis with traditional initialization methods using QAOA on Erd\H{o}s-R\'enyi graph instances, revealing superior optimal approximation ratios. We employ the bilinear strategy to initialize QAOA circuit parameters at greater depths, with reference parameters obtained from GRU-CNN optimization. This approach allows us to forecast parameters for a depth-12 QAOA circuit, yielding a remarkable approximation ratio of 0.998 across 10 qubits, which surpasses that of the random initialization strategy and the PPN2 method at a depth of 10. The proposed hybrid GRU-CNN bilinear optimization method significantly improves the effectiveness and accuracy of parameters initialization, offering a promising iterative framework for QAOA that elevates its performance

Property Improvement of α-Amylase from Bacillus stearothermophilus by Deletion of Amino Acid Residues Arginine 179 and Glycine 180

Proizveden je mutant AmySΔR179-G180 delecijom arginina (Arg179) i glicina (Gly180) u α-amilazi iz bakterije Bacillus stearothermophilus (AmyS) pomoću ciljane mutageneze, a radi poboljšanja njezinih svojstava. Amilaze AmyS i AmySΔR179-G180 eksprimirane su u bakteriji Bacillus subtilis i pročišćene taloženjem pomoću amonijeva sulfata, te su im opisana i uspoređena svojstva. Delecijom aminokiselina Arg179 i Gly180 poboljšala se termostabilnost α-amilaze AmySΔR179-G180, a vrijeme poluraspada pri 100 °C bitno se produljilo s 24 na 33 min. Osim toga, ova je amilaza otpornija na djelovanje kiselina i treba manje kalcija za održavanje aktivnosti. Sekrecijska svojstva rekombinantnog soja ispitana su šaržnom fermentacijom u fermentoru od 7,5 L, te je dobivena velika aktivnost α-amilaze. Postignuta je najveća aktivnost od 3300 U/mL i produktivnost od 45,8 U/(mL•h).To improve the properties of α-amylase from Bacillus stearothermophilus (AmyS), a deletion mutant AmyS∆R179-G180 was constructed by deleting arginine (Arg179) and glycine (Gly180) using site-directed mutagenesis. AmyS and AmyS∆R179-G180 were expressed in Bacillus subtilis and purified by ammonium sulfate precipitation, after which the enzymatic properties were characterized and compared. By deleting amino acids Arg179 and Gly180, the thermostability of α-amylase AmyS∆R179-G180 was enhanced and the half-life at 100 °C significantly increased from 24 to 33 min. In addition, AmyS∆R179-G180 exhibited greater acid resistance and lower calcium requirements to maintain α-amylase activity. The secretory capacity of the recombinant strain was evaluated by fed-batch fermentation in a 7.5-litre fermentor in which high α-amylase activity was obtained. The highest activity reached 3300 U/mL with a high productivity of 45.8 U/(mL•h)

Two Strains of Lactobacilli Effectively Decrease the Colonization of VRE in a Mouse Model

Vancomycin-resistant Enterococcus (VRE) infection is a serious challenge for clinical management and there is no effective treatment at present. Fecal microbiota transplantation (FMT) and probiotic intervention have been shown to be promising approaches for reducing the colonization of certain pathogenic bacteria in the gastrointestinal tract, however, no such studies have been done on VRE. In this study, we evaluated the effect of FMT and two Lactobacillus strains (Y74 and HT121) on the colonization of VRE in a VRE-infection mouse model. We found that both Lactobacilli strains reduced VRE colonization rapidly. Fecal microbiota and colon mRNA expression analyses further showed that mice in FMT and the two Lactobacilli treatment groups restored their intestinal microbiota diversity faster than those in the phosphate buffer saline (PBS) treated group. Administration of Lactobacilli restored Firmicutes more quickly to the normal level, compared to FMT or PBS treatment, but restored Bacteroides to their normal level less quickly than FMT did. Furthermore, these treatments also had an impact on the relative abundance of intestinal microbiota composition from phylum to species level. RNA-seq showed that FMT treatment induced the expression of more genes in the colon, compared to the Lactobacilli treatment. Defense-related genes such as defensin α, Apoa1, and RegIII were down-regulated in both FMT and the two Lactobacilli treatment groups. Taken together, our findings indicate that both FMT and Lactobacilli treatments were effective in decreasing the colonization of VRE in the gut

Distinct magneto-Raman signatures of spin-flip phase transitions in CrI3_{3}

The discovery of 2-dimensional (2D) materials, such as CrI$_{3}$, that retain magnetic ordering at monolayer thickness has resulted in a surge of both pure and applied research in 2D magnetism. Here, we report a magneto-Raman spectroscopy study on multilayered CrI$_{3}$, focusing on two additional features in the spectra that appear below the magnetic ordering temperature and were previously assigned to high frequency magnons. Instead, we conclude these modes are actually zone-folded phonons. We observe a striking evolution of the Raman spectra with increasing magnetic field applied perpendicular to the atomic layers in which clear, sudden changes in intensities of the modes are attributed to the interlayer ordering changing from antiferromagnetic to ferromagnetic at a critical magnetic field. Our work highlights the sensitivity of the Raman modes to weak interlayer spin ordering in CrI$_{3}$

Distinct magneto-Raman signatures of spin-flip phase transitions in CrI3

The discovery of 2-dimensional (2D) materials, such as CrI3, that retain magnetic ordering at monolayer thickness has resulted in a surge of research in 2D magnetism from both pure and applied perspectives. Here, we report a magneto-Raman spectroscopy study on multilayered CrI3, focusing on two new features in the spectra which appear at temperatures below the magnetic ordering temperature and were previously assigned to high frequency magnons. We observe a striking evolution of the Raman spectra with increasing magnetic field in which clear, sudden changes in intensities of the modes are attributed to the interlayer ordering changing from antiferromagnetic to ferromagnetic at a critical magnetic field. Our work highlights the sensitivity of the Raman modes to weak interlayer spin ordering in CrI3. In addition, we theoretically examine potential origins for the new modes, which we deduce are unlikely single magnons