Currently, a significant portion of acoustic scene categorization (ASC) research is centered around utilizing Convolutional Neural Network (CNN) models. This preference is primarily due to CNN’s ability to effectively extract time-frequency information from audio recordings of scenes by employing spectrum data as input. The expression of many dimensions can be achieved by utilizing 2D spectrum characteristics. Nevertheless, the diverse interpretations of the same object’s existence in different positions on the spectrum map can be attributed to the discrepancies between spectrum properties and picture qualities. The lack of distinction between different aspects of input information in ASC-based CNN networks may result in a decline in system performance. Considering this, a feature pyramid segmentation (FPS) approach based on CNN is proposed. The proposed approach involves utilizing spectrum features as the input for the model. These features are split based on a preset scale, and each segment-level feature is then fed into the CNN network for learning. The SoftMax classifier will receive the output of all feature scales, and these high-level features will be fused and fed to it to categorize different scenarios. The experiment provides evidence to support the efficacy of the FPS strategy and its potential to enhance the performance of the ASC system.

Convolutional neural network (CNN) is widely used in acoustic scene classification (ASC) tasks. In most cases, local convolution is utilized to gather time-frequency information between spectrum nodes. It is challenging to adequately express the non-local link between frequency domains in a finite convolution region. In this paper, we propose a dual-path convolutional neural network based on band interaction block (DCNN-bi) for ASC, with mel-spectrogram as the model’s input. We build two parallel CNN paths to learn the high-frequency and low-frequency components of the input feature. Additionally, we have created three band interaction blocks (bi-blocks) to explore the pertinent nodes between various frequency bands, which are connected between two paths. Combining the time-frequency information from two paths, the bi-blocks with three distinct designs acquire non-local information and send it back to the respective paths. The experimental results indicate that the utilization of the bi-block has the potential to improve the initial performance of the CNN substantially. Specifically, when applied to the DCASE 2018 and DCASE 2020 datasets, the CNN exhibited performance improvements of 1.79% and 3.06%, respectively.

Automatic detection of prohibited items is vital in helping security staff be more efficient while improving the public safety index. However, prohibited item detection within X-ray security inspection images is limited by various factors, including the imbalance distribution of categories, diversity of prohibited item scales, and overlap between items. In this paper, we propose to leverage the Poisson blending algorithm with the Canny edge operator to alleviate the imbalance distribution of categories maximally in the X-ray images dataset. Based on this, we improve the cascade network to deal with the other two difficulties. To address the prohibited scale diversity problem, we propose the Re-BiFPN feature fusion method, which includes a coordinate attention atrous spatial pyramid pooling (CA-ASPP) module and a recursive connection. The CA-ASPP module can implicitly extract direction-aware and position-aware information from the feature map. The recursive connection feeds the CA-ASPP module processed multi-scale feature map to the bottom-up backbone layer for further multi-scale feature extraction. In addition, a Rep-CIoU loss function is designed to address the overlapping problem in X-ray images. Extensive experimental results demonstrate that our method can successfully identify ten types of prohibited items, such as Knives, Scissors, Pressure, etc. and achieves 83.4% of mAP, which is 3.8% superior to the original cascade network. Moreover, our method outperforms other mainstream methods by a significant margin.

Acoustic scene classification (ASC) is a fundamental domain within the realm of artificial intelligence classification tasks. ASC-based tasks commonly employ models based on convolutional neural networks (CNNs) that utilize log-Mel spectrograms as input for gathering acoustic features. In this paper, we designed a CNN-based multi-scale pooling (MSP) strategy for ASC. The log-Mel spectrograms are utilized as the input to CNN, which is partitioned into four frequency axis segments. Furthermore, we devised four CNN channels to acquire inputs from distinct frequency ranges. The high-level features extracted from outputs in various frequency bands are integrated through frequency pyramid average pooling layers at multiple levels. Subsequently, a softmax classifier is employed to classify different scenes. Our study demonstrates that the implementation of our designed model leads to a significant enhancement in the model's performance, as evidenced by the testing of two acoustic datasets.

As a potential member of next generation wireless communications, the reconfigurable intelligent surface (RIS) can control the reflected elements to adjust the phase of the transmitted signal with less energy consumption. A novel RIS-assisted index modulation scheme is proposed in this paper, which is named the generalized reflected phase modulation (GRPM). In the GRPM, the transmitted bits are mapped into the reflected phase combination which is conveyed through the reflected elements on the RIS, and detected by the maximum likelihood (ML) detector. The performance analysis of the GRPM with the ML detector is presented, in which the closed form expression of pairwise error probability is derived. The simulation results show the bit error rate (BER) performance of GRPM by comparing with various RIS-assisted index modulation schemes in the conditions of various spectral efficiency and number of antennas.

Measurements of contact discharge current waveforms from an ESD generator with a test voltage of 4kV are conducted with the IEC specified arrangement of a 2m long return current cable in different three calibration environments that all comply with the IEC calibration standard to identify the occurrence source of damped oscillations (ringing), which has remained unclear since contact discharge testing was first adopted in 1989 IEC publication 801-2. Their frequency spectra are analyzed comparing with the spectrum calculated from the ideal contact discharge current waveform without ringing (IEC specified waveform) offered in IEC 61000-4-2 and the spectra derived from a simplified equivalent circuit based on the IEC standard in combination with the measured input impedances of one-ended grounding return current cable with the same arrangement in the same calibration environment as those for the current measurements. The results show that the measured contact discharge waveforms have ringing around the IEC specified waveform after the falling edge of the peak, causing their spectra from 20MHz to 200MHz, but the spectra from 40MHz to 200MHz significantly differ depending on the calibration environments even for the same cable arrangement, which do not almost affect the spectra from 20MHz to 40MHz and over 200MHz. In the calibration environment under the cable arrangement close to the reference ground, the spectral shapes of the measured contact discharge currents and their frequencies of the multiple peaks and dips roughly correspond to the spectral distributions calculated from the simplified equivalent circuit using the measured cable input impedances. These findings reveal that the root cause of ringing is mainly due to the resonances of the return current cable, and calibration environment under the cable arrangement away from the reference ground tends to mitigate the cable resonances.

A metasurface absorber capable of monitoring two-dimensional (2-d) electric field distributions has been developed, where a matrix of lumped resistors between surface patches formed on a mushroom-type structure works as a 2-d array of short dipole sensors. In this paper absorption and reflection of a spherical wave incident on the metasurface absorber are analyzed by numerical computation by the plane-wave spectrum (PWS) technique using 2-d Fourier analysis. The electromagnetic field of the spherical wave incident on the absorber surface is expanded into a large number of plane waves, for each of which the TE and TM reflection and absorption coefficients are applied. Then by synthesizing all the plane wave fields we obtain the spatial distributions of reflected and absorbed fields. The detailed formulation of the computation is described, and the computed field distributions are compared with those obtained by simulation and actual measurement when the spherical wave from a dipole is illuminated onto a metasurface absorber. It is demonstrated that the PWS technique is effective and efficient in obtaining the accurate field distributions of the spherical wave on and around the absorber. This is useful for evaluating the performance of the metasurface absorber to absorb and measure the spherical wave field distributions around an EM source.

Network traffic control and classification have become increasingly dependent on deep packet inspection (DPI) approaches, which are the most precise techniques for intrusion detection and prevention. However, the increasing traffic volumes and link speed exert considerable pressure on DPI techniques to process packets with high performance in restricted available memory. To overcome this problem, we proposed dual cuckoo filter (DCF) as a data structure based on cuckoo filter (CF). The CF can be extended to the parallel mode called parallel Cuckoo Filter (PCF). The proposed data structure employs an extra hash function to obtain two potential indices of entries. The DCF magnifies the superiority of the CF with no additional memory. Moreover, it can be extended to the parallel mode, resulting in a data structure referred to as parallel Dual Cuckoo filter (PDCF). The implementation results show that using the DCF and PDCF as identification tools in a DPI system results in time improvements of up to 2% and 30% over the CF and PCF, respectively.

In human-computer interaction, acoustic scene classification (ASC) is one of the relevant research domains. In real life, the recorded audio may include a lot of noise and quiet clips, making it hard for earlier ASC-based research to isolate the crucial scene information in sound. Furthermore, scene information may be scattered across numerous audio frames; hence, selecting scene-related frames is crucial for ASC. In this context, an integrated convolutional neural network with a fusion attention mechanism (ICNN-FA) is proposed for ASC. Firstly, segmented mel-spectrograms as the input of ICNN can assist the model in learning the short-term time-frequency correlation information. Then, the designed ICNN model is employed to learn these segment-level features. In addition, the proposed global attention layer may gather global information by integrating these segment features. Finally, the developed fusion attention layer is utilized to fuse all segment-level features while the classifier classifies various situations. Experimental findings using ASC datasets from DCASE 2018 and 2019 indicate the efficacy of the suggested method.

In this paper, we propose a heuristic planning method to efficiently accommodate dynamic multilayer path (MLP) demand in multilayer networks consisting of a Time Division Multiplexing (TDM) layer and a Wavelength Division Multiplexing (WDM) layer; the goal is to achieve the flexible accommodation of increasing capacity and diversifying path demands. In addition to the grooming of links at the TDM layer and the route and frequency slots for the elastic optical path to be established, MLP requires the selection of an appropriate operational mode, consisting of a combination of modulation formats and symbol rates supported by digital coherent transceivers. Our proposed MLP planning method defines a planning policy for each of these parameters and embeds the values calculated by combining these policies in an auxiliary graph, which allows the planning parameters to be calculated for MLP demand requirements in a single step. Simulations reveal that the choice of operational mode significantly reduces the blocking probability and demonstrate that the edge weights in the auxiliary graph allow MLP planning with characteristics tailored to MLP demand and network requirements. Furthermore, we quantitatively evaluate the impact of each planning policy on the MLP planning results.

To achieve the extreme high data rate and extreme coverage extension requirements of 6G wireless communication, new spectrum in sub-THz (100-300GHz) and non-terrestrial network (NTN) are two of the macro trends of 6G candidate technologies, respectively. However, non-linearity of power amplifiers (PA) is a critical challenge for both sub-THz and NTN. Therefore, high power efficiency (PE) or low peak to average power ratio (PAPR) waveform design becomes one of the most significant 6G research topics. Meanwhile, high spectral efficiency (SE) and low out-of-band emission (OOBE) are still important key performance indicators (KPIs) for 6G waveform design. Single-carrier waveform discrete Fourier transform spreading orthogonal frequency division multiplexing (DFT-s-OFDM) has achieved many research interests due to its high PE, and it has been supported in 5G New Radio (NR) when uplink coverage is limited. So DFT-s-OFDM can be regarded as a candidate waveform for 6G. Many enhancement schemes based on DFT-s-OFDM have been proposed, including null cyclic prefix (NCP)/unique word (UW), frequency-domain spectral shaping (FDSS), and time-domain compression and expansion (TD-CE), etc. However, there is no unified framework to be compatible with all the enhancement schemes. This paper firstly provides a general description of the 6G candidate waveforms based on DFT-s-OFDM enhancement. Secondly, the more flexible TD-CE supporting methods for unified non-orthogonal waveform (uNOW) are proposed and discussed. Thirdly, a unified waveform framework based on DFT-s-OFDM structure is proposed. By designing the pre-processing and post-processing modules before and after DFT in the unified waveform framework, the three technical methods (NCP/UW, FDSS, and TD-CE) can be integrated to improve three KPIs of DFT-s-OFDM simultaneously with high flexibility. Then the implementation complexity of the 6G candidate waveforms are analyzed and compared. Performance of different DFT-s-OFDM enhancement schemes is investigated by link level simulation, which reveals that uNOW can achieve the best PAPR performance among all the 6G candidate waveforms. When considering PA back-off, uNOW can achieve 124% throughput gain compared to traditional DFT-s-OFDM.

We discuss the spectral efficiency of orthogonal frequency-division multiplexing (OFDM) signals widely adopted in practical systems from a viewpoint of their power spectral density property. Since the conventional OFDM does not make use of pulse shaping filter, its out-of-band (OOB) spectrum may not be negligible especially when the number of subcarriers is small. Thus, in practice, windowing is applied to mitigate OOB emission by smoothing the transition of consecutive OFDM symbols, but its effectiveness has not been well investigated. Furthermore, OFDM signal suffers from nonlinear distortion associated with its high signal peak-to-average power ratio (PAPR), which also leads to OOB radiation. We examine how power amplifier nonlinearity affects the spectral efficiency based on the theoretical results developed in the literature.

The traditional manual inspection is gradually replaced by the unmanned aerial vehicles (UAV) automatic inspection. However, due to the limited computational resources carried by the UAV, the existing deep learning-based algorithm needs a large amount of computational resources, which makes it impossible to realize the online detection. Moreover, there is no effective online detection system at present. To realize the high-precision online detection of electrical equipment, this paper proposes an SSD (Single Shot Multibox Detector) detection algorithm based on the improved Dual network for the images of insulators and spacers taken by UAVs. The proposed algorithm uses MnasNet and MobileNetv3 to form the Dual network to extract multi-level features, which overcomes the shortcoming of single convolutional network-based backbone for feature extraction. Then the features extracted from the two networks are fused together to obtain the features with high-level semantic information. Finally, the proposed algorithm is tested on the public dataset of the insulator and spacer. The experimental results show that the proposed algorithm can detect insulators and spacers efficiently. Compared with other methods, the proposed algorithm has the advantages of smaller model size and higher accuracy. The object detection accuracy of the proposed method is up to 95.1%.

In this study, we consider techniques for searching high-rate convolutional code (CC) encoders using dual code encoders. A low-rate (R = 1/n) CC is a dual code to a high-rate (R = (n - 1)/n) CC. According to our past studies, if a CC encoder has a high performance, a dual code encoder to the CC also tends to have a good performance. However, it is not guaranteed to have the highest performance. We consider a method to obtain a high-rate CC encoder with a high performance using good dual code encoders, namely, high-performance low-rate CC encoders. We also present some CC encoders obtained by searches using our method.

Crowdsensing-based spectrum detection (CSD) is promising to enable full-coverage radio resource availability for the increasingly connected machines in the Internet of Things (IoT) networks. The current CSD scheme consumes a lot of energy and network resources for local sensing, processing, and distributed data reporting for each crowdsensing device. Furthermore, when the amount of reported data is large, the data fusion implemented at the requestor can easily cause high latency. For improving efficiencies in both energy and network resources, this paper proposes a green CSD (GCSD) paradigm. The ambient backscatter (AmB) is used to enable a battery-free mode of operation in which the received spectrum data is reported directly through backscattering without local processing. The energy for backscattering can be provided by ambient radio frequency (RF) sources. Then, relying on air computation (AirComp), the data fusion can be implemented during the backscattering process and over the air by utilizing the summation property of wireless channel. This paper illustrates the model and the implementation process of the GCSD paradigm. Closed-form expressions of detection metrics are derived for the proposed GCSD. Simulation results verify the correctness of the theoretical derivation and demonstrate the green properties of the GCSD paradigm.

In this study, the bending losses of chalcogenide glass channel optical waveguides consisting of an As2Se3 core and an As2S3 lower cladding layer were numerically evaluated across the astronomical N-band, which is the mid-infrared spectral range between the 8 µm and 12 µm wavelengths. The results reveal the design rules for bent waveguides in mid-infrared astrophotonic devices.

Contemporary development projects benefit from code review as it improves the quality of a project. Large ecosystems of inter-dependent projects like OpenStack generate a large number of reviews, which poses new challenges for collaboration (improving patches, fixing defects). Review tools allow developers to link between patches, to indicate patch dependency, competing solutions, or provide broader context. We hypothesize that such patch linkage may also simulate cross-collaboration. With a case study of OpenStack, we take a first step to explore collaborations that occur after a patch linkage was posted between two patches (i.e., cross-patch collaboration). Our empirical results show that although patch linkage that requests collaboration is relatively less prevalent, the probability of collaboration is relatively higher. Interestingly, the results also show that collaborative contributions via patch linkage are non-trivial, i.e, contributions can affect the review outcome (such as voting) or even improve the patch (i.e., revising). This work opens up future directions to understand barriers and opportunities related to this new kind of collaboration, that assists with code review and development tasks in large ecosystems.

S-parameters of InGaAs/InAlAs triple-barrier resonant tunneling diodes (TBRTDs) were measured up to 67 GHz with various mesa areas and various bias voltages. Admittance data of bare TBRTDs are deembedded and evaluated by getting rid of parasitic components with help of electromagnetic simulations for particular fabricated device structures. Admittance spectroscopy up to 67 GHz is applied for bare TBRTDs for the first time and a Kramers-Kronig relation with Lorentzian function is found to be a consistent model for the admittance especially in cases of low bias conditions. Relaxation time included in the Lorentzian function are tentatively evaluated as the order of several pico second.

We proposed a new method for evaluating the deterioration of messenger wires by using terahertz waves. We use terahertz time-domain spectroscopy to measure several twisted wire samples with different levels of deterioration. We find that each twisted wire sample had a different distribution of reflection intensity which was due to the wires' twist structure. We show that it is possible to assess the degradation from the straight lines present in the reflection intensity distribution image. Furthermore, it was confirmed that our method can be applied to wire covered with resin.

This paper investigates the joint problem of user association and spectrum allocation in satellite-terrestrial integrated networks (STINs), where a low earth orbit (LEO) satellite access network cooperating with terrestrial networks constitutes a heterogeneous network, which is beneficial in terms of both providing seamless coverage as well as improving the backhaul capacity for the dense network scenario. However, the orbital movement of satellites results in the dynamic change of accessible satellites and the backhaul capacities. Moreover, spectrum sharing may be faced with severe co-channel interferences (CCIs) caused by overlapping coverage of multiple access points (APs). This paper aims to maximize the total sum rate considering the influences of the dynamic feature of STIN, backhaul capacity limitation and interference management. The optimization problem is then decomposed into two subproblems: resource allocation for terrestrial communications and satellite communications, which are both solved by matching algorithms. Finally, simulation results show the effectiveness of our proposed scheme in terms of STIN's sum rate and spectrum efficiency.