Cooperative Communication is one of the most effective techniques to improve the desired signal quality of the typical user. This paper studies an indoor cellular network system that deploys the Reconfigurable Intelligent Surfaces (RIS) at the position of BSs to enable the cooperative features. To evaluate the network performance, the coverage probability expression of the typical user in the indoor wireless environment with presence of walls and effects of Rayleigh fading is derived. The analytical results shows that the RIS-assisted system outperforms the regular one in terms of coverage probability.

This letter studies the secrecy outage probability (SOP) and the secrecy diversity order of Alamouti STBC with decision feedback (DF) detection over the time-selective fading channels. For given temporal correlations, we have derived the exact SOPs and their asymptotic approximations for all possible combinations of detection schemes including joint maximum likehood (JML), zero-forcing (ZF), and DF at Bob and Eve. We reveal that the SOP is mainly influenced by the detection scheme of the legitimate receiver rather than eavesdropper and the achievable secrecy diversity order converges to two and one for JML only at Bob (i.e., JML-JML/ZF/DF) and for the other cases (i.e., ZF-JML/ZF/DF, DF-JML/ZF/DF), respectively. Here, p-q combination pair indicates that Bob and Eve adopt the detection method p ∈ {JML, ZF, DF} and q ∈ {JML, ZF, DF}, respectively.

The automatic defect detection for fabric images is an essential mission in textile industry. However, there are some inherent difficulties in the detection of fabric images, such as complexity of the background and the highly uneven scales of defects. Moreover, the trade-off between accuracy and speed should be considered in real applications. To address these problems, we propose a novel model based on YOLOv4 to detect defects in fabric images, called Feature Augmentation YOLO (FA-YOLO). In terms of network structure, FA-YOLO adds an additional detection head to improve the detection ability of small defects and builds a powerful Neck structure to enhance feature fusion. First, to reduce information loss during feature fusion, we perform the residual feature augmentation (RFA) on the features after dimensionality reduction by using 1×1 convolution. Afterward, the attention module (SimAM) is embedded into the locations with rich features to improve the adaptation ability to complex backgrounds. Adaptive spatial feature fusion (ASFF) is also applied to output of the Neck to filter inconsistencies across layers. Finally, the cross-stage partial (CSP) structure is introduced for optimization. Experimental results based on three real industrial datasets, including Tianchi fabric dataset (72.5% mAP), ZJU-Leaper fabric dataset (0.714 of average F1-score) and NEU-DET steel dataset (77.2% mAP), demonstrate the proposed FA-YOLO achieves competitive results compared to other state-of-the-art (SoTA) methods.

Software Defined Networking (SDN), a new network architecture, allows for centralized network management by separating the control plane from the forwarding plane. Because forwarding and control is separated, distributed denial of service (DDoS) assaults provide a greater threat to SDN networks. To address the problem, this paper uses a joint high-precision attack detection combining self-attentive mechanism and support vector machine: a trigger mechanism deployed at both control and data layers is proposed to trigger the initial detection of DDoS attacks; the data in the network under attack is screened in detail using a combination of self-attentive mechanism and support vector machine; the control plane is proposed to initiate attack defense using the OpenFlow protocol features to issue flow tables for accurate classification results. The experimental results show that the trigger mechanism can react to the attack in time with less than 20% load, and the accurate detection mechanism is better than the existing inspection and testing methods, with a precision rate of 98.95% and a false alarm rate of only 1.04%. At the same time, the defense strategy can achieve timely recovery of network characteristics.

This paper discusses the resilience of networks based on graph theory and stochastic process. The electric power network where edges may fail simultaneously and the performance of the network is measured by the ratio of connected nodes is supposed for the target network. For the restoration, under the constraint that the resources are limited, the failed edges are repaired one by one, and the order of the repair for several failed edges is determined with the priority to the edge that the amount of increasing system performance is the largest after the completion of repair. Two types of resilience are discussed, one is resilience in the recovery stage according to the conventional definition of resilience and the other is steady state operational resilience considering the long-term operation in which the network state changes stochastically. The second represents a comprehensive capacity of resilience for a system and is analytically derived by Markov analysis. We assume that the large-scale disruption occurs due to the simultaneous failure of edges caused by the common cause failures in the analysis. Marshall-Olkin type shock model and α factor method are incorporated to model the common cause failures. Then two resilience measures, “operational resilience” and “operational resilience in recovery stage” are proposed. We also propose approximation methods to obtain these two operational resilience measures for complex networks.

With the development of drone technology, concerns have arisen about the possibility of drones being equipped with threat payloads for terrorism and other crimes. A drone detection system that can detect drones carrying payloads is needed. A drone’s propeller rotation frequency increases with payload weight. Therefore, a method for estimating propeller rotation frequency will effectively detect the presence or absence of a payload and its weight. In this paper, we propose a method for classifying the payload weight of a drone by estimating its propeller rotation frequency from radar images obtained using a millimeter-wave fast-chirp-modulation multiple-input and multiple-output (MIMO) radar. For each drone model, the proposed method requires a pre-prepared reference dataset that establishes the relationships between the payload weight and propeller rotation frequency. Two experimental measurement cases were conducted to investigate the effectiveness of our proposal. In case 1, we assessed four drones (DJI Matrice 600, DJI Phantom 3, DJI Mavic Pro, and DJI Mavic Mini) to determine whether the propeller rotation frequency of any drone could be correctly estimated. In case 2, experiments were conducted on a hovering Phantom 3 drone with several payloads in a stable position for calculating the accuracy of the payload weight classification. The experimental results indicated that the proposed method could estimate the propeller rotation frequency of any drone and classify payloads in a 250 g step with high accuracy.

In interdependent systems, such as electric power systems, entities or components mutually depend on each other. Due to these interdependencies, a small number of initial failures can propagate throughout the system, resulting in catastrophic system failures. This paper addresses the problem of finding the set of entities whose failures will have the worst effects on the system. To this end, a two-phase algorithm is developed. In the first phase, the tight bound on failure propagation steps is computed using a Boolean Satisfiablility (SAT) solver. In the second phase, the problem is formulated as an Integer Linear Programming (ILP) problem using the obtained step bound and solved with an ILP solver. Experimental results show that the algorithm scales to large problem instances and outperforms a single-phase algorithm that uses a loose step bound.

A wireless sensor terminal module of 5cc size (2.5 cm × 2.5 cm × 0.8 cm) that does not require a battery is proposed by integrating three kinds of circuit technologies. (i) a low-power sensor interface: an FM modulation type CMOS sensor interface circuit that can operate with a typical power consumption of 24.5 μW was fabricated by the 0.7-μm CMOS process technology. (ii) power supply to the sensor interface circuit: a wireless power transmission characteristic to a small-sized PCB spiral coil antenna was clarified and applied to the module. (iii) wireless sensing from the module: backscatter communication technology that modulates the signal from the base terminal equipment with sensor information and reflects it, which is used for the low-power sensing operation. The module fabricated includes a rectifier circuit with the PCB spiral coil antenna that receives wireless power transmitted from base terminal equipment by electromagnetic resonance coupling and converts it into DC power and a sensor interface circuit that operates using the power. The interface circuit modulates the received signal with the sensor information and reflects it back to the base terminal. The module could achieve 100 mm communication distance when 0.4 mW power is feeding to the sensor terminal.

Although baud-rate clock and data recovery (CDR) such as Mueller-Müller (MM) CDR is adopted to ADC-based receivers (RXs), it suffers from false-lock points when the RXs handle PAM4 data pattern because of the absence of edge data. In this paper, a false-lock-aware locking scheme is proposed to address this issue. After the false-lock-aware locking scheme, a clock phase is adjusted to achieve maximum eye height by using a post-1-tap parameter for an FFE in the CDR loop. The proposed techniques are implemented in a 56-Gb/s PAM4 transceiver. A PLL uses an area-efficient “glasses-shaped” inductor. The RX comprises an AFE, a 28-GS/s 7-bit time-interleaved SAR ADC, and a DSP with a 31-tap FFE and a 1-tap DFE. A TX is based on a 7-bit DAC with a 4-tap FFE. The transceiver is fabricated in 16-nm CMOS FinFET technology, and achieves a BER of less than 1e-7 with a 30-dB loss channel. The measurement results show that the MM CDR escapes from false-lock points, and converges to near the optimum point for large eye height.

With the recent advances in IoT, there is a growing interest in multi-surface computing, where a mobile app can cooperatively utilize multiple devices' surfaces. We propose a novel framework that seamlessly augments mobile apps with multi-surface computing capabilities. It enables various apps to employ multiple surfaces with acceptable performance.

Providing students with useful feedback on faulty programs can effectively help students fix programs. Spectrum-Based Fault Location (SBFL), which is a widely studied and lightweight technique, can automatically generate a suspicious value of statement ranking to help users find potential faults in a program. However, the performance of SBFL on student programs is not satisfactory, to improve the accuracy of SBFL in student programs, we propose a novel Multi-Correct Programs based Fault Localization (MCPFL) approach. Specifically, We first collected the correct programs submitted by students on the OJ system according to the programming problem numbers and removed the highly similar correct programs based on code similarity, and then stored them together with the faulty program to be located to construct a set of programs. Afterward, we analyzed the suspiciousness of the term in the faulty program through the Term Frequency-Inverse Document Frequency (TF-IDF). Finally, we designed a formula to calculate the weight of suspiciousness for program statements based on the number of input variables in the statement and weighted it to the spectrum-based fault localization formula. To evaluate the effectiveness of MCPFL, we conducted empirical studies on six student program datasets collected in our OJ system, and the results showed that MCPFL can effectively improve the traditional SBFL methods. In particular, on the EXAM metric, our approach improves by an average of 27.51% on the Dstar formula.

This paper presents the design and implementation of an automated multi-phase facilitation agent based on LLM to realize inclusive facilitation and efficient use of a large language model (LLM) to facilitate realistic discussions. Large-scale discussion support systems have been studied and implemented very widely since they enable a lot of people to discuss remotely and within 24 hours and 7 days. Furthermore, automated facilitation artificial intelligence (AI) agents have been realized since they can efficiently facilitate large-scale discussions. For example, D-Agree is a large-scale discussion support system where an automated facilitation AI agent facilitates discussion among people. Since the current automated facilitation agent was designed following the structure of the issue-based information system (IBIS) and the IBIS-based agent has been proven that it has superior performance. However, there are several problems that need to be addressed with the IBIS-based agent. In this paper, we focus on the following three problems: 1) The IBIS-based agent was designed to only promote other participants' posts by replying to existing posts accordingly, lacking the consideration of different behaviours taken by participants with diverse characteristics, leading to a result that sometimes the discussion is not sufficient. 2) The facilitation messages generated by the IBIS-based agent were not natural enough, leading to consequences that the participants were not sufficiently promoted and the participants did not follow the flow to discuss a topic. 3) Since the IBIS-based agent is not combined with LLM, designing the control of LLM is necessary. Thus, to solve the problems mentioned above, the design of a phase-based facilitation framework is proposed in this paper. Specifically, we propose two significant designs: One is the design for a multi-phase facilitation agent created based on the framework to address problem 1); The other one is the design for the combination with LLM to address problem 2) and 3). Particularly, the language model called “GPT-3.5” is used for the combination by using corresponding APIs from OPENAI. Furthermore, we demonstrate the improvement of our facilitation agent framework by presenting the evaluations and a case study. Besides, we present the difference between our framework and LangChain which has generic features to utilize LLMs.

For 6G mobile communications, it is important to realize a 300 GHz band bandpass filter that fits the occupied bandwidth of wireless communication system to prevent inter-system interference. This paper presents the design of a 300-GHz-band dual-band bandstop filter composed of two types of different sized split ring resonator (SRR) unit cells. The SRR unit cells are formed by a 5-μm-thick gold pattern on a 200-μm-thick quartz substrate. When two different-sized SRR unit cells are placed alternately on the same quartz substrate and the SRR unit cell size is over 260 μm, the stopbands of the dual-band bandstop filter are almost the same as those of the bandstop filter, which is composed of a single SRR unit cell. The insertion loss of the dual-band bandstop filter at 297.4 GHz is 1.8 dB and the 3-dB passband becomes 16.0 GHz (290.4-306.4 GHz). The attenuation in the two stopbands is greater than 20 dB. Six types of dual-band bandstop filters with different arrangement and different distance between SRR unit cells are prototyped, and the effect of the distance and arrangement between different sized SRR unit cells on the transmission characteristics of dual-band bandstop filters were clarified.

A method for bandwidth enhancement of radar cross section (RCS) reduction by metasurfaces was studied. Scattering cancellation is one of common methods for reducing RCS of target scatterers. It occurs when the wave scattered by the target scatterer and the wave scattered by the canceling scatterer are the same amplitude and opposite phase. Since bandwidth of scattering cancellation is usually narrow, we proposed the bandwidth enhancement method using metasurfaces, which can control the frequency dependence of the scattering phase. We designed and fabricated a metasurface composed of a patch array on a grounded dielectric substrate. Numerical and experimental evaluations confirmed that the metasurface enhances the bandwidth of 10dB RCS reduction by 52% bandwidth ratio of the metasurface from 34% bandwidth ratio of metallic cancelling scatterers.

In recent years, to meet strict time-to-market constraints, it has become difficult for only one semiconductor design company to design a VLSI. Thus, design companies purchase IP cores from third-party IP vendors and design only the necessary parts. On the other hand, since IP cores have the disadvantage that copyright infringement can be easily performed, logic locking has to be applied to them. Functional logic locking methods using TTLock are resilient to SAT attacks however vulnerable to FALL attacks. Additionally, it is difficult to design logic locking based on TTLock at the gate level. This paper proposes a logic locking method, CRLock, based on SAT attack and FALL attack resistance at the register transfer level. The CRLock is a logic locking method for controllers at RTL in which the designer selects a protected input pattern and modifies the controller based on the protection input pattern. In experimental results, we applied CRLock to MCNC'91 benchmark circuits and showed that all circuits are resistant to SAT and FALL attacks.

Approximate computing (AC) saves energy and improves performance by introducing approximation into computation in error-torrent applications. This work focuses on an AC strategy that accurately performs important computations and approximates others. In order to make AC circuits practical, we need to determine which computation is how important carefully, and thus need to appropriately approximate the redundant computation for maintaining the required computational quality. In this paper, we focus on the importance of computations at the flip-flop (FF) level and propose a novel importance evaluation methodology. The key idea of the proposed methodology is a two-step fault injection algorithm to extract the near-optimal set of redundant FFs in the circuit. In the first step, the proposed methodology performs the FI simulation for each FF and extracts the candidates of redundant FFs. Then, in the second step, the proposed methodology extracts the set of redundant FFs in a binary search manner. Thanks to the two-step strategy, the proposed algorithm reduces the complexity of architecture exploration from an exponential order to a linear order without understanding the functionality and behavior of the target application program. Experimental results show that the proposed methodology identifies the candidates of redundant FFs depending on the given constraints. In a case study of an image processing accelerator, the truncation for identified redundant FFs reduces the circuit area by 29.6% and saves power dissipation by 44.8% under the ASIC implementation while satisfying the PSNR constraint. Similarly, the dynamic power dissipation is saved by 47.2% under the FPGA implementation.

Adversarial examples (AEs), which cause misclassification by adding subtle perturbations to input images, have been proposed as an attack method on image-classification systems using deep neural networks (DNNs). Physical AEs created by attaching stickers to traffic signs have been reported, which are a threat to traffic-sign-recognition DNNs used in advanced driver assistance systems. We previously proposed an attack method for generating a noise area on images by superimposing an electrical signal on the mobile industry processor interface and showed that it can generate a single adversarial mark that triggers a backdoor attack on the input image. Therefore, we propose a misclassification attack method n DNNs by creating AEs that include small perturbations to multiple places on the image by the fault injection. The perturbation position for AEs is pre-calculated in advance against the target traffic-sign image, which will be captured on future driving. With 5.2% to 5.5% of a specific image on the simulation, the perturbation that induces misclassification to the target label was calculated. As the experimental results, we confirmed that the traffic-sign-recognition DNN on a Raspberry Pi was successfully misclassified when the target traffic sign was captured with. In addition, we created robust AEs that cause misclassification of images with varying positions and size by adding a common perturbation. We propose a method to reduce the amount of robust AEs perturbation. Our results demonstrated successful misclassification of the captured image with a high attack success rate even if the position and size of the captured image are slightly changed.

The war between cyber attackers and security analysts is gradually intensifying. Owing to the ease of obtaining and creating support tools, recent malware continues to diversify into variants and new species. This increases the burden on security analysts and hinders quick analysis. Identifying malware families is crucial for efficiently analyzing diversified malware; thus, numerous low-cost, general-purpose, deep-learning-based classification techniques have been proposed in recent years. Among these methods, malware images that represent binary features as images are often used. However, no models or architectures specific to malware classification have been proposed in previous studies. Herein, we conduct a detailed analysis of the behavior and structure of malware and focus on PE sections that capture the unique characteristics of malware. First, we validate the features of each PE section that can distinguish malware families. Then, we identify PE sections that contain adequate features to classify families. Further, we propose an ensemble learning-based classification method that combines features of highly discriminative PE sections to improve classification accuracy. The validation of two datasets confirms that the proposed method improves accuracy over the baseline, thereby emphasizing its importance.

In this paper we study a recently proposed variant of the r-gathering problem. An r-gathering of customers C to facilities F is an assignment A of C to open facilities F' ⊂ F such that r or more customers are assigned to each open facility. (Each facility needs enough number of customers to open.) Given an opening cost op(f) for each f∈F, and a connecting cost co(c,f) for each pair of c∈C and f∈F, the cost of an r-gathering A is max{maxc∈C{co(c, A(c))}, maxf∈F'{op(f)}}. The r-gathering problem consists of finding an r-gathering having the minimum cost. Assume that F is a set of locations for emergency shelters, op(f) is the time needed to prepare a shelter f∈F, and co(c,f) is the time needed for a person c∈C to reach assigned shelter f=A(c)∈F. Then an r-gathering corresponds to an evacuation plan such that each open shelter serves r or more people, and the r-gathering problem consists of finding an evacuation plan minimizing the evacuation time span. However in a solution above some person may be assigned to a farther open shelter although it has a closer open shelter. It may be difficult for the person to accept such an assignment for an emergency situation. Therefore, Armon considered the problem with one more additional constraint, that is, each customer should be assigned to a closest open facility, and gave a 9-approximation polynomial-time algorithm for the problem. We have designed a simple 3-approximation algorithm for the problem. The running time is O(r|C||F|).

Text-based communication, such as text chat, is commonly employed in various contexts, both professional and personal. However, it lacks the rich emotional cues present in verbal and visual forms of communication, such as facial expressions and tone of voice, making it more challenging to convey emotions and increasing the likelihood of misunderstandings. In this study, we focused on typefaces as emotional cues employed in text-based communication and investigated the influence of font design on impression formation and decision-making through two experiments. The results of the experiments revealed the relationship between Japanese typeface design and impression formation, and indicated that advice presented in a font evoking an impression of high confidence was more likely to be accepted than advice presented in a font evoking an impression of low confidence.