This paper presents a novel approach to deal with the cooperative localization problem in wireless sensor networks based on received signal strength measurements. In cooperative scenarios, the cost function of the localization problem becomes increasingly nonlinear and nonconvex due to the heightened interaction between sensor nodes, making the estimation of the positions of the target nodes more challenging. Although most of existing cooperative localization algorithms assure acceptable localization accuracy, their computational complexity increases dramatically, which may restrict their applicability. To reduce the computational complexity and provide competitive localization accuracy at the same time, we propose a localization algorithm based on the differential evolution with multiple populations, opposite-based learning, redirection, and anchoring. In this work, the cooperative localization cost function is split into several simpler cost functions, each of which accounts only for one individual target node. Then, each cost function is solved by a dedicated population of the proposed algorithm. In addition, an enhanced version of the proposed algorithm which incorporates the population midpoint scheme for further improvement in the localization accuracy is devised. Simulation results demonstrate that the proposed algorithms provide comparative localization accuracy with much lower computational complexity compared with the state-of-the-art algorithms.

Early-stage dementia, Mild Cognitive Impairment (MCI), is challenging to diagnose since it is a transient condition distinct from complete cognitive collapse. Recent clinical research studies have identified that balance impairments can be a significant indicator for predicting dementia in older adults. Accordingly, we aimed to identify key balance biomarkers using wearable inertial sensors for early detection of dementia/MCI. At National Research Center for Dementia (South Korea), 60 participants (30 Cognitively Normal-CN, 30 Mild Cognitive Impairment-MCI) were selected based on assessments conducted by medical professionals at Chosun University Hospital / Chonnam National University Hospital (Gwangju). Shimmer-3 inertial sensor was placed on lower back of participants, with a tri-axial accelerometer and tri-axial gyroscope. The data was collected under four different conditions: eyes-open (EO), eyes-closed (EC), right-leg lift (RL), left-leg lift (LL). A set of 76 postural sway measures were considered with 43 time-domain and 33 frequency-domain features. Feature ranking was executed through the utilization of "Leave-One-Subject-Out (LOSO)" cross-validation technique for each condition. Filter methods: one-way-ANOVA (Analysis of Variance), Mutual Information and Wrapper methods: Random Forest, Support Vector Machine (SVM) were utilized to evaluate feature subsets. The proportional value of each feature was calculated by the number of times it showed in the top feature list. The final score was obtained by combining scores from all the techniques, which indicated the relative weight of characteristics across the various approaches. Further feature analysis was conducted using SHAP (Shapley-Additive-exPlanations). The multi-step methodology, for feature selection, adopted in our research ensured a rigorous process, combining the strengths of Filter and Wrapper methods. In time-domain: root-mean-square, mean-distance, 95%-confidence circle sway-area, path-length, mean sway-velocity were the key features identified. While in frequency-domain: spectral edge-frequency, mean-frequency, peak-frequency, centroid-frequency, spectral-entropy were found to be the principal features of relevance. These features were also included in the top list of features based on SHAP impact value, hence proving the effectiveness of our method in identifying important biomarkers for detection of MCI. The inertial sensors based postural balance assessment provides useful biomarkers that can facilitate early screening of MCI / dementia in home settings.

Dementia is characterized by a progressive loss of cognitive abilities, and diagnosing its early stages Mild Cognitive Impairment (MCI), is difficult since it is a transitory state that is different from total cognitive collapse. Recent clinical research studies have identified that balance impairments can be a significant indicator for predicting dementia in older adults. Accordingly, the current research focuses on finding innovative postural balance-based digital biomarkers by using wearable inertial sensors and pre-screening of MCI in home settings using machine learning techniques. For this research, sixty subjects (30 cognitively normal and 30 MCI) with waist-mounted inertial sensor performed balance tasks in four different standing postures: eyes-open, eyes-closed, right-leg-lift, and left-leg-lift. The significant balance biomarkers for MCI identification are discovered by our research, demonstrating specific characteristics in each of these four states. A robust feature selection approach is ensured by the multi-step methodology that combines the strengths of Filter techniques, Wrapper methods, and SHAP (Shapley Additive exPlanations) technique. The proposed balance biomarkers have the potential to detect MCI (with 75.8% accuracy), as evidenced by the results of machine learning algorithms for classification. This work adds to the growing body of literature targeted at enhancing understanding and proactive management of cognitive loss in older populations and lays the groundwork for future research efforts aimed at refining digital biomarkers, validating findings, and exploring longitudinal perspectives.

The Korean ABIMS deployment in 2023 near southwest seashores showed a promising solution actively monitoring fishery activities. It covered several trench areas, collecting data on communication signals and system performance. Analysis aimed to measure IoFT ABIMS efficacy in reducing ALD fishing gear impact, aiding evidence-based fisheries management for sustainability and ocean health preservation.

Recent advancements in the collaboration between Marine Technology and 4IR Information Technology have led to the development of the Automatic Buoy Identification Monitoring System (ABIMS) in Korea. ABIMS utilizes location tracking, location message communication, machine learning, and data analytics, to monitor fishing buoys and report abnormal behaviors of connected fishing gears, in real-time, addressing the issue of abandoned, lost, or discarded (ALD) fishing gears. This system contributes to sustainable fisheries management and healthier oceans by effectively tracking gear movement, detecting loss or abandonment, and informing evidence-based decision-making for various unexpected happening events related to fishing gears under monitoring. Korea's efforts with the newly developed and experimented ABIMS are expected to minimize the impact of ALD fishing gears and promote sustainable fishing practices.

This article addresses the target tracking problem based on the received signal strength (RSS) and angle of arrival (AOA) in wireless sensor networks (WSNs). The tracking problem is formulated in the framework of the maximum a posteriori (MAP), in which the prior knowledge of moving target nodes (TNs) is exploited. Due to the fact that the cost function of the tracking problem is highly nonlinear and nonconvex, most of the existing algorithms tend to approximate and relax the cost function. As a result, the tracking accuracy is usually compromised. In this article, we propose a tracking algorithm based on evolutionary techniques that do not require an approximation of the cost function, resulting in a considerable improvement in tracking accuracy. The proposed tracking algorithm is compared with state-of-the-art algorithms such as the MAP, particle filter (PF), and Kalman filter (KF). Simulation and real experiment results demonstrate that the proposed tracking algorithm provides an improvement roughly by 16%, 11%, and 18% over the MAP, PF, and KF, respectively, in the tracking accuracy.

Abrupt and continuous nature of scale variation in a crowded scene is a challenging task to enhance crowd counting accuracy in an image. Existing crowd counting techniques generally used multi-column or single-column dilated convolution to tackle scale variation due to perspective distortion. However, due to multi-column nature, they obtain identical features, whereas, the standard dilated convolution (SDC) with expanded receptive field size has sparse pixel sampling rate. Due to sparse nature of SDC, it is highly challenging to obtain relevant contextual information. Further, features at multiple scale are not extracted despite some inception-based model is not used (which is cost effective). To mitigate theses drawbacks in SDC, we therefore, propose a hierarchical dense dilated deep pyramid feature extraction through convolution neural network (CNN) for single image crowd counting (HDPF). It comprises of three modules: general feature extraction module (GFEM), deep pyramid feature extraction module (PFEM) and fusion module (FM). The GFEM is responsible to obtain task independent general features. Whereas, PFEM plays a vital role to obtain the relevant contextual information due to dense pixel sampling rate caused by densely connected dense stacked dilated convolutional modules (DSDCs). Further, due to dense connections among DSDCs, the final feature map acquires multi-scale information with expanded receptive field as compared to SDC. Due to dense pyramid nature, it is very effective to propagate the extracted feature from lower dilated convolutional layers (DCLs) to middle and higher DCLs, which result in better estimation accuracy. The FM is used to fuse the incoming features extracted by other modules. The proposed technique is tested through simulations on three well known datasets: Shanghaitech (Part-A), Shanghaitech (Part-B) and Venice. Results justify its relative effectiveness in terms of selected performance.

The standard differential evolution (DE) algorithm is a prominent population-based evolutionary algorithm that has shown competitive performance in addressing highly complicated problems such as target node localization in wireless sensor networks. The DE performance, however, can be compromised when the localization problem includes additional parameters, such as the transmit power and path-loss exponent in the received signal strength, to be estimated. In this paper, we propose an enhanced DE (EDE) and its variant called vEDE for addressing the deterioration of the DE when solving the localization problem. The proposed EDE and vEDE both incorporate two processes, namely random redirection and generation of midpoint individuals, to enhance their performance. In cases of high complexity, our numerical results reveal that the EDE and vEDE improve the localization accuracy to roughly 53% and 55%, respectively, as compared to the standard DE. The results also show the superiority of the vEDE as compared to the state-of-the-art algorithms based on semi-definite programming and second-order cone programming under various settings.

With the advances in new technological trends and the reduction in prices of sensor nodes, wireless sensor networks (WSNs) and their applications are proliferating in several areas of our society such as healthcare, industry, farming, and housing. Accordingly, in recent years attention on localization has increased significantly since it is one of the main facets in any WSN. In a nutshell, localization is the process in which the position of any sensor node is retrieved by exploiting measurements from and between sensor nodes. Several techniques of localization have been proposed in the literature with different localization accuracy, complexity, and hence different applicability. The localization accuracy is limited by fundamental limitations, theoretical and practical, that restrict the localization accuracy regardless of the technique employed in the localization process. In this paper, we pay special attention to such fundamental limitations from the theoretical and practical points of view and provide a comprehensive review of the state-of-the-art solutions that deal with such limitations. Additionally, discussion on the theoretical and practical limitations together with their recent solutions, remaining challenges, and perspectives are presented.

The differential evolution (DE) is a well known population-based evolutionary algorithm that has shown capabilities for solving real-world problems such as resource allocation, multicast routing, and localization of target nodes. However, the accuracy of the DE, like other evolutionary algorithms, depends on the settings of its control parameters. The localization of target nodes is highly nonlinear and multi-modal, which may trap the DE in a local optimum. A local optimum may be avoided by a proper selection of the control parameters. One of the key control parameters is the population size (PS), which affects directly the localization accuracy and computational complexity. Finding an adequate PS throughout the evolution process is a challenging task. Even if an adequate PS is found it may not be the adequate PS anymore when the scenario of a problem changes. Although several approaches have been proposed for adapting the PS, they have not been evaluated when solving the localization problem. In this paper, a comprehensive comparison in terms of accuracy and computational demand is conducted among the state-of-the-art PS adaptation techniques when employed with the DE for solving the localization problem of target nodes in various scenarios. We also propose three new PS adaptation techniques, namely, exponential, parabolic, and logistic reduction. The results from extensive numerical simulations show that, after setting the initial PS properly, there is no technique that outperforms the others in practically all the scenario of the localization problem. Additionally, the DE with the proposed techniques provides competitive localization accuracy with considerably less computational complexity. Specifically, The proposed approaches reduce the computational demand by approximately 50 % over the standard DE in all the scenarios considered here.

The mild symptoms in Mild Cognitive Impairment (MCI), a precursor of dementia, often go unnoticed and are assumed as normal aging signs. Such negligence result in late visits which consequently, lead to the diagnosis and progression of dementia. An instrumented gait assessment in home settings may facilitate the detection of subtle MCI-related motor deficits thus, allowing early diagnosis and intervention. This paper investigates potential gait biomarkers derived from shank mounted inertial sensors signals under normal and dual-task walking conditions using data collected from thirty MCI and thirty cognitively normal (CN) subjects. To identify potential gait biomarkers for MCI screening, we assess the variance and predictive power of each feature. Moreover, multiple classification models using different machine learning and feature selection techniques are built to automate MCI detection by leveraging the gait biomarkers. Statistical analysis reveal multiple gait parameters that are significantly different under both single and dual-task settings. However, we show that dual-task walking provides better distinction between MCI and CN subjects. The machine learning model employed for MCI pre-screening based on the inertial sensor-derived gait biomarkers achieves accuracy and sensitivity of 71.67% and 83.33%, respectively.

In order to investigate the effect of asynchronous fast frequency hopping-multiple access (FFH-MA) systems due to the frequency offset, we evaluate the bit error rate (BER) performance, using noncoherent Mary frequency shift keying (FSK) modulation in the Rayleigh fading channel. While the frequency offset increases at a given signal to noise ratio (SNR), the BER is severely degraded due to the loss of orthogonality of received symbols. With 10% frequency offset, about 5 dB SNR is required additionally to obtain 2 x 10-3 BER, compared to that in the perfectly frequency synchronized case. For the SNR of more than 20 dB, the threshold level of the receiver suffering from the frequency offsets should be greater than that of the perfectly synchronized receiver.

전기자동차 충전은 시간에 따른 충전 부하 변화율이 높기 때문에 다수의 전기자동차(Electric Vehicle, EV)들 을 충전할 경우 전력망의 부하 계통에 커다란 문제를 야 기할 수 있다. 따라서 전력망 입장에서 실제 데이터 기반 EV 충전 전력량 예측 시스템은 전력망의 안정적인 전력 운용 관리 및 공급 안정화에 있어 매우 중요한 문제이다 [1]. 최근 기본적인 기계학습 알고리즘에서 나아가 앙상 블 기법과 딥러닝 신경망을 활용한 전기자동차 충전 전 력 수요량 예측을 높이려는 연구가 활발히 진행되고 있 으나 서로 다른 기계학습과 딥러닝 알고리즘들을 결합하 는 앙상블에 대한 연구는 초기 단계이다. EV 충전 전력 수요 관련 특성(충전 세션별 사용자 수, 충전 간격 번호, 충전 시간, 요일 정보)를 여러 기계학습 알고리즘들을 통해 학습하여 예측하였다 [2]. 기본적인 특성만 고려한 것에서 나아가 EV 사용자별 과거 충전 행동 패턴을 K-Means와 K-NN 알고리즘을 사용하여 클러스터링 분류하여 이를 개별 기계학습 알고리즘의 추 가적인 특성 정보로 활용하여 [2]의 알고리즘보다 예측 성능을 향상한 연구가 있었다 [3]. 하지만 EV 전력 충전 은 시간대별 EV 충전 부하율이 다른 특성을 보이는데 [3]의 기계학습 알고리즘은 이러한 시간적 연속성을 고 려하지 못한 단점이 있다. 이를 해결하기 위해 기계학습 알 고리즘에서 나아가 순환 신경망을 활용하여 EV 충전 전력 수요량을 예측하려는 연구가 진행되었다 [4]. 유전학 분야에서 딥러닝 기반의 스태킹 앙상블 방법이 최근 제안된 바 있다. 신경 근육 장애 판별 정확도를 높 이기 위해 다중 기계학습 알고리즘들과 컨볼루션 심층 신경망을 결합하여 판별의 정확도를 높였다 [5]. 본 연 구는 이에 착안하여 기계학습 알고리즘들과 순환 신경망 (Recurrent Neural Network, RNN), 장단기 메모리 순 환 신경망(Long Short-Term Memory, LSTM)을 결합 한 새로운 딥러닝 신경망 기반의 스태킹 앙상블 구조를 제안하였다. 제안한 방법이 기계학습 알고리즘과 순환 신 경망과의 모의 실험을 통해 EV 충전 전력 수요량 예측 성능을 비교한다.

In electronic warfare, source enumeration and direction-of-arrival estimation are important. The source enumeration method based on eigenvalues of covariance matrix from received is one of the most used methods. However, there are some drawbacks such as accuracy less than 100 % at high SNR, poor performance at low SNR and reduction of maximum number of estimating sources. We suggested new method based on eigenvalues gaps, which is named AREG(Accumulated Ratio of Eigenvalues Gaps). Meanwhile, FGML(Fast Gridless Maximum Likelihood) which reconstructs the covariance matrix was suggested by Wu et al., and it improves performance of the existing source enumeration methods without modification of algorithms. In this paper, first, we combine AREG with FGML to improve the performance. Second, we compare the performance of source enumeration and direction-of-arrival estimation methods in Rayleigh fading. Third, we suggest new method named REG(Ratio of Eigenvalues Gaps) to reduce performance degradation in Rayleigh Fading environment of AREG.

Ultra wideband (UWB) technology has obtained lots of attention as a strong candidate for short range indoor wireless communication because of low power consumption, low cost implementation and the robustness against multipath fading. It uses trains of short pulses which widely spread the signal energy in frequency domain. Since such large bandwidth can cause interference with other narrow band communication systems, the federal communications commission (FCC) has restricted not only the frequency region from 3.1GHz to 10.6GHz but also the transmission power level for commercial use of UWB systems. The effects of timing jitter on time hopping binary phase shift keying (TH-BPSK) UWB systems applying the FCC-constraint pulses are investigated under flat Nakagami-mfading channel and additive white Gaussian noise (AWGN). The numerical results show that two FCC-constraint pulses, PSP and MMNHP, have almost same sensitivity to the timing jitter even though they have different transceiver complexity. Additionally, the additional required power due to the timing jitter exponentially increases, but that due to the amplitude fading is not exceeded over 4dB.

Source enumeration is an important procedure for radio direction-of-arrival finding in the multiple signal classification (MUSIC) algorithm. The most widely used source enumeration approaches are based on the eigenvalues themselves of the covariance matrix obtained from the received signal. However, they have shortcomings such as the imperfect accuracy even at a high signal-to-noise ratio (SNR), the poor performance at low SNR, and the limited detection number of sources. This paper proposestwo source enumeration approaches using the ratio of eigenvalue gaps and the threshold trained by a machine learning based clustering algorithm for gaps of normalized eigenvalues, respectively. In the first approach, a criterion formula derived with eigenvalue gaps is used to determine the number of sources, where the formula has maximum value. In the second approach, datasets of normalized eigenvalue gaps are generated for the machine learning based clustering algorithm and the optimal threshold for estimation of the number of sources are derived, which minimizes source enumeration error probability. Simulation results show that our proposed approaches are superior to the conventional approaches from both the estimation accuracy and numerical detectability extent points of view. The results demonstrate that the second proposed approach has the feasibility to improve source enumeration performance if appropriate learning datasets are sufficiently provided.

Crowd counting is a challenging task due to large perspective, density, and scale variations. CNN-based crowd counting techniques have achieved significant performance in sparse to dense environments. However, crowd counting in high perspective-varying scenes (images) is getting harder due to different density levels occupied by the same number of pixels. In this way large variations for objects in the same spatial area make it difficult to count accurately. Further, existing CNN-based crowd counting methods are used to extract rich deep features; however, these features are used locally and disseminated while propagating through intermediate layers. This results in high counting errors, especially in dense and high perspective-variation scenes. Further, class-specific responses along channel dimensions are underestimated. To address these above mentioned issues, we therefore propose a CNN-based dense feature extraction network for accurate crowd counting. Our proposed model comprises three main modules: (1) backbone network, (2) dense feature extraction modules (DFEMs), and (3) channel attention module (CAM). The backbone network is used to obtain general features with strong transfer learning ability. The DFEM is composed of multiple sub-modules called dense stacked convolution modules (DSCMs), densely connected with each other. In this way features extracted from lower and middle-lower layers are propagated to higher layers through dense connections. In addition, combinations of task independent general features obtained by the former modules and task-specific features obtained by later ones are incorporated to obtain high counting accuracy in large perspective-varying scenes. Further, to exploit the class-specific response between background and foreground, CAM is incorporated at the end to obtain high-level features along channel dimensions for better counting accuracy. Moreover, we have evaluated the proposed method on three well known datasets: Shanghaitech (Part-A), Shanghaitech (Part-B), and Venice. The performance of the proposed technique justifies its relative effectiveness in terms of selected performance compared to state-of-the-art techniques.

During mild cognitive impairment (MCI), a precursor of dementia, often the mild symptoms go unnoticed and are assumed as normal aging signs by elderly people, resulting in late diagnosis and progression to dementia. Recently, several studies investigated the potential of behavioral analysis (ADL assessment, speech analysis etc.) based biomarkers for automated early screening of MCI in home settings. In this work, different deep learning models with walking data have been examined and a 2D‐CNN‐GRU model for automatic diagnosis of MCI based on wearable inertial sensors gait data is proposed.