Recovery of Low-Rank Matrices under Affine Constraints via a Smoothed Rank Function

In this paper, the problem of matrix rank minimization under affine constraints is addressed. The state-of-the-art algorithms can recover matrices with a rank much less than what is sufficient for the uniqueness of the solution of this optimization problem. We propose an algorithm based on a smooth approximation of the rank function, which practically improves recovery limits on the rank of the solution. This approximation leads to a non-convex program; thus, to avoid getting trapped in local solutions, we use the following scheme. Initially, a rough approximation of the rank function subject to the affine constraints is optimized. As the algorithm proceeds, finer approximations of the rank are optimized and the solver is initialized with the solution of the previous approximation until reaching the desired accuracy. On the theoretical side, benefiting from the spherical section property, we will show that the sequence of the solutions of the approximating function converges to the minimum rank solution. On the experimental side, it will be shown that the proposed algorithm, termed SRF standing for Smoothed Rank Function, can recover matrices which are unique solutions of the rank minimization problem and yet not recoverable by nuclear norm minimization. Furthermore, it will be demonstrated that, in completing partially observed matrices, the accuracy of SRF is considerably and consistently better than some famous algorithms when the number of revealed entries is close to the minimum number of parameters that uniquely represent a low-rank matrix.Comment: Accepted in IEEE TSP on December 4th, 201

Aprendizaje evolutivo supervisado: Uso de histograma de gradiente y algoritmo de enjambre de partículas para detección y seguimiento de peatones en secuencia de imágenes infrarrojas

Recently, tracking and pedestrian detection from various images have become one of the major issues in the field of image processing and statistical identification.  In this regard, using evolutionary learning-based approaches to improve performance in different contexts can greatly influence the appropriate response.  There are problems with pedestrian tracking/identification, such as low accuracy for detection, high processing time, and uncertainty in response to answers.  Researchers are looking for new processing models that can accurately monitor one's position on the move.  In this study, a hybrid algorithm for the automatic detection of pedestrian position is presented.  It is worth noting that this method, contrary to the analysis of visible images, examines pedestrians' thermal and infrared components while walking and combines a neural network with maximum learning capability, wavelet kernel (Wavelet transform), and particle swarm optimization (PSO) to find parameters of learner model. Gradient histograms have a high effect on extracting features in infrared images.  As well, the neural network algorithm can achieve its goal (pedestrian detection and tracking) by maximizing learning.  The proposed method, despite the possibility of maximum learning, has a high speed in education, and results of various data sets in this field have been analyzed. The result indicates a negligible error in observing the infrared sequence of pedestrian movements, and it is suggested to use neural networks because of their precision and trying to boost the selection of their hyperparameters based on evolutionary algorithms

Discrete Markov Chain Based Spectrum Sensing for Cognitive Radio

Spectrum sensing is one of the functionalities of cognitive radios to exploit spectrum holes without interrupting primary users transmission. The more efficient of the spectrum sensing, the highest the throughput of secondary and primary network. This paper presents spectrum sensing method based on phase type modelling that is simple to do for secondary users to conclude about the channel state (idle or busy) under collision constraint. The parameters of phase type model can be adjusted based on desired operating point of the receiver sensor in its ROC curve. The presented approach can run a trade off between sensing time and the two error probabilities of sensor false alarm and miss-detection

L-carnitine versus Propranolol for pediatric migraine prophylaxis

  Objective Carnitine plays a significant role in fatty acid transportation in mitochondria and has been shown to have a prophylactic effect on adult migraine. The aim of this randomized controlled trial was to compare and evaluate the effects of L-carnitine supplementation versus propranolol in the prevention of pediatric migraine. Materials & Methods A total of 60 pediatric patients with episodic migraine were randomly allocated to 2 independent groups to receive either 50 mg/kg/day L-carnitine or 1 mg/kg/day propranolol as a prophylactic drug. Frequency, severity, and duration of migraine attacks and headache disability based on the Pediatric Migraine Disability Assessment Score (PedMIDAS) were studied at the baseline and after 2, 4, and 12 weeks. Results A total of 56 patients were evaluated in the study: 23 girls (41%) and 33 boys (59%) with a mean age of 9.7 ± 2.1 years. Frequency of migraine headaches per month reduced from 11.4 ± 7.1 to 5.34 ± 2.4 in the L-carnitine group and from 10.7 ± 6.2 to 4.96 ± 3.9 in the propranolol group by the end of the study. Headache severity score was also reduced from 19.38 ± 14 to 2.88 ± 7.4 and from 12.92 ± 13 to 0.82 ± 1.3 in the L-carnitine and propranolol groups, respectively. We found a significant decrease in frequency, severity, and duration of headache attacks in both groups (P < 0.01). No significant difference was observed between the efficacies of the 2 drugs. Conclusion This study concluded that L-carnitine supplementation can play a prophylactic role in the management of pediatric migraine

DT-DDNN: A Physical Layer Security Attack Detector in 5G RF Domain for CAVs

The Synchronization Signal Block (SSB) is a fundamental component of the 5G New Radio (NR) air interface, crucial for the initial access procedure of Connected and Automated Vehicles (CAVs), and serves several key purposes in the network's operation. However, due to the predictable nature of SSB transmission, including the Primary and Secondary Synchronization Signals (PSS and SSS), jamming attacks are critical threats. These attacks, which can be executed without requiring high power or complex equipment, pose substantial risks to the 5G network, particularly as a result of the unencrypted transmission of control signals. Leveraging RF domain knowledge, this work presents a novel deep learning-based technique for detecting jammers in CAV networks. Unlike the existing jamming detection algorithms that mostly rely on network parameters, we introduce a double-threshold deep learning jamming detector by focusing on the SSB. The detection method is focused on RF domain features and improves the robustness of the network without requiring integration with the pre-existing network infrastructure. By integrating a preprocessing block to extract PSS correlation and energy per null resource elements (EPNRE) characteristics, our method distinguishes between normal and jammed received signals with high precision. Additionally, by incorporating of Discrete Wavelet Transform (DWT), the efficacy of training and detection are optimized. A double-threshold double Deep Neural Network (DT-DDNN) is also introduced to the architecture complemented by a deep cascade learning model to increase the sensitivity of the model to variations of signal-to-jamming noise ratio (SJNR). Results show that the proposed method achieves 96.4% detection rate in extra low jamming power, i.e., SJNR between 15 to 30 dB. Further, performance of DT-DDNN is validated by analyzing real 5G signals obtained from a practical testbed.Comment: 15 pages, 16 figure

Bypassing a Reactive Jammer via NOMA-Based Transmissions in Critical Missions

Wireless networks can be vulnerable to radio jamming attacks. The quality of service under a jamming attack is not guaranteed and the service requirements such as reliability, latency, and effective rate, specifically in mission-critical military applications, can be deeply affected by the jammer's actions. This paper analyzes the effect of a reactive jammer. Particularly, reliability, average transmission delay, and the effective sum rate (ESR) for a NOMA-based scheme with finite blocklength transmissions are mathematically derived taking the detection probability of the jammer into account. Furthermore, the effect of UEs' allocated power and blocklength on the network metrics is explored. Contrary to the existing literature, results show that gNB can mitigate the impact of reactive jamming by decreasing transmit power, making the transmissions covert at the jammer side. Finally, an optimization problem is formulated to maximize the ESR under reliability, delay, and transmit power constraints. It is shown that by adjusting the allocated transmit power to UEs by gNB, the gNB can bypass the jammer effect to fulfill the 0.99999 reliability and the latency of 5ms without the need for packet re-transmission.Comment: 6 pages, 7 figures, IEEE International Conference on Communications (ICC) 202

Comparison of 180° and 360° Arc Data Acquisition to Measure Scintigraphic Parameters from Gated Single Photon Emission Computed Tomography Myocardial Perfusion Imaging: Is There Any Difference?

Objective: The aim of the current study was to compare 180° and 360° data collection modes to measure end diastolic volume (EDV), end systolic volume (ESV) and ejection fraction (EF) values of the cardiac system by gated myocardial perfusion tomography. Methods: Thirty-three patients underwent gated myocardial perfusion tomography. Single photon emission computed tomography data of patients’ heart were acquired by 180°, 45° left posterior oblique to 45° right anterior oblique, and 360° to obtain EDV, ESV, EF and cardiac volume changes (V1, V2, V3, V4, V5, V6, V7 and V8) throughout each cardiac cycle. Results: Results of the current study indicated that there were no significant differences between 180° and 360° angular sampling in terms of measuring EDV, ESV and EF in myocardial perfusion imaging. Cardiac volume change patterns during a cardiac cycle were also similar in 360° and 180° scans. We also observed that there was no difference in EDV, ESV and EF values between the group with stress induced by exercise and the group with stress imposed by dipyridamole. Conclusion: As there is no difference between 180°and 360° cardiac scanning in terms of EDV, ESV and EF, half-orbit scan is recommended to study these cardiac system parameters because it offers more comfort to patients and a shorter scanning time. Keywords: 180° data collection, 360° data collection, ejection fraction, end diastolic volume, end systolic volume, gated myocardial perfusio

Avoidance dental visit, the impact of predictor factors: A cross-sectional study in Kerman, Iran

BACKGROUND AND AIM: In spite of different reforms and programs, the evidence indicates that Iran dental health sector has not been able to improve the dental situation and decrease the unmet needs. This study assessed the factors which affect the avoiding dental visit during one year ago because of its costs. METHODS: This cross-sectional study was performed on 1158 household heads which were selected through multi-stage sampling in Kerman, Iran, and questioned house by house using trained interviewers. The association of dichotomous outcome variables of “have you avoided or postponed dental visit during 1 years ago in spite of need because of its costs?” with 3 types of predictor variables of household heads including sex as binary predictor, age as continuous predictor, and income and education as categorical variables studied using regression logistics. RESULTS: The odds ratio (OR) of avoiding dental visit because of its costs in men was 1.4 times more than women (P = 0.035). The OR decreased by 0.01 with each year increase in age (P = 0.017). Furthermore, the OR is people with incomes 267-803, 803-1339 and > 1339 USD in comparison with the heads incomes under 267 USD was 0.31, 0.02, 0.01, respectively (P = 0.001), and in people with educational level < diploma, diploma, Bachelor of Science (BSc), Master of Science (MSc) and Doctor of Medicine (MD)/Doctor of Philosophy (PhD) in comparison with illiterate/elementary decreased by 0.51, 0.13, 0.04, 0.01 and 0.02, respectively (P < 0.001). CONCLUSION: The factors of older ages, being a woman, increasing education and income level decrease the rate of avoiding a dental visit. In the absence of strong dental health insurance, these factors determine the utilization of the services which lead to horizontal inequality. KEYWORDS: Odds Ratio; Utilization; Dental Car