Wideband Spectrum Acquisition for UAV Swarm Using the Sparse Coding Fourier Transform

As the trend towards small, safe, smart, speedy and swarm development grows, unmanned aerial vehicles (UAVs) are becoming increasingly popular for a wide range of applications. In this letter, the challenge of wideband spectrum acquisition for the UAV swarms is studied by proposing a processing method that features lower power consumption, higher compression rates, and a lower signal-to-noise ratio. Our system is equipped with multiple UAVs, each with a different sub-sampling rate. That allows for frequency backetization and estimation based on sparse Fourier transform theory. Unlike other techniques, the collisions and iterations caused by non-sparsity environ-ments are considered. We introduce sparse coding Fourier transform to address these issues. The key is to code the entire spectrum and decode it through spectrum correlation in the code. Simulation results show that our proposed method performs well in acquiring both narrowband and wideband signals simultaneously, compared to the other methods

Case report: Adrenal myelolipoma resected by laparoscopic surgery

IntroductionAdrenal myelolipomas are benign tumors composed mainly of lipomatous elements with myeloid cells. With the development of medical imaging technology, the detection rate has gradually increased. We report a case of adrenal myelolipoma successfully excised through the laparoscope and reviewed existing literature in recent ten years to summarize the feasibility of the laparoscopic approach for this tumor.Case presentationHerein, we described a case of adrenal myelolipoma resected by laparoscope in a 63-year-old male patient. He did not have any other symptoms except the incidental finding of a left adrenal mass. An abdominal CT examination revealed a mixed-density lesion containing some amount of adipose tissue. In conjunction with the patient’s willingness, we performed a laparoscopic operation to remove the lump. The definite diagnosis was confirmed as an adrenal myelolipoma according to the pathology. The patient recovered well postoperatively and without signs of recurrence at a 5-month follow-up.ConclusionAdrenal myelolipoma is commonly benign, asymptomatic, and hormonal inactivity. A surgical strategy is suggested for high-complication-risk patients. The laparoscopic approach is safe and effective with an obvious advantage over open procedures

Wideband Power Spectrum Sensing: a Fast Practical Solution for Nyquist Folding Receiver

The limited availability of spectrum resources has been growing into a critical problem in wireless communications, remote sensing, and electronic surveillance, etc. To address the high-speed sampling bottleneck of wideband spectrum sensing, a fast and practical solution of power spectrum estimation for Nyquist folding receiver (NYFR) is proposed in this paper. The NYFR architectures is can theoretically achieve the full-band signal sensing with a hundred percent of probability of intercept. But the existing algorithm is difficult to realize in real-time due to its high complexity and complicated calculations. By exploring the sub-sampling principle inherent in NYFR, a computationally efficient method is introduced with compressive covariance sensing. That can be efficient implemented via only the non-uniform fast Fourier transform, fast Fourier transform, and some simple multiplication operations. Meanwhile, the state-of-the-art power spectrum reconstruction model for NYFR of time-domain and frequency-domain is constructed in this paper as a comparison. Furthermore, the computational complexity of the proposed method scales linearly with the Nyquist-rate sampled number of samples and the sparsity of spectrum occupancy. Simulation results and discussion demonstrate that the low complexity in sampling and computation is a more practical solution to meet the real-time wideband spectrum sensing applications

Distributed UAV Swarm Augmented Wideband Spectrum Sensing Using Nyquist Folding Receiver

Distributed unmanned aerial vehicle (UAV) swarms are formed by multiple UAVs with increased portability, higher levels of sensing capabilities, and more powerful autonomy. These features make them attractive for many recent applica-tions, potentially increasing the shortage of spectrum resources. In this paper, wideband spectrum sensing augmented technology is discussed for distributed UAV swarms to improve the utilization of spectrum. However, the sub-Nyquist sampling applied in existing schemes has high hardware complexity, power consumption, and low recovery efficiency for non-strictly sparse conditions. Thus, the Nyquist folding receiver (NYFR) is considered for the distributed UAV swarms, which can theoretically achieve full-band spectrum detection and reception using a single analog-to-digital converter (ADC) at low speed for all circuit components. There is a focus on the sensing model of two multichannel scenarios for the distributed UAV swarms, one with a complete functional receiver for the UAV swarm with RIS, and another with a decentralized UAV swarm equipped with a complete functional receiver for each UAV element. The key issue is to consider whether the application of RIS technology will bring advantages to spectrum sensing and the data fusion problem of decentralized UAV swarms based on the NYFR architecture. Therefore, the property for multiple pulse reconstruction is analyzed through the Gershgorin circle theorem, especially for very short pulses. Further, the block sparse recovery property is analyzed for wide bandwidth signals. The proposed technology can improve the processing capability for multiple signals and wide bandwidth signals while reducing interference from folded noise and subsampled harmonics. Experiment results show augmented spectrum sensing efficiency under non-strictly sparse conditions

DataSheet_1_Case report: Adrenal myelolipoma resected by laparoscopic surgery.pdf

IntroductionAdrenal myelolipomas are benign tumors composed mainly of lipomatous elements with myeloid cells. With the development of medical imaging technology, the detection rate has gradually increased. We report a case of adrenal myelolipoma successfully excised through the laparoscope and reviewed existing literature in recent ten years to summarize the feasibility of the laparoscopic approach for this tumor.Case presentationHerein, we described a case of adrenal myelolipoma resected by laparoscope in a 63-year-old male patient. He did not have any other symptoms except the incidental finding of a left adrenal mass. An abdominal CT examination revealed a mixed-density lesion containing some amount of adipose tissue. In conjunction with the patient’s willingness, we performed a laparoscopic operation to remove the lump. The definite diagnosis was confirmed as an adrenal myelolipoma according to the pathology. The patient recovered well postoperatively and without signs of recurrence at a 5-month follow-up.ConclusionAdrenal myelolipoma is commonly benign, asymptomatic, and hormonal inactivity. A surgical strategy is suggested for high-complication-risk patients. The laparoscopic approach is safe and effective with an obvious advantage over open procedures.</p

Parkinson's disease diagnosis via joint learning from multiple modalities and relations

Parkinson's disease (PD) is a neurodegenerative progressive disease that mainly affects the motor systems of patients. To slow this disease deterioration, early and accurate diagnosis of PD is an effective way, which alleviates mental and physical sufferings by clinical intervention. In this paper, we propose a joint regression and classification framework for PD diagnosis via magnetic resonance and diffusion tensor imaging data. Specifically, we devise a unified multitask feature selection model to explore multiple relationships among features, samples, and clinical scores. We regress four clinical variables of depression, sleep, olfaction, cognition scores, as well as perform the classification of PD disease from the multimodal data. The multitask model explores the relationships at the level of clinical scores, image features, and subjects, to select the most informative and diseased-related features for diagnosis. The proposed method is evaluated on the public Parkinson's progression markers initiative dataset. The extensive experimental results show that the multitask framework can effectively boost the performance of regression and classification and outperforms other state-of-the-art methods. The computerized predictions of clinical scores and label for PD diagnosis may offer quantitative reference for decision support as well.Accepted versio

Gene-related Parkinson's disease diagnosis via feature-based multi-branch octave convolution network

Parkinson's disease (PD) is a common neurodegenerative disease in the elderly population. PD is irreversible and its diagnosis mainly relies on clinical symptoms. Hence, its effective diagnosis is vital. PD has the related gene mutation called gene-related PD, which can be diagnosed not only in the specific PD patients, but also in the healthiest people without clinical symptoms of PD. Since mutations in PD-related genes can affect healthy people, and unaffected PD-related gene carriers can develop into PD patients, it is very necessary to distinguish gene-related PD diseases. The magnetic resonance imaging (MRI) has a lot of information about brain tissue, which can distinguish gene-related PD diseases. However, the limited amount of the gene-related cohort in PD is a challenge for further diagnosis. Therefore, we develop a joint learning framework called feature-based multi-branch octave convolution network (FMOCNN), which uses MRI data for gene-related cohort PD diagnosis. FMOCNN performs sample-feature selection to learn discriminative samples and features and contains a deep neural network to obtain high-level feature representation from various feature types. Specifically, we first train a cardinality constrained sample-feature selection (CCSFS) model to select informative samples and features. We then establish a multi-branch octave convolution neural network (MBOCNN) to jointly train multiple feature inputs. High/low-frequency learning in MBOCNN is exploited to reduce redundant feature information and enhance the feature expression ability. Our method is validated on the publicly available Parkinson's Progression Markers Initiative (PPMI) dataset. Experiments demonstrate that our method achieves promising classification performance and outperforms similar algorithms.This work was supported partly by National Natural Science Foundation of Guangdong Province (No. 2020A1515010649), Guangdong Basic and Applied Basic Research Foundation (Nos. 2019B1515120029 and 2019A1515111205), (Key) Project of Department of Education of Guangdong Province (No. 2019KZDZX1015), Shenzhen Key Basic Research Project (No. JCYJ20190808165209410), and National Taipei University of Technology- Shenzhen University Joint Research Program (NTUT-SZU Joint Research Program) (No. 2020003)