Design of dual-band microstrip reflectar-ray using single layer multiresonance double cross elements

A multiresonance double cross element is used to design a dual-band reflectarray with dual linear polarization. The proposed element has a single conductive layer structure which makes it easy to manufacture. The results presented in this paper show that the mutual effect between the elements of the two bands is negligible. Hence, it is easy to achieve the phase compensation for each band separately. The simulated and measured results for an element designed to cover the X- and K-bands have confirmed the suitability of the proposed element to build a dual-band reflectarray

Theoretical Investigation into Spectral Coexistence of CDMA and TDMA Systems

The scarcity of available radio spectrum presently limits the extension of modern multimedia systems. This paper presents a theoretical investigation into the possibility of using a frequency overlay of a narrowband Code Division Multiple Access (CDMA) System and a Time Division Multiple Access (TDMA) System to provide a greater spectral efficiency. This paper shows that under certain conditions the two systems can operate in the same frequency band and in the same area with a considerable improvement in the overall capacity of the whole system

Wideband microwave crossover using double vertical microstrip-CPW interconnect

The paper presents the design of a novel ultra-wideband microwave crossover for the use in microstrip circuits. The proposed structure includes a double microstrip-coplanar waveguide (CPW) vertical interconnect in single-layer substrate technology which allows an inclusion of a finite-width coplanar waveguide (CPW) on the top side of the substrate to achieve the required cross-link. The presented design is verified using the full-wave electromagnetic simulator Ansoft HFSS v.13 and experimental tests. The obtained experimental results show that in the frequency band of 3.2–11 GHz, the crossover has an isolation of 20 dB accompanied by insertion losses of no more than 1.5 dB

Pulse compression with minimum uncertainty: An efficient microwave medical imaging technique

A pulse compression technique that gives an optimum contrast and visibility of targets in radar-based medical imaging is presented. A smoothing window for microwave beamforming technique which more properly alleviates the effect of abrupt truncation in finite length signals with the aid of the uncertainty principle is utilized. It is found that using a closer output signal shape to the Gaussian pulse results in a lower uncertainty and ambiguity in the reconstructed images. Hence, when the back-scattered signal passes through a window whose uncertainty is the least, the visibility of the target in the imaged domain will be the highest with high signal-to-noise ratio and fine resolution in microwave medical imaging. The accumulation of the above properties together increases the chance of detecting any abnormality in the human body at early stages and thus resulting in a higher chance of survival. The idea is tested on a real-sized head model surrounded by an array of dipoles operating across the band 1.3-1.4 GHz. The results are compared with the most commonly used beamforming techniques to show the achieved improvements in practice

Tacrolimus-Induced Intestinal Angioedema: Diagnosis by Capsule Endoscopy

Small intestinal angioedema has been reported with angiotensin converting enzyme inhibitors therapy, but not in implanted patients treated with tacrolimus. We present a kidney transplanted patient, hospitalized with severe diarrhea, diagnosed with tacrolimus-induced intestinal angioedema with abdominal computerized tomography and capsule endoscopy. To the best of our knowledge this is the first described case of tacrolimus-induced small bowel angioedema diagnosed with capsule endoscopy

Artificial intelligence in cancer imaging: Clinical challenges and applications

Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care

Collateral damage: the impact on outcomes from cancer surgery of the COVID-19 pandemic.

BACKGROUND: Cancer diagnostics and surgery have been disrupted by the response of health care services to the coronavirus disease 2019 (COVID-19) pandemic. Progression of cancers during delay will impact on patients' long-term survival. PATIENTS AND METHODS: We generated per-day hazard ratios of cancer progression from observational studies and applied these to age-specific, stage-specific cancer survival for England 2013-2017. We modelled per-patient delay of 3 and 6 months and periods of disruption of 1 and 2 years. Using health care resource costing, we contextualise attributable lives saved and life-years gained (LYGs) from cancer surgery to equivalent volumes of COVID-19 hospitalisations. RESULTS: Per year, 94 912 resections for major cancers result in 80 406 long-term survivors and 1 717 051 LYGs. Per-patient delay of 3/6 months would cause attributable death of 4755/10 760 of these individuals with loss of 92 214/208 275 life-years, respectively. For cancer surgery, average LYGs per patient are 18.1 under standard conditions and 17.1/15.9 with a delay of 3/6 months (an average loss of 0.97/2.19 LYGs per patient), respectively. Taking into account health care resource units (HCRUs), surgery results on average per patient in 2.25 resource-adjusted life-years gained (RALYGs) under standard conditions and 2.12/1.97 RALYGs following delay of 3/6 months. For 94 912 hospital COVID-19 admissions, there are 482 022 LYGs requiring 1 052 949 HCRUs. Hospitalisation of community-acquired COVID-19 patients yields on average per patient 5.08 LYG and 0.46 RALYGs. CONCLUSIONS: Modest delays in surgery for cancer incur significant impact on survival. Delay of 3/6 months in surgery for incident cancers would mitigate 19%/43% of LYGs, respectively, by hospitalisation of an equivalent volume of admissions for community-acquired COVID-19. This rises to 26%/59%, respectively, when considering RALYGs. To avoid a downstream public health crisis of avoidable cancer deaths, cancer diagnostic and surgical pathways must be maintained at normal throughput, with rapid attention to any backlog already accrued