Study of Smart Antenna Wide Band Multi Beam by Algorithm Switch Beam

The use of wideband antennas in radio frequency (RF) systems are intended to improve the efficiency of the system economically. So that problems arise due to differences in RF system frequency allocation in each country can be overcome. Other than that, the need for an antenna that can optimize the direction of the beam becomes one which became a consideration, for that to develop a smart antenna that is capable of producing the different beam. In various studies, wideband antennas have been built only able to work on a single beam. Meanwhile, the antenna has also been proven to be working in multi-beam but still works on a single frequency. The researcher intends to develop an antenna that can work as a smart antenna that applies multi-beam with switching algorithms by having a wide working frequency (wideband). Multi-beam with wideband can be produced by combining wideband antenna array with a Butler matrix that applies the switching beam algorithm with phase array technique so that it can be a smart antenna because the antenna can be adjusted of the beam as desired

Peningkatan Koefisien Refleksi Antena Mikrostrip 28 GHz dengan Slit

5G communication technology requires antennas that have high performance. Microstrip antenna is one of the most widely used antennas in telecommunications because of its light mass, small dimensions and easy fabrication. This study designed a rectangular microstrip patch antenna with miniaturization and slit at a working frequency of 28 GHz. The use of slits on the patch can improve the reflection coefficient of 39.3%.Teknologi komunikasi 5G membutuhkan antena yang mempunyai performansi tinggi. Antena mikrostrip merupakan salah satu antena yang paling banyak digunakan dalam telekomunikasi karena massanya yang ringan, dimensi nya yang kecil dan mudah dipabrikasi. Penelitian ini merancang antena mikrostrip patch persegi panjang dengan miniaturisasi dan slit pada frekuensi kerja 28 GHz. Penggunaan slit pada patch mampu memperbaiki koefisien refleksi sebesar 39.3%

Diagnosis of Genetic White Matter Disorders by Singleton Whole-Exome and Genome Sequencing Using Interactome-Driven Prioritization

Background and Objectives Genetic white matter disorders (GWMD) are of heterogeneous origin, with >100 causal genes identified to date. Classic targeted approaches achieve a molecular diagnosis in only half of all patients. We aimed to determine the clinical utility of singleton whole-exome sequencing and whole-genome sequencing (sWES-WGS) interpreted with a phenotype- and interactome-driven prioritization algorithm to diagnose GWMD while identifying novel phenotypes and candidate genes. Methods A case series of patients of all ages with undiagnosed GWMD despite extensive standard-of-care paraclinical studies were recruited between April 2017 and December 2019 in a collaborative study at the Bellvitge Biomedical Research Institute (IDIBELL) and neurology units of tertiary Spanish hospitals. We ran sWES and WGS and applied our interactome-prioritization algorithm based on the network expansion of a seed group of GWMD-related genes derived from the Human Phenotype Ontology terms of each patient. Results We evaluated 126 patients (101 children and 25 adults) with ages ranging from 1 month to 74 years. We obtained a first molecular diagnosis by singleton WES in 59% of cases, which increased to 68% after annual reanalysis, and reached 72% after WGS was performed in 16 of the remaining negative cases. We identified variants in 57 different genes among 91 diagnosed cases, with the most frequent being RNASEH2B, EIF2B5, POLR3A, and PLP1, and a dual diagnosis underlying complex phenotypes in 6 families, underscoring the importance of genomic analysis to solve these cases. We discovered 9 candidate genes causing novel diseases and propose additional putative novel candidate genes for yet-to-be discovered GWMD. Discussion Our strategy enables a high diagnostic yield and is a good alternative to trio WES/WGS for GWMD. It shortens the time to diagnosis compared to the classical targeted approach, thus optimizing appropriate management. Furthermore, the interactome-driven prioritization pipeline enables the discovery of novel disease-causing genes and phenotypes, and predicts novel putative candidate genes, shedding light on etiopathogenic mechanisms that are pivotal for myelin generation and maintenance

ClinPrior: an algorithm for diagnosis and novel gene discovery by network-based prioritization

BackgroundWhole-exome sequencing (WES) and whole-genome sequencing (WGS) have become indispensable tools to solve rare Mendelian genetic conditions. Nevertheless, there is still an urgent need for sensitive, fast algorithms to maximise WES/WGS diagnostic yield in rare disease patients. Most tools devoted to this aim take advantage of patient phenotype information for prioritization of genomic data, although are often limited by incomplete gene-phenotype knowledge stored in biomedical databases and a lack of proper benchmarking on real-world patient cohorts.MethodsWe developed ClinPrior, a novel method for the analysis of WES/WGS data that ranks candidate causal variants based on the patient's standardized phenotypic features (in Human Phenotype Ontology (HPO) terms). The algorithm propagates the data through an interactome network-based prioritization approach. This algorithm was thoroughly benchmarked using a synthetic patient cohort and was subsequently tested on a heterogeneous prospective, real-world series of 135 families affected by hereditary spastic paraplegia (HSP) and/or cerebellar ataxia (CA).ResultsClinPrior successfully identified causative variants achieving a final positive diagnostic yield of 70% in our real-world cohort. This includes 10 novel candidate genes not previously associated with disease, 7 of which were functionally validated within this project. We used the knowledge generated by ClinPrior to create a specific interactome for HSP/CA disorders thus enabling future diagnoses as well as the discovery of novel disease genes.ConclusionsClinPrior is an algorithm that uses standardized phenotype information and interactome data to improve clinical genomic diagnosis. It helps in identifying atypical cases and efficiently predicts novel disease-causing genes. This leads to increasing diagnostic yield, shortening of the diagnostic Odysseys and advancing our understanding of human illnesses

Evaluation of cellular network performance involving the LTE 1800 band and LTE 2100 band using the drive test method

In the 4G Network on the cellular system, the possibility of high traffic increase is a big problem for users, the proposed solution is to reduce the possibility of full traffic and decrease the quality of the cellular system by dividing the frequency channel into several parts. The purpose of this paper is to study the effect of network optimization on the value of Key Performance Indicator (KPI) in the LTE 1800 and LTE 2100 bands. KPI values, In the LTE 1800 and LTE 2100 bands tested using the drive test method using the Telkomsel sim card provider, the results show that the LTE 2100 band on the TML 013 site has a very high CSSR number compared to the band LTE 1800 which is 99.73% after optimization. The results showed that the LTE band 2100 is better than the LTE band 1800 in terms of KPI Summary

Frequency and phenotypic spectrum of spinocerebellar ataxia <scp>27B</scp> and other genetic ataxias in a Spanish cohort of late‐onset cerebellar ataxia

Background and purpose: Dominantly inherited GAA repeat expansions in the fibroblast growth factor 14 (FGF14) gene have recently been shown to cause spinocerebellar ataxia 27B (SCA27B). We aimed to study the frequency and phenotype of SCA27B in a cohort of patients with unsolved late-onset cerebellar ataxia (LOCA). We also assessed the frequency of SCA27B relative to other genetically defined LOCAs. Methods: We recruited a consecutive series of 107 patients with LOCA, of whom 64 remained genetically undiagnosed. We creened these 64 patients for the FGF14 GAA repeat expansion. We next analysed the frequency of SCA27B relative to other genetically defined forms of LOCA in the cohort of 107 patients. Results: Eighteen of 64 patients (28%) carried an FGF14 (GAA)≥250 expansion. The median (range) age at onset was 62.5 (39–72) years. The most common clinical features included gait ataxia (100%) and mild cerebellar dysarthria (67%). In addition, episodic symptoms and downbeat nystagmus were present in 39% (7/18) and 37% (6/16) of patients, respectively. SCA27B was the most common cause of LOCA in our cohort (17%, 18/107). Among patients with genetically defined LOCA, SCA27B was the main cause of pure ataxia, RFC1-related disease of ataxia with neuropathy, and SPG7 of ataxia with spasticity. Conclusion: We showed that SCA27B is the most common cause of LOCA in our cohort. Our results support the use of FGF14 GAA repeat expansion screening as a first-tier genetic test in patients with LOCA