DNA (Deoxyribonucleic Acid) must be extracted from the cell before it can be amplified using the PCR (Polymerase Chain Reaction) to generate an STR (Short Tandem Repeat) profile. Although extraction methods such as binding DNA to silica remove PCR inhibitors, they have complex procedures that can lead to partial DNA loss. Evaluating the efficiency of an extraction method can be difficult, as the original amount of input DNA may be unknown. Knowledge of the amount of DNA expected to be present in a sample can provide quantitative information on an extraction method’s efficiency, as the recovery of DNA from a DNA extraction process can be calculated using the expected DNA value. Theoretically, while using a direct lysis extraction method, all the cells in the sample are lysed and present in that single tube, so quantitative PCR (qPCR) values of samples extracted using a direct lysis method such as forensicGEMTM can be used as an expected yield value. Validation studies determine if a method is reliable, reproducible, and robust. An internal validation study, following the Federal Bureau of Investigation’s Quality Assurance Standards (QAS) for Forensic DNA Analysis Methods, was performed for the Maxwell® FSC instrument, a robotic DNA extraction instrument designed to remove PCR inhibitors from forensic samples. Results from this study were compared to results obtained on a previously validated Maxwell® 16 instrument. The validation study was successful, as the study demonstrated the instrument could process known/non-probative evidentiary samples, is sensitive, precise, accurate, can process mixture samples, and had no detectable contamination in the process. An added study, done post validation, compared the amount of DNA obtained using a “direct lysis method” with the amount of DNA obtained using samples equivalent to those used in the instrument validation. However, upon comparing the amount of DNA recovered using the Maxwell® FSC instrument to the amount of DNA obtained using a direct lysis method, which in theory would recover 100% of the DNA, the percent yield for serially diluted blood samples was low. Only 10.79% of the DNA was recovered for the 1:10 dilution, 14.44% was recovered for the 1:100 dilution, and 8.00% was recovered for the 1:1000 dilution. The DNA IQTM System – Small Sample Casework kit uses the same chemistry and the same reagents as the Maxwell® FSC DNA IQTM Casework Kit. To provide additional data on DNA recovery, the Maxwell® FSC Study was replicated using the DNA IQTM System – Small Sample Casework Kit, following the manufacturer’s instructions for manual extractions. The study showed that about 77.71% of the DNA is lost when using neat samples, 96.88% is lost for the 1:10 dilution samples, 95.22% is lost for 1:100, and 99.00% for 1:1000. Steps from the DNA IQTM System – Small Sample Casework Protocol were identified where, potentially, the DNA loss could have occurred. These included DNA left in the waste products, DNA left on the swatch, and DNA left on the Resin after elution. Study data identify experimentally two major sources of DNA loss: the waste and the swatch. When quantifying the “waste,” for almost every individual sample, more DNA remained in the waste than was found in the initial extract using the DNA IQTM System – Small Sample Casework Kit. The high amounts of DNA in the waste indicates that the DNA IQ™ Resin does not bind to all the available nucleic acid in the solution. The data and other data characterizing DNA loss at particular steps of the protocol are described in this study. Modifications to the protocol are described, which were made, in an attempt, to increase the amount of DNA bound to the Resin that could subsequently be recovered. Laboratories that use DNA IQTM reagents need to be aware that a significant amount of the sample may be lost when following the procedure. Preliminary results indicate that yield can be improved by modifying the procedure to either increase the Resin volume, increase the incubation temperature, or recapture DNA from the first waste.

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No description available.

No description available.

Adaptive Behavior skills are the conceptual, social, and practical skills that individuals learn to be able to function in their everyday lives (AAIDD, 2008). Measuring adaptive behavior is a way to summarize the effectiveness with which individuals meet the standards of personal independence and social responsibility expected for their age and cultural group. This paper discusses the history and development of adaptive behavior as a construct, its measurement, and its relationship to intelligence. Previous research has examined the relationship between adaptive and intellectual functioning; this study investigates adaptive performance among children with disabilities while controlling for the influence of intellectual level. Children with autism, specific learning disabilities, and mental retardation were studied to determine how they fared in the adaptive subdomains of communication, socialization, and activities of daily living. Data for the study were gathered by reviewing archives from special education records in a large, urban school district. Results indicated a positive and moderate relationship between intelligence and adaptive behavior, but only in the autism group. The groups differed in their performance on the subdomains of adaptive behavior; however, the pattern of adaptive skills for each diagnostic group was unique. Children with autism were found to have deficits in socialization, children with learning disabilities were found to have deficits in communication, and children with mental retardation showed deficits in all domains. These patterns held up even when IQ was controlled; however, the groups no longer differed on communication skills, suggesting that IQ is most strongly related to communication. Finally, the study revealed that full scale IQ, activities of daily living, and communication skills discriminate mental retardation from the other groups while socialization skills discriminate autism from the other groups. Implications of these findings are discussed relative to assessment practices, differential diagnosis, program development, and progress monitoring. / School Psychology

Direct-Down Conversion (DDC) principle based transceiver architectures are of interest to meet the diverse needs of present and future wireless systems. DDC transceivers have a simple structure with fewer analog components and offer low-cost, flexible and multi-standard solutions. However, DDC transceivers have certain circuit impairments affecting their performance in wide-band, high data rate and multi-user systems. IQ imbalance is one of the problems of DDC transceivers that limits their image rejection capabilities. Compensation techniques for frequency independent IQI arising due to gain and phase mismatches of the mixers in the I/Q paths of the transceiver have been widely discussed in the literature. However for wideband multi-channel transceivers, it is becoming increasingly important to address frequency dependent IQI arising due to mismatches in the analog I/Q lowpass filters. A hardware-efficient and standard independent digital estimation and compensation technique for frequency dependent IQI is introduced which is also capable of tracking time-varying IQI changes. The technique is blind and adaptive in nature, based on the second order statistical properties of complex random signals such as properness/circularity. A detailed performance analysis of the introduced technique is executed through computer simulations for various real-time operating scenarios. A novel technique for finding the optimal number of taps required for the adaptive IQI compensation filter is proposed and the performance of this technique is validated. In addition, a metric for the measure of properness is developed and used for error power and step size analysis. / Master of Science / A wireless transceiver consists of two major building blocks namely the RF front-end and digital baseband. The front-end performs functions such as frequency conversion, filtering, and amplification. Impurities because of deep-submicron fabrication lead to non-idealities of the front-end components which limit their accuracy and affect the performance of the overall transceiver. Complex (I/Q) mixing of baseband signals is preferred over real mixing because of its inherent trait of bandwidth efficiency. The I/Q paths enabling this complex mixing in the front-end may not be exactly identical thereby disturbing the perfect orthogonality of inphase and quadrature components leading to IQ Imbalance. The resultant IQ imbalance leads to an image of the signal formed at its mirror frequencies. Imbalances arising from mixers lead to an image of constant strength whereas I/Q low-pass filter mismatches lead to an image of varying strength across the Nyquist range. In addition, temperature effects cause slow variation in IQ imbalance with time. In this thesis a hardware efficient and standard-independent technique is introduced to compensate for performance degrading IQ imbalance. The technique is blind and adaptive in nature and uses second order statistical signal properties like circularity or properness for IQ imbalance estimation. The contribution of this work, which gives a key insight into the optimal number of taps required for the adaptive compensation filter improves the state-of-the-art technique. The performance of the technique is evaluated under various scenarios of interest and a detailed analysis of the results is presented.

This Bachelor thesis investigates the influence of prompt engineering and the integration of an Interaction Quality (IQ) feedback loop on the performance of ChatGPT-3.5 as a voice assistant. By analysing empirical data across multiple configurations, this study explores how these interventions affect response accuracy and efficiency. Findings suggest that prompt engineering tends to enhance system performance, though the benefits of the IQ feedback loop remain less clear and require further investigation. This study contributes to the field by delineating the potential for targeted modifications to improve dialogue system outputs in real-time applications.

Specific Emitter Identification (SEI) is the association of a received signal to an emitter, and is made possible by the unique and unintentional characteristics an emitter imparts onto each transmission, known as its radio frequency (RF) fingerprint. SEI systems are of vital importance to the military for applications such as early warning systems, emitter tracking, and emitter location. More recently, cognitive radio systems have started making use of SEI systems to enforce Dynamic Spectrum Access (DSA) rules. The use of pre-determined and expert defined signal features to characterize the RF fingerprint of emitters of interest limits current state-of-the-art SEI systems in numerous ways. Recent work in RF Machine Learning (RFML) and Convolutional Neural Networks (CNNs) has shown the capability to perform signal processing tasks such as modulation classification, without the need for pre-defined expert features. Given this success, the work presented in this thesis investigates the ability to use CNNs, in place of a traditional expert-defined feature extraction process, to improve upon traditional SEI systems, by developing and analyzing two distinct approaches for performing SEI using CNNs. Neither approach assumes a priori knowledge of the emitters of interest. Further, both approaches use only raw IQ data as input, and are designed to be easily tuned or modified for new operating environments. Results show CNNs can be used to both estimate expert-defined features and to learn emitter-specific features to effectively identify emitters. / Master of Science / When a device sends a signal, it unintentionally modifies the signal due to small variations and imperfections in the device’s hardware. These modifications, which are typically called the device’s radio frequency (RF) fingerprint, are unique to each device, and, generally, are independent of the data contained within the signal. The goal of a Specific Emitter Identification (SEI) system is to use these RF fingerprints to match received signals to the devices, or emitters, which sent the given signals. SEI systems are often used for military applications, and, more recently, have been used to help make more efficient use of the highly congested RF spectrum. Traditional state-of-the-art SEI systems detect the RF fingerprint embedded in each received signal by extracting one or more features from the signal. These features have been defined by experts in the field, and are determined ahead of time, in order to best capture the RF fingerprints of the emitters the system will likely encounter. However, this use of pre-determined expert features in traditional SEI systems limits the system in a variety of ways. The work presented in this thesis investigates the ability to use Machine Learning (ML) techniques in place of the typically used expert-defined feature extraction processes, in order to improve upon traditional SEI systems. More specifically, in this thesis, two distinct approaches for performing SEI using Convolutional Neural Networks (CNNs) are developed and evaluated. These approaches are designed to have no knowledge of the emitters they may encounter and to be easily modified, unlike traditional SEI systems

This thesis contains design of beamforming network designed for passive radar antennas. The first part contains theory of passive radars and beamforming networks. The next part implies design of beamforming network at the block digram level. Then are choosed circuits for amplitude and phase control, including the design of control communication. It follows by realization of IQ phase shifter and his automatic measurement. Based on this results is phase shifter adjusted and PCB of 2x2 beamforming network is designed. Last part includes design of control application (Matlab) and control program for STM32F407VG microcontroller.

There are more and more people watching video streams over the Internet, and this has led to an increase in companies that compete for viewers. To improve the users experience, these companies can measure how their services are performing. The aim of this thesis was to recommend a way to measure the quality of service for a real time video streaming service. Three methods were presented; to buy the information from a content delivery network, extend existing analytics software or build a custom solution using packet sniffing. It was decided to extend existing analytics software. An evaluation was made on which software to extend. Four solutions were compared: Google Analytics, Mixpanel, Ooyala IQ and Piwik. The comparison was made using the analytic hierarchy process, comparing each alternative in their performance in criteria such as API maturity, flexibility, visualization and support. The recommended software to extend when building a real time video streaming service is Ooyala IQ which excel at flexibility and is easy to implement into existing solutions. It also had great capacity, offering no limit on how many events it can track per month, and finally it offers great dedicated support via telephone or email. / Det finns fler och fler personer som tittar på video strömmar på Internet, detta har lett till att nya företag har startats som konkurerar om tittare. För att förbättra kundupplevelsen kan man mäta hur tjänsten presterar. Målet med examensarbetet var att rekommendera hur man kan mäta tjänstekvalite för en realtidsvideoströmningstjänst. Tre olika lösningsförslag presenterades; att köpa informationen från en content delivery network, att bygga vidare på tillgängliga analytisk mjukvara eller att bygga ett eget paketsniffarprogram. Det bestämdes att bygga vidare på tillgänglig analytisk mjukvara. Fyra olika mjukvara jämfördes: Google Analytics, Mixpanel, Ooyala IQ och Piwik. Jämförelsen gjordes med hjälp av analytical hierarchy process, de olika alternativen jämfördes med avseende på: hur moget API:et var, flexibilitet, visualiseringen av data och support. Rekommendationen är att använda sig av Ooyala IQ som utmärker sig med avseende på flexibilitet, det var enkelt att använda deras API i sin egen lösning, det fanns ingen gräns på hur många händelser man kunde lagra per månad, och slutligen så fanns det dedikerad supportpersonal att nå via telefon eller email.