Localized Dielectric Loss Heating in Dielectrophoresis Devices

Temperature increases during dielectrophoresis (DEP) can affect the response of biological entities, and ignoring the effect can result in misleading analysis. The heating mechanism of a DEP device is typically considered to be the result of Joule heating and is overlooked without an appropriate analysis. Our experiment and analysis indicate that the heating mechanism is due to the dielectric loss (Debye relaxation). A temperature increase between interdigitated electrodes (IDEs) has been measured with an integrated micro temperature sensor between IDEs to be as high as 70 °C at 1.5 MHz with a 30 Vpp applied voltage to our ultra-low thermal mass DEP device. Analytical and numerical analysis of the power dissipation due to the dielectric loss are in good agreement with the experiment data

On effective secrecy throughput of underlay spectrum sharing α - μ/ Málaga hybrid model under interference-and-transmit power constraints

The underlay cognitive radio-based hybrid radio frequency/free-space optical (RF/FSO) systems have emerged as a promising technology due to their ability to eliminate spectrum scarcity and spectrum under-utilization problems. The physical layer security of such a network with a primary user, a secondary source, a secondary receiver, and an eavesdropper is therefore examined in this work. In this network, secret communication occurs between two reliable secondary peers over the RF and FSO links simultaneously, and the eavesdropper can only overhear the RF link. In particular, the maximum transmits power limitation at the secondary user as well as the permissible interference power restriction at the primary user are also taken into consideration. All the RF and FSO links are modeled with α - μ fading and Málaga turbulence with link blockage and pointing error impairments. At the receiver, the selection combining diversity technique is utilized to select the signal with the best electrical signal-to-ratio (SNR). Furthermore, to examine the secrecy performance taking into account the effects of each system parameter, closed-form expressions for the secrecy outage probability and effective secrecy throughput are derived. The resultant expressions are finally verified by Monte-Carlo simulations

BaDLAD: A Large Multi-Domain Bengali Document Layout Analysis Dataset

While strides have been made in deep learning based Bengali Optical Character Recognition (OCR) in the past decade, the absence of large Document Layout Analysis (DLA) datasets has hindered the application of OCR in document transcription, e.g., transcribing historical documents and newspapers. Moreover, rule-based DLA systems that are currently being employed in practice are not robust to domain variations and out-of-distribution layouts. To this end, we present the first multidomain large Bengali Document Layout Analysis Dataset: BaDLAD. This dataset contains 33,695 human annotated document samples from six domains - i) books and magazines, ii) public domain govt. documents, iii) liberation war documents, iv) newspapers, v) historical newspapers, and vi) property deeds, with 710K polygon annotations for four unit types: text-box, paragraph, image, and table. Through preliminary experiments benchmarking the performance of existing state-of-the-art deep learning architectures for English DLA, we demonstrate the efficacy of our dataset in training deep learning based Bengali document digitization models

A Temperature Sensor for Measuring Dielectric Loss Heating in a Dielectrophoresis Device

Dielectrophoresis is an extensively used technique in the field of biomedical science that manipulates particles in a non-uniform electric field. One of its drawbacks is heat dissipation during the DEP operation that raises the local temperature of the DEP device. Temperature increases during dielectrophoresis (DEP) can affect the response of biological entities and ignoring the effect can mislead the result of the analysis. The heating mechanism of a DEP device is typically considered to be the result of Joule heating as bare electrodes are used in a conductive solution. However, when electrodes are insulated from the solution the presence of heat is overlooked without appropriate analysis. The measurement of the local temperature of a microdevice is a complex task. A temperature increase between interdigitated electrodes (IDEs) in presence of DI solution has been measured with an integrated micro temperature sensor between IDEs to be as high as 9 °C at 1.5 MHz with a 26 Vpp applied voltage to our ultra-low thermal mass DEP device. Our experiment and analysis indicate that the heating mechanism in insulated DEP electrodes is due to the dielectric loss (Debye relaxation). The analytical result of the power dissipation due to the dielectric loss is in good agreement with the experiment data

\u3cem\u3eOn-Site/In Situ\u3c/em\u3e Continuous Detecting ppb-Level Metal Ions in Drinking Water Using Block Loop-Gap Resonators and Machine Learning

Microwave measurements and machine learning algorithms are presented to estimate metal ion concentrations in drinking water. A novel block loop gap resonator (BLGR) as a microwave probe is designed and fabricated to estimate Pb ion concentrations in city water as low as 1 ppb with an rms error of 0.18 ppb. No physical contact between the BLGR probe and the water sample allows on-site/in situ continuous detection of ppb-level metal ion concentrations. The S11 raw data (amplitude and phase) from the BLGR are used to classify and estimate metal ion concentrations using a support vector regression algorithm. The performance of the proposed method to estimate Pb concentrations in the presence of interfering metal ions (Cu 2+ , Fe 3+ , and Zn 2+ ) is also evaluated, and it is found that the average measurement error remains less than 13%

Android malware Detection using Machine learning: A Review

Malware for Android is becoming increasingly dangerous to the safety of mobile devices and the data they hold. Although machine learning techniques have been shown to be effective at detecting malware for Android, a comprehensive analysis of the methods used is required. We review the current state of Android malware detection using machine learning in this paper. We begin by providing an overview of Android malware and the security issues it causes. Then, we look at the various supervised, unsupervised, and deep learning machine learning approaches that have been utilized for Android malware detection. Additionally, we present a comparison of the performance of various Android malware detection methods and talk about the performance evaluation metrics that are utilized to evaluate their efficacy. Finally, we draw attention to the drawbacks and difficulties of the methods that are currently in use and suggest possible future directions for research in this area. In addition to providing insights into the current state of Android malware detection using machine learning, our review provides a comprehensive overview of the subject.</p

OOD-Speech: A Large Bengali Speech Recognition Dataset for Out-of-Distribution Benchmarking

We present OOD-Speech, the first out-of-distribution (OOD) benchmarking dataset for Bengali automatic speech recognition (ASR). Being one of the most spoken languages globally, Bengali portrays large diversity in dialects and prosodic features, which demands ASR frameworks to be robust towards distribution shifts. For example, islamic religious sermons in Bengali are delivered with a tonality that is significantly different from regular speech. Our training dataset is collected via massively online crowdsourcing campaigns which resulted in 1177.94 hours collected and curated from $22,645$ native Bengali speakers from South Asia. Our test dataset comprises 23.03 hours of speech collected and manually annotated from 17 different sources, e.g., Bengali TV drama, Audiobook, Talk show, Online class, and Islamic sermons to name a few. OOD-Speech is jointly the largest publicly available speech dataset, as well as the first out-of-distribution ASR benchmarking dataset for Bengali