Academic literature on the topic 'Abnormal Battery Drain issue'

Nexus 6P smartphones have been beset by battery drain issues, which have been causing premature shutdown of the phone even when the charge indicator displays a significant remaining runtime. To investigate the premature battery drain issue, two Nexus 6P smartphones (one new and one used) were disassembled and their batteries were evaluated using computerized tomography (CT) scan analysis, electrical performance (capacity, resistance, and impedance) tests, and cycle life capacity fade tests. The “used” smartphone battery delivered only 20% of the rated capacity when tested in a first capacity cycle and then 15% of the rated capacity in a second cycle. The new smartphone battery exceeded the rated capacity when first taken out of the box, but exhibited an accelerated capacity fade under C/2 rate cycling and decreased to 10% of its initial capacity in just 50 cycles. The CT scan results revealed the presence of contaminant materials inside the used battery, raising questions about the quality of the manufacturing process.

Power consumption is a key factor in how final users rate the quality of service in mobile networks; however, its characterization is a challenging issue due to the many parameters involved and the complexity of their dependencies. Traditional battery drain testing in the field does not provide a suitable environment to reach accurate conclusions. In this paper we address this problem providing a controlled environment, more compact and accurate than those currently found in the literature, designed to measure the effects that different factors have on the global energy consumption.

Battery driven computing devices such as laptops and cellular phones have become a necessity in this era. Mobile applications help us in daily life activities and with the rise of Internet of Things (IoT) new opportunities are open up to automate different task. However, batteries have their own limitations such as weight, cost, and size. Multiple applications and background processes running in parallel easily drain phone’s battery within 24 hours consequently annoying users by limited battery capacity. Repeated charge, recharge cycles steadily diminish the full capacity of batteries resulting in the immense decreased performance of the device. Therefore, mobile devices and mobile applications are in great need of energy-aware modules. In this paper, a survey is performed to identify the needs of the mobile user in the context of energy consumption problem. The results of survey lead authors to propose a middle layer energy aware framework to address this issue. The proposed framework highly relies on the association between the operating system, application, and end user. The main objective of the proposed framework is to maintain an energy-aware capability to facilitate end user and mobile applications. The major components of the proposed framework are processing engine, application classifier, application resource management, system profiling, application modes, power estimator and power policy management. Proposed framework also offers a policy manager algorithm based on research community feedback and survey's results. Proposed framework emphasizes on energy efficient execution of mobile operations for end user and operating systems.

In battery energy storage stations (BESSs), the power conversion system (PCS) as the interface between the battery and the power grid is responsible for battery charging and discharging control and grid connection. Any anomaly in the data of a PCS will threaten the security of the BESS. It is difficult to detect anomalies in real-time data because of the large scale, chaos, and small deviations between normal and abnormal values. In this paper, the density-based clustering algorithm DBSCAN is used for data anomaly detection. However, the traditional DBSCAN has a limitation in that it has difficulty in the parameter selection and the parameter is strongly correlated to the detection accuracy. To address this issue, we propose a parameter self-selection-based improved DBSCAN model for detecting PCS anomalies in BESSs. The detection is achieved by mining the correlations between data sets and combining them with the DBSCAN algorithm, and the model is updated in real time based on the normal data of the PCSs. The proposed method is further validated using a comparative experiment based on real-world BESS data.

In this paper, a c ´ uk converter balancing method by using a coupled inductor for lithium based batteries is investigated. The proposed circuit is an active balancing circuit that will equalize eight battery cells in a series. In electrical vehicles (EV), a battery management system (BMS) is a vital task to achieve the best performance of the batteries and longer lifetime. The problem of voltage difference in a battery pack is an important issue to be improved. To overcome the voltage differences in battery string, an equalizing method is mandatory. The conventional c ´ uk converter requires 2(n − 1) switches to balance n cells, while the proposed circuit requires only n switches for n cells in series. In addition, the proposed developed topology uses coupled inductors instead of un-coupled inductors, unlike the traditional c ´ uk converter balancing method. Since the c ´ uk balancing transfers the energy among two adjacent cells, it requires a proportionately long equalization time particularly for long string battery packs, but the coupled inductor c ´ uk converter type overcomes this problem. The switches are N-channel metal-oxide field-effect transistor (MOSFET) to achieve lower drain-source on-resistance, R D S ( o n ) , and less voltage drop as compared to the P-channels. The switches are triggered by complementary signals. The coupled inductor is made in such a way to hold the same magnetizing inductance. It can be done by using five wires in one hand. The circuit contains five inductors, one magnetic core, with five winding for eight cells, and one capacitor for two cells. Therefore, the overall circuitry and complexity of the circuit are reduced, resulting in a more cost-effective and easy to implement circuit. The system also does not demand complicated control for battery equalizing. The experimental circuit was implemented and simulation results were obtained to confirm the validity of the proposed system.

The introduction of the Internet of Things (IoT) and Big Data applications have garnered a massive amount of digital data. Processing and analysing these data demand vast computing resources, proportionately. The major downside of producing and using computing resources in such volumes is the deterioration of the Earth's environment. The production process of the electronic devices involves hazardous and toxic substances which not only harm human and other living being’s health but also contaminate the water and soil. The production and operations of these computers in largescale also results in massive energy consumption and greenhouse gas generation. Moreover, the low use cycle of these devices produces a huge amount of not-easy-to-decompose e-waste. In this outlook, instead of buying new devices, it is advisable to use the existing resources to their fullest, which will minimize the environmental penalties of production and e-waste. This paper advocates for using smartphones and smartphone crowd computing (SCC) to ease off the use of PCs/laptops and centralized high-performance computers (HPCs) such as data centres and supercomputers. The paper aims to establish SCC as the most feasible computing system solution for sustainable computing. Detailed comparisons, in terms of environmental effects (e.g., energy consumption, greenhouse gas generation, etc.), between SCC and supercomputers and other green computing initiatives such as Grid and Cloud Computing, are presented. The key enablers of SCC are identified and discussed. One of them is today's computationally powerful smartphones. A comprehensive statistical survey of the various commercial CPUs, GPUs, SoCs for smartphones is presented confirming the capability of the SCC as an alternative to HPC. The challenges involved in realizing SCC are also considered. One of the major challenges is handling the issue of limited battery in smartphones. The reasons for battery drain are recognized with probable measures. An exhaustive survey is presented on the present and optimistic future of the continuous improvement and research on different aspects of smartphone battery and other alternative power sources which will allow users to use their smartphones for SCC without worrying about the battery running out.

Background Smartphone use is widely spreading in society. Their embedded functions and sensors may play an important role in therapy monitoring and planning. However, the use of smartphones for intrapersonal behavioral and physical monitoring is not yet fully supported by adequate studies addressing technical reliability and acceptance. Objective The objective of this paper is to identify and discuss technical issues that may impact on the wide use of smartphones as clinical monitoring tools. The focus is on the quality of the data and transparency of the acquisition process. Methods QuantifyMyPerson is a platform for continuous monitoring of smartphone use and embedded sensors data. The platform consists of an app for data acquisition, a backend cloud server for data storage and processing, and a web-based dashboard for data management and visualization. The data processing aims to extract meaningful features for the description of daily life such as phone status, calls, app use, GPS, and accelerometer data. A total of health subjects installed the app on their smartphones, running it for 7 months. The acquired data were analyzed to assess impact on smartphone performance (ie, battery consumption and anomalies in functioning) and data integrity. Relevance of the selected features in describing changes in daily life was assessed through the computation of a k-nearest neighbors global anomaly score to detect days that differ from others. Results The effectiveness of smartphone-based monitoring depends on the acceptability and interoperability of the system as user retention and data integrity are key aspects. Acceptability was confirmed by the full transparency of the app and the absence of any conflicts with daily smartphone use. The only perceived issue was the battery consumption even though the trend of battery drain with and without the app running was comparable. Regarding interoperability, the app was successfully installed and run on several Android brands. The study shows that some smartphone manufacturers implement power-saving policies not allowing continuous sensor data acquisition and impacting integrity. Data integrity was 96% on smartphones whose power-saving policies do not impact the embedded sensor management and 84% overall. Conclusions The main technological barriers to continuous behavioral and physical monitoring (ie, battery consumption and power-saving policies of manufacturers) may be overcome. Battery consumption increase is mainly due to GPS triangulation and may be limited, while data missing because of power-saving policies are related only to periods of nonuse of the phone since the embedded sensors are reactivated by any smartphone event. Overall, smartphone-based passive sensing is fully feasible and scalable despite the Android market fragmentation.

The issue of progressive cognitive decline in patients with schizophrenia has been debated. We performed a cross-sectional study of patients with chronic schizophrenia, aged from 18 to 69 years, in order to address this issue. The patients included in this study passed a rigorous screen for any comorbid condition with an adverse impact on central nervous system function. We assessed intellectual deterioration with a battery of neuropsychological tests known to be sensitive to cognitive impairment in progressive dementia. No evidence of accelerated intellectual decline was found. No significant differences were found between the five age-derived cohorts (18–29, 30–39, 40–49, 50–59, and 60–69 years of age) on the Mini-Mental State Examination, Dementia Rating Scale, or other tests sensitive to dementia. While performance on the Boston Naming Test significantly declined with age, this was mainly due to age rather than duration of illness. However, it is important to note that mean performances on the majority of the tests were abnormal across all cohorts studied. These results suggest that intellectual function does not markedly decline during the adulthood of patients with schizophrenia. The course of schizophrenia is more consistent with a static encephalopathy than a dementing disorder.

Recently, IoT devices have become the targets of large-scale cyberattacks, and their security issues have been increasingly serious. However, due to the limited memory and battery power of IoT devices, it is hardly possible to install traditional security software, such as antivirus software for security defense. Meanwhile, network-based traffic detection is difficult to obtain the internal behavior states and conduct in-depth security analysis because more and more IoT devices use encrypted traffic. Therefore, how to obtain complex security behaviors and states inside IoT devices and perform security detection and defense is an issue that needs to be solved urgently. Aiming at this issue, we propose IoT-DeepSense, a behavioral security detection system of IoT devices based on firmware virtualization and deep learning. IoT-DeepSense constructs the real operating environment of the IoT device system to capture the fine-grained system behaviors and then leverages an LSTM-based IoT system behavior abnormality detection approach to effectively extract the hidden features of the system’s behavior sequence and enforce the security detection of the abnormal behavior of the IoT devices. The design and implementation of IoT-DeepSense are carried out on an independent Internet of things behavior detection server, without modifying the limited resources of IoT devices, and have strong scalability. The evaluation results show that IoT-DeepSense achieves a high behavioral detection rate of 92%, with negligible impact on the performance of IoT devices.

Although, dynamic power in portable mobile devices can be reduced by reducing power supply VDD on the cost of increased leakage current. Therefore, maintaining low leakage current in the device is serious issue for minimizing overall power consumption of the circuit and improving the battery life. The conventional Metal Oxide Field Effect Transistor (MOSFET) requires at least 60 mV of gate voltage for better current drive at room temperature which is difficult to achieve due to thermal limit. This limitation of gate voltage requirement degrades the performance of the device at lower VDD. Tunnel Field Effect Transistor (TFET) is a potential candidate to replace CMOS in deep-submicron region due to its lower subthreshold slope SS (< 60 mV/decade) at room temperature. Steep switching in TFET can extend the supply voltage scaling with improved energy efficiency for both digital and analog applications. Despite those advantages, TFETs are suffering from lower ON current and larger ambipolar current. To overcome these shortcomings, a new structure, known as Hetero-dielectric gate TFET (HDG TFET), has been proposed in the literature. Since, in the absence of the compact analytical model, it is difficult to understand the electrical behaviour of the HDG TFET device, therefore, the present paper presents an analytical model of transconductance parameter of HDG TFET device. The electrical performance analysis of HDG TFET device reflects that on current can be increased considerably by choosing gate material of higher work function near the source region which also suppresses the ambipolar current. It is also observed that a thinner silicon film and larger drain bias result in larger transconductance value.

Abstract Gate-to-drain contact short issue in floating gate memory has been studied. Two cases will be discussed, floating-gate to drain contact short, and control-gate to drain contact short, both caused by leakage bridge defect. The abnormal electrical device characteristic combined with modeling gives further insight into the failure mode. Nano-prober measurement results not only provide an evidence of short-contact issue but also measures the current behaviors between drain and gate in floating gate configuration. These results help to predict the defect location and successfully monitor the bridge-failure through electrical analysis.